mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-10 00:28:31 +00:00
Merge branch 'main' of github.com:crewAIInc/crewAI into lorenze/agent-executor-flow-pattern
This commit is contained in:
lorenzejay
23
.github/workflows/publish.yml
vendored
23
.github/workflows/publish.yml
vendored
@@ -1,9 +1,14 @@
|
||||
name: Publish to PyPI
|
||||
|
||||
on:
|
||||
release:
|
||||
types: [ published ]
|
||||
repository_dispatch:
|
||||
types: [deployment-tests-passed]
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
release_tag:
|
||||
description: 'Release tag to publish'
|
||||
required: false
|
||||
type: string
|
||||
|
||||
jobs:
|
||||
build:
|
||||
@@ -12,7 +17,21 @@ jobs:
|
||||
permissions:
|
||||
contents: read
|
||||
steps:
|
||||
- name: Determine release tag
|
||||
id: release
|
||||
run: |
|
||||
# Priority: workflow_dispatch input > repository_dispatch payload > default branch
|
||||
if [ -n "${{ inputs.release_tag }}" ]; then
|
||||
echo "tag=${{ inputs.release_tag }}" >> $GITHUB_OUTPUT
|
||||
elif [ -n "${{ github.event.client_payload.release_tag }}" ]; then
|
||||
echo "tag=${{ github.event.client_payload.release_tag }}" >> $GITHUB_OUTPUT
|
||||
else
|
||||
echo "tag=" >> $GITHUB_OUTPUT
|
||||
fi
|
||||
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
ref: ${{ steps.release.outputs.tag || github.ref }}
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
|
||||
18
.github/workflows/trigger-deployment-tests.yml
vendored
Normal file
18
.github/workflows/trigger-deployment-tests.yml
vendored
Normal file
@@ -0,0 +1,18 @@
|
||||
name: Trigger Deployment Tests
|
||||
|
||||
on:
|
||||
release:
|
||||
types: [published]
|
||||
|
||||
jobs:
|
||||
trigger:
|
||||
name: Trigger deployment tests
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Trigger deployment tests
|
||||
uses: peter-evans/repository-dispatch@v3
|
||||
with:
|
||||
token: ${{ secrets.CREWAI_DEPLOYMENTS_PAT }}
|
||||
repository: ${{ secrets.CREWAI_DEPLOYMENTS_REPOSITORY }}
|
||||
event-type: crewai-release
|
||||
client-payload: '{"release_tag": "${{ github.event.release.tag_name }}", "release_name": "${{ github.event.release.name }}"}'
|
||||
@@ -24,4 +24,10 @@ repos:
|
||||
rev: 0.9.3
|
||||
hooks:
|
||||
- id: uv-lock
|
||||
- repo: https://github.com/commitizen-tools/commitizen
|
||||
rev: v4.10.1
|
||||
hooks:
|
||||
- id: commitizen
|
||||
- id: commitizen-branch
|
||||
stages: [ pre-push ]
|
||||
|
||||
|
||||
@@ -136,6 +136,10 @@ def _filter_request_headers(request: Request) -> Request: # type: ignore[no-any
|
||||
|
||||
def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Filter sensitive headers from response before recording."""
|
||||
# Remove Content-Encoding to prevent decompression issues on replay
|
||||
for encoding_header in ["Content-Encoding", "content-encoding"]:
|
||||
response["headers"].pop(encoding_header, None)
|
||||
|
||||
for header_name, replacement in HEADERS_TO_FILTER.items():
|
||||
for variant in [header_name, header_name.upper(), header_name.title()]:
|
||||
if variant in response["headers"]:
|
||||
|
||||
@@ -308,6 +308,7 @@
|
||||
"en/learn/hierarchical-process",
|
||||
"en/learn/human-input-on-execution",
|
||||
"en/learn/human-in-the-loop",
|
||||
"en/learn/human-feedback-in-flows",
|
||||
"en/learn/kickoff-async",
|
||||
"en/learn/kickoff-for-each",
|
||||
"en/learn/llm-connections",
|
||||
@@ -735,6 +736,7 @@
|
||||
"pt-BR/learn/hierarchical-process",
|
||||
"pt-BR/learn/human-input-on-execution",
|
||||
"pt-BR/learn/human-in-the-loop",
|
||||
"pt-BR/learn/human-feedback-in-flows",
|
||||
"pt-BR/learn/kickoff-async",
|
||||
"pt-BR/learn/kickoff-for-each",
|
||||
"pt-BR/learn/llm-connections",
|
||||
@@ -1171,6 +1173,7 @@
|
||||
"ko/learn/hierarchical-process",
|
||||
"ko/learn/human-input-on-execution",
|
||||
"ko/learn/human-in-the-loop",
|
||||
"ko/learn/human-feedback-in-flows",
|
||||
"ko/learn/kickoff-async",
|
||||
"ko/learn/kickoff-for-each",
|
||||
"ko/learn/llm-connections",
|
||||
|
||||
@@ -16,16 +16,17 @@ Welcome to the CrewAI AOP API reference. This API allows you to programmatically
|
||||
Navigate to your crew's detail page in the CrewAI AOP dashboard and copy your Bearer Token from the Status tab.
|
||||
</Step>
|
||||
|
||||
<Step title="Discover Required Inputs">
|
||||
Use the `GET /inputs` endpoint to see what parameters your crew expects.
|
||||
</Step>
|
||||
<Step title="Discover Required Inputs">
|
||||
Use the `GET /inputs` endpoint to see what parameters your crew expects.
|
||||
</Step>
|
||||
|
||||
<Step title="Start a Crew Execution">
|
||||
Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
|
||||
</Step>
|
||||
<Step title="Start a Crew Execution">
|
||||
Call `POST /kickoff` with your inputs to start the crew execution and receive
|
||||
a `kickoff_id`.
|
||||
</Step>
|
||||
|
||||
<Step title="Monitor Progress">
|
||||
Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
|
||||
Use `GET /{kickoff_id}/status` to check execution status and retrieve results.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
@@ -40,13 +41,14 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
|
||||
### Token Types
|
||||
|
||||
| Token Type | Scope | Use Case |
|
||||
|:-----------|:--------|:----------|
|
||||
| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
|
||||
| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
|
||||
| Token Type | Scope | Use Case |
|
||||
| :-------------------- | :------------------------ | :----------------------------------------------------------- |
|
||||
| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
|
||||
| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
|
||||
|
||||
<Tip>
|
||||
You can find both token types in the Status tab of your crew's detail page in the CrewAI AOP dashboard.
|
||||
You can find both token types in the Status tab of your crew's detail page in
|
||||
the CrewAI AOP dashboard.
|
||||
</Tip>
|
||||
|
||||
## Base URL
|
||||
@@ -63,29 +65,33 @@ Replace `your-crew-name` with your actual crew's URL from the dashboard.
|
||||
|
||||
1. **Discovery**: Call `GET /inputs` to understand what your crew needs
|
||||
2. **Execution**: Submit inputs via `POST /kickoff` to start processing
|
||||
3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
|
||||
3. **Monitoring**: Poll `GET /{kickoff_id}/status` until completion
|
||||
4. **Results**: Extract the final output from the completed response
|
||||
|
||||
## Error Handling
|
||||
|
||||
The API uses standard HTTP status codes:
|
||||
|
||||
| Code | Meaning |
|
||||
|------|:--------|
|
||||
| `200` | Success |
|
||||
| `400` | Bad Request - Invalid input format |
|
||||
| `401` | Unauthorized - Invalid bearer token |
|
||||
| `404` | Not Found - Resource doesn't exist |
|
||||
| Code | Meaning |
|
||||
| ----- | :----------------------------------------- |
|
||||
| `200` | Success |
|
||||
| `400` | Bad Request - Invalid input format |
|
||||
| `401` | Unauthorized - Invalid bearer token |
|
||||
| `404` | Not Found - Resource doesn't exist |
|
||||
| `422` | Validation Error - Missing required inputs |
|
||||
| `500` | Server Error - Contact support |
|
||||
| `500` | Server Error - Contact support |
|
||||
|
||||
## Interactive Testing
|
||||
|
||||
<Info>
|
||||
**Why no "Send" button?** Since each CrewAI AOP user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
|
||||
**Why no "Send" button?** Since each CrewAI AOP user has their own unique crew
|
||||
URL, we use **reference mode** instead of an interactive playground to avoid
|
||||
confusion. This shows you exactly what the requests should look like without
|
||||
non-functional send buttons.
|
||||
</Info>
|
||||
|
||||
Each endpoint page shows you:
|
||||
|
||||
- ✅ **Exact request format** with all parameters
|
||||
- ✅ **Response examples** for success and error cases
|
||||
- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
|
||||
@@ -103,6 +109,7 @@ Each endpoint page shows you:
|
||||
</CardGroup>
|
||||
|
||||
**Example workflow:**
|
||||
|
||||
1. **Copy this cURL example** from any endpoint page
|
||||
2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
|
||||
3. **Replace the Bearer token** with your real token from the dashboard
|
||||
@@ -111,10 +118,18 @@ Each endpoint page shows you:
|
||||
## Need Help?
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
|
||||
<Card
|
||||
title="Enterprise Support"
|
||||
icon="headset"
|
||||
href="mailto:support@crewai.com"
|
||||
>
|
||||
Get help with API integration and troubleshooting
|
||||
</Card>
|
||||
<Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
|
||||
<Card
|
||||
title="Enterprise Dashboard"
|
||||
icon="chart-line"
|
||||
href="https://app.crewai.com"
|
||||
>
|
||||
Manage your crews and view execution logs
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
---
|
||||
title: "GET /status/{kickoff_id}"
|
||||
title: "GET /{kickoff_id}/status"
|
||||
description: "Get execution status"
|
||||
openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
|
||||
openapi: "/enterprise-api.en.yaml GET /{kickoff_id}/status"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
|
||||
|
||||
@@ -572,6 +572,55 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
|
||||
|
||||
When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
|
||||
|
||||
### Human in the Loop (human feedback)
|
||||
|
||||
The `@human_feedback` decorator enables human-in-the-loop workflows by pausing flow execution to collect feedback from a human. This is useful for approval gates, quality review, and decision points that require human judgment.
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Do you approve this content?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Content to be reviewed..."
|
||||
|
||||
@listen("approved")
|
||||
def on_approval(self, result: HumanFeedbackResult):
|
||||
print(f"Approved! Feedback: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def on_rejection(self, result: HumanFeedbackResult):
|
||||
print(f"Rejected. Reason: {result.feedback}")
|
||||
```
|
||||
|
||||
When `emit` is specified, the human's free-form feedback is interpreted by an LLM and collapsed into one of the specified outcomes, which then triggers the corresponding `@listen` decorator.
|
||||
|
||||
You can also use `@human_feedback` without routing to simply collect feedback:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(message="Any comments on this output?")
|
||||
def my_method(self):
|
||||
return "Output for review"
|
||||
|
||||
@listen(my_method)
|
||||
def next_step(self, result: HumanFeedbackResult):
|
||||
# Access feedback via result.feedback
|
||||
# Access original output via result.output
|
||||
pass
|
||||
```
|
||||
|
||||
Access all feedback collected during a flow via `self.last_human_feedback` (most recent) or `self.human_feedback_history` (all feedback as a list).
|
||||
|
||||
For a complete guide on human feedback in flows, including **async/non-blocking feedback** with custom providers (Slack, webhooks, etc.), see [Human Feedback in Flows](/en/learn/human-feedback-in-flows).
|
||||
|
||||
## Adding Agents to Flows
|
||||
|
||||
Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
|
||||
|
||||
@@ -187,6 +187,97 @@ You can also deploy your crews directly through the CrewAI AOP web interface by
|
||||
|
||||
</Steps>
|
||||
|
||||
## Option 3: Redeploy Using API (CI/CD Integration)
|
||||
|
||||
For automated deployments in CI/CD pipelines, you can use the CrewAI API to trigger redeployments of existing crews. This is particularly useful for GitHub Actions, Jenkins, or other automation workflows.
|
||||
|
||||
<Steps>
|
||||
<Step title="Get Your Personal Access Token">
|
||||
|
||||
Navigate to your CrewAI AOP account settings to generate an API token:
|
||||
|
||||
1. Go to [app.crewai.com](https://app.crewai.com)
|
||||
2. Click on **Settings** → **Account** → **Personal Access Token**
|
||||
3. Generate a new token and copy it securely
|
||||
4. Store this token as a secret in your CI/CD system
|
||||
|
||||
</Step>
|
||||
|
||||
<Step title="Find Your Automation UUID">
|
||||
|
||||
Locate the unique identifier for your deployed crew:
|
||||
|
||||
1. Go to **Automations** in your CrewAI AOP dashboard
|
||||
2. Select your existing automation/crew
|
||||
3. Click on **Additional Details**
|
||||
4. Copy the **UUID** - this identifies your specific crew deployment
|
||||
|
||||
</Step>
|
||||
|
||||
<Step title="Trigger Redeployment via API">
|
||||
|
||||
Use the Deploy API endpoint to trigger a redeployment:
|
||||
|
||||
```bash
|
||||
curl -i -X POST \
|
||||
-H "Authorization: Bearer YOUR_PERSONAL_ACCESS_TOKEN" \
|
||||
https://app.crewai.com/crewai_plus/api/v1/crews/YOUR-AUTOMATION-UUID/deploy
|
||||
|
||||
# HTTP/2 200
|
||||
# content-type: application/json
|
||||
#
|
||||
# {
|
||||
# "uuid": "your-automation-uuid",
|
||||
# "status": "Deploy Enqueued",
|
||||
# "public_url": "https://your-crew-deployment.crewai.com",
|
||||
# "token": "your-bearer-token"
|
||||
# }
|
||||
```
|
||||
|
||||
<Info>
|
||||
If your automation was first created connected to Git, the API will automatically pull the latest changes from your repository before redeploying.
|
||||
</Info>
|
||||
|
||||
|
||||
</Step>
|
||||
|
||||
<Step title="GitHub Actions Integration Example">
|
||||
|
||||
Here's a GitHub Actions workflow with more complex deployment triggers:
|
||||
|
||||
```yaml
|
||||
name: Deploy CrewAI Automation
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ main ]
|
||||
pull_request:
|
||||
types: [ labeled ]
|
||||
release:
|
||||
types: [ published ]
|
||||
|
||||
jobs:
|
||||
deploy:
|
||||
runs-on: ubuntu-latest
|
||||
if: |
|
||||
(github.event_name == 'push' && github.ref == 'refs/heads/main') ||
|
||||
(github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'deploy')) ||
|
||||
(github.event_name == 'release')
|
||||
steps:
|
||||
- name: Trigger CrewAI Redeployment
|
||||
run: |
|
||||
curl -X POST \
|
||||
-H "Authorization: Bearer ${{ secrets.CREWAI_PAT }}" \
|
||||
https://app.crewai.com/crewai_plus/api/v1/crews/${{ secrets.CREWAI_AUTOMATION_UUID }}/deploy
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Add `CREWAI_PAT` and `CREWAI_AUTOMATION_UUID` as repository secrets. For PR deployments, add a "deploy" label to trigger the workflow.
|
||||
</Tip>
|
||||
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## ⚠️ Environment Variable Security Requirements
|
||||
|
||||
<Warning>
|
||||
|
||||
@@ -62,13 +62,13 @@ Test your Gmail trigger integration locally using the CrewAI CLI:
|
||||
crewai triggers list
|
||||
|
||||
# Simulate a Gmail trigger with realistic payload
|
||||
crewai triggers run gmail/new_email
|
||||
crewai triggers run gmail/new_email_received
|
||||
```
|
||||
|
||||
The `crewai triggers run` command will execute your crew with a complete Gmail payload, allowing you to test your parsing logic before deployment.
|
||||
|
||||
<Warning>
|
||||
Use `crewai triggers run gmail/new_email` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
|
||||
Use `crewai triggers run gmail/new_email_received` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
|
||||
</Warning>
|
||||
|
||||
## Monitoring Executions
|
||||
@@ -83,6 +83,6 @@ Track history and performance of triggered runs:
|
||||
|
||||
- Ensure Gmail is connected in Tools & Integrations
|
||||
- Verify the Gmail Trigger is enabled on the Triggers tab
|
||||
- Test locally with `crewai triggers run gmail/new_email` to see the exact payload structure
|
||||
- Test locally with `crewai triggers run gmail/new_email_received` to see the exact payload structure
|
||||
- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
|
||||
- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution
|
||||
|
||||
581
docs/en/learn/human-feedback-in-flows.mdx
Normal file
581
docs/en/learn/human-feedback-in-flows.mdx
Normal file
@@ -0,0 +1,581 @@
|
||||
---
|
||||
title: Human Feedback in Flows
|
||||
description: Learn how to integrate human feedback directly into your CrewAI Flows using the @human_feedback decorator
|
||||
icon: user-check
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
The `@human_feedback` decorator enables human-in-the-loop (HITL) workflows directly within CrewAI Flows. It allows you to pause flow execution, present output to a human for review, collect their feedback, and optionally route to different listeners based on the feedback outcome.
|
||||
|
||||
This is particularly valuable for:
|
||||
|
||||
- **Quality assurance**: Review AI-generated content before it's used downstream
|
||||
- **Decision gates**: Let humans make critical decisions in automated workflows
|
||||
- **Approval workflows**: Implement approve/reject/revise patterns
|
||||
- **Interactive refinement**: Collect feedback to improve outputs iteratively
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A[Flow Method] --> B[Output Generated]
|
||||
B --> C[Human Reviews]
|
||||
C --> D{Feedback}
|
||||
D -->|emit specified| E[LLM Collapses to Outcome]
|
||||
D -->|no emit| F[HumanFeedbackResult]
|
||||
E --> G["@listen('approved')"]
|
||||
E --> H["@listen('rejected')"]
|
||||
F --> I[Next Listener]
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
|
||||
Here's the simplest way to add human feedback to a flow:
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback
|
||||
|
||||
class SimpleReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(message="Please review this content:")
|
||||
def generate_content(self):
|
||||
return "This is AI-generated content that needs review."
|
||||
|
||||
@listen(generate_content)
|
||||
def process_feedback(self, result):
|
||||
print(f"Content: {result.output}")
|
||||
print(f"Human said: {result.feedback}")
|
||||
|
||||
flow = SimpleReviewFlow()
|
||||
flow.kickoff()
|
||||
```
|
||||
|
||||
When this flow runs, it will:
|
||||
1. Execute `generate_content` and return the string
|
||||
2. Display the output to the user with the request message
|
||||
3. Wait for the user to type feedback (or press Enter to skip)
|
||||
4. Pass a `HumanFeedbackResult` object to `process_feedback`
|
||||
|
||||
## The @human_feedback Decorator
|
||||
|
||||
### Parameters
|
||||
|
||||
| Parameter | Type | Required | Description |
|
||||
|-----------|------|----------|-------------|
|
||||
| `message` | `str` | Yes | The message shown to the human alongside the method output |
|
||||
| `emit` | `Sequence[str]` | No | List of possible outcomes. Feedback is collapsed to one of these, which triggers `@listen` decorators |
|
||||
| `llm` | `str \| BaseLLM` | When `emit` specified | LLM used to interpret feedback and map to an outcome |
|
||||
| `default_outcome` | `str` | No | Outcome to use if no feedback provided. Must be in `emit` |
|
||||
| `metadata` | `dict` | No | Additional data for enterprise integrations |
|
||||
| `provider` | `HumanFeedbackProvider` | No | Custom provider for async/non-blocking feedback. See [Async Human Feedback](#async-human-feedback-non-blocking) |
|
||||
|
||||
### Basic Usage (No Routing)
|
||||
|
||||
When you don't specify `emit`, the decorator simply collects feedback and passes a `HumanFeedbackResult` to the next listener:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(message="What do you think of this analysis?")
|
||||
def analyze_data(self):
|
||||
return "Analysis results: Revenue up 15%, costs down 8%"
|
||||
|
||||
@listen(analyze_data)
|
||||
def handle_feedback(self, result):
|
||||
# result is a HumanFeedbackResult
|
||||
print(f"Analysis: {result.output}")
|
||||
print(f"Feedback: {result.feedback}")
|
||||
```
|
||||
|
||||
### Routing with emit
|
||||
|
||||
When you specify `emit`, the decorator becomes a router. The human's free-form feedback is interpreted by an LLM and collapsed into one of the specified outcomes:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Do you approve this content for publication?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_content(self):
|
||||
return "Draft blog post content here..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
print(f"Publishing! User said: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def discard(self, result):
|
||||
print(f"Discarding. Reason: {result.feedback}")
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise(self, result):
|
||||
print(f"Revising based on: {result.feedback}")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
The LLM uses structured outputs (function calling) when available to guarantee the response is one of your specified outcomes. This makes routing reliable and predictable.
|
||||
</Tip>
|
||||
|
||||
## HumanFeedbackResult
|
||||
|
||||
The `HumanFeedbackResult` dataclass contains all information about a human feedback interaction:
|
||||
|
||||
```python Code
|
||||
from crewai.flow.human_feedback import HumanFeedbackResult
|
||||
|
||||
@dataclass
|
||||
class HumanFeedbackResult:
|
||||
output: Any # The original method output shown to the human
|
||||
feedback: str # The raw feedback text from the human
|
||||
outcome: str | None # The collapsed outcome (if emit was specified)
|
||||
timestamp: datetime # When the feedback was received
|
||||
method_name: str # Name of the decorated method
|
||||
metadata: dict # Any metadata passed to the decorator
|
||||
```
|
||||
|
||||
### Accessing in Listeners
|
||||
|
||||
When a listener is triggered by a `@human_feedback` method with `emit`, it receives the `HumanFeedbackResult`:
|
||||
|
||||
```python Code
|
||||
@listen("approved")
|
||||
def on_approval(self, result: HumanFeedbackResult):
|
||||
print(f"Original output: {result.output}")
|
||||
print(f"User feedback: {result.feedback}")
|
||||
print(f"Outcome: {result.outcome}") # "approved"
|
||||
print(f"Received at: {result.timestamp}")
|
||||
```
|
||||
|
||||
## Accessing Feedback History
|
||||
|
||||
The `Flow` class provides two attributes for accessing human feedback:
|
||||
|
||||
### last_human_feedback
|
||||
|
||||
Returns the most recent `HumanFeedbackResult`:
|
||||
|
||||
```python Code
|
||||
@listen(some_method)
|
||||
def check_feedback(self):
|
||||
if self.last_human_feedback:
|
||||
print(f"Last feedback: {self.last_human_feedback.feedback}")
|
||||
```
|
||||
|
||||
### human_feedback_history
|
||||
|
||||
A list of all `HumanFeedbackResult` objects collected during the flow:
|
||||
|
||||
```python Code
|
||||
@listen(final_step)
|
||||
def summarize(self):
|
||||
print(f"Total feedback collected: {len(self.human_feedback_history)}")
|
||||
for i, fb in enumerate(self.human_feedback_history):
|
||||
print(f"{i+1}. {fb.method_name}: {fb.outcome or 'no routing'}")
|
||||
```
|
||||
|
||||
<Warning>
|
||||
Each `HumanFeedbackResult` is appended to `human_feedback_history`, so multiple feedback steps won't overwrite each other. Use this list to access all feedback collected during the flow.
|
||||
</Warning>
|
||||
|
||||
## Complete Example: Content Approval Workflow
|
||||
|
||||
Here's a full example implementing a content review and approval workflow:
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class ContentState(BaseModel):
|
||||
topic: str = ""
|
||||
draft: str = ""
|
||||
final_content: str = ""
|
||||
revision_count: int = 0
|
||||
|
||||
|
||||
class ContentApprovalFlow(Flow[ContentState]):
|
||||
"""A flow that generates content and gets human approval."""
|
||||
|
||||
@start()
|
||||
def get_topic(self):
|
||||
self.state.topic = input("What topic should I write about? ")
|
||||
return self.state.topic
|
||||
|
||||
@listen(get_topic)
|
||||
def generate_draft(self, topic):
|
||||
# In real use, this would call an LLM
|
||||
self.state.draft = f"# {topic}\n\nThis is a draft about {topic}..."
|
||||
return self.state.draft
|
||||
|
||||
@listen(generate_draft)
|
||||
@human_feedback(
|
||||
message="Please review this draft. Reply 'approved', 'rejected', or provide revision feedback:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_draft(self, draft):
|
||||
return draft
|
||||
|
||||
@listen("approved")
|
||||
def publish_content(self, result: HumanFeedbackResult):
|
||||
self.state.final_content = result.output
|
||||
print("\n✅ Content approved and published!")
|
||||
print(f"Reviewer comment: {result.feedback}")
|
||||
return "published"
|
||||
|
||||
@listen("rejected")
|
||||
def handle_rejection(self, result: HumanFeedbackResult):
|
||||
print("\n❌ Content rejected")
|
||||
print(f"Reason: {result.feedback}")
|
||||
return "rejected"
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise_content(self, result: HumanFeedbackResult):
|
||||
self.state.revision_count += 1
|
||||
print(f"\n📝 Revision #{self.state.revision_count} requested")
|
||||
print(f"Feedback: {result.feedback}")
|
||||
|
||||
# In a real flow, you might loop back to generate_draft
|
||||
# For this example, we just acknowledge
|
||||
return "revision_requested"
|
||||
|
||||
|
||||
# Run the flow
|
||||
flow = ContentApprovalFlow()
|
||||
result = flow.kickoff()
|
||||
print(f"\nFlow completed. Revisions requested: {flow.state.revision_count}")
|
||||
```
|
||||
|
||||
```text Output
|
||||
What topic should I write about? AI Safety
|
||||
|
||||
==================================================
|
||||
OUTPUT FOR REVIEW:
|
||||
==================================================
|
||||
# AI Safety
|
||||
|
||||
This is a draft about AI Safety...
|
||||
==================================================
|
||||
|
||||
Please review this draft. Reply 'approved', 'rejected', or provide revision feedback:
|
||||
(Press Enter to skip, or type your feedback)
|
||||
|
||||
Your feedback: Looks good, approved!
|
||||
|
||||
✅ Content approved and published!
|
||||
Reviewer comment: Looks good, approved!
|
||||
|
||||
Flow completed. Revisions requested: 0
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## Combining with Other Decorators
|
||||
|
||||
The `@human_feedback` decorator works with other flow decorators. Place it as the innermost decorator (closest to the function):
|
||||
|
||||
```python Code
|
||||
# Correct: @human_feedback is innermost (closest to the function)
|
||||
@start()
|
||||
@human_feedback(message="Review this:")
|
||||
def my_start_method(self):
|
||||
return "content"
|
||||
|
||||
@listen(other_method)
|
||||
@human_feedback(message="Review this too:")
|
||||
def my_listener(self, data):
|
||||
return f"processed: {data}"
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Place `@human_feedback` as the innermost decorator (last/closest to the function) so it wraps the method directly and can capture the return value before passing to the flow system.
|
||||
</Tip>
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Write Clear Request Messages
|
||||
|
||||
The `request` parameter is what the human sees. Make it actionable:
|
||||
|
||||
```python Code
|
||||
# ✅ Good - clear and actionable
|
||||
@human_feedback(message="Does this summary accurately capture the key points? Reply 'yes' or explain what's missing:")
|
||||
|
||||
# ❌ Bad - vague
|
||||
@human_feedback(message="Review this:")
|
||||
```
|
||||
|
||||
### 2. Choose Meaningful Outcomes
|
||||
|
||||
When using `emit`, pick outcomes that map naturally to human responses:
|
||||
|
||||
```python Code
|
||||
# ✅ Good - natural language outcomes
|
||||
emit=["approved", "rejected", "needs_more_detail"]
|
||||
|
||||
# ❌ Bad - technical or unclear
|
||||
emit=["state_1", "state_2", "state_3"]
|
||||
```
|
||||
|
||||
### 3. Always Provide a Default Outcome
|
||||
|
||||
Use `default_outcome` to handle cases where users press Enter without typing:
|
||||
|
||||
```python Code
|
||||
@human_feedback(
|
||||
message="Approve? (press Enter to request revision)",
|
||||
emit=["approved", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision", # Safe default
|
||||
)
|
||||
```
|
||||
|
||||
### 4. Use Feedback History for Audit Trails
|
||||
|
||||
Access `human_feedback_history` to create audit logs:
|
||||
|
||||
```python Code
|
||||
@listen(final_step)
|
||||
def create_audit_log(self):
|
||||
log = []
|
||||
for fb in self.human_feedback_history:
|
||||
log.append({
|
||||
"step": fb.method_name,
|
||||
"outcome": fb.outcome,
|
||||
"feedback": fb.feedback,
|
||||
"timestamp": fb.timestamp.isoformat(),
|
||||
})
|
||||
return log
|
||||
```
|
||||
|
||||
### 5. Handle Both Routed and Non-Routed Feedback
|
||||
|
||||
When designing flows, consider whether you need routing:
|
||||
|
||||
| Scenario | Use |
|
||||
|----------|-----|
|
||||
| Simple review, just need the feedback text | No `emit` |
|
||||
| Need to branch to different paths based on response | Use `emit` |
|
||||
| Approval gates with approve/reject/revise | Use `emit` |
|
||||
| Collecting comments for logging only | No `emit` |
|
||||
|
||||
## Async Human Feedback (Non-Blocking)
|
||||
|
||||
By default, `@human_feedback` blocks execution waiting for console input. For production applications, you may need **async/non-blocking** feedback that integrates with external systems like Slack, email, webhooks, or APIs.
|
||||
|
||||
### The Provider Abstraction
|
||||
|
||||
Use the `provider` parameter to specify a custom feedback collection strategy:
|
||||
|
||||
```python Code
|
||||
from crewai.flow import Flow, start, human_feedback, HumanFeedbackProvider, HumanFeedbackPending, PendingFeedbackContext
|
||||
|
||||
class WebhookProvider(HumanFeedbackProvider):
|
||||
"""Provider that pauses flow and waits for webhook callback."""
|
||||
|
||||
def __init__(self, webhook_url: str):
|
||||
self.webhook_url = webhook_url
|
||||
|
||||
def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
|
||||
# Notify external system (e.g., send Slack message, create ticket)
|
||||
self.send_notification(context)
|
||||
|
||||
# Pause execution - framework handles persistence automatically
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={"webhook_url": f"{self.webhook_url}/{context.flow_id}"}
|
||||
)
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review this content:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
provider=WebhookProvider("https://myapp.com/api"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "AI-generated content..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
return "Published!"
|
||||
```
|
||||
|
||||
<Tip>
|
||||
The flow framework **automatically persists state** when `HumanFeedbackPending` is raised. Your provider only needs to notify the external system and raise the exception—no manual persistence calls required.
|
||||
</Tip>
|
||||
|
||||
### Handling Paused Flows
|
||||
|
||||
When using an async provider, `kickoff()` returns a `HumanFeedbackPending` object instead of raising an exception:
|
||||
|
||||
```python Code
|
||||
flow = ReviewFlow()
|
||||
result = flow.kickoff()
|
||||
|
||||
if isinstance(result, HumanFeedbackPending):
|
||||
# Flow is paused, state is automatically persisted
|
||||
print(f"Waiting for feedback at: {result.callback_info['webhook_url']}")
|
||||
print(f"Flow ID: {result.context.flow_id}")
|
||||
else:
|
||||
# Normal completion
|
||||
print(f"Flow completed: {result}")
|
||||
```
|
||||
|
||||
### Resuming a Paused Flow
|
||||
|
||||
When feedback arrives (e.g., via webhook), resume the flow:
|
||||
|
||||
```python Code
|
||||
# Sync handler:
|
||||
def handle_feedback_webhook(flow_id: str, feedback: str):
|
||||
flow = ReviewFlow.from_pending(flow_id)
|
||||
result = flow.resume(feedback)
|
||||
return result
|
||||
|
||||
# Async handler (FastAPI, aiohttp, etc.):
|
||||
async def handle_feedback_webhook(flow_id: str, feedback: str):
|
||||
flow = ReviewFlow.from_pending(flow_id)
|
||||
result = await flow.resume_async(feedback)
|
||||
return result
|
||||
```
|
||||
|
||||
### Key Types
|
||||
|
||||
| Type | Description |
|
||||
|------|-------------|
|
||||
| `HumanFeedbackProvider` | Protocol for custom feedback providers |
|
||||
| `PendingFeedbackContext` | Contains all info needed to resume a paused flow |
|
||||
| `HumanFeedbackPending` | Returned by `kickoff()` when flow is paused for feedback |
|
||||
| `ConsoleProvider` | Default blocking console input provider |
|
||||
|
||||
### PendingFeedbackContext
|
||||
|
||||
The context contains everything needed to resume:
|
||||
|
||||
```python Code
|
||||
@dataclass
|
||||
class PendingFeedbackContext:
|
||||
flow_id: str # Unique identifier for this flow execution
|
||||
flow_class: str # Fully qualified class name
|
||||
method_name: str # Method that triggered feedback
|
||||
method_output: Any # Output shown to the human
|
||||
message: str # The request message
|
||||
emit: list[str] | None # Possible outcomes for routing
|
||||
default_outcome: str | None
|
||||
metadata: dict # Custom metadata
|
||||
llm: str | None # LLM for outcome collapsing
|
||||
requested_at: datetime
|
||||
```
|
||||
|
||||
### Complete Async Flow Example
|
||||
|
||||
```python Code
|
||||
from crewai.flow import (
|
||||
Flow, start, listen, human_feedback,
|
||||
HumanFeedbackProvider, HumanFeedbackPending, PendingFeedbackContext
|
||||
)
|
||||
|
||||
class SlackNotificationProvider(HumanFeedbackProvider):
|
||||
"""Provider that sends Slack notifications and pauses for async feedback."""
|
||||
|
||||
def __init__(self, channel: str):
|
||||
self.channel = channel
|
||||
|
||||
def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
|
||||
# Send Slack notification (implement your own)
|
||||
slack_thread_id = self.post_to_slack(
|
||||
channel=self.channel,
|
||||
message=f"Review needed:\n\n{context.method_output}\n\n{context.message}",
|
||||
)
|
||||
|
||||
# Pause execution - framework handles persistence automatically
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={
|
||||
"slack_channel": self.channel,
|
||||
"thread_id": slack_thread_id,
|
||||
}
|
||||
)
|
||||
|
||||
class ContentPipeline(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Approve this content for publication?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
provider=SlackNotificationProvider("#content-reviews"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "AI-generated blog post content..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
print(f"Publishing! Reviewer said: {result.feedback}")
|
||||
return {"status": "published"}
|
||||
|
||||
@listen("rejected")
|
||||
def archive(self, result):
|
||||
print(f"Archived. Reason: {result.feedback}")
|
||||
return {"status": "archived"}
|
||||
|
||||
@listen("needs_revision")
|
||||
def queue_revision(self, result):
|
||||
print(f"Queued for revision: {result.feedback}")
|
||||
return {"status": "revision_needed"}
|
||||
|
||||
|
||||
# Starting the flow (will pause and wait for Slack response)
|
||||
def start_content_pipeline():
|
||||
flow = ContentPipeline()
|
||||
result = flow.kickoff()
|
||||
|
||||
if isinstance(result, HumanFeedbackPending):
|
||||
return {"status": "pending", "flow_id": result.context.flow_id}
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# Resuming when Slack webhook fires (sync handler)
|
||||
def on_slack_feedback(flow_id: str, slack_message: str):
|
||||
flow = ContentPipeline.from_pending(flow_id)
|
||||
result = flow.resume(slack_message)
|
||||
return result
|
||||
|
||||
|
||||
# If your handler is async (FastAPI, aiohttp, Slack Bolt async, etc.)
|
||||
async def on_slack_feedback_async(flow_id: str, slack_message: str):
|
||||
flow = ContentPipeline.from_pending(flow_id)
|
||||
result = await flow.resume_async(slack_message)
|
||||
return result
|
||||
```
|
||||
|
||||
<Warning>
|
||||
If you're using an async web framework (FastAPI, aiohttp, Slack Bolt async mode), use `await flow.resume_async()` instead of `flow.resume()`. Calling `resume()` from within a running event loop will raise a `RuntimeError`.
|
||||
</Warning>
|
||||
|
||||
### Best Practices for Async Feedback
|
||||
|
||||
1. **Check the return type**: `kickoff()` returns `HumanFeedbackPending` when paused—no try/except needed
|
||||
2. **Use the right resume method**: Use `resume()` in sync code, `await resume_async()` in async code
|
||||
3. **Store callback info**: Use `callback_info` to store webhook URLs, ticket IDs, etc.
|
||||
4. **Implement idempotency**: Your resume handler should be idempotent for safety
|
||||
5. **Automatic persistence**: State is automatically saved when `HumanFeedbackPending` is raised and uses `SQLiteFlowPersistence` by default
|
||||
6. **Custom persistence**: Pass a custom persistence instance to `from_pending()` if needed
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- [Flows Overview](/en/concepts/flows) - Learn about CrewAI Flows
|
||||
- [Flow State Management](/en/guides/flows/mastering-flow-state) - Managing state in flows
|
||||
- [Flow Persistence](/en/concepts/flows#persistence) - Persisting flow state
|
||||
- [Routing with @router](/en/concepts/flows#router) - More about conditional routing
|
||||
- [Human Input on Execution](/en/learn/human-input-on-execution) - Task-level human input
|
||||
@@ -5,9 +5,22 @@ icon: "user-check"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
Human-in-the-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.
|
||||
Human-in-the-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. CrewAI provides multiple ways to implement HITL depending on your needs.
|
||||
|
||||
## Setting Up HITL Workflows
|
||||
## Choosing Your HITL Approach
|
||||
|
||||
CrewAI offers two main approaches for implementing human-in-the-loop workflows:
|
||||
|
||||
| Approach | Best For | Integration |
|
||||
|----------|----------|-------------|
|
||||
| **Flow-based** (`@human_feedback` decorator) | Local development, console-based review, synchronous workflows | [Human Feedback in Flows](/en/learn/human-feedback-in-flows) |
|
||||
| **Webhook-based** (Enterprise) | Production deployments, async workflows, external integrations (Slack, Teams, etc.) | This guide |
|
||||
|
||||
<Tip>
|
||||
If you're building flows and want to add human review steps with routing based on feedback, check out the [Human Feedback in Flows](/en/learn/human-feedback-in-flows) guide for the `@human_feedback` decorator.
|
||||
</Tip>
|
||||
|
||||
## Setting Up Webhook-Based HITL Workflows
|
||||
|
||||
<Steps>
|
||||
<Step title="Configure Your Task">
|
||||
|
||||
@@ -35,7 +35,7 @@ info:
|
||||
|
||||
1. **Discover inputs** using `GET /inputs`
|
||||
2. **Start execution** using `POST /kickoff`
|
||||
3. **Monitor progress** using `GET /status/{kickoff_id}`
|
||||
3. **Monitor progress** using `GET /{kickoff_id}/status`
|
||||
version: 1.0.0
|
||||
contact:
|
||||
name: CrewAI Support
|
||||
@@ -63,7 +63,7 @@ paths:
|
||||
Use this endpoint to discover what inputs you need to provide when starting a crew execution.
|
||||
operationId: getRequiredInputs
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Successfully retrieved required inputs
|
||||
content:
|
||||
application/json:
|
||||
@@ -84,13 +84,21 @@ paths:
|
||||
outreach_crew:
|
||||
summary: Outreach crew inputs
|
||||
value:
|
||||
inputs: ["name", "title", "company", "industry", "our_product", "linkedin_url"]
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
$ref: '#/components/responses/NotFoundError'
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
inputs:
|
||||
[
|
||||
"name",
|
||||
"title",
|
||||
"company",
|
||||
"industry",
|
||||
"our_product",
|
||||
"linkedin_url",
|
||||
]
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
$ref: "#/components/responses/NotFoundError"
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/kickoff:
|
||||
post:
|
||||
@@ -170,7 +178,7 @@ paths:
|
||||
taskWebhookUrl: "https://api.example.com/webhooks/task"
|
||||
crewWebhookUrl: "https://api.example.com/webhooks/crew"
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Crew execution started successfully
|
||||
content:
|
||||
application/json:
|
||||
@@ -182,24 +190,24 @@ paths:
|
||||
format: uuid
|
||||
description: Unique identifier for tracking this execution
|
||||
example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
'400':
|
||||
"400":
|
||||
description: Invalid request body or missing required inputs
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'422':
|
||||
$ref: "#/components/schemas/Error"
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"422":
|
||||
description: Validation error - ensure all required inputs are provided
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ValidationError'
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
$ref: "#/components/schemas/ValidationError"
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/status/{kickoff_id}:
|
||||
/{kickoff_id}/status:
|
||||
get:
|
||||
summary: Get Execution Status
|
||||
description: |
|
||||
@@ -222,15 +230,15 @@ paths:
|
||||
format: uuid
|
||||
example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Successfully retrieved execution status
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/ExecutionRunning'
|
||||
- $ref: '#/components/schemas/ExecutionCompleted'
|
||||
- $ref: '#/components/schemas/ExecutionError'
|
||||
- $ref: "#/components/schemas/ExecutionRunning"
|
||||
- $ref: "#/components/schemas/ExecutionCompleted"
|
||||
- $ref: "#/components/schemas/ExecutionError"
|
||||
examples:
|
||||
running:
|
||||
summary: Execution in progress
|
||||
@@ -262,19 +270,19 @@ paths:
|
||||
status: "error"
|
||||
error: "Task execution failed: Invalid API key for external service"
|
||||
execution_time: 23.1
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
description: Kickoff ID not found
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Execution not found"
|
||||
message: "No execution found with ID: abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/resume:
|
||||
post:
|
||||
@@ -354,7 +362,7 @@ paths:
|
||||
taskWebhookUrl: "https://api.example.com/webhooks/task"
|
||||
crewWebhookUrl: "https://api.example.com/webhooks/crew"
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Execution resumed successfully
|
||||
content:
|
||||
application/json:
|
||||
@@ -381,28 +389,28 @@ paths:
|
||||
value:
|
||||
status: "retrying"
|
||||
message: "Task will be retried with your feedback"
|
||||
'400':
|
||||
"400":
|
||||
description: Invalid request body or execution not in pending state
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Invalid Request"
|
||||
message: "Execution is not in pending human input state"
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
description: Execution ID or Task ID not found
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Not Found"
|
||||
message: "Execution ID not found"
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
components:
|
||||
securitySchemes:
|
||||
@@ -458,7 +466,7 @@ components:
|
||||
tasks:
|
||||
type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/TaskResult'
|
||||
$ref: "#/components/schemas/TaskResult"
|
||||
execution_time:
|
||||
type: number
|
||||
description: Total execution time in seconds
|
||||
@@ -536,7 +544,7 @@ components:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Unauthorized"
|
||||
message: "Invalid or missing bearer token"
|
||||
@@ -546,7 +554,7 @@ components:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Not Found"
|
||||
message: "The requested resource was not found"
|
||||
@@ -556,7 +564,7 @@ components:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Internal Server Error"
|
||||
message: "An unexpected error occurred"
|
||||
|
||||
@@ -35,7 +35,7 @@ info:
|
||||
|
||||
1. **Discover inputs** using `GET /inputs`
|
||||
2. **Start execution** using `POST /kickoff`
|
||||
3. **Monitor progress** using `GET /status/{kickoff_id}`
|
||||
3. **Monitor progress** using `GET /{kickoff_id}/status`
|
||||
version: 1.0.0
|
||||
contact:
|
||||
name: CrewAI Support
|
||||
@@ -63,7 +63,7 @@ paths:
|
||||
Use this endpoint to discover what inputs you need to provide when starting a crew execution.
|
||||
operationId: getRequiredInputs
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Successfully retrieved required inputs
|
||||
content:
|
||||
application/json:
|
||||
@@ -84,13 +84,21 @@ paths:
|
||||
outreach_crew:
|
||||
summary: Outreach crew inputs
|
||||
value:
|
||||
inputs: ["name", "title", "company", "industry", "our_product", "linkedin_url"]
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
$ref: '#/components/responses/NotFoundError'
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
inputs:
|
||||
[
|
||||
"name",
|
||||
"title",
|
||||
"company",
|
||||
"industry",
|
||||
"our_product",
|
||||
"linkedin_url",
|
||||
]
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
$ref: "#/components/responses/NotFoundError"
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/kickoff:
|
||||
post:
|
||||
@@ -170,7 +178,7 @@ paths:
|
||||
taskWebhookUrl: "https://api.example.com/webhooks/task"
|
||||
crewWebhookUrl: "https://api.example.com/webhooks/crew"
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Crew execution started successfully
|
||||
content:
|
||||
application/json:
|
||||
@@ -182,24 +190,24 @@ paths:
|
||||
format: uuid
|
||||
description: Unique identifier for tracking this execution
|
||||
example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
'400':
|
||||
"400":
|
||||
description: Invalid request body or missing required inputs
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'422':
|
||||
$ref: "#/components/schemas/Error"
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"422":
|
||||
description: Validation error - ensure all required inputs are provided
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ValidationError'
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
$ref: "#/components/schemas/ValidationError"
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/status/{kickoff_id}:
|
||||
/{kickoff_id}/status:
|
||||
get:
|
||||
summary: Get Execution Status
|
||||
description: |
|
||||
@@ -222,15 +230,15 @@ paths:
|
||||
format: uuid
|
||||
example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Successfully retrieved execution status
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/ExecutionRunning'
|
||||
- $ref: '#/components/schemas/ExecutionCompleted'
|
||||
- $ref: '#/components/schemas/ExecutionError'
|
||||
- $ref: "#/components/schemas/ExecutionRunning"
|
||||
- $ref: "#/components/schemas/ExecutionCompleted"
|
||||
- $ref: "#/components/schemas/ExecutionError"
|
||||
examples:
|
||||
running:
|
||||
summary: Execution in progress
|
||||
@@ -262,19 +270,19 @@ paths:
|
||||
status: "error"
|
||||
error: "Task execution failed: Invalid API key for external service"
|
||||
execution_time: 23.1
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
description: Kickoff ID not found
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Execution not found"
|
||||
message: "No execution found with ID: abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/resume:
|
||||
post:
|
||||
@@ -354,7 +362,7 @@ paths:
|
||||
taskWebhookUrl: "https://api.example.com/webhooks/task"
|
||||
crewWebhookUrl: "https://api.example.com/webhooks/crew"
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Execution resumed successfully
|
||||
content:
|
||||
application/json:
|
||||
@@ -381,28 +389,28 @@ paths:
|
||||
value:
|
||||
status: "retrying"
|
||||
message: "Task will be retried with your feedback"
|
||||
'400':
|
||||
"400":
|
||||
description: Invalid request body or execution not in pending state
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Invalid Request"
|
||||
message: "Execution is not in pending human input state"
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
description: Execution ID or Task ID not found
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Not Found"
|
||||
message: "Execution ID not found"
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
components:
|
||||
securitySchemes:
|
||||
@@ -458,7 +466,7 @@ components:
|
||||
tasks:
|
||||
type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/TaskResult'
|
||||
$ref: "#/components/schemas/TaskResult"
|
||||
execution_time:
|
||||
type: number
|
||||
description: Total execution time in seconds
|
||||
@@ -536,7 +544,7 @@ components:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Unauthorized"
|
||||
message: "Invalid or missing bearer token"
|
||||
@@ -546,7 +554,7 @@ components:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Not Found"
|
||||
message: "The requested resource was not found"
|
||||
@@ -556,7 +564,7 @@ components:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Internal Server Error"
|
||||
message: "An unexpected error occurred"
|
||||
|
||||
@@ -84,7 +84,7 @@ paths:
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
|
||||
/status/{kickoff_id}:
|
||||
/{kickoff_id}/status:
|
||||
get:
|
||||
summary: 실행 상태 조회
|
||||
description: |
|
||||
|
||||
@@ -35,7 +35,7 @@ info:
|
||||
|
||||
1. **Descubra os inputs** usando `GET /inputs`
|
||||
2. **Inicie a execução** usando `POST /kickoff`
|
||||
3. **Monitore o progresso** usando `GET /status/{kickoff_id}`
|
||||
3. **Monitore o progresso** usando `GET /{kickoff_id}/status`
|
||||
version: 1.0.0
|
||||
contact:
|
||||
name: CrewAI Suporte
|
||||
@@ -56,7 +56,7 @@ paths:
|
||||
Retorna a lista de parâmetros de entrada que sua crew espera.
|
||||
operationId: getRequiredInputs
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Inputs requeridos obtidos com sucesso
|
||||
content:
|
||||
application/json:
|
||||
@@ -69,12 +69,12 @@ paths:
|
||||
type: string
|
||||
description: Nomes dos parâmetros de entrada
|
||||
example: ["budget", "interests", "duration", "age"]
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
$ref: '#/components/responses/NotFoundError'
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
$ref: "#/components/responses/NotFoundError"
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/kickoff:
|
||||
post:
|
||||
@@ -104,7 +104,7 @@ paths:
|
||||
age: "35"
|
||||
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Execução iniciada com sucesso
|
||||
content:
|
||||
application/json:
|
||||
@@ -115,12 +115,12 @@ paths:
|
||||
type: string
|
||||
format: uuid
|
||||
example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/status/{kickoff_id}:
|
||||
/{kickoff_id}/status:
|
||||
get:
|
||||
summary: Obter Status da Execução
|
||||
description: |
|
||||
@@ -136,25 +136,25 @@ paths:
|
||||
type: string
|
||||
format: uuid
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Status recuperado com sucesso
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/ExecutionRunning'
|
||||
- $ref: '#/components/schemas/ExecutionCompleted'
|
||||
- $ref: '#/components/schemas/ExecutionError'
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
- $ref: "#/components/schemas/ExecutionRunning"
|
||||
- $ref: "#/components/schemas/ExecutionCompleted"
|
||||
- $ref: "#/components/schemas/ExecutionError"
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
description: Kickoff ID não encontrado
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
$ref: "#/components/schemas/Error"
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
/resume:
|
||||
post:
|
||||
@@ -234,7 +234,7 @@ paths:
|
||||
taskWebhookUrl: "https://api.example.com/webhooks/task"
|
||||
crewWebhookUrl: "https://api.example.com/webhooks/crew"
|
||||
responses:
|
||||
'200':
|
||||
"200":
|
||||
description: Execution resumed successfully
|
||||
content:
|
||||
application/json:
|
||||
@@ -261,28 +261,28 @@ paths:
|
||||
value:
|
||||
status: "retrying"
|
||||
message: "Task will be retried with your feedback"
|
||||
'400':
|
||||
"400":
|
||||
description: Invalid request body or execution not in pending state
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Invalid Request"
|
||||
message: "Execution is not in pending human input state"
|
||||
'401':
|
||||
$ref: '#/components/responses/UnauthorizedError'
|
||||
'404':
|
||||
"401":
|
||||
$ref: "#/components/responses/UnauthorizedError"
|
||||
"404":
|
||||
description: Execution ID or Task ID not found
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
example:
|
||||
error: "Not Found"
|
||||
message: "Execution ID not found"
|
||||
'500':
|
||||
$ref: '#/components/responses/ServerError'
|
||||
"500":
|
||||
$ref: "#/components/responses/ServerError"
|
||||
|
||||
components:
|
||||
securitySchemes:
|
||||
@@ -324,7 +324,7 @@ components:
|
||||
tasks:
|
||||
type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/TaskResult'
|
||||
$ref: "#/components/schemas/TaskResult"
|
||||
execution_time:
|
||||
type: number
|
||||
|
||||
@@ -380,16 +380,16 @@ components:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
NotFoundError:
|
||||
description: Recurso não encontrado
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
ServerError:
|
||||
description: Erro interno do servidor
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/Error'
|
||||
$ref: "#/components/schemas/Error"
|
||||
|
||||
@@ -16,16 +16,17 @@ CrewAI 엔터프라이즈 API 참고 자료에 오신 것을 환영합니다.
|
||||
CrewAI AOP 대시보드에서 자신의 crew 상세 페이지로 이동하여 Status 탭에서 Bearer Token을 복사하세요.
|
||||
</Step>
|
||||
|
||||
<Step title="필수 입력값 확인하기">
|
||||
`GET /inputs` 엔드포인트를 사용하여 crew가 기대하는 파라미터를 확인하세요.
|
||||
</Step>
|
||||
<Step title="필수 입력값 확인하기">
|
||||
`GET /inputs` 엔드포인트를 사용하여 crew가 기대하는 파라미터를 확인하세요.
|
||||
</Step>
|
||||
|
||||
<Step title="Crew 실행 시작하기">
|
||||
입력값과 함께 `POST /kickoff`를 호출하여 crew 실행을 시작하고 `kickoff_id`를 받으세요.
|
||||
</Step>
|
||||
<Step title="Crew 실행 시작하기">
|
||||
입력값과 함께 `POST /kickoff`를 호출하여 crew 실행을 시작하고 `kickoff_id`를
|
||||
받으세요.
|
||||
</Step>
|
||||
|
||||
<Step title="진행 상황 모니터링">
|
||||
`GET /status/{kickoff_id}`를 사용하여 실행 상태를 확인하고 결과를 조회하세요.
|
||||
`GET /{kickoff_id}/status`를 사용하여 실행 상태를 확인하고 결과를 조회하세요.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
@@ -40,13 +41,14 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
|
||||
### 토큰 유형
|
||||
|
||||
| 토큰 유형 | 범위 | 사용 사례 |
|
||||
|:-----------|:--------|:----------|
|
||||
| **Bearer Token** | 조직 단위 접근 | 전체 crew 운영, 서버 간 통합에 이상적 |
|
||||
| **User Bearer Token** | 사용자 범위 접근 | 제한된 권한, 사용자별 작업에 적합 |
|
||||
| 토큰 유형 | 범위 | 사용 사례 |
|
||||
| :-------------------- | :--------------- | :------------------------------------ |
|
||||
| **Bearer Token** | 조직 단위 접근 | 전체 crew 운영, 서버 간 통합에 이상적 |
|
||||
| **User Bearer Token** | 사용자 범위 접근 | 제한된 권한, 사용자별 작업에 적합 |
|
||||
|
||||
<Tip>
|
||||
두 토큰 유형 모두 CrewAI AOP 대시보드의 crew 상세 페이지 Status 탭에서 확인할 수 있습니다.
|
||||
두 토큰 유형 모두 CrewAI AOP 대시보드의 crew 상세 페이지 Status 탭에서 확인할
|
||||
수 있습니다.
|
||||
</Tip>
|
||||
|
||||
## 기본 URL
|
||||
@@ -63,29 +65,33 @@ https://your-crew-name.crewai.com
|
||||
|
||||
1. **탐색**: `GET /inputs`를 호출하여 crew가 필요한 것을 파악합니다.
|
||||
2. **실행**: `POST /kickoff`를 통해 입력값을 제출하여 처리를 시작합니다.
|
||||
3. **모니터링**: 완료될 때까지 `GET /status/{kickoff_id}`를 주기적으로 조회합니다.
|
||||
3. **모니터링**: 완료될 때까지 `GET /{kickoff_id}/status`를 주기적으로 조회합니다.
|
||||
4. **결과**: 완료된 응답에서 최종 출력을 추출합니다.
|
||||
|
||||
## 오류 처리
|
||||
|
||||
API는 표준 HTTP 상태 코드를 사용합니다:
|
||||
|
||||
| 코드 | 의미 |
|
||||
|------|:--------|
|
||||
| `200` | 성공 |
|
||||
| `400` | 잘못된 요청 - 잘못된 입력 형식 |
|
||||
| `401` | 인증 실패 - 잘못된 베어러 토큰 |
|
||||
| 코드 | 의미 |
|
||||
| ----- | :------------------------------------ |
|
||||
| `200` | 성공 |
|
||||
| `400` | 잘못된 요청 - 잘못된 입력 형식 |
|
||||
| `401` | 인증 실패 - 잘못된 베어러 토큰 |
|
||||
| `404` | 찾을 수 없음 - 리소스가 존재하지 않음 |
|
||||
| `422` | 유효성 검사 오류 - 필수 입력 누락 |
|
||||
| `500` | 서버 오류 - 지원팀에 문의하십시오 |
|
||||
| `422` | 유효성 검사 오류 - 필수 입력 누락 |
|
||||
| `500` | 서버 오류 - 지원팀에 문의하십시오 |
|
||||
|
||||
## 인터랙티브 테스트
|
||||
|
||||
<Info>
|
||||
**왜 "전송" 버튼이 없나요?** 각 CrewAI AOP 사용자는 고유한 crew URL을 가지므로, 혼동을 피하기 위해 인터랙티브 플레이그라운드 대신 **참조 모드**를 사용합니다. 이를 통해 비작동 전송 버튼 없이 요청이 어떻게 생겼는지 정확히 보여줍니다.
|
||||
**왜 "전송" 버튼이 없나요?** 각 CrewAI AOP 사용자는 고유한 crew URL을
|
||||
가지므로, 혼동을 피하기 위해 인터랙티브 플레이그라운드 대신 **참조 모드**를
|
||||
사용합니다. 이를 통해 비작동 전송 버튼 없이 요청이 어떻게 생겼는지 정확히
|
||||
보여줍니다.
|
||||
</Info>
|
||||
|
||||
각 엔드포인트 페이지에서는 다음을 확인할 수 있습니다:
|
||||
|
||||
- ✅ 모든 파라미터가 포함된 **정확한 요청 형식**
|
||||
- ✅ 성공 및 오류 사례에 대한 **응답 예시**
|
||||
- ✅ 여러 언어(cURL, Python, JavaScript 등)로 제공되는 **코드 샘플**
|
||||
@@ -103,6 +109,7 @@ API는 표준 HTTP 상태 코드를 사용합니다:
|
||||
</CardGroup>
|
||||
|
||||
**예시 작업 흐름:**
|
||||
|
||||
1. **cURL 예제를 복사**합니다 (엔드포인트 페이지에서)
|
||||
2. **`your-actual-crew-name.crewai.com`**을(를) 실제 crew URL로 교체합니다
|
||||
3. **Bearer 토큰을** 대시보드에서 복사한 실제 토큰으로 교체합니다
|
||||
@@ -111,10 +118,18 @@ API는 표준 HTTP 상태 코드를 사용합니다:
|
||||
## 도움이 필요하신가요?
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
|
||||
<Card
|
||||
title="Enterprise Support"
|
||||
icon="headset"
|
||||
href="mailto:support@crewai.com"
|
||||
>
|
||||
API 통합 및 문제 해결에 대한 지원을 받으세요
|
||||
</Card>
|
||||
<Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
|
||||
<Card
|
||||
title="Enterprise Dashboard"
|
||||
icon="chart-line"
|
||||
href="https://app.crewai.com"
|
||||
>
|
||||
crew를 관리하고 실행 로그를 확인하세요
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
---
|
||||
title: "GET /status/{kickoff_id}"
|
||||
title: "GET /{kickoff_id}/status"
|
||||
description: "실행 상태 조회"
|
||||
openapi: "/enterprise-api.ko.yaml GET /status/{kickoff_id}"
|
||||
openapi: "/enterprise-api.ko.yaml GET /{kickoff_id}/status"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
|
||||
|
||||
@@ -33,6 +33,7 @@ crewAI에서 crew는 일련의 작업을 달성하기 위해 함께 협력하는
|
||||
| **Planning** *(선택사항)* | `planning` | Crew에 계획 수립 기능을 추가. 활성화하면 각 Crew 반복 전에 모든 Crew 데이터를 AgentPlanner로 전송하여 작업계획을 세우고, 이 계획이 각 작업 설명에 추가됨. |
|
||||
| **Planning LLM** *(선택사항)* | `planning_llm` | 계획 과정에서 AgentPlanner가 사용하는 언어 모델. |
|
||||
| **Knowledge Sources** _(선택사항)_ | `knowledge_sources` | crew 수준에서 사용 가능한 지식 소스. 모든 agent가 접근 가능. |
|
||||
| **Stream** _(선택사항)_ | `stream` | 스트리밍 출력을 활성화하여 crew 실행 중 실시간 업데이트를 받을 수 있습니다. 청크를 반복할 수 있는 `CrewStreamingOutput` 객체를 반환합니다. 기본값은 `False`. |
|
||||
|
||||
<Tip>
|
||||
**Crew Max RPM**: `max_rpm` 속성은 crew가 분당 처리할 수 있는 최대 요청 수를 설정하며, 개별 agent의 `max_rpm` 설정을 crew 단위로 지정할 경우 오버라이드합니다.
|
||||
@@ -306,12 +307,27 @@ print(result)
|
||||
|
||||
### Crew를 시작하는 다양한 방법
|
||||
|
||||
crew가 구성되면, 적절한 시작 방법으로 workflow를 시작하세요. CrewAI는 kickoff 프로세스를 더 잘 제어할 수 있도록 여러 방법을 제공합니다: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, 그리고 `kickoff_for_each_async()`.
|
||||
crew가 구성되면, 적절한 시작 방법으로 workflow를 시작하세요. CrewAI는 kickoff 프로세스를 더 잘 제어할 수 있도록 여러 방법을 제공합니다.
|
||||
|
||||
#### 동기 메서드
|
||||
|
||||
- `kickoff()`: 정의된 process flow에 따라 실행 프로세스를 시작합니다.
|
||||
- `kickoff_for_each()`: 입력 이벤트나 컬렉션 내 각 항목에 대해 순차적으로 task를 실행합니다.
|
||||
- `kickoff_async()`: 비동기적으로 workflow를 시작합니다.
|
||||
- `kickoff_for_each_async()`: 입력 이벤트나 각 항목에 대해 비동기 처리를 활용하여 task를 동시에 실행합니다.
|
||||
|
||||
#### 비동기 메서드
|
||||
|
||||
CrewAI는 비동기 실행을 위해 두 가지 접근 방식을 제공합니다:
|
||||
|
||||
| 메서드 | 타입 | 설명 |
|
||||
|--------|------|-------------|
|
||||
| `akickoff()` | 네이티브 async | 전체 실행 체인에서 진정한 async/await 사용 |
|
||||
| `akickoff_for_each()` | 네이티브 async | 리스트의 각 입력에 대해 네이티브 async 실행 |
|
||||
| `kickoff_async()` | 스레드 기반 | 동기 실행을 `asyncio.to_thread`로 래핑 |
|
||||
| `kickoff_for_each_async()` | 스레드 기반 | 리스트의 각 입력에 대해 스레드 기반 async |
|
||||
|
||||
<Note>
|
||||
고동시성 워크로드의 경우 `akickoff()` 및 `akickoff_for_each()`가 권장됩니다. 이들은 작업 실행, 메모리 작업, 지식 검색에 네이티브 async를 사용합니다.
|
||||
</Note>
|
||||
|
||||
```python Code
|
||||
# Start the crew's task execution
|
||||
@@ -324,19 +340,53 @@ results = my_crew.kickoff_for_each(inputs=inputs_array)
|
||||
for result in results:
|
||||
print(result)
|
||||
|
||||
# Example of using kickoff_async
|
||||
# Example of using native async with akickoff
|
||||
inputs = {'topic': 'AI in healthcare'}
|
||||
async_result = await my_crew.akickoff(inputs=inputs)
|
||||
print(async_result)
|
||||
|
||||
# Example of using native async with akickoff_for_each
|
||||
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
|
||||
async_results = await my_crew.akickoff_for_each(inputs=inputs_array)
|
||||
for async_result in async_results:
|
||||
print(async_result)
|
||||
|
||||
# Example of using thread-based kickoff_async
|
||||
inputs = {'topic': 'AI in healthcare'}
|
||||
async_result = await my_crew.kickoff_async(inputs=inputs)
|
||||
print(async_result)
|
||||
|
||||
# Example of using kickoff_for_each_async
|
||||
# Example of using thread-based kickoff_for_each_async
|
||||
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
|
||||
async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
|
||||
for async_result in async_results:
|
||||
print(async_result)
|
||||
```
|
||||
|
||||
이러한 메서드는 crew 내에서 task를 관리하고 실행하는 데 유연성을 제공하며, 동기 및 비동기 workflow 모두 필요에 맞게 사용할 수 있도록 지원합니다.
|
||||
이러한 메서드는 crew 내에서 task를 관리하고 실행하는 데 유연성을 제공하며, 동기 및 비동기 workflow 모두 필요에 맞게 사용할 수 있도록 지원합니다. 자세한 비동기 예제는 [Crew 비동기 시작](/ko/learn/kickoff-async) 가이드를 참조하세요.
|
||||
|
||||
### 스트리밍 Crew 실행
|
||||
|
||||
crew 실행을 실시간으로 확인하려면 스트리밍을 활성화하여 출력이 생성되는 대로 받을 수 있습니다:
|
||||
|
||||
```python Code
|
||||
# 스트리밍 활성화
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
# 스트리밍 출력을 반복
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# 최종 결과 접근
|
||||
result = streaming.result
|
||||
```
|
||||
|
||||
스트리밍에 대한 자세한 내용은 [스트리밍 Crew 실행](/ko/learn/streaming-crew-execution) 가이드를 참조하세요.
|
||||
|
||||
### 특정 Task에서 다시 실행하기
|
||||
|
||||
|
||||
@@ -565,6 +565,55 @@ Fourth method running
|
||||
|
||||
이 Flow를 실행하면, `start_method`에서 생성된 랜덤 불리언 값에 따라 출력값이 달라집니다.
|
||||
|
||||
### Human in the Loop (인간 피드백)
|
||||
|
||||
`@human_feedback` 데코레이터는 인간의 피드백을 수집하기 위해 플로우 실행을 일시 중지하는 human-in-the-loop 워크플로우를 가능하게 합니다. 이는 승인 게이트, 품질 검토, 인간의 판단이 필요한 결정 지점에 유용합니다.
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="이 콘텐츠를 승인하시겠습니까?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def generate_content(self):
|
||||
return "검토할 콘텐츠..."
|
||||
|
||||
@listen("approved")
|
||||
def on_approval(self, result: HumanFeedbackResult):
|
||||
print(f"승인됨! 피드백: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def on_rejection(self, result: HumanFeedbackResult):
|
||||
print(f"거부됨. 이유: {result.feedback}")
|
||||
```
|
||||
|
||||
`emit`이 지정되면, 인간의 자유 형식 피드백이 LLM에 의해 해석되어 지정된 outcome 중 하나로 매핑되고, 해당 `@listen` 데코레이터를 트리거합니다.
|
||||
|
||||
라우팅 없이 단순히 피드백만 수집할 수도 있습니다:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(message="이 출력에 대한 코멘트가 있으신가요?")
|
||||
def my_method(self):
|
||||
return "검토할 출력"
|
||||
|
||||
@listen(my_method)
|
||||
def next_step(self, result: HumanFeedbackResult):
|
||||
# result.feedback로 피드백에 접근
|
||||
# result.output으로 원래 출력에 접근
|
||||
pass
|
||||
```
|
||||
|
||||
플로우 실행 중 수집된 모든 피드백은 `self.last_human_feedback` (가장 최근) 또는 `self.human_feedback_history` (리스트 형태의 모든 피드백)를 통해 접근할 수 있습니다.
|
||||
|
||||
플로우에서의 인간 피드백에 대한 완전한 가이드는 비동기/논블로킹 피드백과 커스텀 프로바이더(Slack, 웹훅 등)를 포함하여 [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows)을 참조하세요.
|
||||
|
||||
## 플로우에 에이전트 추가하기
|
||||
|
||||
에이전트는 플로우에 원활하게 통합할 수 있으며, 단순하고 집중된 작업 실행이 필요할 때 전체 Crew의 경량 대안으로 활용됩니다. 아래는 에이전트를 플로우 내에서 사용하여 시장 조사를 수행하는 예시입니다:
|
||||
|
||||
@@ -62,13 +62,13 @@ CrewAI CLI를 사용하여 Gmail 트리거 통합을 로컬에서 테스트하
|
||||
crewai triggers list
|
||||
|
||||
# 실제 payload로 Gmail 트리거 시뮬레이션
|
||||
crewai triggers run gmail/new_email
|
||||
crewai triggers run gmail/new_email_received
|
||||
```
|
||||
|
||||
`crewai triggers run` 명령은 완전한 Gmail payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
|
||||
|
||||
<Warning>
|
||||
개발 중에는 `crewai triggers run gmail/new_email`을 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
개발 중에는 `crewai triggers run gmail/new_email_received`을 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
</Warning>
|
||||
|
||||
## Monitoring Executions
|
||||
@@ -83,6 +83,6 @@ Track history and performance of triggered runs:
|
||||
|
||||
- Ensure Gmail is connected in Tools & Integrations
|
||||
- Verify the Gmail Trigger is enabled on the Triggers tab
|
||||
- `crewai triggers run gmail/new_email`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
|
||||
- `crewai triggers run gmail/new_email_received`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
|
||||
- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
|
||||
- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)
|
||||
|
||||
581
docs/ko/learn/human-feedback-in-flows.mdx
Normal file
581
docs/ko/learn/human-feedback-in-flows.mdx
Normal file
@@ -0,0 +1,581 @@
|
||||
---
|
||||
title: Flow에서 인간 피드백
|
||||
description: "@human_feedback 데코레이터를 사용하여 CrewAI Flow에 인간 피드백을 직접 통합하는 방법을 알아보세요"
|
||||
icon: user-check
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## 개요
|
||||
|
||||
`@human_feedback` 데코레이터는 CrewAI Flow 내에서 직접 human-in-the-loop(HITL) 워크플로우를 가능하게 합니다. Flow 실행을 일시 중지하고, 인간에게 검토를 위해 출력을 제시하고, 피드백을 수집하고, 선택적으로 피드백 결과에 따라 다른 리스너로 라우팅할 수 있습니다.
|
||||
|
||||
이는 특히 다음과 같은 경우에 유용합니다:
|
||||
|
||||
- **품질 보증**: AI가 생성한 콘텐츠를 다운스트림에서 사용하기 전에 검토
|
||||
- **결정 게이트**: 자동화된 워크플로우에서 인간이 중요한 결정을 내리도록 허용
|
||||
- **승인 워크플로우**: 승인/거부/수정 패턴 구현
|
||||
- **대화형 개선**: 출력을 반복적으로 개선하기 위해 피드백 수집
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A[Flow 메서드] --> B[출력 생성됨]
|
||||
B --> C[인간이 검토]
|
||||
C --> D{피드백}
|
||||
D -->|emit 지정됨| E[LLM이 Outcome으로 매핑]
|
||||
D -->|emit 없음| F[HumanFeedbackResult]
|
||||
E --> G["@listen('approved')"]
|
||||
E --> H["@listen('rejected')"]
|
||||
F --> I[다음 리스너]
|
||||
```
|
||||
|
||||
## 빠른 시작
|
||||
|
||||
Flow에 인간 피드백을 추가하는 가장 간단한 방법은 다음과 같습니다:
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback
|
||||
|
||||
class SimpleReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(message="이 콘텐츠를 검토해 주세요:")
|
||||
def generate_content(self):
|
||||
return "검토가 필요한 AI 생성 콘텐츠입니다."
|
||||
|
||||
@listen(generate_content)
|
||||
def process_feedback(self, result):
|
||||
print(f"콘텐츠: {result.output}")
|
||||
print(f"인간의 의견: {result.feedback}")
|
||||
|
||||
flow = SimpleReviewFlow()
|
||||
flow.kickoff()
|
||||
```
|
||||
|
||||
이 Flow를 실행하면:
|
||||
1. `generate_content`를 실행하고 문자열을 반환합니다
|
||||
2. 요청 메시지와 함께 사용자에게 출력을 표시합니다
|
||||
3. 사용자가 피드백을 입력할 때까지 대기합니다 (또는 Enter를 눌러 건너뜁니다)
|
||||
4. `HumanFeedbackResult` 객체를 `process_feedback`에 전달합니다
|
||||
|
||||
## @human_feedback 데코레이터
|
||||
|
||||
### 매개변수
|
||||
|
||||
| 매개변수 | 타입 | 필수 | 설명 |
|
||||
|----------|------|------|------|
|
||||
| `message` | `str` | 예 | 메서드 출력과 함께 인간에게 표시되는 메시지 |
|
||||
| `emit` | `Sequence[str]` | 아니오 | 가능한 outcome 목록. 피드백이 이 중 하나로 매핑되어 `@listen` 데코레이터를 트리거합니다 |
|
||||
| `llm` | `str \| BaseLLM` | `emit` 지정 시 | 피드백을 해석하고 outcome에 매핑하는 데 사용되는 LLM |
|
||||
| `default_outcome` | `str` | 아니오 | 피드백이 제공되지 않을 때 사용할 outcome. `emit`에 있어야 합니다 |
|
||||
| `metadata` | `dict` | 아니오 | 엔터프라이즈 통합을 위한 추가 데이터 |
|
||||
| `provider` | `HumanFeedbackProvider` | 아니오 | 비동기/논블로킹 피드백을 위한 커스텀 프로바이더. [비동기 인간 피드백](#비동기-인간-피드백-논블로킹) 참조 |
|
||||
|
||||
### 기본 사용법 (라우팅 없음)
|
||||
|
||||
`emit`을 지정하지 않으면, 데코레이터는 단순히 피드백을 수집하고 다음 리스너에 `HumanFeedbackResult`를 전달합니다:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(message="이 분석에 대해 어떻게 생각하시나요?")
|
||||
def analyze_data(self):
|
||||
return "분석 결과: 매출 15% 증가, 비용 8% 감소"
|
||||
|
||||
@listen(analyze_data)
|
||||
def handle_feedback(self, result):
|
||||
# result는 HumanFeedbackResult입니다
|
||||
print(f"분석: {result.output}")
|
||||
print(f"피드백: {result.feedback}")
|
||||
```
|
||||
|
||||
### emit을 사용한 라우팅
|
||||
|
||||
`emit`을 지정하면, 데코레이터는 라우터가 됩니다. 인간의 자유 형식 피드백이 LLM에 의해 해석되어 지정된 outcome 중 하나로 매핑됩니다:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="이 콘텐츠의 출판을 승인하시겠습니까?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_content(self):
|
||||
return "블로그 게시물 초안 내용..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
print(f"출판 중! 사용자 의견: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def discard(self, result):
|
||||
print(f"폐기됨. 이유: {result.feedback}")
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise(self, result):
|
||||
print(f"다음을 기반으로 수정 중: {result.feedback}")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
LLM은 가능한 경우 구조화된 출력(function calling)을 사용하여 응답이 지정된 outcome 중 하나임을 보장합니다. 이로 인해 라우팅이 신뢰할 수 있고 예측 가능해집니다.
|
||||
</Tip>
|
||||
|
||||
## HumanFeedbackResult
|
||||
|
||||
`HumanFeedbackResult` 데이터클래스는 인간 피드백 상호작용에 대한 모든 정보를 포함합니다:
|
||||
|
||||
```python Code
|
||||
from crewai.flow.human_feedback import HumanFeedbackResult
|
||||
|
||||
@dataclass
|
||||
class HumanFeedbackResult:
|
||||
output: Any # 인간에게 표시된 원래 메서드 출력
|
||||
feedback: str # 인간의 원시 피드백 텍스트
|
||||
outcome: str | None # 매핑된 outcome (emit이 지정된 경우)
|
||||
timestamp: datetime # 피드백이 수신된 시간
|
||||
method_name: str # 데코레이터된 메서드의 이름
|
||||
metadata: dict # 데코레이터에 전달된 모든 메타데이터
|
||||
```
|
||||
|
||||
### 리스너에서 접근하기
|
||||
|
||||
`emit`이 있는 `@human_feedback` 메서드에 의해 리스너가 트리거되면, `HumanFeedbackResult`를 받습니다:
|
||||
|
||||
```python Code
|
||||
@listen("approved")
|
||||
def on_approval(self, result: HumanFeedbackResult):
|
||||
print(f"원래 출력: {result.output}")
|
||||
print(f"사용자 피드백: {result.feedback}")
|
||||
print(f"Outcome: {result.outcome}") # "approved"
|
||||
print(f"수신 시간: {result.timestamp}")
|
||||
```
|
||||
|
||||
## 피드백 히스토리 접근하기
|
||||
|
||||
`Flow` 클래스는 인간 피드백에 접근하기 위한 두 가지 속성을 제공합니다:
|
||||
|
||||
### last_human_feedback
|
||||
|
||||
가장 최근의 `HumanFeedbackResult`를 반환합니다:
|
||||
|
||||
```python Code
|
||||
@listen(some_method)
|
||||
def check_feedback(self):
|
||||
if self.last_human_feedback:
|
||||
print(f"마지막 피드백: {self.last_human_feedback.feedback}")
|
||||
```
|
||||
|
||||
### human_feedback_history
|
||||
|
||||
Flow 동안 수집된 모든 `HumanFeedbackResult` 객체의 리스트입니다:
|
||||
|
||||
```python Code
|
||||
@listen(final_step)
|
||||
def summarize(self):
|
||||
print(f"수집된 총 피드백: {len(self.human_feedback_history)}")
|
||||
for i, fb in enumerate(self.human_feedback_history):
|
||||
print(f"{i+1}. {fb.method_name}: {fb.outcome or '라우팅 없음'}")
|
||||
```
|
||||
|
||||
<Warning>
|
||||
각 `HumanFeedbackResult`는 `human_feedback_history`에 추가되므로, 여러 피드백 단계가 서로 덮어쓰지 않습니다. 이 리스트를 사용하여 Flow 동안 수집된 모든 피드백에 접근하세요.
|
||||
</Warning>
|
||||
|
||||
## 완전한 예제: 콘텐츠 승인 워크플로우
|
||||
|
||||
콘텐츠 검토 및 승인 워크플로우를 구현하는 전체 예제입니다:
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class ContentState(BaseModel):
|
||||
topic: str = ""
|
||||
draft: str = ""
|
||||
final_content: str = ""
|
||||
revision_count: int = 0
|
||||
|
||||
|
||||
class ContentApprovalFlow(Flow[ContentState]):
|
||||
"""콘텐츠를 생성하고 인간의 승인을 받는 Flow입니다."""
|
||||
|
||||
@start()
|
||||
def get_topic(self):
|
||||
self.state.topic = input("어떤 주제에 대해 글을 쓸까요? ")
|
||||
return self.state.topic
|
||||
|
||||
@listen(get_topic)
|
||||
def generate_draft(self, topic):
|
||||
# 실제 사용에서는 LLM을 호출합니다
|
||||
self.state.draft = f"# {topic}\n\n{topic}에 대한 초안입니다..."
|
||||
return self.state.draft
|
||||
|
||||
@listen(generate_draft)
|
||||
@human_feedback(
|
||||
message="이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_draft(self, draft):
|
||||
return draft
|
||||
|
||||
@listen("approved")
|
||||
def publish_content(self, result: HumanFeedbackResult):
|
||||
self.state.final_content = result.output
|
||||
print("\n✅ 콘텐츠가 승인되어 출판되었습니다!")
|
||||
print(f"검토자 코멘트: {result.feedback}")
|
||||
return "published"
|
||||
|
||||
@listen("rejected")
|
||||
def handle_rejection(self, result: HumanFeedbackResult):
|
||||
print("\n❌ 콘텐츠가 거부되었습니다")
|
||||
print(f"이유: {result.feedback}")
|
||||
return "rejected"
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise_content(self, result: HumanFeedbackResult):
|
||||
self.state.revision_count += 1
|
||||
print(f"\n📝 수정 #{self.state.revision_count} 요청됨")
|
||||
print(f"피드백: {result.feedback}")
|
||||
|
||||
# 실제 Flow에서는 generate_draft로 돌아갈 수 있습니다
|
||||
# 이 예제에서는 단순히 확인합니다
|
||||
return "revision_requested"
|
||||
|
||||
|
||||
# Flow 실행
|
||||
flow = ContentApprovalFlow()
|
||||
result = flow.kickoff()
|
||||
print(f"\nFlow 완료. 요청된 수정: {flow.state.revision_count}")
|
||||
```
|
||||
|
||||
```text Output
|
||||
어떤 주제에 대해 글을 쓸까요? AI 안전
|
||||
|
||||
==================================================
|
||||
OUTPUT FOR REVIEW:
|
||||
==================================================
|
||||
# AI 안전
|
||||
|
||||
AI 안전에 대한 초안입니다...
|
||||
==================================================
|
||||
|
||||
이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요:
|
||||
(Press Enter to skip, or type your feedback)
|
||||
|
||||
Your feedback: 좋아 보입니다, 승인!
|
||||
|
||||
✅ 콘텐츠가 승인되어 출판되었습니다!
|
||||
검토자 코멘트: 좋아 보입니다, 승인!
|
||||
|
||||
Flow 완료. 요청된 수정: 0
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## 다른 데코레이터와 결합하기
|
||||
|
||||
`@human_feedback` 데코레이터는 다른 Flow 데코레이터와 함께 작동합니다. 가장 안쪽 데코레이터(함수에 가장 가까운)로 배치하세요:
|
||||
|
||||
```python Code
|
||||
# 올바름: @human_feedback이 가장 안쪽(함수에 가장 가까움)
|
||||
@start()
|
||||
@human_feedback(message="이것을 검토해 주세요:")
|
||||
def my_start_method(self):
|
||||
return "content"
|
||||
|
||||
@listen(other_method)
|
||||
@human_feedback(message="이것도 검토해 주세요:")
|
||||
def my_listener(self, data):
|
||||
return f"processed: {data}"
|
||||
```
|
||||
|
||||
<Tip>
|
||||
`@human_feedback`를 가장 안쪽 데코레이터(마지막/함수에 가장 가까움)로 배치하여 메서드를 직접 래핑하고 Flow 시스템에 전달하기 전에 반환 값을 캡처할 수 있도록 하세요.
|
||||
</Tip>
|
||||
|
||||
## 모범 사례
|
||||
|
||||
### 1. 명확한 요청 메시지 작성
|
||||
|
||||
`message` 매개변수는 인간이 보는 것입니다. 실행 가능하게 만드세요:
|
||||
|
||||
```python Code
|
||||
# ✅ 좋음 - 명확하고 실행 가능
|
||||
@human_feedback(message="이 요약이 핵심 포인트를 정확하게 캡처했나요? '예'로 답하거나 무엇이 빠졌는지 설명해 주세요:")
|
||||
|
||||
# ❌ 나쁨 - 모호함
|
||||
@human_feedback(message="이것을 검토해 주세요:")
|
||||
```
|
||||
|
||||
### 2. 의미 있는 Outcome 선택
|
||||
|
||||
`emit`을 사용할 때, 인간의 응답에 자연스럽게 매핑되는 outcome을 선택하세요:
|
||||
|
||||
```python Code
|
||||
# ✅ 좋음 - 자연어 outcome
|
||||
emit=["approved", "rejected", "needs_more_detail"]
|
||||
|
||||
# ❌ 나쁨 - 기술적이거나 불명확
|
||||
emit=["state_1", "state_2", "state_3"]
|
||||
```
|
||||
|
||||
### 3. 항상 기본 Outcome 제공
|
||||
|
||||
사용자가 입력 없이 Enter를 누르는 경우를 처리하기 위해 `default_outcome`을 사용하세요:
|
||||
|
||||
```python Code
|
||||
@human_feedback(
|
||||
message="승인하시겠습니까? (수정 요청하려면 Enter 누르세요)",
|
||||
emit=["approved", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision", # 안전한 기본값
|
||||
)
|
||||
```
|
||||
|
||||
### 4. 감사 추적을 위한 피드백 히스토리 사용
|
||||
|
||||
감사 로그를 생성하기 위해 `human_feedback_history`에 접근하세요:
|
||||
|
||||
```python Code
|
||||
@listen(final_step)
|
||||
def create_audit_log(self):
|
||||
log = []
|
||||
for fb in self.human_feedback_history:
|
||||
log.append({
|
||||
"step": fb.method_name,
|
||||
"outcome": fb.outcome,
|
||||
"feedback": fb.feedback,
|
||||
"timestamp": fb.timestamp.isoformat(),
|
||||
})
|
||||
return log
|
||||
```
|
||||
|
||||
### 5. 라우팅된 피드백과 라우팅되지 않은 피드백 모두 처리
|
||||
|
||||
Flow를 설계할 때, 라우팅이 필요한지 고려하세요:
|
||||
|
||||
| 시나리오 | 사용 |
|
||||
|----------|------|
|
||||
| 간단한 검토, 피드백 텍스트만 필요 | `emit` 없음 |
|
||||
| 응답에 따라 다른 경로로 분기 필요 | `emit` 사용 |
|
||||
| 승인/거부/수정이 있는 승인 게이트 | `emit` 사용 |
|
||||
| 로깅만을 위한 코멘트 수집 | `emit` 없음 |
|
||||
|
||||
## 비동기 인간 피드백 (논블로킹)
|
||||
|
||||
기본적으로 `@human_feedback`은 콘솔 입력을 기다리며 실행을 차단합니다. 프로덕션 애플리케이션에서는 Slack, 이메일, 웹훅 또는 API와 같은 외부 시스템과 통합되는 **비동기/논블로킹** 피드백이 필요할 수 있습니다.
|
||||
|
||||
### Provider 추상화
|
||||
|
||||
커스텀 피드백 수집 전략을 지정하려면 `provider` 매개변수를 사용하세요:
|
||||
|
||||
```python Code
|
||||
from crewai.flow import Flow, start, human_feedback, HumanFeedbackProvider, HumanFeedbackPending, PendingFeedbackContext
|
||||
|
||||
class WebhookProvider(HumanFeedbackProvider):
|
||||
"""웹훅 콜백을 기다리며 Flow를 일시 중지하는 Provider."""
|
||||
|
||||
def __init__(self, webhook_url: str):
|
||||
self.webhook_url = webhook_url
|
||||
|
||||
def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
|
||||
# 외부 시스템에 알림 (예: Slack 메시지 전송, 티켓 생성)
|
||||
self.send_notification(context)
|
||||
|
||||
# 실행 일시 중지 - 프레임워크가 자동으로 영속성 처리
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={"webhook_url": f"{self.webhook_url}/{context.flow_id}"}
|
||||
)
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="이 콘텐츠를 검토해 주세요:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
provider=WebhookProvider("https://myapp.com/api"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "AI가 생성한 콘텐츠..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
return "출판됨!"
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Flow 프레임워크는 `HumanFeedbackPending`이 발생하면 **자동으로 상태를 영속화**합니다. Provider는 외부 시스템에 알리고 예외를 발생시키기만 하면 됩니다—수동 영속성 호출이 필요하지 않습니다.
|
||||
</Tip>
|
||||
|
||||
### 일시 중지된 Flow 처리
|
||||
|
||||
비동기 provider를 사용하면 `kickoff()`는 예외를 발생시키는 대신 `HumanFeedbackPending` 객체를 반환합니다:
|
||||
|
||||
```python Code
|
||||
flow = ReviewFlow()
|
||||
result = flow.kickoff()
|
||||
|
||||
if isinstance(result, HumanFeedbackPending):
|
||||
# Flow가 일시 중지됨, 상태가 자동으로 영속화됨
|
||||
print(f"피드백 대기 중: {result.callback_info['webhook_url']}")
|
||||
print(f"Flow ID: {result.context.flow_id}")
|
||||
else:
|
||||
# 정상 완료
|
||||
print(f"Flow 완료: {result}")
|
||||
```
|
||||
|
||||
### 일시 중지된 Flow 재개
|
||||
|
||||
피드백이 도착하면 (예: 웹훅을 통해) Flow를 재개합니다:
|
||||
|
||||
```python Code
|
||||
# 동기 핸들러:
|
||||
def handle_feedback_webhook(flow_id: str, feedback: str):
|
||||
flow = ReviewFlow.from_pending(flow_id)
|
||||
result = flow.resume(feedback)
|
||||
return result
|
||||
|
||||
# 비동기 핸들러 (FastAPI, aiohttp 등):
|
||||
async def handle_feedback_webhook(flow_id: str, feedback: str):
|
||||
flow = ReviewFlow.from_pending(flow_id)
|
||||
result = await flow.resume_async(feedback)
|
||||
return result
|
||||
```
|
||||
|
||||
### 주요 타입
|
||||
|
||||
| 타입 | 설명 |
|
||||
|------|------|
|
||||
| `HumanFeedbackProvider` | 커스텀 피드백 provider를 위한 프로토콜 |
|
||||
| `PendingFeedbackContext` | 일시 중지된 Flow를 재개하는 데 필요한 모든 정보 포함 |
|
||||
| `HumanFeedbackPending` | Flow가 피드백을 위해 일시 중지되면 `kickoff()`에서 반환됨 |
|
||||
| `ConsoleProvider` | 기본 블로킹 콘솔 입력 provider |
|
||||
|
||||
### PendingFeedbackContext
|
||||
|
||||
컨텍스트는 재개에 필요한 모든 것을 포함합니다:
|
||||
|
||||
```python Code
|
||||
@dataclass
|
||||
class PendingFeedbackContext:
|
||||
flow_id: str # 이 Flow 실행의 고유 식별자
|
||||
flow_class: str # 정규화된 클래스 이름
|
||||
method_name: str # 피드백을 트리거한 메서드
|
||||
method_output: Any # 인간에게 표시된 출력
|
||||
message: str # 요청 메시지
|
||||
emit: list[str] | None # 라우팅을 위한 가능한 outcome
|
||||
default_outcome: str | None
|
||||
metadata: dict # 커스텀 메타데이터
|
||||
llm: str | None # outcome 매핑을 위한 LLM
|
||||
requested_at: datetime
|
||||
```
|
||||
|
||||
### 완전한 비동기 Flow 예제
|
||||
|
||||
```python Code
|
||||
from crewai.flow import (
|
||||
Flow, start, listen, human_feedback,
|
||||
HumanFeedbackProvider, HumanFeedbackPending, PendingFeedbackContext
|
||||
)
|
||||
|
||||
class SlackNotificationProvider(HumanFeedbackProvider):
|
||||
"""Slack 알림을 보내고 비동기 피드백을 위해 일시 중지하는 Provider."""
|
||||
|
||||
def __init__(self, channel: str):
|
||||
self.channel = channel
|
||||
|
||||
def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
|
||||
# Slack 알림 전송 (직접 구현)
|
||||
slack_thread_id = self.post_to_slack(
|
||||
channel=self.channel,
|
||||
message=f"검토 필요:\n\n{context.method_output}\n\n{context.message}",
|
||||
)
|
||||
|
||||
# 실행 일시 중지 - 프레임워크가 자동으로 영속성 처리
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={
|
||||
"slack_channel": self.channel,
|
||||
"thread_id": slack_thread_id,
|
||||
}
|
||||
)
|
||||
|
||||
class ContentPipeline(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="이 콘텐츠의 출판을 승인하시겠습니까?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
provider=SlackNotificationProvider("#content-reviews"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "AI가 생성한 블로그 게시물 콘텐츠..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
print(f"출판 중! 검토자 의견: {result.feedback}")
|
||||
return {"status": "published"}
|
||||
|
||||
@listen("rejected")
|
||||
def archive(self, result):
|
||||
print(f"보관됨. 이유: {result.feedback}")
|
||||
return {"status": "archived"}
|
||||
|
||||
@listen("needs_revision")
|
||||
def queue_revision(self, result):
|
||||
print(f"수정 대기열에 추가됨: {result.feedback}")
|
||||
return {"status": "revision_needed"}
|
||||
|
||||
|
||||
# Flow 시작 (Slack 응답을 기다리며 일시 중지)
|
||||
def start_content_pipeline():
|
||||
flow = ContentPipeline()
|
||||
result = flow.kickoff()
|
||||
|
||||
if isinstance(result, HumanFeedbackPending):
|
||||
return {"status": "pending", "flow_id": result.context.flow_id}
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# Slack 웹훅이 실행될 때 재개 (동기 핸들러)
|
||||
def on_slack_feedback(flow_id: str, slack_message: str):
|
||||
flow = ContentPipeline.from_pending(flow_id)
|
||||
result = flow.resume(slack_message)
|
||||
return result
|
||||
|
||||
|
||||
# 핸들러가 비동기인 경우 (FastAPI, aiohttp, Slack Bolt 비동기 등)
|
||||
async def on_slack_feedback_async(flow_id: str, slack_message: str):
|
||||
flow = ContentPipeline.from_pending(flow_id)
|
||||
result = await flow.resume_async(slack_message)
|
||||
return result
|
||||
```
|
||||
|
||||
<Warning>
|
||||
비동기 웹 프레임워크(FastAPI, aiohttp, Slack Bolt 비동기 모드)를 사용하는 경우 `flow.resume()` 대신 `await flow.resume_async()`를 사용하세요. 실행 중인 이벤트 루프 내에서 `resume()`을 호출하면 `RuntimeError`가 발생합니다.
|
||||
</Warning>
|
||||
|
||||
### 비동기 피드백 모범 사례
|
||||
|
||||
1. **반환 타입 확인**: `kickoff()`는 일시 중지되면 `HumanFeedbackPending`을 반환합니다—try/except가 필요하지 않습니다
|
||||
2. **올바른 resume 메서드 사용**: 동기 코드에서는 `resume()`, 비동기 코드에서는 `await resume_async()` 사용
|
||||
3. **콜백 정보 저장**: `callback_info`를 사용하여 웹훅 URL, 티켓 ID 등을 저장
|
||||
4. **멱등성 구현**: 안전을 위해 resume 핸들러는 멱등해야 합니다
|
||||
5. **자동 영속성**: `HumanFeedbackPending`이 발생하면 상태가 자동으로 저장되며 기본적으로 `SQLiteFlowPersistence` 사용
|
||||
6. **커스텀 영속성**: 필요한 경우 `from_pending()`에 커스텀 영속성 인스턴스 전달
|
||||
|
||||
## 관련 문서
|
||||
|
||||
- [Flow 개요](/ko/concepts/flows) - CrewAI Flow에 대해 알아보기
|
||||
- [Flow 상태 관리](/ko/guides/flows/mastering-flow-state) - Flow에서 상태 관리하기
|
||||
- [Flow 영속성](/ko/concepts/flows#persistence) - Flow 상태 영속화
|
||||
- [@router를 사용한 라우팅](/ko/concepts/flows#router) - 조건부 라우팅에 대해 더 알아보기
|
||||
- [실행 시 인간 입력](/ko/learn/human-input-on-execution) - 태스크 수준 인간 입력
|
||||
@@ -7,17 +7,28 @@ mode: "wide"
|
||||
|
||||
## 소개
|
||||
|
||||
CrewAI는 crew를 비동기적으로 시작할 수 있는 기능을 제공합니다. 이를 통해 crew 실행을 블로킹(blocking) 없이 시작할 수 있습니다.
|
||||
CrewAI는 crew를 비동기적으로 시작할 수 있는 기능을 제공합니다. 이를 통해 crew 실행을 블로킹(blocking) 없이 시작할 수 있습니다.
|
||||
이 기능은 여러 개의 crew를 동시에 실행하거나 crew가 실행되는 동안 다른 작업을 수행해야 할 때 특히 유용합니다.
|
||||
|
||||
## 비동기 Crew 실행
|
||||
CrewAI는 비동기 실행을 위해 두 가지 접근 방식을 제공합니다:
|
||||
|
||||
Crew를 비동기적으로 시작하려면 `kickoff_async()` 메서드를 사용하세요. 이 메서드는 별도의 스레드에서 crew 실행을 시작하여, 메인 스레드가 다른 작업을 계속 실행할 수 있도록 합니다.
|
||||
| 메서드 | 타입 | 설명 |
|
||||
|--------|------|-------------|
|
||||
| `akickoff()` | 네이티브 async | 전체 실행 체인에서 진정한 async/await 사용 |
|
||||
| `kickoff_async()` | 스레드 기반 | 동기 실행을 `asyncio.to_thread`로 래핑 |
|
||||
|
||||
<Note>
|
||||
고동시성 워크로드의 경우 `akickoff()`가 권장됩니다. 이는 작업 실행, 메모리 작업, 지식 검색에 네이티브 async를 사용합니다.
|
||||
</Note>
|
||||
|
||||
## `akickoff()`를 사용한 네이티브 비동기 실행
|
||||
|
||||
`akickoff()` 메서드는 작업 실행, 메모리 작업, 지식 쿼리를 포함한 전체 실행 체인에서 async/await를 사용하여 진정한 네이티브 비동기 실행을 제공합니다.
|
||||
|
||||
### 메서드 시그니처
|
||||
|
||||
```python Code
|
||||
def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
async def akickoff(self, inputs: dict) -> CrewOutput:
|
||||
```
|
||||
|
||||
### 매개변수
|
||||
@@ -28,23 +39,13 @@ def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
|
||||
- `CrewOutput`: crew 실행 결과를 나타내는 객체입니다.
|
||||
|
||||
## 잠재적 사용 사례
|
||||
|
||||
- **병렬 콘텐츠 생성**: 여러 개의 독립적인 crew를 비동기적으로 시작하여, 각 crew가 다른 주제에 대한 콘텐츠 생성을 담당합니다. 예를 들어, 한 crew는 AI 트렌드에 대한 기사 조사 및 초안을 작성하는 반면, 또 다른 crew는 신제품 출시와 관련된 소셜 미디어 게시물을 생성할 수 있습니다. 각 crew는 독립적으로 운영되므로 콘텐츠 생산을 효율적으로 확장할 수 있습니다.
|
||||
|
||||
- **동시 시장 조사 작업**: 여러 crew를 비동기적으로 시작하여 시장 조사를 병렬로 수행합니다. 한 crew는 업계 동향을 분석하고, 또 다른 crew는 경쟁사 전략을 조사하며, 또 다른 crew는 소비자 감정을 평가할 수 있습니다. 각 crew는 독립적으로 자신의 작업을 완료하므로 더 빠르고 포괄적인 인사이트를 얻을 수 있습니다.
|
||||
|
||||
- **독립적인 여행 계획 모듈**: 각각 독립적으로 여행의 다양한 측면을 계획하도록 crew를 따로 실행합니다. 한 crew는 항공편 옵션을, 다른 crew는 숙박을, 세 번째 crew는 활동 계획을 담당할 수 있습니다. 각 crew는 비동기적으로 작업하므로 여행의 다양한 요소를 동시에 그리고 독립적으로 더 빠르게 계획할 수 있습니다.
|
||||
|
||||
## 예시: 단일 비동기 crew 실행
|
||||
|
||||
다음은 asyncio를 사용하여 crew를 비동기적으로 시작하고 결과를 await하는 방법의 예시입니다:
|
||||
### 예시: 네이티브 비동기 Crew 실행
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
# Create an agent with code execution enabled
|
||||
# 에이전트 생성
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
@@ -52,37 +53,165 @@ coding_agent = Agent(
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
# Create a task that requires code execution
|
||||
# 작업 생성
|
||||
data_analysis_task = Task(
|
||||
description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
# Create a crew and add the task
|
||||
# Crew 생성
|
||||
analysis_crew = Crew(
|
||||
agents=[coding_agent],
|
||||
tasks=[data_analysis_task]
|
||||
)
|
||||
|
||||
# Async function to kickoff the crew asynchronously
|
||||
async def async_crew_execution():
|
||||
result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
# 네이티브 비동기 실행
|
||||
async def main():
|
||||
result = await analysis_crew.akickoff(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
print("Crew Result:", result)
|
||||
|
||||
# Run the async function
|
||||
asyncio.run(async_crew_execution())
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## 예제: 다중 비동기 Crew 실행
|
||||
### 예시: 여러 네이티브 비동기 Crew
|
||||
|
||||
이 예제에서는 여러 Crew를 비동기적으로 시작하고 `asyncio.gather()`를 사용하여 모두 완료될 때까지 기다리는 방법을 보여줍니다:
|
||||
`asyncio.gather()`를 사용하여 네이티브 async로 여러 crew를 동시에 실행:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
task_1 = Task(
|
||||
description="Analyze the first dataset and calculate the average age. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
task_2 = Task(
|
||||
description="Analyze the second dataset and calculate the average age. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
|
||||
crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
|
||||
|
||||
async def main():
|
||||
results = await asyncio.gather(
|
||||
crew_1.akickoff(inputs={"ages": [25, 30, 35, 40, 45]}),
|
||||
crew_2.akickoff(inputs={"ages": [20, 22, 24, 28, 30]})
|
||||
)
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"Crew {i} Result:", result)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
### 예시: 여러 입력에 대한 네이티브 비동기
|
||||
|
||||
`akickoff_for_each()`를 사용하여 네이티브 async로 여러 입력에 대해 crew를 동시에 실행:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
data_analysis_task = Task(
|
||||
description="Analyze the dataset and calculate the average age. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
analysis_crew = Crew(
|
||||
agents=[coding_agent],
|
||||
tasks=[data_analysis_task]
|
||||
)
|
||||
|
||||
async def main():
|
||||
datasets = [
|
||||
{"ages": [25, 30, 35, 40, 45]},
|
||||
{"ages": [20, 22, 24, 28, 30]},
|
||||
{"ages": [30, 35, 40, 45, 50]}
|
||||
]
|
||||
|
||||
results = await analysis_crew.akickoff_for_each(datasets)
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"Dataset {i} Result:", result)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## `kickoff_async()`를 사용한 스레드 기반 비동기
|
||||
|
||||
`kickoff_async()` 메서드는 동기 `kickoff()`를 스레드로 래핑하여 비동기 실행을 제공합니다. 이는 더 간단한 비동기 통합이나 하위 호환성에 유용합니다.
|
||||
|
||||
### 메서드 시그니처
|
||||
|
||||
```python Code
|
||||
async def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
```
|
||||
|
||||
### 매개변수
|
||||
|
||||
- `inputs` (dict): 작업에 필요한 입력 데이터를 포함하는 딕셔너리입니다.
|
||||
|
||||
### 반환
|
||||
|
||||
- `CrewOutput`: crew 실행 결과를 나타내는 객체입니다.
|
||||
|
||||
### 예시: 스레드 기반 비동기 실행
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
data_analysis_task = Task(
|
||||
description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
analysis_crew = Crew(
|
||||
agents=[coding_agent],
|
||||
tasks=[data_analysis_task]
|
||||
)
|
||||
|
||||
async def async_crew_execution():
|
||||
result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
print("Crew Result:", result)
|
||||
|
||||
asyncio.run(async_crew_execution())
|
||||
```
|
||||
|
||||
### 예시: 여러 스레드 기반 비동기 Crew
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
# Create an agent with code execution enabled
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
@@ -90,7 +219,6 @@ coding_agent = Agent(
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
# Create tasks that require code execution
|
||||
task_1 = Task(
|
||||
description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
@@ -103,22 +231,76 @@ task_2 = Task(
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
# Create two crews and add tasks
|
||||
crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
|
||||
crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
|
||||
|
||||
# Async function to kickoff multiple crews asynchronously and wait for all to finish
|
||||
async def async_multiple_crews():
|
||||
# Create coroutines for concurrent execution
|
||||
result_1 = crew_1.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
result_2 = crew_2.kickoff_async(inputs={"ages": [20, 22, 24, 28, 30]})
|
||||
|
||||
# Wait for both crews to finish
|
||||
results = await asyncio.gather(result_1, result_2)
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"Crew {i} Result:", result)
|
||||
|
||||
# Run the async function
|
||||
asyncio.run(async_multiple_crews())
|
||||
```
|
||||
```
|
||||
|
||||
## 비동기 스트리밍
|
||||
|
||||
두 비동기 메서드 모두 crew에 `stream=True`가 설정된 경우 스트리밍을 지원합니다:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research and summarize topics",
|
||||
backstory="You are an expert researcher."
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Research the topic: {topic}",
|
||||
agent=agent,
|
||||
expected_output="A comprehensive summary of the topic."
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
stream=True # 스트리밍 활성화
|
||||
)
|
||||
|
||||
async def main():
|
||||
streaming_output = await crew.akickoff(inputs={"topic": "AI trends in 2024"})
|
||||
|
||||
# 스트리밍 청크에 대한 비동기 반복
|
||||
async for chunk in streaming_output:
|
||||
print(f"Chunk: {chunk.content}")
|
||||
|
||||
# 스트리밍 완료 후 최종 결과 접근
|
||||
result = streaming_output.result
|
||||
print(f"Final result: {result.raw}")
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## 잠재적 사용 사례
|
||||
|
||||
- **병렬 콘텐츠 생성**: 여러 개의 독립적인 crew를 비동기적으로 시작하여, 각 crew가 다른 주제에 대한 콘텐츠 생성을 담당합니다. 예를 들어, 한 crew는 AI 트렌드에 대한 기사 조사 및 초안을 작성하는 반면, 또 다른 crew는 신제품 출시와 관련된 소셜 미디어 게시물을 생성할 수 있습니다.
|
||||
|
||||
- **동시 시장 조사 작업**: 여러 crew를 비동기적으로 시작하여 시장 조사를 병렬로 수행합니다. 한 crew는 업계 동향을 분석하고, 또 다른 crew는 경쟁사 전략을 조사하며, 또 다른 crew는 소비자 감정을 평가할 수 있습니다.
|
||||
|
||||
- **독립적인 여행 계획 모듈**: 각각 독립적으로 여행의 다양한 측면을 계획하도록 crew를 따로 실행합니다. 한 crew는 항공편 옵션을, 다른 crew는 숙박을, 세 번째 crew는 활동 계획을 담당할 수 있습니다.
|
||||
|
||||
## `akickoff()`와 `kickoff_async()` 선택하기
|
||||
|
||||
| 기능 | `akickoff()` | `kickoff_async()` |
|
||||
|---------|--------------|-------------------|
|
||||
| 실행 모델 | 네이티브 async/await | 스레드 기반 래퍼 |
|
||||
| 작업 실행 | `aexecute_sync()`로 비동기 | 스레드 풀에서 동기 |
|
||||
| 메모리 작업 | 비동기 | 스레드 풀에서 동기 |
|
||||
| 지식 검색 | 비동기 | 스레드 풀에서 동기 |
|
||||
| 적합한 용도 | 고동시성, I/O 바운드 워크로드 | 간단한 비동기 통합 |
|
||||
| 스트리밍 지원 | 예 | 예 |
|
||||
|
||||
356
docs/ko/learn/streaming-crew-execution.mdx
Normal file
356
docs/ko/learn/streaming-crew-execution.mdx
Normal file
@@ -0,0 +1,356 @@
|
||||
---
|
||||
title: 스트리밍 Crew 실행
|
||||
description: CrewAI crew 실행에서 실시간 출력을 스트리밍하기
|
||||
icon: wave-pulse
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## 소개
|
||||
|
||||
CrewAI는 crew 실행 중 실시간 출력을 스트리밍하는 기능을 제공하여, 전체 프로세스가 완료될 때까지 기다리지 않고 결과가 생성되는 대로 표시할 수 있습니다. 이 기능은 대화형 애플리케이션을 구축하거나, 사용자 피드백을 제공하거나, 장시간 실행되는 프로세스를 모니터링할 때 특히 유용합니다.
|
||||
|
||||
## 스트리밍 작동 방식
|
||||
|
||||
스트리밍이 활성화되면 CrewAI는 LLM 응답과 도구 호출을 실시간으로 캡처하여, 어떤 task와 agent가 실행 중인지에 대한 컨텍스트를 포함한 구조화된 청크로 패키징합니다. 이러한 청크를 실시간으로 반복 처리하고 실행이 완료되면 최종 결과에 접근할 수 있습니다.
|
||||
|
||||
## 스트리밍 활성화
|
||||
|
||||
스트리밍을 활성화하려면 crew를 생성할 때 `stream` 파라미터를 `True`로 설정하세요:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Crew, Task
|
||||
|
||||
# 에이전트와 태스크 생성
|
||||
researcher = Agent(
|
||||
role="Research Analyst",
|
||||
goal="Gather comprehensive information on topics",
|
||||
backstory="You are an experienced researcher with excellent analytical skills.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Research the latest developments in AI",
|
||||
expected_output="A detailed report on recent AI advancements",
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
# 스트리밍 활성화
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True # 스트리밍 출력 활성화
|
||||
)
|
||||
```
|
||||
|
||||
## 동기 스트리밍
|
||||
|
||||
스트리밍이 활성화된 crew에서 `kickoff()`를 호출하면, 청크가 도착할 때마다 반복 처리할 수 있는 `CrewStreamingOutput` 객체가 반환됩니다:
|
||||
|
||||
```python Code
|
||||
# 스트리밍 실행 시작
|
||||
streaming = crew.kickoff(inputs={"topic": "artificial intelligence"})
|
||||
|
||||
# 청크가 도착할 때마다 반복
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# 스트리밍 완료 후 최종 결과 접근
|
||||
result = streaming.result
|
||||
print(f"\n\n최종 출력: {result.raw}")
|
||||
```
|
||||
|
||||
### 스트림 청크 정보
|
||||
|
||||
각 청크는 실행에 대한 풍부한 컨텍스트를 제공합니다:
|
||||
|
||||
```python Code
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
|
||||
for chunk in streaming:
|
||||
print(f"Task: {chunk.task_name} (인덱스 {chunk.task_index})")
|
||||
print(f"Agent: {chunk.agent_role}")
|
||||
print(f"Content: {chunk.content}")
|
||||
print(f"Type: {chunk.chunk_type}") # TEXT 또는 TOOL_CALL
|
||||
if chunk.tool_call:
|
||||
print(f"Tool: {chunk.tool_call.tool_name}")
|
||||
print(f"Arguments: {chunk.tool_call.arguments}")
|
||||
```
|
||||
|
||||
### 스트리밍 결과 접근
|
||||
|
||||
`CrewStreamingOutput` 객체는 여러 유용한 속성을 제공합니다:
|
||||
|
||||
```python Code
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
|
||||
# 청크 반복 및 수집
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# 반복 완료 후
|
||||
print(f"\n완료됨: {streaming.is_completed}")
|
||||
print(f"전체 텍스트: {streaming.get_full_text()}")
|
||||
print(f"전체 청크 수: {len(streaming.chunks)}")
|
||||
print(f"최종 결과: {streaming.result.raw}")
|
||||
```
|
||||
|
||||
## 비동기 스트리밍
|
||||
|
||||
비동기 애플리케이션의 경우, 비동기 반복과 함께 `akickoff()`(네이티브 async) 또는 `kickoff_async()`(스레드 기반)를 사용할 수 있습니다:
|
||||
|
||||
### `akickoff()`를 사용한 네이티브 Async
|
||||
|
||||
`akickoff()` 메서드는 전체 체인에서 진정한 네이티브 async 실행을 제공합니다:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
|
||||
async def stream_crew():
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
# 네이티브 async 스트리밍 시작
|
||||
streaming = await crew.akickoff(inputs={"topic": "AI"})
|
||||
|
||||
# 청크에 대한 비동기 반복
|
||||
async for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# 최종 결과 접근
|
||||
result = streaming.result
|
||||
print(f"\n\n최종 출력: {result.raw}")
|
||||
|
||||
asyncio.run(stream_crew())
|
||||
```
|
||||
|
||||
### `kickoff_async()`를 사용한 스레드 기반 Async
|
||||
|
||||
더 간단한 async 통합이나 하위 호환성을 위해:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
|
||||
async def stream_crew():
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
# 스레드 기반 async 스트리밍 시작
|
||||
streaming = await crew.kickoff_async(inputs={"topic": "AI"})
|
||||
|
||||
# 청크에 대한 비동기 반복
|
||||
async for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# 최종 결과 접근
|
||||
result = streaming.result
|
||||
print(f"\n\n최종 출력: {result.raw}")
|
||||
|
||||
asyncio.run(stream_crew())
|
||||
```
|
||||
|
||||
<Note>
|
||||
고동시성 워크로드의 경우, 태스크 실행, 메모리 작업, 지식 검색에 네이티브 async를 사용하는 `akickoff()`가 권장됩니다. 자세한 내용은 [Crew 비동기 시작](/ko/learn/kickoff-async) 가이드를 참조하세요.
|
||||
</Note>
|
||||
|
||||
## kickoff_for_each를 사용한 스트리밍
|
||||
|
||||
`kickoff_for_each()`로 여러 입력에 대해 crew를 실행할 때, 동기 또는 비동기 여부에 따라 스트리밍이 다르게 작동합니다:
|
||||
|
||||
### 동기 kickoff_for_each
|
||||
|
||||
동기 `kickoff_for_each()`를 사용하면, 각 입력에 대해 하나씩 `CrewStreamingOutput` 객체의 리스트가 반환됩니다:
|
||||
|
||||
```python Code
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
inputs_list = [
|
||||
{"topic": "AI in healthcare"},
|
||||
{"topic": "AI in finance"}
|
||||
]
|
||||
|
||||
# 스트리밍 출력 리스트 반환
|
||||
streaming_outputs = crew.kickoff_for_each(inputs=inputs_list)
|
||||
|
||||
# 각 스트리밍 출력에 대해 반복
|
||||
for i, streaming in enumerate(streaming_outputs):
|
||||
print(f"\n=== 입력 {i + 1} ===")
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
result = streaming.result
|
||||
print(f"\n\n결과 {i + 1}: {result.raw}")
|
||||
```
|
||||
|
||||
### 비동기 kickoff_for_each_async
|
||||
|
||||
비동기 `kickoff_for_each_async()`를 사용하면, 모든 crew의 청크가 동시에 도착하는 대로 반환하는 단일 `CrewStreamingOutput`이 반환됩니다:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
|
||||
async def stream_multiple_crews():
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
inputs_list = [
|
||||
{"topic": "AI in healthcare"},
|
||||
{"topic": "AI in finance"}
|
||||
]
|
||||
|
||||
# 모든 crew에 대한 단일 스트리밍 출력 반환
|
||||
streaming = await crew.kickoff_for_each_async(inputs=inputs_list)
|
||||
|
||||
# 모든 crew의 청크가 생성되는 대로 도착
|
||||
async for chunk in streaming:
|
||||
print(f"[{chunk.task_name}] {chunk.content}", end="", flush=True)
|
||||
|
||||
# 모든 결과 접근
|
||||
results = streaming.results # CrewOutput 객체 리스트
|
||||
for i, result in enumerate(results):
|
||||
print(f"\n\n결과 {i + 1}: {result.raw}")
|
||||
|
||||
asyncio.run(stream_multiple_crews())
|
||||
```
|
||||
|
||||
## 스트림 청크 타입
|
||||
|
||||
청크는 `chunk_type` 필드로 표시되는 다양한 타입을 가질 수 있습니다:
|
||||
|
||||
### TEXT 청크
|
||||
|
||||
LLM 응답의 표준 텍스트 콘텐츠:
|
||||
|
||||
```python Code
|
||||
for chunk in streaming:
|
||||
if chunk.chunk_type == StreamChunkType.TEXT:
|
||||
print(chunk.content, end="", flush=True)
|
||||
```
|
||||
|
||||
### TOOL_CALL 청크
|
||||
|
||||
수행 중인 도구 호출에 대한 정보:
|
||||
|
||||
```python Code
|
||||
for chunk in streaming:
|
||||
if chunk.chunk_type == StreamChunkType.TOOL_CALL:
|
||||
print(f"\n도구 호출: {chunk.tool_call.tool_name}")
|
||||
print(f"인자: {chunk.tool_call.arguments}")
|
||||
```
|
||||
|
||||
## 실용적인 예시: 스트리밍을 사용한 UI 구축
|
||||
|
||||
다음은 스트리밍을 사용한 대화형 애플리케이션을 구축하는 방법을 보여주는 완전한 예시입니다:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Agent, Crew, Task
|
||||
from crewai.types.streaming import StreamChunkType
|
||||
|
||||
async def interactive_research():
|
||||
# 스트리밍이 활성화된 crew 생성
|
||||
researcher = Agent(
|
||||
role="Research Analyst",
|
||||
goal="Provide detailed analysis on any topic",
|
||||
backstory="You are an expert researcher with broad knowledge.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Research and analyze: {topic}",
|
||||
expected_output="A comprehensive analysis with key insights",
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True,
|
||||
verbose=False
|
||||
)
|
||||
|
||||
# 사용자 입력 받기
|
||||
topic = input("연구할 주제를 입력하세요: ")
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"연구 중: {topic}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
# 스트리밍 실행 시작
|
||||
streaming = await crew.kickoff_async(inputs={"topic": topic})
|
||||
|
||||
current_task = ""
|
||||
async for chunk in streaming:
|
||||
# 태스크 전환 표시
|
||||
if chunk.task_name != current_task:
|
||||
current_task = chunk.task_name
|
||||
print(f"\n[{chunk.agent_role}] 작업 중: {chunk.task_name}")
|
||||
print("-" * 60)
|
||||
|
||||
# 텍스트 청크 표시
|
||||
if chunk.chunk_type == StreamChunkType.TEXT:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# 도구 호출 표시
|
||||
elif chunk.chunk_type == StreamChunkType.TOOL_CALL and chunk.tool_call:
|
||||
print(f"\n🔧 도구 사용: {chunk.tool_call.tool_name}")
|
||||
|
||||
# 최종 결과 표시
|
||||
result = streaming.result
|
||||
print(f"\n\n{'='*60}")
|
||||
print("분석 완료!")
|
||||
print(f"{'='*60}")
|
||||
print(f"\n토큰 사용량: {result.token_usage}")
|
||||
|
||||
asyncio.run(interactive_research())
|
||||
```
|
||||
|
||||
## 사용 사례
|
||||
|
||||
스트리밍은 다음과 같은 경우에 특히 유용합니다:
|
||||
|
||||
- **대화형 애플리케이션**: 에이전트가 작업하는 동안 사용자에게 실시간 피드백 제공
|
||||
- **장시간 실행 태스크**: 연구, 분석 또는 콘텐츠 생성의 진행 상황 표시
|
||||
- **디버깅 및 모니터링**: 에이전트 동작과 의사 결정을 실시간으로 관찰
|
||||
- **사용자 경험**: 점진적인 결과를 표시하여 체감 지연 시간 감소
|
||||
- **라이브 대시보드**: crew 실행 상태를 표시하는 모니터링 인터페이스 구축
|
||||
|
||||
## 중요 사항
|
||||
|
||||
- 스트리밍은 crew의 모든 에이전트에 대해 자동으로 LLM 스트리밍을 활성화합니다
|
||||
- `.result` 속성에 접근하기 전에 모든 청크를 반복해야 합니다
|
||||
- 스트리밍을 사용하는 `kickoff_for_each_async()`의 경우, 모든 출력을 가져오려면 `.results`(복수형)를 사용하세요
|
||||
- 스트리밍은 최소한의 오버헤드를 추가하며 실제로 체감 성능을 향상시킬 수 있습니다
|
||||
- 각 청크는 풍부한 UI를 위한 전체 컨텍스트(태스크, 에이전트, 청크 타입)를 포함합니다
|
||||
|
||||
## 오류 처리
|
||||
|
||||
스트리밍 실행 중 오류 처리:
|
||||
|
||||
```python Code
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
|
||||
try:
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
result = streaming.result
|
||||
print(f"\n성공: {result.raw}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n스트리밍 중 오류 발생: {e}")
|
||||
if streaming.is_completed:
|
||||
print("스트리밍은 완료되었지만 오류가 발생했습니다")
|
||||
```
|
||||
|
||||
스트리밍을 활용하면 CrewAI로 더 반응성이 좋고 대화형인 애플리케이션을 구축하여 사용자에게 에이전트 실행과 결과에 대한 실시간 가시성을 제공할 수 있습니다.
|
||||
@@ -16,16 +16,17 @@ Bem-vindo à referência da API do CrewAI AOP. Esta API permite que você intera
|
||||
Navegue até a página de detalhes do seu crew no painel do CrewAI AOP e copie seu Bearer Token na aba Status.
|
||||
</Step>
|
||||
|
||||
<Step title="Descubra os Inputs Necessários">
|
||||
Use o endpoint `GET /inputs` para ver quais parâmetros seu crew espera.
|
||||
</Step>
|
||||
<Step title="Descubra os Inputs Necessários">
|
||||
Use o endpoint `GET /inputs` para ver quais parâmetros seu crew espera.
|
||||
</Step>
|
||||
|
||||
<Step title="Inicie uma Execução de Crew">
|
||||
Chame `POST /kickoff` com seus inputs para iniciar a execução do crew e receber um `kickoff_id`.
|
||||
</Step>
|
||||
<Step title="Inicie uma Execução de Crew">
|
||||
Chame `POST /kickoff` com seus inputs para iniciar a execução do crew e
|
||||
receber um `kickoff_id`.
|
||||
</Step>
|
||||
|
||||
<Step title="Monitore o Progresso">
|
||||
Use `GET /status/{kickoff_id}` para checar o status da execução e recuperar os resultados.
|
||||
Use `GET /{kickoff_id}/status` para checar o status da execução e recuperar os resultados.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
@@ -40,13 +41,14 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
|
||||
|
||||
### Tipos de Token
|
||||
|
||||
| Tipo de Token | Escopo | Caso de Uso |
|
||||
|:--------------------|:------------------------|:---------------------------------------------------------|
|
||||
| **Bearer Token** | Acesso em nível de organização | Operações completas de crew, ideal para integração server-to-server |
|
||||
| **User Bearer Token** | Acesso com escopo de usuário | Permissões limitadas, adequado para operações específicas de usuário |
|
||||
| Tipo de Token | Escopo | Caso de Uso |
|
||||
| :-------------------- | :----------------------------- | :------------------------------------------------------------------- |
|
||||
| **Bearer Token** | Acesso em nível de organização | Operações completas de crew, ideal para integração server-to-server |
|
||||
| **User Bearer Token** | Acesso com escopo de usuário | Permissões limitadas, adequado para operações específicas de usuário |
|
||||
|
||||
<Tip>
|
||||
Você pode encontrar ambos os tipos de token na aba Status da página de detalhes do seu crew no painel do CrewAI AOP.
|
||||
Você pode encontrar ambos os tipos de token na aba Status da página de
|
||||
detalhes do seu crew no painel do CrewAI AOP.
|
||||
</Tip>
|
||||
|
||||
## URL Base
|
||||
@@ -63,29 +65,33 @@ Substitua `your-crew-name` pela URL real do seu crew no painel.
|
||||
|
||||
1. **Descoberta**: Chame `GET /inputs` para entender o que seu crew precisa
|
||||
2. **Execução**: Envie os inputs via `POST /kickoff` para iniciar o processamento
|
||||
3. **Monitoramento**: Faça polling em `GET /status/{kickoff_id}` até a conclusão
|
||||
3. **Monitoramento**: Faça polling em `GET /{kickoff_id}/status` até a conclusão
|
||||
4. **Resultados**: Extraia o output final da resposta concluída
|
||||
|
||||
## Tratamento de Erros
|
||||
|
||||
A API utiliza códigos de status HTTP padrão:
|
||||
|
||||
| Código | Significado |
|
||||
|--------|:--------------------------------------|
|
||||
| `200` | Sucesso |
|
||||
| `400` | Requisição Inválida - Formato de input inválido |
|
||||
| `401` | Não Autorizado - Bearer token inválido |
|
||||
| `404` | Não Encontrado - Recurso não existe |
|
||||
| Código | Significado |
|
||||
| ------ | :----------------------------------------------- |
|
||||
| `200` | Sucesso |
|
||||
| `400` | Requisição Inválida - Formato de input inválido |
|
||||
| `401` | Não Autorizado - Bearer token inválido |
|
||||
| `404` | Não Encontrado - Recurso não existe |
|
||||
| `422` | Erro de Validação - Inputs obrigatórios ausentes |
|
||||
| `500` | Erro no Servidor - Contate o suporte |
|
||||
| `500` | Erro no Servidor - Contate o suporte |
|
||||
|
||||
## Testes Interativos
|
||||
|
||||
<Info>
|
||||
**Por que não há botão "Enviar"?** Como cada usuário do CrewAI AOP possui sua própria URL de crew, utilizamos o **modo referência** em vez de um playground interativo para evitar confusão. Isso mostra exatamente como as requisições devem ser feitas, sem botões de envio não funcionais.
|
||||
**Por que não há botão "Enviar"?** Como cada usuário do CrewAI AOP possui sua
|
||||
própria URL de crew, utilizamos o **modo referência** em vez de um playground
|
||||
interativo para evitar confusão. Isso mostra exatamente como as requisições
|
||||
devem ser feitas, sem botões de envio não funcionais.
|
||||
</Info>
|
||||
|
||||
Cada página de endpoint mostra para você:
|
||||
|
||||
- ✅ **Formato exato da requisição** com todos os parâmetros
|
||||
- ✅ **Exemplos de resposta** para casos de sucesso e erro
|
||||
- ✅ **Exemplos de código** em várias linguagens (cURL, Python, JavaScript, etc.)
|
||||
@@ -103,6 +109,7 @@ Cada página de endpoint mostra para você:
|
||||
</CardGroup>
|
||||
|
||||
**Exemplo de fluxo:**
|
||||
|
||||
1. **Copie este exemplo cURL** de qualquer página de endpoint
|
||||
2. **Substitua `your-actual-crew-name.crewai.com`** pela URL real do seu crew
|
||||
3. **Substitua o Bearer token** pelo seu token real do painel
|
||||
@@ -111,10 +118,18 @@ Cada página de endpoint mostra para você:
|
||||
## Precisa de Ajuda?
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Suporte Enterprise" icon="headset" href="mailto:support@crewai.com">
|
||||
<Card
|
||||
title="Suporte Enterprise"
|
||||
icon="headset"
|
||||
href="mailto:support@crewai.com"
|
||||
>
|
||||
Obtenha ajuda com integração da API e resolução de problemas
|
||||
</Card>
|
||||
<Card title="Painel Enterprise" icon="chart-line" href="https://app.crewai.com">
|
||||
<Card
|
||||
title="Painel Enterprise"
|
||||
icon="chart-line"
|
||||
href="https://app.crewai.com"
|
||||
>
|
||||
Gerencie seus crews e visualize logs de execução
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
---
|
||||
title: "GET /status/{kickoff_id}"
|
||||
title: "GET /{kickoff_id}/status"
|
||||
description: "Obter o status da execução"
|
||||
openapi: "/enterprise-api.pt-BR.yaml GET /status/{kickoff_id}"
|
||||
openapi: "/enterprise-api.pt-BR.yaml GET /{kickoff_id}/status"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
|
||||
|
||||
@@ -32,6 +32,8 @@ Uma crew no crewAI representa um grupo colaborativo de agentes trabalhando em co
|
||||
| **Prompt File** _(opcional)_ | `prompt_file` | Caminho para o arquivo JSON de prompt a ser utilizado pela crew. |
|
||||
| **Planning** *(opcional)* | `planning` | Adiciona habilidade de planejamento à Crew. Quando ativado, antes de cada iteração, todos os dados da Crew são enviados a um AgentPlanner que planejará as tasks e este plano será adicionado à descrição de cada task. |
|
||||
| **Planning LLM** *(opcional)* | `planning_llm` | O modelo de linguagem usado pelo AgentPlanner em um processo de planejamento. |
|
||||
| **Knowledge Sources** _(opcional)_ | `knowledge_sources` | Fontes de conhecimento disponíveis no nível da crew, acessíveis a todos os agentes. |
|
||||
| **Stream** _(opcional)_ | `stream` | Habilita saída em streaming para receber atualizações em tempo real durante a execução da crew. Retorna um objeto `CrewStreamingOutput` que pode ser iterado para chunks. O padrão é `False`. |
|
||||
|
||||
<Tip>
|
||||
**Crew Max RPM**: O atributo `max_rpm` define o número máximo de requisições por minuto que a crew pode executar para evitar limites de taxa e irá sobrescrever as configurações de `max_rpm` dos agentes individuais se você o definir.
|
||||
@@ -303,12 +305,27 @@ print(result)
|
||||
|
||||
### Diferentes Formas de Iniciar uma Crew
|
||||
|
||||
Assim que sua crew estiver definida, inicie o fluxo de trabalho com o método kickoff apropriado. O CrewAI oferece vários métodos para melhor controle do processo: `kickoff()`, `kickoff_for_each()`, `kickoff_async()` e `kickoff_for_each_async()`.
|
||||
Assim que sua crew estiver definida, inicie o fluxo de trabalho com o método kickoff apropriado. O CrewAI oferece vários métodos para melhor controle do processo.
|
||||
|
||||
#### Métodos Síncronos
|
||||
|
||||
- `kickoff()`: Inicia o processo de execução seguindo o fluxo definido.
|
||||
- `kickoff_for_each()`: Executa tasks sequencialmente para cada evento de entrada ou item da coleção fornecida.
|
||||
- `kickoff_async()`: Inicia o workflow de forma assíncrona.
|
||||
- `kickoff_for_each_async()`: Executa as tasks concorrentemente para cada entrada, aproveitando o processamento assíncrono.
|
||||
|
||||
#### Métodos Assíncronos
|
||||
|
||||
O CrewAI oferece duas abordagens para execução assíncrona:
|
||||
|
||||
| Método | Tipo | Descrição |
|
||||
|--------|------|-------------|
|
||||
| `akickoff()` | Async nativo | Async/await verdadeiro em toda a cadeia de execução |
|
||||
| `akickoff_for_each()` | Async nativo | Execução async nativa para cada entrada em uma lista |
|
||||
| `kickoff_async()` | Baseado em thread | Envolve execução síncrona em `asyncio.to_thread` |
|
||||
| `kickoff_for_each_async()` | Baseado em thread | Async baseado em thread para cada entrada em uma lista |
|
||||
|
||||
<Note>
|
||||
Para cargas de trabalho de alta concorrência, `akickoff()` e `akickoff_for_each()` são recomendados pois usam async nativo para execução de tasks, operações de memória e recuperação de conhecimento.
|
||||
</Note>
|
||||
|
||||
```python Code
|
||||
# Iniciar execução das tasks da crew
|
||||
@@ -321,19 +338,53 @@ results = my_crew.kickoff_for_each(inputs=inputs_array)
|
||||
for result in results:
|
||||
print(result)
|
||||
|
||||
# Exemplo com kickoff_async
|
||||
# Exemplo usando async nativo com akickoff
|
||||
inputs = {'topic': 'AI in healthcare'}
|
||||
async_result = await my_crew.akickoff(inputs=inputs)
|
||||
print(async_result)
|
||||
|
||||
# Exemplo usando async nativo com akickoff_for_each
|
||||
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
|
||||
async_results = await my_crew.akickoff_for_each(inputs=inputs_array)
|
||||
for async_result in async_results:
|
||||
print(async_result)
|
||||
|
||||
# Exemplo usando kickoff_async baseado em thread
|
||||
inputs = {'topic': 'AI in healthcare'}
|
||||
async_result = await my_crew.kickoff_async(inputs=inputs)
|
||||
print(async_result)
|
||||
|
||||
# Exemplo com kickoff_for_each_async
|
||||
# Exemplo usando kickoff_for_each_async baseado em thread
|
||||
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
|
||||
async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
|
||||
for async_result in async_results:
|
||||
print(async_result)
|
||||
```
|
||||
|
||||
Esses métodos fornecem flexibilidade para gerenciar e executar tasks dentro de sua crew, permitindo fluxos de trabalho síncronos e assíncronos de acordo com sua necessidade.
|
||||
Esses métodos fornecem flexibilidade para gerenciar e executar tasks dentro de sua crew, permitindo fluxos de trabalho síncronos e assíncronos de acordo com sua necessidade. Para exemplos detalhados de async, consulte o guia [Inicie uma Crew de Forma Assíncrona](/pt-BR/learn/kickoff-async).
|
||||
|
||||
### Streaming na Execução da Crew
|
||||
|
||||
Para visibilidade em tempo real da execução da crew, você pode habilitar streaming para receber saída conforme é gerada:
|
||||
|
||||
```python Code
|
||||
# Habilitar streaming
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
# Iterar sobre saída em streaming
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Acessar resultado final
|
||||
result = streaming.result
|
||||
```
|
||||
|
||||
Saiba mais sobre streaming no guia [Streaming na Execução da Crew](/pt-BR/learn/streaming-crew-execution).
|
||||
|
||||
### Repetindo Execução a partir de uma Task Específica
|
||||
|
||||
|
||||
@@ -307,6 +307,55 @@ Os métodos `third_method` e `fourth_method` escutam a saída do `second_method`
|
||||
|
||||
Ao executar esse Flow, a saída será diferente dependendo do valor booleano aleatório gerado pelo `start_method`.
|
||||
|
||||
### Human in the Loop (feedback humano)
|
||||
|
||||
O decorador `@human_feedback` permite fluxos de trabalho human-in-the-loop, pausando a execução do flow para coletar feedback de um humano. Isso é útil para portões de aprovação, revisão de qualidade e pontos de decisão que requerem julgamento humano.
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Você aprova este conteúdo?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Conteúdo para revisão..."
|
||||
|
||||
@listen("approved")
|
||||
def on_approval(self, result: HumanFeedbackResult):
|
||||
print(f"Aprovado! Feedback: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def on_rejection(self, result: HumanFeedbackResult):
|
||||
print(f"Rejeitado. Motivo: {result.feedback}")
|
||||
```
|
||||
|
||||
Quando `emit` é especificado, o feedback livre do humano é interpretado por um LLM e mapeado para um dos outcomes especificados, que então dispara o decorador `@listen` correspondente.
|
||||
|
||||
Você também pode usar `@human_feedback` sem roteamento para simplesmente coletar feedback:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(message="Algum comentário sobre esta saída?")
|
||||
def my_method(self):
|
||||
return "Saída para revisão"
|
||||
|
||||
@listen(my_method)
|
||||
def next_step(self, result: HumanFeedbackResult):
|
||||
# Acesse o feedback via result.feedback
|
||||
# Acesse a saída original via result.output
|
||||
pass
|
||||
```
|
||||
|
||||
Acesse todo o feedback coletado durante um flow via `self.last_human_feedback` (mais recente) ou `self.human_feedback_history` (todo o feedback em uma lista).
|
||||
|
||||
Para um guia completo sobre feedback humano em flows, incluindo feedback assíncrono/não-bloqueante com providers customizados (Slack, webhooks, etc.), veja [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows).
|
||||
|
||||
## Adicionando Agentes aos Flows
|
||||
|
||||
Os agentes podem ser integrados facilmente aos seus flows, oferecendo uma alternativa leve às crews completas quando você precisar executar tarefas simples e focadas. Veja um exemplo de como utilizar um agente em um flow para realizar uma pesquisa de mercado:
|
||||
|
||||
@@ -62,13 +62,13 @@ Teste sua integração de trigger do Gmail localmente usando a CLI da CrewAI:
|
||||
crewai triggers list
|
||||
|
||||
# Simule um trigger do Gmail com payload realista
|
||||
crewai triggers run gmail/new_email
|
||||
crewai triggers run gmail/new_email_received
|
||||
```
|
||||
|
||||
O comando `crewai triggers run` executará sua crew com um payload completo do Gmail, permitindo que você teste sua lógica de parsing antes do deployment.
|
||||
|
||||
<Warning>
|
||||
Use `crewai triggers run gmail/new_email` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger.
|
||||
Use `crewai triggers run gmail/new_email_received` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger.
|
||||
</Warning>
|
||||
|
||||
## Monitoring Executions
|
||||
@@ -83,6 +83,6 @@ Track history and performance of triggered runs:
|
||||
|
||||
- Ensure Gmail is connected in Tools & Integrations
|
||||
- Verify the Gmail Trigger is enabled on the Triggers tab
|
||||
- Teste localmente com `crewai triggers run gmail/new_email` para ver a estrutura exata do payload
|
||||
- Teste localmente com `crewai triggers run gmail/new_email_received` para ver a estrutura exata do payload
|
||||
- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
|
||||
- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger
|
||||
|
||||
581
docs/pt-BR/learn/human-feedback-in-flows.mdx
Normal file
581
docs/pt-BR/learn/human-feedback-in-flows.mdx
Normal file
@@ -0,0 +1,581 @@
|
||||
---
|
||||
title: Feedback Humano em Flows
|
||||
description: Aprenda como integrar feedback humano diretamente nos seus CrewAI Flows usando o decorador @human_feedback
|
||||
icon: user-check
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Visão Geral
|
||||
|
||||
O decorador `@human_feedback` permite fluxos de trabalho human-in-the-loop (HITL) diretamente nos CrewAI Flows. Ele permite pausar a execução do flow, apresentar a saída para um humano revisar, coletar seu feedback e, opcionalmente, rotear para diferentes listeners com base no resultado do feedback.
|
||||
|
||||
Isso é particularmente valioso para:
|
||||
|
||||
- **Garantia de qualidade**: Revisar conteúdo gerado por IA antes de ser usado downstream
|
||||
- **Portões de decisão**: Deixar humanos tomarem decisões críticas em fluxos automatizados
|
||||
- **Fluxos de aprovação**: Implementar padrões de aprovar/rejeitar/revisar
|
||||
- **Refinamento interativo**: Coletar feedback para melhorar saídas iterativamente
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A[Método do Flow] --> B[Saída Gerada]
|
||||
B --> C[Humano Revisa]
|
||||
C --> D{Feedback}
|
||||
D -->|emit especificado| E[LLM Mapeia para Outcome]
|
||||
D -->|sem emit| F[HumanFeedbackResult]
|
||||
E --> G["@listen('approved')"]
|
||||
E --> H["@listen('rejected')"]
|
||||
F --> I[Próximo Listener]
|
||||
```
|
||||
|
||||
## Início Rápido
|
||||
|
||||
Aqui está a maneira mais simples de adicionar feedback humano a um flow:
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback
|
||||
|
||||
class SimpleReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(message="Por favor, revise este conteúdo:")
|
||||
def generate_content(self):
|
||||
return "Este é um conteúdo gerado por IA que precisa de revisão."
|
||||
|
||||
@listen(generate_content)
|
||||
def process_feedback(self, result):
|
||||
print(f"Conteúdo: {result.output}")
|
||||
print(f"Humano disse: {result.feedback}")
|
||||
|
||||
flow = SimpleReviewFlow()
|
||||
flow.kickoff()
|
||||
```
|
||||
|
||||
Quando este flow é executado, ele irá:
|
||||
1. Executar `generate_content` e retornar a string
|
||||
2. Exibir a saída para o usuário com a mensagem de solicitação
|
||||
3. Aguardar o usuário digitar o feedback (ou pressionar Enter para pular)
|
||||
4. Passar um objeto `HumanFeedbackResult` para `process_feedback`
|
||||
|
||||
## O Decorador @human_feedback
|
||||
|
||||
### Parâmetros
|
||||
|
||||
| Parâmetro | Tipo | Obrigatório | Descrição |
|
||||
|-----------|------|-------------|-----------|
|
||||
| `message` | `str` | Sim | A mensagem mostrada ao humano junto com a saída do método |
|
||||
| `emit` | `Sequence[str]` | Não | Lista de possíveis outcomes. O feedback é mapeado para um destes, que dispara decoradores `@listen` |
|
||||
| `llm` | `str \| BaseLLM` | Quando `emit` especificado | LLM usado para interpretar o feedback e mapear para um outcome |
|
||||
| `default_outcome` | `str` | Não | Outcome a usar se nenhum feedback for fornecido. Deve estar em `emit` |
|
||||
| `metadata` | `dict` | Não | Dados adicionais para integrações enterprise |
|
||||
| `provider` | `HumanFeedbackProvider` | Não | Provider customizado para feedback assíncrono/não-bloqueante. Veja [Feedback Humano Assíncrono](#feedback-humano-assíncrono-não-bloqueante) |
|
||||
|
||||
### Uso Básico (Sem Roteamento)
|
||||
|
||||
Quando você não especifica `emit`, o decorador simplesmente coleta o feedback e passa um `HumanFeedbackResult` para o próximo listener:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(message="O que você acha desta análise?")
|
||||
def analyze_data(self):
|
||||
return "Resultados da análise: Receita aumentou 15%, custos diminuíram 8%"
|
||||
|
||||
@listen(analyze_data)
|
||||
def handle_feedback(self, result):
|
||||
# result é um HumanFeedbackResult
|
||||
print(f"Análise: {result.output}")
|
||||
print(f"Feedback: {result.feedback}")
|
||||
```
|
||||
|
||||
### Roteamento com emit
|
||||
|
||||
Quando você especifica `emit`, o decorador se torna um roteador. O feedback livre do humano é interpretado por um LLM e mapeado para um dos outcomes especificados:
|
||||
|
||||
```python Code
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Você aprova este conteúdo para publicação?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_content(self):
|
||||
return "Rascunho do post do blog aqui..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
print(f"Publicando! Usuário disse: {result.feedback}")
|
||||
|
||||
@listen("rejected")
|
||||
def discard(self, result):
|
||||
print(f"Descartando. Motivo: {result.feedback}")
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise(self, result):
|
||||
print(f"Revisando baseado em: {result.feedback}")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
O LLM usa saídas estruturadas (function calling) quando disponível para garantir que a resposta seja um dos seus outcomes especificados. Isso torna o roteamento confiável e previsível.
|
||||
</Tip>
|
||||
|
||||
## HumanFeedbackResult
|
||||
|
||||
O dataclass `HumanFeedbackResult` contém todas as informações sobre uma interação de feedback humano:
|
||||
|
||||
```python Code
|
||||
from crewai.flow.human_feedback import HumanFeedbackResult
|
||||
|
||||
@dataclass
|
||||
class HumanFeedbackResult:
|
||||
output: Any # A saída original do método mostrada ao humano
|
||||
feedback: str # O texto bruto do feedback do humano
|
||||
outcome: str | None # O outcome mapeado (se emit foi especificado)
|
||||
timestamp: datetime # Quando o feedback foi recebido
|
||||
method_name: str # Nome do método decorado
|
||||
metadata: dict # Qualquer metadata passado ao decorador
|
||||
```
|
||||
|
||||
### Acessando em Listeners
|
||||
|
||||
Quando um listener é disparado por um método `@human_feedback` com `emit`, ele recebe o `HumanFeedbackResult`:
|
||||
|
||||
```python Code
|
||||
@listen("approved")
|
||||
def on_approval(self, result: HumanFeedbackResult):
|
||||
print(f"Saída original: {result.output}")
|
||||
print(f"Feedback do usuário: {result.feedback}")
|
||||
print(f"Outcome: {result.outcome}") # "approved"
|
||||
print(f"Recebido em: {result.timestamp}")
|
||||
```
|
||||
|
||||
## Acessando o Histórico de Feedback
|
||||
|
||||
A classe `Flow` fornece dois atributos para acessar o feedback humano:
|
||||
|
||||
### last_human_feedback
|
||||
|
||||
Retorna o `HumanFeedbackResult` mais recente:
|
||||
|
||||
```python Code
|
||||
@listen(some_method)
|
||||
def check_feedback(self):
|
||||
if self.last_human_feedback:
|
||||
print(f"Último feedback: {self.last_human_feedback.feedback}")
|
||||
```
|
||||
|
||||
### human_feedback_history
|
||||
|
||||
Uma lista de todos os objetos `HumanFeedbackResult` coletados durante o flow:
|
||||
|
||||
```python Code
|
||||
@listen(final_step)
|
||||
def summarize(self):
|
||||
print(f"Total de feedbacks coletados: {len(self.human_feedback_history)}")
|
||||
for i, fb in enumerate(self.human_feedback_history):
|
||||
print(f"{i+1}. {fb.method_name}: {fb.outcome or 'sem roteamento'}")
|
||||
```
|
||||
|
||||
<Warning>
|
||||
Cada `HumanFeedbackResult` é adicionado a `human_feedback_history`, então múltiplos passos de feedback não sobrescrevem uns aos outros. Use esta lista para acessar todo o feedback coletado durante o flow.
|
||||
</Warning>
|
||||
|
||||
## Exemplo Completo: Fluxo de Aprovação de Conteúdo
|
||||
|
||||
Aqui está um exemplo completo implementando um fluxo de revisão e aprovação de conteúdo:
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```python Code
|
||||
from crewai.flow.flow import Flow, start, listen
|
||||
from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class ContentState(BaseModel):
|
||||
topic: str = ""
|
||||
draft: str = ""
|
||||
final_content: str = ""
|
||||
revision_count: int = 0
|
||||
|
||||
|
||||
class ContentApprovalFlow(Flow[ContentState]):
|
||||
"""Um flow que gera conteúdo e obtém aprovação humana."""
|
||||
|
||||
@start()
|
||||
def get_topic(self):
|
||||
self.state.topic = input("Sobre qual tópico devo escrever? ")
|
||||
return self.state.topic
|
||||
|
||||
@listen(get_topic)
|
||||
def generate_draft(self, topic):
|
||||
# Em uso real, isso chamaria um LLM
|
||||
self.state.draft = f"# {topic}\n\nEste é um rascunho sobre {topic}..."
|
||||
return self.state.draft
|
||||
|
||||
@listen(generate_draft)
|
||||
@human_feedback(
|
||||
message="Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_draft(self, draft):
|
||||
return draft
|
||||
|
||||
@listen("approved")
|
||||
def publish_content(self, result: HumanFeedbackResult):
|
||||
self.state.final_content = result.output
|
||||
print("\n✅ Conteúdo aprovado e publicado!")
|
||||
print(f"Comentário do revisor: {result.feedback}")
|
||||
return "published"
|
||||
|
||||
@listen("rejected")
|
||||
def handle_rejection(self, result: HumanFeedbackResult):
|
||||
print("\n❌ Conteúdo rejeitado")
|
||||
print(f"Motivo: {result.feedback}")
|
||||
return "rejected"
|
||||
|
||||
@listen("needs_revision")
|
||||
def revise_content(self, result: HumanFeedbackResult):
|
||||
self.state.revision_count += 1
|
||||
print(f"\n📝 Revisão #{self.state.revision_count} solicitada")
|
||||
print(f"Feedback: {result.feedback}")
|
||||
|
||||
# Em um flow real, você pode voltar para generate_draft
|
||||
# Para este exemplo, apenas reconhecemos
|
||||
return "revision_requested"
|
||||
|
||||
|
||||
# Executar o flow
|
||||
flow = ContentApprovalFlow()
|
||||
result = flow.kickoff()
|
||||
print(f"\nFlow concluído. Revisões solicitadas: {flow.state.revision_count}")
|
||||
```
|
||||
|
||||
```text Output
|
||||
Sobre qual tópico devo escrever? Segurança em IA
|
||||
|
||||
==================================================
|
||||
OUTPUT FOR REVIEW:
|
||||
==================================================
|
||||
# Segurança em IA
|
||||
|
||||
Este é um rascunho sobre Segurança em IA...
|
||||
==================================================
|
||||
|
||||
Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão:
|
||||
(Press Enter to skip, or type your feedback)
|
||||
|
||||
Your feedback: Parece bom, aprovado!
|
||||
|
||||
✅ Conteúdo aprovado e publicado!
|
||||
Comentário do revisor: Parece bom, aprovado!
|
||||
|
||||
Flow concluído. Revisões solicitadas: 0
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## Combinando com Outros Decoradores
|
||||
|
||||
O decorador `@human_feedback` funciona com outros decoradores de flow. Coloque-o como o decorador mais interno (mais próximo da função):
|
||||
|
||||
```python Code
|
||||
# Correto: @human_feedback é o mais interno (mais próximo da função)
|
||||
@start()
|
||||
@human_feedback(message="Revise isto:")
|
||||
def my_start_method(self):
|
||||
return "content"
|
||||
|
||||
@listen(other_method)
|
||||
@human_feedback(message="Revise isto também:")
|
||||
def my_listener(self, data):
|
||||
return f"processed: {data}"
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Coloque `@human_feedback` como o decorador mais interno (último/mais próximo da função) para que ele envolva o método diretamente e possa capturar o valor de retorno antes de passar para o sistema de flow.
|
||||
</Tip>
|
||||
|
||||
## Melhores Práticas
|
||||
|
||||
### 1. Escreva Mensagens de Solicitação Claras
|
||||
|
||||
O parâmetro `message` é o que o humano vê. Torne-o acionável:
|
||||
|
||||
```python Code
|
||||
# ✅ Bom - claro e acionável
|
||||
@human_feedback(message="Este resumo captura com precisão os pontos-chave? Responda 'sim' ou explique o que está faltando:")
|
||||
|
||||
# ❌ Ruim - vago
|
||||
@human_feedback(message="Revise isto:")
|
||||
```
|
||||
|
||||
### 2. Escolha Outcomes Significativos
|
||||
|
||||
Ao usar `emit`, escolha outcomes que mapeiem naturalmente para respostas humanas:
|
||||
|
||||
```python Code
|
||||
# ✅ Bom - outcomes em linguagem natural
|
||||
emit=["approved", "rejected", "needs_more_detail"]
|
||||
|
||||
# ❌ Ruim - técnico ou pouco claro
|
||||
emit=["state_1", "state_2", "state_3"]
|
||||
```
|
||||
|
||||
### 3. Sempre Forneça um Outcome Padrão
|
||||
|
||||
Use `default_outcome` para lidar com casos onde usuários pressionam Enter sem digitar:
|
||||
|
||||
```python Code
|
||||
@human_feedback(
|
||||
message="Aprovar? (pressione Enter para solicitar revisão)",
|
||||
emit=["approved", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision", # Padrão seguro
|
||||
)
|
||||
```
|
||||
|
||||
### 4. Use o Histórico de Feedback para Trilhas de Auditoria
|
||||
|
||||
Acesse `human_feedback_history` para criar logs de auditoria:
|
||||
|
||||
```python Code
|
||||
@listen(final_step)
|
||||
def create_audit_log(self):
|
||||
log = []
|
||||
for fb in self.human_feedback_history:
|
||||
log.append({
|
||||
"step": fb.method_name,
|
||||
"outcome": fb.outcome,
|
||||
"feedback": fb.feedback,
|
||||
"timestamp": fb.timestamp.isoformat(),
|
||||
})
|
||||
return log
|
||||
```
|
||||
|
||||
### 5. Trate Feedback Roteado e Não Roteado
|
||||
|
||||
Ao projetar flows, considere se você precisa de roteamento:
|
||||
|
||||
| Cenário | Use |
|
||||
|---------|-----|
|
||||
| Revisão simples, só precisa do texto do feedback | Sem `emit` |
|
||||
| Precisa ramificar para caminhos diferentes baseado na resposta | Use `emit` |
|
||||
| Portões de aprovação com aprovar/rejeitar/revisar | Use `emit` |
|
||||
| Coletando comentários apenas para logging | Sem `emit` |
|
||||
|
||||
## Feedback Humano Assíncrono (Não-Bloqueante - Human in the loop)
|
||||
|
||||
Por padrão, `@human_feedback` bloqueia a execução aguardando entrada no console. Para aplicações de produção, você pode precisar de feedback **assíncrono/não-bloqueante** que se integre com sistemas externos como Slack, email, webhooks ou APIs.
|
||||
|
||||
### A Abstração de Provider
|
||||
|
||||
Use o parâmetro `provider` para especificar uma estratégia customizada de coleta de feedback:
|
||||
|
||||
```python Code
|
||||
from crewai.flow import Flow, start, human_feedback, HumanFeedbackProvider, HumanFeedbackPending, PendingFeedbackContext
|
||||
|
||||
class WebhookProvider(HumanFeedbackProvider):
|
||||
"""Provider que pausa o flow e aguarda callback de webhook."""
|
||||
|
||||
def __init__(self, webhook_url: str):
|
||||
self.webhook_url = webhook_url
|
||||
|
||||
def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
|
||||
# Notifica sistema externo (ex: envia mensagem Slack, cria ticket)
|
||||
self.send_notification(context)
|
||||
|
||||
# Pausa execução - framework cuida da persistência automaticamente
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={"webhook_url": f"{self.webhook_url}/{context.flow_id}"}
|
||||
)
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Revise este conteúdo:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
provider=WebhookProvider("https://myapp.com/api"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Conteúdo gerado por IA..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
return "Publicado!"
|
||||
```
|
||||
|
||||
<Tip>
|
||||
O framework de flow **persiste automaticamente o estado** quando `HumanFeedbackPending` é lançado. Seu provider só precisa notificar o sistema externo e lançar a exceção—não são necessárias chamadas manuais de persistência.
|
||||
</Tip>
|
||||
|
||||
### Tratando Flows Pausados
|
||||
|
||||
Ao usar um provider assíncrono, `kickoff()` retorna um objeto `HumanFeedbackPending` em vez de lançar uma exceção:
|
||||
|
||||
```python Code
|
||||
flow = ReviewFlow()
|
||||
result = flow.kickoff()
|
||||
|
||||
if isinstance(result, HumanFeedbackPending):
|
||||
# Flow está pausado, estado é automaticamente persistido
|
||||
print(f"Aguardando feedback em: {result.callback_info['webhook_url']}")
|
||||
print(f"Flow ID: {result.context.flow_id}")
|
||||
else:
|
||||
# Conclusão normal
|
||||
print(f"Flow concluído: {result}")
|
||||
```
|
||||
|
||||
### Retomando um Flow Pausado
|
||||
|
||||
Quando o feedback chega (ex: via webhook), retome o flow:
|
||||
|
||||
```python Code
|
||||
# Handler síncrono:
|
||||
def handle_feedback_webhook(flow_id: str, feedback: str):
|
||||
flow = ReviewFlow.from_pending(flow_id)
|
||||
result = flow.resume(feedback)
|
||||
return result
|
||||
|
||||
# Handler assíncrono (FastAPI, aiohttp, etc.):
|
||||
async def handle_feedback_webhook(flow_id: str, feedback: str):
|
||||
flow = ReviewFlow.from_pending(flow_id)
|
||||
result = await flow.resume_async(feedback)
|
||||
return result
|
||||
```
|
||||
|
||||
### Tipos Principais
|
||||
|
||||
| Tipo | Descrição |
|
||||
|------|-----------|
|
||||
| `HumanFeedbackProvider` | Protocolo para providers de feedback customizados |
|
||||
| `PendingFeedbackContext` | Contém todas as informações necessárias para retomar um flow pausado |
|
||||
| `HumanFeedbackPending` | Retornado por `kickoff()` quando o flow está pausado para feedback |
|
||||
| `ConsoleProvider` | Provider padrão de entrada bloqueante no console |
|
||||
|
||||
### PendingFeedbackContext
|
||||
|
||||
O contexto contém tudo necessário para retomar:
|
||||
|
||||
```python Code
|
||||
@dataclass
|
||||
class PendingFeedbackContext:
|
||||
flow_id: str # Identificador único desta execução de flow
|
||||
flow_class: str # Nome qualificado completo da classe
|
||||
method_name: str # Método que disparou o feedback
|
||||
method_output: Any # Saída mostrada ao humano
|
||||
message: str # A mensagem de solicitação
|
||||
emit: list[str] | None # Outcomes possíveis para roteamento
|
||||
default_outcome: str | None
|
||||
metadata: dict # Metadata customizado
|
||||
llm: str | None # LLM para mapeamento de outcome
|
||||
requested_at: datetime
|
||||
```
|
||||
|
||||
### Exemplo Completo de Flow Assíncrono
|
||||
|
||||
```python Code
|
||||
from crewai.flow import (
|
||||
Flow, start, listen, human_feedback,
|
||||
HumanFeedbackProvider, HumanFeedbackPending, PendingFeedbackContext
|
||||
)
|
||||
|
||||
class SlackNotificationProvider(HumanFeedbackProvider):
|
||||
"""Provider que envia notificações Slack e pausa para feedback assíncrono."""
|
||||
|
||||
def __init__(self, channel: str):
|
||||
self.channel = channel
|
||||
|
||||
def request_feedback(self, context: PendingFeedbackContext, flow: Flow) -> str:
|
||||
# Envia notificação Slack (implemente você mesmo)
|
||||
slack_thread_id = self.post_to_slack(
|
||||
channel=self.channel,
|
||||
message=f"Revisão necessária:\n\n{context.method_output}\n\n{context.message}",
|
||||
)
|
||||
|
||||
# Pausa execução - framework cuida da persistência automaticamente
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={
|
||||
"slack_channel": self.channel,
|
||||
"thread_id": slack_thread_id,
|
||||
}
|
||||
)
|
||||
|
||||
class ContentPipeline(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Aprova este conteúdo para publicação?",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
provider=SlackNotificationProvider("#content-reviews"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Conteúdo de blog post gerado por IA..."
|
||||
|
||||
@listen("approved")
|
||||
def publish(self, result):
|
||||
print(f"Publicando! Revisor disse: {result.feedback}")
|
||||
return {"status": "published"}
|
||||
|
||||
@listen("rejected")
|
||||
def archive(self, result):
|
||||
print(f"Arquivado. Motivo: {result.feedback}")
|
||||
return {"status": "archived"}
|
||||
|
||||
@listen("needs_revision")
|
||||
def queue_revision(self, result):
|
||||
print(f"Na fila para revisão: {result.feedback}")
|
||||
return {"status": "revision_needed"}
|
||||
|
||||
|
||||
# Iniciando o flow (vai pausar e aguardar resposta do Slack)
|
||||
def start_content_pipeline():
|
||||
flow = ContentPipeline()
|
||||
result = flow.kickoff()
|
||||
|
||||
if isinstance(result, HumanFeedbackPending):
|
||||
return {"status": "pending", "flow_id": result.context.flow_id}
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# Retomando quando webhook do Slack dispara (handler síncrono)
|
||||
def on_slack_feedback(flow_id: str, slack_message: str):
|
||||
flow = ContentPipeline.from_pending(flow_id)
|
||||
result = flow.resume(slack_message)
|
||||
return result
|
||||
|
||||
|
||||
# Se seu handler é assíncrono (FastAPI, aiohttp, Slack Bolt async, etc.)
|
||||
async def on_slack_feedback_async(flow_id: str, slack_message: str):
|
||||
flow = ContentPipeline.from_pending(flow_id)
|
||||
result = await flow.resume_async(slack_message)
|
||||
return result
|
||||
```
|
||||
|
||||
<Warning>
|
||||
Se você está usando um framework web assíncrono (FastAPI, aiohttp, Slack Bolt modo async), use `await flow.resume_async()` em vez de `flow.resume()`. Chamar `resume()` de dentro de um event loop em execução vai lançar um `RuntimeError`.
|
||||
</Warning>
|
||||
|
||||
### Melhores Práticas para Feedback Assíncrono
|
||||
|
||||
1. **Verifique o tipo de retorno**: `kickoff()` retorna `HumanFeedbackPending` quando pausado—não precisa de try/except
|
||||
2. **Use o método resume correto**: Use `resume()` em código síncrono, `await resume_async()` em código assíncrono
|
||||
3. **Armazene informações de callback**: Use `callback_info` para armazenar URLs de webhook, IDs de tickets, etc.
|
||||
4. **Implemente idempotência**: Seu handler de resume deve ser idempotente por segurança
|
||||
5. **Persistência automática**: O estado é automaticamente salvo quando `HumanFeedbackPending` é lançado e usa `SQLiteFlowPersistence` por padrão
|
||||
6. **Persistência customizada**: Passe uma instância de persistência customizada para `from_pending()` se necessário
|
||||
|
||||
## Documentação Relacionada
|
||||
|
||||
- [Visão Geral de Flows](/pt-BR/concepts/flows) - Aprenda sobre CrewAI Flows
|
||||
- [Gerenciamento de Estado em Flows](/pt-BR/guides/flows/mastering-flow-state) - Gerenciando estado em flows
|
||||
- [Persistência de Flows](/pt-BR/concepts/flows#persistence) - Persistindo estado de flows
|
||||
- [Roteamento com @router](/pt-BR/concepts/flows#router) - Mais sobre roteamento condicional
|
||||
- [Input Humano na Execução](/pt-BR/learn/human-input-on-execution) - Input humano no nível de task
|
||||
@@ -7,17 +7,28 @@ mode: "wide"
|
||||
|
||||
## Introdução
|
||||
|
||||
A CrewAI oferece a capacidade de iniciar uma crew de forma assíncrona, permitindo que você comece a execução da crew de maneira não bloqueante.
|
||||
A CrewAI oferece a capacidade de iniciar uma crew de forma assíncrona, permitindo que você comece a execução da crew de maneira não bloqueante.
|
||||
Esse recurso é especialmente útil quando você deseja executar múltiplas crews simultaneamente ou quando precisa realizar outras tarefas enquanto a crew está em execução.
|
||||
|
||||
## Execução Assíncrona de Crew
|
||||
O CrewAI oferece duas abordagens para execução assíncrona:
|
||||
|
||||
Para iniciar uma crew de forma assíncrona, utilize o método `kickoff_async()`. Este método inicia a execução da crew em uma thread separada, permitindo que a thread principal continue executando outras tarefas.
|
||||
| Método | Tipo | Descrição |
|
||||
|--------|------|-------------|
|
||||
| `akickoff()` | Async nativo | Async/await verdadeiro em toda a cadeia de execução |
|
||||
| `kickoff_async()` | Baseado em thread | Envolve execução síncrona em `asyncio.to_thread` |
|
||||
|
||||
<Note>
|
||||
Para cargas de trabalho de alta concorrência, `akickoff()` é recomendado pois usa async nativo para execução de tasks, operações de memória e recuperação de conhecimento.
|
||||
</Note>
|
||||
|
||||
## Execução Async Nativa com `akickoff()`
|
||||
|
||||
O método `akickoff()` fornece execução async nativa verdadeira, usando async/await em toda a cadeia de execução, incluindo execução de tasks, operações de memória e consultas de conhecimento.
|
||||
|
||||
### Assinatura do Método
|
||||
|
||||
```python Code
|
||||
def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
async def akickoff(self, inputs: dict) -> CrewOutput:
|
||||
```
|
||||
|
||||
### Parâmetros
|
||||
@@ -28,97 +39,268 @@ def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
|
||||
- `CrewOutput`: Um objeto que representa o resultado da execução da crew.
|
||||
|
||||
## Possíveis Casos de Uso
|
||||
|
||||
- **Geração Paralela de Conteúdo**: Inicie múltiplas crews independentes de forma assíncrona, cada uma responsável por gerar conteúdo sobre temas diferentes. Por exemplo, uma crew pode pesquisar e redigir um artigo sobre tendências em IA, enquanto outra gera posts para redes sociais sobre o lançamento de um novo produto. Cada crew atua de forma independente, permitindo a escala eficiente da produção de conteúdo.
|
||||
|
||||
- **Tarefas Conjuntas de Pesquisa de Mercado**: Lance múltiplas crews de forma assíncrona para realizar pesquisas de mercado em paralelo. Uma crew pode analisar tendências do setor, outra examinar estratégias de concorrentes e ainda outra avaliar o sentimento do consumidor. Cada crew conclui sua tarefa de forma independente, proporcionando insights mais rápidos e abrangentes.
|
||||
|
||||
- **Módulos Independentes de Planejamento de Viagem**: Execute crews separadas para planejar diferentes aspectos de uma viagem de forma independente. Uma crew pode cuidar das opções de voo, outra das acomodações e uma terceira do planejamento das atividades. Cada crew trabalha de maneira assíncrona, permitindo que os vários componentes da viagem sejam planejados ao mesmo tempo e de maneira independente, para resultados mais rápidos.
|
||||
|
||||
## Exemplo: Execução Assíncrona de uma Única Crew
|
||||
|
||||
Veja um exemplo de como iniciar uma crew de forma assíncrona utilizando asyncio e aguardando o resultado:
|
||||
### Exemplo: Execução Async Nativa de Crew
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
# Create an agent with code execution enabled
|
||||
# Criar um agente
|
||||
coding_agent = Agent(
|
||||
role="Analista de Dados Python",
|
||||
goal="Analisar dados e fornecer insights usando Python",
|
||||
backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
# Create a task that requires code execution
|
||||
# Criar uma tarefa
|
||||
data_analysis_task = Task(
|
||||
description="Analise o conjunto de dados fornecido e calcule a idade média dos participantes. Idades: {ages}",
|
||||
description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="A idade média dos participantes."
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
# Create a crew and add the task
|
||||
# Criar uma crew
|
||||
analysis_crew = Crew(
|
||||
agents=[coding_agent],
|
||||
tasks=[data_analysis_task]
|
||||
)
|
||||
|
||||
# Async function to kickoff the crew asynchronously
|
||||
async def async_crew_execution():
|
||||
result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
# Execução async nativa
|
||||
async def main():
|
||||
result = await analysis_crew.akickoff(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
print("Crew Result:", result)
|
||||
|
||||
# Run the async function
|
||||
asyncio.run(async_crew_execution())
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## Exemplo: Execução Assíncrona de Múltiplas Crews
|
||||
### Exemplo: Múltiplas Crews Async Nativas
|
||||
|
||||
Neste exemplo, mostraremos como iniciar múltiplas crews de forma assíncrona e aguardar todas serem concluídas usando `asyncio.gather()`:
|
||||
Execute múltiplas crews concorrentemente usando `asyncio.gather()` com async nativo:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
# Create an agent with code execution enabled
|
||||
coding_agent = Agent(
|
||||
role="Analista de Dados Python",
|
||||
goal="Analisar dados e fornecer insights usando Python",
|
||||
backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
# Create tasks that require code execution
|
||||
task_1 = Task(
|
||||
description="Analise o primeiro conjunto de dados e calcule a idade média dos participantes. Idades: {ages}",
|
||||
description="Analyze the first dataset and calculate the average age. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="A idade média dos participantes."
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
task_2 = Task(
|
||||
description="Analise o segundo conjunto de dados e calcule a idade média dos participantes. Idades: {ages}",
|
||||
description="Analyze the second dataset and calculate the average age. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="A idade média dos participantes."
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
|
||||
crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
|
||||
|
||||
async def main():
|
||||
results = await asyncio.gather(
|
||||
crew_1.akickoff(inputs={"ages": [25, 30, 35, 40, 45]}),
|
||||
crew_2.akickoff(inputs={"ages": [20, 22, 24, 28, 30]})
|
||||
)
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"Crew {i} Result:", result)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
### Exemplo: Async Nativo para Múltiplas Entradas
|
||||
|
||||
Use `akickoff_for_each()` para executar sua crew contra múltiplas entradas concorrentemente com async nativo:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
data_analysis_task = Task(
|
||||
description="Analyze the dataset and calculate the average age. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
analysis_crew = Crew(
|
||||
agents=[coding_agent],
|
||||
tasks=[data_analysis_task]
|
||||
)
|
||||
|
||||
async def main():
|
||||
datasets = [
|
||||
{"ages": [25, 30, 35, 40, 45]},
|
||||
{"ages": [20, 22, 24, 28, 30]},
|
||||
{"ages": [30, 35, 40, 45, 50]}
|
||||
]
|
||||
|
||||
results = await analysis_crew.akickoff_for_each(datasets)
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"Dataset {i} Result:", result)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## Async Baseado em Thread com `kickoff_async()`
|
||||
|
||||
O método `kickoff_async()` fornece execução async envolvendo o `kickoff()` síncrono em uma thread. Isso é útil para integração async mais simples ou compatibilidade retroativa.
|
||||
|
||||
### Assinatura do Método
|
||||
|
||||
```python Code
|
||||
async def kickoff_async(self, inputs: dict) -> CrewOutput:
|
||||
```
|
||||
|
||||
### Parâmetros
|
||||
|
||||
- `inputs` (dict): Um dicionário contendo os dados de entrada necessários para as tarefas.
|
||||
|
||||
### Retorno
|
||||
|
||||
- `CrewOutput`: Um objeto que representa o resultado da execução da crew.
|
||||
|
||||
### Exemplo: Execução Async Baseada em Thread
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
data_analysis_task = Task(
|
||||
description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
analysis_crew = Crew(
|
||||
agents=[coding_agent],
|
||||
tasks=[data_analysis_task]
|
||||
)
|
||||
|
||||
async def async_crew_execution():
|
||||
result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
print("Crew Result:", result)
|
||||
|
||||
asyncio.run(async_crew_execution())
|
||||
```
|
||||
|
||||
### Exemplo: Múltiplas Crews Async Baseadas em Thread
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
coding_agent = Agent(
|
||||
role="Python Data Analyst",
|
||||
goal="Analyze data and provide insights using Python",
|
||||
backstory="You are an experienced data analyst with strong Python skills.",
|
||||
allow_code_execution=True
|
||||
)
|
||||
|
||||
task_1 = Task(
|
||||
description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
task_2 = Task(
|
||||
description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
|
||||
agent=coding_agent,
|
||||
expected_output="The average age of the participants."
|
||||
)
|
||||
|
||||
# Create two crews and add tasks
|
||||
crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
|
||||
crew_2 = Crew(agents=[coding_agent], tasks=[task_2])
|
||||
|
||||
# Async function to kickoff multiple crews asynchronously and wait for all to finish
|
||||
async def async_multiple_crews():
|
||||
# Create coroutines for concurrent execution
|
||||
result_1 = crew_1.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
|
||||
result_2 = crew_2.kickoff_async(inputs={"ages": [20, 22, 24, 28, 30]})
|
||||
|
||||
# Wait for both crews to finish
|
||||
results = await asyncio.gather(result_1, result_2)
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"Crew {i} Result:", result)
|
||||
|
||||
# Run the async function
|
||||
asyncio.run(async_multiple_crews())
|
||||
```
|
||||
```
|
||||
|
||||
## Streaming Assíncrono
|
||||
|
||||
Ambos os métodos async suportam streaming quando `stream=True` está definido na crew:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Crew, Agent, Task
|
||||
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research and summarize topics",
|
||||
backstory="You are an expert researcher."
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Research the topic: {topic}",
|
||||
agent=agent,
|
||||
expected_output="A comprehensive summary of the topic."
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
stream=True # Habilitar streaming
|
||||
)
|
||||
|
||||
async def main():
|
||||
streaming_output = await crew.akickoff(inputs={"topic": "AI trends in 2024"})
|
||||
|
||||
# Iteração async sobre chunks de streaming
|
||||
async for chunk in streaming_output:
|
||||
print(f"Chunk: {chunk.content}")
|
||||
|
||||
# Acessar resultado final após streaming completar
|
||||
result = streaming_output.result
|
||||
print(f"Final result: {result.raw}")
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## Possíveis Casos de Uso
|
||||
|
||||
- **Geração Paralela de Conteúdo**: Inicie múltiplas crews independentes de forma assíncrona, cada uma responsável por gerar conteúdo sobre temas diferentes. Por exemplo, uma crew pode pesquisar e redigir um artigo sobre tendências em IA, enquanto outra gera posts para redes sociais sobre o lançamento de um novo produto.
|
||||
|
||||
- **Tarefas Conjuntas de Pesquisa de Mercado**: Lance múltiplas crews de forma assíncrona para realizar pesquisas de mercado em paralelo. Uma crew pode analisar tendências do setor, outra examinar estratégias de concorrentes e ainda outra avaliar o sentimento do consumidor.
|
||||
|
||||
- **Módulos Independentes de Planejamento de Viagem**: Execute crews separadas para planejar diferentes aspectos de uma viagem de forma independente. Uma crew pode cuidar das opções de voo, outra das acomodações e uma terceira do planejamento das atividades.
|
||||
|
||||
## Escolhendo entre `akickoff()` e `kickoff_async()`
|
||||
|
||||
| Recurso | `akickoff()` | `kickoff_async()` |
|
||||
|---------|--------------|-------------------|
|
||||
| Modelo de execução | Async/await nativo | Wrapper baseado em thread |
|
||||
| Execução de tasks | Async com `aexecute_sync()` | Síncrono em thread pool |
|
||||
| Operações de memória | Async | Síncrono em thread pool |
|
||||
| Recuperação de conhecimento | Async | Síncrono em thread pool |
|
||||
| Melhor para | Alta concorrência, cargas I/O-bound | Integração async simples |
|
||||
| Suporte a streaming | Sim | Sim |
|
||||
|
||||
356
docs/pt-BR/learn/streaming-crew-execution.mdx
Normal file
356
docs/pt-BR/learn/streaming-crew-execution.mdx
Normal file
@@ -0,0 +1,356 @@
|
||||
---
|
||||
title: Streaming na Execução da Crew
|
||||
description: Transmita saída em tempo real da execução da sua crew no CrewAI
|
||||
icon: wave-pulse
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Introdução
|
||||
|
||||
O CrewAI fornece a capacidade de transmitir saída em tempo real durante a execução da crew, permitindo que você exiba resultados conforme são gerados, em vez de esperar que todo o processo seja concluído. Este recurso é particularmente útil para construir aplicações interativas, fornecer feedback ao usuário e monitorar processos de longa duração.
|
||||
|
||||
## Como o Streaming Funciona
|
||||
|
||||
Quando o streaming está ativado, o CrewAI captura respostas do LLM e chamadas de ferramentas conforme acontecem, empacotando-as em chunks estruturados que incluem contexto sobre qual task e agent está executando. Você pode iterar sobre esses chunks em tempo real e acessar o resultado final quando a execução for concluída.
|
||||
|
||||
## Ativando o Streaming
|
||||
|
||||
Para ativar o streaming, defina o parâmetro `stream` como `True` ao criar sua crew:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Crew, Task
|
||||
|
||||
# Crie seus agentes e tasks
|
||||
researcher = Agent(
|
||||
role="Research Analyst",
|
||||
goal="Gather comprehensive information on topics",
|
||||
backstory="You are an experienced researcher with excellent analytical skills.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Research the latest developments in AI",
|
||||
expected_output="A detailed report on recent AI advancements",
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
# Ativar streaming
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True # Ativar saída em streaming
|
||||
)
|
||||
```
|
||||
|
||||
## Streaming Síncrono
|
||||
|
||||
Quando você chama `kickoff()` em uma crew com streaming ativado, ele retorna um objeto `CrewStreamingOutput` que você pode iterar para receber chunks conforme chegam:
|
||||
|
||||
```python Code
|
||||
# Iniciar execução com streaming
|
||||
streaming = crew.kickoff(inputs={"topic": "artificial intelligence"})
|
||||
|
||||
# Iterar sobre chunks conforme chegam
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Acessar o resultado final após o streaming completar
|
||||
result = streaming.result
|
||||
print(f"\n\nSaída final: {result.raw}")
|
||||
```
|
||||
|
||||
### Informações do Chunk de Stream
|
||||
|
||||
Cada chunk fornece contexto rico sobre a execução:
|
||||
|
||||
```python Code
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
|
||||
for chunk in streaming:
|
||||
print(f"Task: {chunk.task_name} (índice {chunk.task_index})")
|
||||
print(f"Agent: {chunk.agent_role}")
|
||||
print(f"Content: {chunk.content}")
|
||||
print(f"Type: {chunk.chunk_type}") # TEXT ou TOOL_CALL
|
||||
if chunk.tool_call:
|
||||
print(f"Tool: {chunk.tool_call.tool_name}")
|
||||
print(f"Arguments: {chunk.tool_call.arguments}")
|
||||
```
|
||||
|
||||
### Acessando Resultados do Streaming
|
||||
|
||||
O objeto `CrewStreamingOutput` fornece várias propriedades úteis:
|
||||
|
||||
```python Code
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
|
||||
# Iterar e coletar chunks
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Após a iteração completar
|
||||
print(f"\nCompletado: {streaming.is_completed}")
|
||||
print(f"Texto completo: {streaming.get_full_text()}")
|
||||
print(f"Todos os chunks: {len(streaming.chunks)}")
|
||||
print(f"Resultado final: {streaming.result.raw}")
|
||||
```
|
||||
|
||||
## Streaming Assíncrono
|
||||
|
||||
Para aplicações assíncronas, você pode usar `akickoff()` (async nativo) ou `kickoff_async()` (baseado em threads) com iteração assíncrona:
|
||||
|
||||
### Async Nativo com `akickoff()`
|
||||
|
||||
O método `akickoff()` fornece execução async nativa verdadeira em toda a cadeia:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
|
||||
async def stream_crew():
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
# Iniciar streaming async nativo
|
||||
streaming = await crew.akickoff(inputs={"topic": "AI"})
|
||||
|
||||
# Iteração assíncrona sobre chunks
|
||||
async for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Acessar resultado final
|
||||
result = streaming.result
|
||||
print(f"\n\nSaída final: {result.raw}")
|
||||
|
||||
asyncio.run(stream_crew())
|
||||
```
|
||||
|
||||
### Async Baseado em Threads com `kickoff_async()`
|
||||
|
||||
Para integração async mais simples ou compatibilidade retroativa:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
|
||||
async def stream_crew():
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
# Iniciar streaming async baseado em threads
|
||||
streaming = await crew.kickoff_async(inputs={"topic": "AI"})
|
||||
|
||||
# Iteração assíncrona sobre chunks
|
||||
async for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Acessar resultado final
|
||||
result = streaming.result
|
||||
print(f"\n\nSaída final: {result.raw}")
|
||||
|
||||
asyncio.run(stream_crew())
|
||||
```
|
||||
|
||||
<Note>
|
||||
Para cargas de trabalho de alta concorrência, `akickoff()` é recomendado pois usa async nativo para execução de tasks, operações de memória e recuperação de conhecimento. Consulte o guia [Iniciar Crew de Forma Assíncrona](/pt-BR/learn/kickoff-async) para mais detalhes.
|
||||
</Note>
|
||||
|
||||
## Streaming com kickoff_for_each
|
||||
|
||||
Ao executar uma crew para múltiplas entradas com `kickoff_for_each()`, o streaming funciona de forma diferente dependendo se você usa síncrono ou assíncrono:
|
||||
|
||||
### kickoff_for_each Síncrono
|
||||
|
||||
Com `kickoff_for_each()` síncrono, você obtém uma lista de objetos `CrewStreamingOutput`, um para cada entrada:
|
||||
|
||||
```python Code
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
inputs_list = [
|
||||
{"topic": "AI in healthcare"},
|
||||
{"topic": "AI in finance"}
|
||||
]
|
||||
|
||||
# Retorna lista de saídas de streaming
|
||||
streaming_outputs = crew.kickoff_for_each(inputs=inputs_list)
|
||||
|
||||
# Iterar sobre cada saída de streaming
|
||||
for i, streaming in enumerate(streaming_outputs):
|
||||
print(f"\n=== Entrada {i + 1} ===")
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
result = streaming.result
|
||||
print(f"\n\nResultado {i + 1}: {result.raw}")
|
||||
```
|
||||
|
||||
### kickoff_for_each_async Assíncrono
|
||||
|
||||
Com `kickoff_for_each_async()` assíncrono, você obtém um único `CrewStreamingOutput` que produz chunks de todas as crews conforme chegam concorrentemente:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
|
||||
async def stream_multiple_crews():
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True
|
||||
)
|
||||
|
||||
inputs_list = [
|
||||
{"topic": "AI in healthcare"},
|
||||
{"topic": "AI in finance"}
|
||||
]
|
||||
|
||||
# Retorna saída de streaming única para todas as crews
|
||||
streaming = await crew.kickoff_for_each_async(inputs=inputs_list)
|
||||
|
||||
# Chunks de todas as crews chegam conforme são gerados
|
||||
async for chunk in streaming:
|
||||
print(f"[{chunk.task_name}] {chunk.content}", end="", flush=True)
|
||||
|
||||
# Acessar todos os resultados
|
||||
results = streaming.results # Lista de objetos CrewOutput
|
||||
for i, result in enumerate(results):
|
||||
print(f"\n\nResultado {i + 1}: {result.raw}")
|
||||
|
||||
asyncio.run(stream_multiple_crews())
|
||||
```
|
||||
|
||||
## Tipos de Chunk de Stream
|
||||
|
||||
Chunks podem ser de diferentes tipos, indicados pelo campo `chunk_type`:
|
||||
|
||||
### Chunks TEXT
|
||||
|
||||
Conteúdo de texto padrão de respostas do LLM:
|
||||
|
||||
```python Code
|
||||
for chunk in streaming:
|
||||
if chunk.chunk_type == StreamChunkType.TEXT:
|
||||
print(chunk.content, end="", flush=True)
|
||||
```
|
||||
|
||||
### Chunks TOOL_CALL
|
||||
|
||||
Informações sobre chamadas de ferramentas sendo feitas:
|
||||
|
||||
```python Code
|
||||
for chunk in streaming:
|
||||
if chunk.chunk_type == StreamChunkType.TOOL_CALL:
|
||||
print(f"\nChamando ferramenta: {chunk.tool_call.tool_name}")
|
||||
print(f"Argumentos: {chunk.tool_call.arguments}")
|
||||
```
|
||||
|
||||
## Exemplo Prático: Construindo uma UI com Streaming
|
||||
|
||||
Aqui está um exemplo completo mostrando como construir uma aplicação interativa com streaming:
|
||||
|
||||
```python Code
|
||||
import asyncio
|
||||
from crewai import Agent, Crew, Task
|
||||
from crewai.types.streaming import StreamChunkType
|
||||
|
||||
async def interactive_research():
|
||||
# Criar crew com streaming ativado
|
||||
researcher = Agent(
|
||||
role="Research Analyst",
|
||||
goal="Provide detailed analysis on any topic",
|
||||
backstory="You are an expert researcher with broad knowledge.",
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Research and analyze: {topic}",
|
||||
expected_output="A comprehensive analysis with key insights",
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
stream=True,
|
||||
verbose=False
|
||||
)
|
||||
|
||||
# Obter entrada do usuário
|
||||
topic = input("Digite um tópico para pesquisar: ")
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Pesquisando: {topic}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
# Iniciar execução com streaming
|
||||
streaming = await crew.kickoff_async(inputs={"topic": topic})
|
||||
|
||||
current_task = ""
|
||||
async for chunk in streaming:
|
||||
# Mostrar transições de task
|
||||
if chunk.task_name != current_task:
|
||||
current_task = chunk.task_name
|
||||
print(f"\n[{chunk.agent_role}] Trabalhando em: {chunk.task_name}")
|
||||
print("-" * 60)
|
||||
|
||||
# Exibir chunks de texto
|
||||
if chunk.chunk_type == StreamChunkType.TEXT:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
# Exibir chamadas de ferramentas
|
||||
elif chunk.chunk_type == StreamChunkType.TOOL_CALL and chunk.tool_call:
|
||||
print(f"\n🔧 Usando ferramenta: {chunk.tool_call.tool_name}")
|
||||
|
||||
# Mostrar resultado final
|
||||
result = streaming.result
|
||||
print(f"\n\n{'='*60}")
|
||||
print("Análise Completa!")
|
||||
print(f"{'='*60}")
|
||||
print(f"\nUso de Tokens: {result.token_usage}")
|
||||
|
||||
asyncio.run(interactive_research())
|
||||
```
|
||||
|
||||
## Casos de Uso
|
||||
|
||||
O streaming é particularmente valioso para:
|
||||
|
||||
- **Aplicações Interativas**: Fornecer feedback em tempo real aos usuários enquanto os agentes trabalham
|
||||
- **Tasks de Longa Duração**: Mostrar progresso para pesquisa, análise ou geração de conteúdo
|
||||
- **Depuração e Monitoramento**: Observar comportamento e tomada de decisão dos agentes em tempo real
|
||||
- **Experiência do Usuário**: Reduzir latência percebida mostrando resultados incrementais
|
||||
- **Dashboards ao Vivo**: Construir interfaces de monitoramento que exibem status de execução da crew
|
||||
|
||||
## Notas Importantes
|
||||
|
||||
- O streaming ativa automaticamente o streaming do LLM para todos os agentes na crew
|
||||
- Você deve iterar através de todos os chunks antes de acessar a propriedade `.result`
|
||||
- Para `kickoff_for_each_async()` com streaming, use `.results` (plural) para obter todas as saídas
|
||||
- O streaming adiciona overhead mínimo e pode realmente melhorar a performance percebida
|
||||
- Cada chunk inclui contexto completo (task, agente, tipo de chunk) para UIs ricas
|
||||
|
||||
## Tratamento de Erros
|
||||
|
||||
Trate erros durante a execução com streaming:
|
||||
|
||||
```python Code
|
||||
streaming = crew.kickoff(inputs={"topic": "AI"})
|
||||
|
||||
try:
|
||||
for chunk in streaming:
|
||||
print(chunk.content, end="", flush=True)
|
||||
|
||||
result = streaming.result
|
||||
print(f"\nSucesso: {result.raw}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\nErro durante o streaming: {e}")
|
||||
if streaming.is_completed:
|
||||
print("O streaming foi completado mas ocorreu um erro")
|
||||
```
|
||||
|
||||
Ao aproveitar o streaming, você pode construir aplicações mais responsivas e interativas com o CrewAI, fornecendo aos usuários visibilidade em tempo real da execução dos agentes e resultados.
|
||||
@@ -12,7 +12,7 @@ dependencies = [
|
||||
"pytube~=15.0.0",
|
||||
"requests~=2.32.5",
|
||||
"docker~=7.1.0",
|
||||
"crewai==1.7.0",
|
||||
"crewai==1.7.2",
|
||||
"lancedb~=0.5.4",
|
||||
"tiktoken~=0.8.0",
|
||||
"beautifulsoup4~=4.13.4",
|
||||
|
||||
@@ -291,4 +291,4 @@ __all__ = [
|
||||
"ZapierActionTools",
|
||||
]
|
||||
|
||||
__version__ = "1.7.0"
|
||||
__version__ = "1.7.2"
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
"""Crewai Enterprise Tools."""
|
||||
|
||||
import os
|
||||
import json
|
||||
import re
|
||||
from typing import Any, Optional, Union, cast, get_origin
|
||||
@@ -432,7 +432,11 @@ class CrewAIPlatformActionTool(BaseTool):
|
||||
payload = cleaned_kwargs
|
||||
|
||||
response = requests.post(
|
||||
url=api_url, headers=headers, json=payload, timeout=60
|
||||
url=api_url,
|
||||
headers=headers,
|
||||
json=payload,
|
||||
timeout=60,
|
||||
verify=os.environ.get("CREWAI_FACTORY", "false").lower() != "true",
|
||||
)
|
||||
|
||||
data = response.json()
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from typing import Any
|
||||
|
||||
import os
|
||||
from crewai.tools import BaseTool
|
||||
import requests
|
||||
|
||||
@@ -37,6 +37,7 @@ class CrewaiPlatformToolBuilder:
|
||||
headers=headers,
|
||||
timeout=30,
|
||||
params={"apps": ",".join(self._apps)},
|
||||
verify=os.environ.get("CREWAI_FACTORY", "false").lower() != "true",
|
||||
)
|
||||
response.raise_for_status()
|
||||
except Exception:
|
||||
|
||||
@@ -1,4 +1,6 @@
|
||||
from typing import Union, get_args, get_origin
|
||||
from unittest.mock import patch, Mock
|
||||
import os
|
||||
|
||||
from crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool import (
|
||||
CrewAIPlatformActionTool,
|
||||
@@ -249,3 +251,109 @@ class TestSchemaProcessing:
|
||||
result_type = tool._process_schema_type(test_schema, "TestFieldAllOfMixed")
|
||||
|
||||
assert result_type is str
|
||||
|
||||
class TestCrewAIPlatformActionToolVerify:
|
||||
"""Test suite for SSL verification behavior based on CREWAI_FACTORY environment variable"""
|
||||
|
||||
def setup_method(self):
|
||||
self.action_schema = {
|
||||
"function": {
|
||||
"name": "test_action",
|
||||
"parameters": {
|
||||
"properties": {
|
||||
"test_param": {
|
||||
"type": "string",
|
||||
"description": "Test parameter"
|
||||
}
|
||||
},
|
||||
"required": []
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
def create_test_tool(self):
|
||||
return CrewAIPlatformActionTool(
|
||||
description="Test action tool",
|
||||
action_name="test_action",
|
||||
action_schema=self.action_schema
|
||||
)
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}, clear=True)
|
||||
@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool.requests.post")
|
||||
def test_run_with_ssl_verification_default(self, mock_post):
|
||||
"""Test that _run uses SSL verification by default when CREWAI_FACTORY is not set"""
|
||||
mock_response = Mock()
|
||||
mock_response.ok = True
|
||||
mock_response.json.return_value = {"result": "success"}
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
tool = self.create_test_tool()
|
||||
tool._run(test_param="test_value")
|
||||
|
||||
mock_post.assert_called_once()
|
||||
call_args = mock_post.call_args
|
||||
assert call_args.kwargs["verify"] is True
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "false"}, clear=True)
|
||||
@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool.requests.post")
|
||||
def test_run_with_ssl_verification_factory_false(self, mock_post):
|
||||
"""Test that _run uses SSL verification when CREWAI_FACTORY is 'false'"""
|
||||
mock_response = Mock()
|
||||
mock_response.ok = True
|
||||
mock_response.json.return_value = {"result": "success"}
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
tool = self.create_test_tool()
|
||||
tool._run(test_param="test_value")
|
||||
|
||||
mock_post.assert_called_once()
|
||||
call_args = mock_post.call_args
|
||||
assert call_args.kwargs["verify"] is True
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "FALSE"}, clear=True)
|
||||
@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool.requests.post")
|
||||
def test_run_with_ssl_verification_factory_false_uppercase(self, mock_post):
|
||||
"""Test that _run uses SSL verification when CREWAI_FACTORY is 'FALSE' (case-insensitive)"""
|
||||
mock_response = Mock()
|
||||
mock_response.ok = True
|
||||
mock_response.json.return_value = {"result": "success"}
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
tool = self.create_test_tool()
|
||||
tool._run(test_param="test_value")
|
||||
|
||||
mock_post.assert_called_once()
|
||||
call_args = mock_post.call_args
|
||||
assert call_args.kwargs["verify"] is True
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "true"}, clear=True)
|
||||
@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool.requests.post")
|
||||
def test_run_without_ssl_verification_factory_true(self, mock_post):
|
||||
"""Test that _run disables SSL verification when CREWAI_FACTORY is 'true'"""
|
||||
mock_response = Mock()
|
||||
mock_response.ok = True
|
||||
mock_response.json.return_value = {"result": "success"}
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
tool = self.create_test_tool()
|
||||
tool._run(test_param="test_value")
|
||||
|
||||
mock_post.assert_called_once()
|
||||
call_args = mock_post.call_args
|
||||
assert call_args.kwargs["verify"] is False
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "TRUE"}, clear=True)
|
||||
@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool.requests.post")
|
||||
def test_run_without_ssl_verification_factory_true_uppercase(self, mock_post):
|
||||
"""Test that _run disables SSL verification when CREWAI_FACTORY is 'TRUE' (case-insensitive)"""
|
||||
mock_response = Mock()
|
||||
mock_response.ok = True
|
||||
mock_response.json.return_value = {"result": "success"}
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
tool = self.create_test_tool()
|
||||
tool._run(test_param="test_value")
|
||||
|
||||
mock_post.assert_called_once()
|
||||
call_args = mock_post.call_args
|
||||
assert call_args.kwargs["verify"] is False
|
||||
|
||||
@@ -258,3 +258,98 @@ class TestCrewaiPlatformToolBuilder(unittest.TestCase):
|
||||
assert "simple_string" in description_text
|
||||
assert "nested_object" in description_text
|
||||
assert "array_prop" in description_text
|
||||
|
||||
|
||||
|
||||
class TestCrewaiPlatformToolBuilderVerify(unittest.TestCase):
|
||||
"""Test suite for SSL verification behavior in CrewaiPlatformToolBuilder"""
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}, clear=True)
|
||||
@patch(
|
||||
"crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
|
||||
)
|
||||
def test_fetch_actions_with_ssl_verification_default(self, mock_get):
|
||||
"""Test that _fetch_actions uses SSL verification by default when CREWAI_FACTORY is not set"""
|
||||
mock_response = Mock()
|
||||
mock_response.raise_for_status.return_value = None
|
||||
mock_response.json.return_value = {"actions": {}}
|
||||
mock_get.return_value = mock_response
|
||||
|
||||
builder = CrewaiPlatformToolBuilder(apps=["github"])
|
||||
builder._fetch_actions()
|
||||
|
||||
mock_get.assert_called_once()
|
||||
call_args = mock_get.call_args
|
||||
assert call_args.kwargs["verify"] is True
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "false"}, clear=True)
|
||||
@patch(
|
||||
"crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
|
||||
)
|
||||
def test_fetch_actions_with_ssl_verification_factory_false(self, mock_get):
|
||||
"""Test that _fetch_actions uses SSL verification when CREWAI_FACTORY is 'false'"""
|
||||
mock_response = Mock()
|
||||
mock_response.raise_for_status.return_value = None
|
||||
mock_response.json.return_value = {"actions": {}}
|
||||
mock_get.return_value = mock_response
|
||||
|
||||
builder = CrewaiPlatformToolBuilder(apps=["github"])
|
||||
builder._fetch_actions()
|
||||
|
||||
mock_get.assert_called_once()
|
||||
call_args = mock_get.call_args
|
||||
assert call_args.kwargs["verify"] is True
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "FALSE"}, clear=True)
|
||||
@patch(
|
||||
"crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
|
||||
)
|
||||
def test_fetch_actions_with_ssl_verification_factory_false_uppercase(self, mock_get):
|
||||
"""Test that _fetch_actions uses SSL verification when CREWAI_FACTORY is 'FALSE' (case-insensitive)"""
|
||||
mock_response = Mock()
|
||||
mock_response.raise_for_status.return_value = None
|
||||
mock_response.json.return_value = {"actions": {}}
|
||||
mock_get.return_value = mock_response
|
||||
|
||||
builder = CrewaiPlatformToolBuilder(apps=["github"])
|
||||
builder._fetch_actions()
|
||||
|
||||
mock_get.assert_called_once()
|
||||
call_args = mock_get.call_args
|
||||
assert call_args.kwargs["verify"] is True
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "true"}, clear=True)
|
||||
@patch(
|
||||
"crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
|
||||
)
|
||||
def test_fetch_actions_without_ssl_verification_factory_true(self, mock_get):
|
||||
"""Test that _fetch_actions disables SSL verification when CREWAI_FACTORY is 'true'"""
|
||||
mock_response = Mock()
|
||||
mock_response.raise_for_status.return_value = None
|
||||
mock_response.json.return_value = {"actions": {}}
|
||||
mock_get.return_value = mock_response
|
||||
|
||||
builder = CrewaiPlatformToolBuilder(apps=["github"])
|
||||
builder._fetch_actions()
|
||||
|
||||
mock_get.assert_called_once()
|
||||
call_args = mock_get.call_args
|
||||
assert call_args.kwargs["verify"] is False
|
||||
|
||||
@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token", "CREWAI_FACTORY": "TRUE"}, clear=True)
|
||||
@patch(
|
||||
"crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
|
||||
)
|
||||
def test_fetch_actions_without_ssl_verification_factory_true_uppercase(self, mock_get):
|
||||
"""Test that _fetch_actions disables SSL verification when CREWAI_FACTORY is 'TRUE' (case-insensitive)"""
|
||||
mock_response = Mock()
|
||||
mock_response.raise_for_status.return_value = None
|
||||
mock_response.json.return_value = {"actions": {}}
|
||||
mock_get.return_value = mock_response
|
||||
|
||||
builder = CrewaiPlatformToolBuilder(apps=["github"])
|
||||
builder._fetch_actions()
|
||||
|
||||
mock_get.assert_called_once()
|
||||
call_args = mock_get.call_args
|
||||
assert call_args.kwargs["verify"] is False
|
||||
|
||||
@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = [
|
||||
"crewai-tools==1.7.0",
|
||||
"crewai-tools==1.7.2",
|
||||
]
|
||||
embeddings = [
|
||||
"tiktoken~=0.8.0"
|
||||
@@ -84,7 +84,7 @@ bedrock = [
|
||||
"boto3~=1.40.45",
|
||||
]
|
||||
google-genai = [
|
||||
"google-genai~=1.2.0",
|
||||
"google-genai~=1.49.0",
|
||||
]
|
||||
azure-ai-inference = [
|
||||
"azure-ai-inference~=1.0.0b9",
|
||||
|
||||
@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
|
||||
_suppress_pydantic_deprecation_warnings()
|
||||
|
||||
__version__ = "1.7.0"
|
||||
__version__ = "1.7.2"
|
||||
_telemetry_submitted = False
|
||||
|
||||
|
||||
|
||||
@@ -16,7 +16,7 @@ from crewai.events.types.knowledge_events import (
|
||||
KnowledgeSearchQueryFailedEvent,
|
||||
)
|
||||
from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
|
||||
from crewai.utilities.converter import generate_model_description
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
||||
@@ -5,10 +5,9 @@ from __future__ import annotations
|
||||
from abc import ABC, abstractmethod
|
||||
import json
|
||||
import re
|
||||
from typing import TYPE_CHECKING, Final, Literal
|
||||
|
||||
from crewai.utilities.converter import generate_model_description
|
||||
from typing import TYPE_CHECKING, Any, Final, Literal
|
||||
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@@ -42,7 +41,7 @@ class BaseConverterAdapter(ABC):
|
||||
"""
|
||||
self.agent_adapter = agent_adapter
|
||||
self._output_format: Literal["json", "pydantic"] | None = None
|
||||
self._schema: str | None = None
|
||||
self._schema: dict[str, Any] | None = None
|
||||
|
||||
@abstractmethod
|
||||
def configure_structured_output(self, task: Task) -> None:
|
||||
@@ -129,7 +128,7 @@ class BaseConverterAdapter(ABC):
|
||||
@staticmethod
|
||||
def _configure_format_from_task(
|
||||
task: Task,
|
||||
) -> tuple[Literal["json", "pydantic"] | None, str | None]:
|
||||
) -> tuple[Literal["json", "pydantic"] | None, dict[str, Any] | None]:
|
||||
"""Determine output format and schema from task requirements.
|
||||
|
||||
This is a helper method that examines the task's output requirements
|
||||
|
||||
@@ -4,6 +4,7 @@ This module contains the OpenAIConverterAdapter class that handles structured
|
||||
output conversion for OpenAI agents, supporting JSON and Pydantic model formats.
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
|
||||
@@ -61,7 +62,7 @@ class OpenAIConverterAdapter(BaseConverterAdapter):
|
||||
output_schema: str = (
|
||||
get_i18n()
|
||||
.slice("formatted_task_instructions")
|
||||
.format(output_format=self._schema)
|
||||
.format(output_format=json.dumps(self._schema, indent=2))
|
||||
)
|
||||
|
||||
return f"{base_prompt}\n\n{output_schema}"
|
||||
|
||||
@@ -149,7 +149,9 @@ class AuthenticationCommand:
|
||||
return
|
||||
|
||||
if token_data["error"] not in ("authorization_pending", "slow_down"):
|
||||
raise requests.HTTPError(token_data["error_description"])
|
||||
raise requests.HTTPError(
|
||||
token_data.get("error_description") or token_data.get("error")
|
||||
)
|
||||
|
||||
time.sleep(device_code_data["interval"])
|
||||
attempts += 1
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from typing import Any
|
||||
from urllib.parse import urljoin
|
||||
|
||||
import os
|
||||
import requests
|
||||
|
||||
from crewai.cli.config import Settings
|
||||
@@ -33,9 +33,7 @@ class PlusAPI:
|
||||
if settings.org_uuid:
|
||||
self.headers["X-Crewai-Organization-Id"] = settings.org_uuid
|
||||
|
||||
self.base_url = (
|
||||
str(settings.enterprise_base_url) or DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
)
|
||||
self.base_url = os.getenv("CREWAI_PLUS_URL") or str(settings.enterprise_base_url) or DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
|
||||
def _make_request(
|
||||
self, method: str, endpoint: str, **kwargs: Any
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.7.0"
|
||||
"crewai[tools]==1.7.2"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.7.0"
|
||||
"crewai[tools]==1.7.2"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import base64
|
||||
from json import JSONDecodeError
|
||||
import os
|
||||
from pathlib import Path
|
||||
import subprocess
|
||||
@@ -11,6 +12,7 @@ from rich.console import Console
|
||||
from crewai.cli import git
|
||||
from crewai.cli.command import BaseCommand, PlusAPIMixin
|
||||
from crewai.cli.config import Settings
|
||||
from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
from crewai.cli.utils import (
|
||||
build_env_with_tool_repository_credentials,
|
||||
extract_available_exports,
|
||||
@@ -130,10 +132,13 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
self._validate_response(publish_response)
|
||||
|
||||
published_handle = publish_response.json()["handle"]
|
||||
settings = Settings()
|
||||
base_url = settings.enterprise_base_url or DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
|
||||
console.print(
|
||||
f"Successfully published `{published_handle}` ({project_version}).\n\n"
|
||||
+ "⚠️ Security checks are running in the background. Your tool will be available once these are complete.\n"
|
||||
+ f"You can monitor the status or access your tool here:\nhttps://app.crewai.com/crewai_plus/tools/{published_handle}",
|
||||
+ f"You can monitor the status or access your tool here:\n{base_url}/crewai_plus/tools/{published_handle}",
|
||||
style="bold green",
|
||||
)
|
||||
|
||||
@@ -162,9 +167,19 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
|
||||
if login_response.status_code != 200:
|
||||
console.print(
|
||||
"Authentication failed. Verify if the currently active organization access to the tool repository, and run 'crewai login' again. ",
|
||||
"Authentication failed. Verify if the currently active organization can access the tool repository, and run 'crewai login' again.",
|
||||
style="bold red",
|
||||
)
|
||||
try:
|
||||
console.print(
|
||||
f"[{login_response.status_code} error - {login_response.json().get('message', 'Unknown error')}]",
|
||||
style="bold red italic",
|
||||
)
|
||||
except JSONDecodeError:
|
||||
console.print(
|
||||
f"[{login_response.status_code} error - Unknown error - Invalid JSON response]",
|
||||
style="bold red italic",
|
||||
)
|
||||
raise SystemExit
|
||||
|
||||
login_response_json = login_response.json()
|
||||
|
||||
@@ -1017,10 +1017,26 @@ class Crew(FlowTrackable, BaseModel):
|
||||
tasks=self.tasks, planning_agent_llm=self.planning_llm
|
||||
)._handle_crew_planning()
|
||||
|
||||
for task, step_plan in zip(
|
||||
self.tasks, result.list_of_plans_per_task, strict=False
|
||||
):
|
||||
task.description += step_plan.plan
|
||||
plan_map: dict[int, str] = {}
|
||||
for step_plan in result.list_of_plans_per_task:
|
||||
if step_plan.task_number in plan_map:
|
||||
self._logger.log(
|
||||
"warning",
|
||||
f"Duplicate plan for Task Number {step_plan.task_number}, "
|
||||
"using the first plan",
|
||||
)
|
||||
else:
|
||||
plan_map[step_plan.task_number] = step_plan.plan
|
||||
|
||||
for idx, task in enumerate(self.tasks):
|
||||
task_number = idx + 1
|
||||
if task_number in plan_map:
|
||||
task.description += plan_map[task_number]
|
||||
else:
|
||||
self._logger.log(
|
||||
"warning",
|
||||
f"No plan found for Task Number {task_number}",
|
||||
)
|
||||
|
||||
def _store_execution_log(
|
||||
self,
|
||||
|
||||
@@ -38,9 +38,11 @@ from crewai.events.types.crew_events import (
|
||||
from crewai.events.types.flow_events import (
|
||||
FlowCreatedEvent,
|
||||
FlowFinishedEvent,
|
||||
FlowPausedEvent,
|
||||
FlowStartedEvent,
|
||||
MethodExecutionFailedEvent,
|
||||
MethodExecutionFinishedEvent,
|
||||
MethodExecutionPausedEvent,
|
||||
MethodExecutionStartedEvent,
|
||||
)
|
||||
from crewai.events.types.knowledge_events import (
|
||||
@@ -363,6 +365,28 @@ class EventListener(BaseEventListener):
|
||||
)
|
||||
self.method_branches[event.method_name] = updated_branch
|
||||
|
||||
@crewai_event_bus.on(MethodExecutionPausedEvent)
|
||||
def on_method_execution_paused(
|
||||
_: Any, event: MethodExecutionPausedEvent
|
||||
) -> None:
|
||||
method_branch = self.method_branches.get(event.method_name)
|
||||
updated_branch = self.formatter.update_method_status(
|
||||
method_branch,
|
||||
self.formatter.current_flow_tree,
|
||||
event.method_name,
|
||||
"paused",
|
||||
)
|
||||
self.method_branches[event.method_name] = updated_branch
|
||||
|
||||
@crewai_event_bus.on(FlowPausedEvent)
|
||||
def on_flow_paused(_: Any, event: FlowPausedEvent) -> None:
|
||||
self.formatter.update_flow_status(
|
||||
self.formatter.current_flow_tree,
|
||||
event.flow_name,
|
||||
event.flow_id,
|
||||
"paused",
|
||||
)
|
||||
|
||||
# ----------- TOOL USAGE EVENTS -----------
|
||||
|
||||
@crewai_event_bus.on(ToolUsageStartedEvent)
|
||||
|
||||
@@ -9,6 +9,8 @@ from rich.console import Console
|
||||
from rich.panel import Panel
|
||||
|
||||
from crewai.cli.authentication.token import AuthError, get_auth_token
|
||||
from crewai.cli.config import Settings
|
||||
from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
from crewai.cli.plus_api import PlusAPI
|
||||
from crewai.cli.version import get_crewai_version
|
||||
from crewai.events.listeners.tracing.types import TraceEvent
|
||||
@@ -16,7 +18,6 @@ from crewai.events.listeners.tracing.utils import (
|
||||
is_tracing_enabled_in_context,
|
||||
should_auto_collect_first_time_traces,
|
||||
)
|
||||
from crewai.utilities.constants import CREWAI_BASE_URL
|
||||
|
||||
|
||||
logger = getLogger(__name__)
|
||||
@@ -326,10 +327,12 @@ class TraceBatchManager:
|
||||
if response.status_code == 200:
|
||||
access_code = response.json().get("access_code", None)
|
||||
console = Console()
|
||||
settings = Settings()
|
||||
base_url = settings.enterprise_base_url or DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
return_link = (
|
||||
f"{CREWAI_BASE_URL}/crewai_plus/trace_batches/{self.trace_batch_id}"
|
||||
f"{base_url}/crewai_plus/trace_batches/{self.trace_batch_id}"
|
||||
if not self.is_current_batch_ephemeral and access_code is None
|
||||
else f"{CREWAI_BASE_URL}/crewai_plus/ephemeral_trace_batches/{self.trace_batch_id}?access_code={access_code}"
|
||||
else f"{base_url}/crewai_plus/ephemeral_trace_batches/{self.trace_batch_id}?access_code={access_code}"
|
||||
)
|
||||
|
||||
if self.is_current_batch_ephemeral:
|
||||
|
||||
@@ -58,6 +58,29 @@ class MethodExecutionFailedEvent(FlowEvent):
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
|
||||
class MethodExecutionPausedEvent(FlowEvent):
|
||||
"""Event emitted when a flow method is paused waiting for human feedback.
|
||||
|
||||
This event is emitted when a @human_feedback decorated method with an
|
||||
async provider raises HumanFeedbackPending to pause execution.
|
||||
|
||||
Attributes:
|
||||
flow_name: Name of the flow that is paused.
|
||||
method_name: Name of the method waiting for feedback.
|
||||
state: Current flow state when paused.
|
||||
flow_id: Unique identifier for this flow execution.
|
||||
message: The message shown when requesting feedback.
|
||||
emit: Optional list of possible outcomes for routing.
|
||||
"""
|
||||
|
||||
method_name: str
|
||||
state: dict[str, Any] | BaseModel
|
||||
flow_id: str
|
||||
message: str
|
||||
emit: list[str] | None = None
|
||||
type: str = "method_execution_paused"
|
||||
|
||||
|
||||
class FlowFinishedEvent(FlowEvent):
|
||||
"""Event emitted when a flow completes execution"""
|
||||
|
||||
@@ -67,8 +90,71 @@ class FlowFinishedEvent(FlowEvent):
|
||||
state: dict[str, Any] | BaseModel
|
||||
|
||||
|
||||
class FlowPausedEvent(FlowEvent):
|
||||
"""Event emitted when a flow is paused waiting for human feedback.
|
||||
|
||||
This event is emitted when a flow is paused due to a @human_feedback
|
||||
decorated method with an async provider raising HumanFeedbackPending.
|
||||
|
||||
Attributes:
|
||||
flow_name: Name of the flow that is paused.
|
||||
flow_id: Unique identifier for this flow execution.
|
||||
method_name: Name of the method waiting for feedback.
|
||||
state: Current flow state when paused.
|
||||
message: The message shown when requesting feedback.
|
||||
emit: Optional list of possible outcomes for routing.
|
||||
"""
|
||||
|
||||
flow_id: str
|
||||
method_name: str
|
||||
state: dict[str, Any] | BaseModel
|
||||
message: str
|
||||
emit: list[str] | None = None
|
||||
type: str = "flow_paused"
|
||||
|
||||
|
||||
class FlowPlotEvent(FlowEvent):
|
||||
"""Event emitted when a flow plot is created"""
|
||||
|
||||
flow_name: str
|
||||
type: str = "flow_plot"
|
||||
|
||||
|
||||
class HumanFeedbackRequestedEvent(FlowEvent):
|
||||
"""Event emitted when human feedback is requested.
|
||||
|
||||
This event is emitted when a @human_feedback decorated method
|
||||
requires input from a human reviewer.
|
||||
|
||||
Attributes:
|
||||
flow_name: Name of the flow requesting feedback.
|
||||
method_name: Name of the method decorated with @human_feedback.
|
||||
output: The method output shown to the human for review.
|
||||
message: The message displayed when requesting feedback.
|
||||
emit: Optional list of possible outcomes for routing.
|
||||
"""
|
||||
|
||||
method_name: str
|
||||
output: Any
|
||||
message: str
|
||||
emit: list[str] | None = None
|
||||
type: str = "human_feedback_requested"
|
||||
|
||||
|
||||
class HumanFeedbackReceivedEvent(FlowEvent):
|
||||
"""Event emitted when human feedback is received.
|
||||
|
||||
This event is emitted after a human provides feedback in response
|
||||
to a @human_feedback decorated method.
|
||||
|
||||
Attributes:
|
||||
flow_name: Name of the flow that received feedback.
|
||||
method_name: Name of the method that received feedback.
|
||||
feedback: The raw text feedback provided by the human.
|
||||
outcome: The collapsed outcome string (if emit was specified).
|
||||
"""
|
||||
|
||||
method_name: str
|
||||
feedback: str
|
||||
outcome: str | None = None
|
||||
type: str = "human_feedback_received"
|
||||
|
||||
@@ -19,9 +19,9 @@ class SignalType(IntEnum):
|
||||
|
||||
SIGTERM = signal.SIGTERM
|
||||
SIGINT = signal.SIGINT
|
||||
SIGHUP = signal.SIGHUP
|
||||
SIGTSTP = signal.SIGTSTP
|
||||
SIGCONT = signal.SIGCONT
|
||||
SIGHUP = getattr(signal, "SIGHUP", 1)
|
||||
SIGTSTP = getattr(signal, "SIGTSTP", 20)
|
||||
SIGCONT = getattr(signal, "SIGCONT", 18)
|
||||
|
||||
|
||||
class SigTermEvent(BaseEvent):
|
||||
|
||||
@@ -453,41 +453,48 @@ To enable tracing, do any one of these:
|
||||
if flow_tree is None:
|
||||
return
|
||||
|
||||
# Determine status-specific labels and styles
|
||||
if status == "completed":
|
||||
label_prefix = "✅ Flow Finished:"
|
||||
style = "green"
|
||||
node_text = "✅ Flow Completed"
|
||||
content_text = "Flow Execution Completed"
|
||||
panel_title = "Flow Completion"
|
||||
elif status == "paused":
|
||||
label_prefix = "⏳ Flow Paused:"
|
||||
style = "cyan"
|
||||
node_text = "⏳ Waiting for Human Feedback"
|
||||
content_text = "Flow Paused - Waiting for Feedback"
|
||||
panel_title = "Flow Paused"
|
||||
else:
|
||||
label_prefix = "❌ Flow Failed:"
|
||||
style = "red"
|
||||
node_text = "❌ Flow Failed"
|
||||
content_text = "Flow Execution Failed"
|
||||
panel_title = "Flow Failure"
|
||||
|
||||
# Update main flow label
|
||||
self.update_tree_label(
|
||||
flow_tree,
|
||||
"✅ Flow Finished:" if status == "completed" else "❌ Flow Failed:",
|
||||
label_prefix,
|
||||
flow_name,
|
||||
"green" if status == "completed" else "red",
|
||||
style,
|
||||
)
|
||||
|
||||
# Update initialization node status
|
||||
for child in flow_tree.children:
|
||||
if "Starting Flow" in str(child.label):
|
||||
child.label = Text(
|
||||
(
|
||||
"✅ Flow Completed"
|
||||
if status == "completed"
|
||||
else "❌ Flow Failed"
|
||||
),
|
||||
style="green" if status == "completed" else "red",
|
||||
)
|
||||
child.label = Text(node_text, style=style)
|
||||
break
|
||||
|
||||
content = self.create_status_content(
|
||||
(
|
||||
"Flow Execution Completed"
|
||||
if status == "completed"
|
||||
else "Flow Execution Failed"
|
||||
),
|
||||
content_text,
|
||||
flow_name,
|
||||
"green" if status == "completed" else "red",
|
||||
style,
|
||||
ID=flow_id,
|
||||
)
|
||||
self.print(flow_tree)
|
||||
self.print_panel(
|
||||
content, "Flow Completion", "green" if status == "completed" else "red"
|
||||
)
|
||||
self.print_panel(content, panel_title, style)
|
||||
|
||||
def update_method_status(
|
||||
self,
|
||||
@@ -508,6 +515,12 @@ To enable tracing, do any one of these:
|
||||
if "Starting Flow" in str(child.label):
|
||||
child.label = Text("Flow Method Step", style="white")
|
||||
break
|
||||
elif status == "paused":
|
||||
prefix, style = "⏳ Paused:", "cyan"
|
||||
for child in flow_tree.children:
|
||||
if "Starting Flow" in str(child.label):
|
||||
child.label = Text("⏳ Waiting for Feedback", style="cyan")
|
||||
break
|
||||
else:
|
||||
prefix, style = "❌ Failed:", "red"
|
||||
for child in flow_tree.children:
|
||||
|
||||
@@ -1,4 +1,11 @@
|
||||
from crewai.flow.async_feedback import (
|
||||
ConsoleProvider,
|
||||
HumanFeedbackPending,
|
||||
HumanFeedbackProvider,
|
||||
PendingFeedbackContext,
|
||||
)
|
||||
from crewai.flow.flow import Flow, and_, listen, or_, router, start
|
||||
from crewai.flow.human_feedback import HumanFeedbackResult, human_feedback
|
||||
from crewai.flow.persistence import persist
|
||||
from crewai.flow.visualization import (
|
||||
FlowStructure,
|
||||
@@ -8,10 +15,16 @@ from crewai.flow.visualization import (
|
||||
|
||||
|
||||
__all__ = [
|
||||
"ConsoleProvider",
|
||||
"Flow",
|
||||
"FlowStructure",
|
||||
"HumanFeedbackPending",
|
||||
"HumanFeedbackProvider",
|
||||
"HumanFeedbackResult",
|
||||
"PendingFeedbackContext",
|
||||
"and_",
|
||||
"build_flow_structure",
|
||||
"human_feedback",
|
||||
"listen",
|
||||
"or_",
|
||||
"persist",
|
||||
|
||||
41
lib/crewai/src/crewai/flow/async_feedback/__init__.py
Normal file
41
lib/crewai/src/crewai/flow/async_feedback/__init__.py
Normal file
@@ -0,0 +1,41 @@
|
||||
"""Async human feedback support for CrewAI Flows.
|
||||
|
||||
This module provides abstractions for non-blocking human-in-the-loop workflows,
|
||||
allowing integration with external systems like Slack, Teams, webhooks, or APIs.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from crewai.flow import Flow, start, human_feedback
|
||||
from crewai.flow.async_feedback import HumanFeedbackProvider, HumanFeedbackPending
|
||||
|
||||
class SlackProvider(HumanFeedbackProvider):
|
||||
def request_feedback(self, context, flow):
|
||||
self.send_slack_notification(context)
|
||||
raise HumanFeedbackPending(context=context)
|
||||
|
||||
class MyFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review this:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
provider=SlackProvider(),
|
||||
)
|
||||
def review(self):
|
||||
return "Content to review"
|
||||
```
|
||||
"""
|
||||
|
||||
from crewai.flow.async_feedback.types import (
|
||||
HumanFeedbackPending,
|
||||
HumanFeedbackProvider,
|
||||
PendingFeedbackContext,
|
||||
)
|
||||
from crewai.flow.async_feedback.providers import ConsoleProvider
|
||||
|
||||
__all__ = [
|
||||
"ConsoleProvider",
|
||||
"HumanFeedbackPending",
|
||||
"HumanFeedbackProvider",
|
||||
"PendingFeedbackContext",
|
||||
]
|
||||
124
lib/crewai/src/crewai/flow/async_feedback/providers.py
Normal file
124
lib/crewai/src/crewai/flow/async_feedback/providers.py
Normal file
@@ -0,0 +1,124 @@
|
||||
"""Default provider implementations for human feedback.
|
||||
|
||||
This module provides the ConsoleProvider, which is the default synchronous
|
||||
provider that collects feedback via console input.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from crewai.flow.async_feedback.types import PendingFeedbackContext
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.flow import Flow
|
||||
|
||||
|
||||
class ConsoleProvider:
|
||||
"""Default synchronous console-based feedback provider.
|
||||
|
||||
This provider blocks execution and waits for console input from the user.
|
||||
It displays the method output with formatting and prompts for feedback.
|
||||
|
||||
This is the default provider used when no custom provider is specified
|
||||
in the @human_feedback decorator.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from crewai.flow.async_feedback import ConsoleProvider
|
||||
|
||||
# Explicitly use console provider
|
||||
@human_feedback(
|
||||
message="Review this:",
|
||||
provider=ConsoleProvider(),
|
||||
)
|
||||
def my_method(self):
|
||||
return "Content to review"
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, verbose: bool = True):
|
||||
"""Initialize the console provider.
|
||||
|
||||
Args:
|
||||
verbose: Whether to display formatted output. If False, only
|
||||
shows the prompt message.
|
||||
"""
|
||||
self.verbose = verbose
|
||||
|
||||
def request_feedback(
|
||||
self,
|
||||
context: PendingFeedbackContext,
|
||||
flow: Flow,
|
||||
) -> str:
|
||||
"""Request feedback via console input (blocking).
|
||||
|
||||
Displays the method output with formatting and waits for the user
|
||||
to type their feedback. Press Enter to skip (returns empty string).
|
||||
|
||||
Args:
|
||||
context: The pending feedback context with output and message.
|
||||
flow: The Flow instance (used for event emission).
|
||||
|
||||
Returns:
|
||||
The user's feedback as a string, or empty string if skipped.
|
||||
"""
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.event_listener import event_listener
|
||||
from crewai.events.types.flow_events import (
|
||||
HumanFeedbackReceivedEvent,
|
||||
HumanFeedbackRequestedEvent,
|
||||
)
|
||||
|
||||
# Emit feedback requested event
|
||||
crewai_event_bus.emit(
|
||||
flow,
|
||||
HumanFeedbackRequestedEvent(
|
||||
type="human_feedback_requested",
|
||||
flow_name=flow.name or flow.__class__.__name__,
|
||||
method_name=context.method_name,
|
||||
output=context.method_output,
|
||||
message=context.message,
|
||||
emit=context.emit,
|
||||
),
|
||||
)
|
||||
|
||||
# Pause live updates during human input
|
||||
formatter = event_listener.formatter
|
||||
formatter.pause_live_updates()
|
||||
|
||||
try:
|
||||
console = formatter.console
|
||||
|
||||
if self.verbose:
|
||||
# Display output with formatting using Rich console
|
||||
console.print("\n" + "═" * 50, style="bold cyan")
|
||||
console.print(" OUTPUT FOR REVIEW", style="bold cyan")
|
||||
console.print("═" * 50 + "\n", style="bold cyan")
|
||||
console.print(context.method_output)
|
||||
console.print("\n" + "═" * 50 + "\n", style="bold cyan")
|
||||
|
||||
# Show message and prompt for feedback
|
||||
console.print(context.message, style="yellow")
|
||||
console.print(
|
||||
"(Press Enter to skip, or type your feedback)\n", style="cyan"
|
||||
)
|
||||
|
||||
feedback = input("Your feedback: ").strip()
|
||||
|
||||
# Emit feedback received event
|
||||
crewai_event_bus.emit(
|
||||
flow,
|
||||
HumanFeedbackReceivedEvent(
|
||||
type="human_feedback_received",
|
||||
flow_name=flow.name or flow.__class__.__name__,
|
||||
method_name=context.method_name,
|
||||
feedback=feedback,
|
||||
outcome=None, # Will be determined after collapsing
|
||||
),
|
||||
)
|
||||
|
||||
return feedback
|
||||
finally:
|
||||
# Resume live updates
|
||||
formatter.resume_live_updates()
|
||||
264
lib/crewai/src/crewai/flow/async_feedback/types.py
Normal file
264
lib/crewai/src/crewai/flow/async_feedback/types.py
Normal file
@@ -0,0 +1,264 @@
|
||||
"""Core types for async human feedback in Flows.
|
||||
|
||||
This module defines the protocol, exception, and context types used for
|
||||
non-blocking human-in-the-loop workflows.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.flow import Flow
|
||||
|
||||
|
||||
@dataclass
|
||||
class PendingFeedbackContext:
|
||||
"""Context capturing everything needed to resume a paused flow.
|
||||
|
||||
When a flow is paused waiting for async human feedback, this dataclass
|
||||
stores all the information needed to:
|
||||
1. Identify which flow execution is waiting
|
||||
2. What method triggered the feedback request
|
||||
3. What was shown to the human
|
||||
4. How to route the response when it arrives
|
||||
|
||||
Attributes:
|
||||
flow_id: Unique identifier for the flow instance (from state.id)
|
||||
flow_class: Fully qualified class name (e.g., "myapp.flows.ReviewFlow")
|
||||
method_name: Name of the method that triggered feedback request
|
||||
method_output: The output that was shown to the human for review
|
||||
message: The message displayed when requesting feedback
|
||||
emit: Optional list of outcome strings for routing
|
||||
default_outcome: Outcome to use when no feedback is provided
|
||||
metadata: Optional metadata for external system integration
|
||||
llm: LLM model string for outcome collapsing
|
||||
requested_at: When the feedback was requested
|
||||
|
||||
Example:
|
||||
```python
|
||||
context = PendingFeedbackContext(
|
||||
flow_id="abc-123",
|
||||
flow_class="myapp.ReviewFlow",
|
||||
method_name="review_content",
|
||||
method_output={"title": "Draft", "body": "..."},
|
||||
message="Please review and approve or reject:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
```
|
||||
"""
|
||||
|
||||
flow_id: str
|
||||
flow_class: str
|
||||
method_name: str
|
||||
method_output: Any
|
||||
message: str
|
||||
emit: list[str] | None = None
|
||||
default_outcome: str | None = None
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
llm: str | None = None
|
||||
requested_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
"""Serialize context to a dictionary for persistence.
|
||||
|
||||
Returns:
|
||||
Dictionary representation suitable for JSON serialization.
|
||||
"""
|
||||
return {
|
||||
"flow_id": self.flow_id,
|
||||
"flow_class": self.flow_class,
|
||||
"method_name": self.method_name,
|
||||
"method_output": self.method_output,
|
||||
"message": self.message,
|
||||
"emit": self.emit,
|
||||
"default_outcome": self.default_outcome,
|
||||
"metadata": self.metadata,
|
||||
"llm": self.llm,
|
||||
"requested_at": self.requested_at.isoformat(),
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> PendingFeedbackContext:
|
||||
"""Deserialize context from a dictionary.
|
||||
|
||||
Args:
|
||||
data: Dictionary representation of the context.
|
||||
|
||||
Returns:
|
||||
Reconstructed PendingFeedbackContext instance.
|
||||
"""
|
||||
requested_at = data.get("requested_at")
|
||||
if isinstance(requested_at, str):
|
||||
requested_at = datetime.fromisoformat(requested_at)
|
||||
elif requested_at is None:
|
||||
requested_at = datetime.now()
|
||||
|
||||
return cls(
|
||||
flow_id=data["flow_id"],
|
||||
flow_class=data["flow_class"],
|
||||
method_name=data["method_name"],
|
||||
method_output=data.get("method_output"),
|
||||
message=data.get("message", ""),
|
||||
emit=data.get("emit"),
|
||||
default_outcome=data.get("default_outcome"),
|
||||
metadata=data.get("metadata", {}),
|
||||
llm=data.get("llm"),
|
||||
requested_at=requested_at,
|
||||
)
|
||||
|
||||
|
||||
class HumanFeedbackPending(Exception): # noqa: N818 - Not an error, a control flow signal
|
||||
"""Signal that flow execution should pause for async human feedback.
|
||||
|
||||
When raised by a provider, the flow framework will:
|
||||
1. Stop execution at the current method
|
||||
2. Automatically persist state and context (if persistence is configured)
|
||||
3. Return this object to the caller (not re-raise it)
|
||||
|
||||
The caller receives this as a return value from `flow.kickoff()`, enabling
|
||||
graceful handling of the paused state without try/except blocks:
|
||||
|
||||
```python
|
||||
result = flow.kickoff()
|
||||
if isinstance(result, HumanFeedbackPending):
|
||||
# Flow is paused, handle async feedback
|
||||
print(f"Waiting for feedback: {result.context.flow_id}")
|
||||
else:
|
||||
# Normal completion
|
||||
print(f"Flow completed: {result}")
|
||||
```
|
||||
|
||||
Note:
|
||||
The flow framework automatically saves pending feedback when this
|
||||
exception is raised. Providers do NOT need to call `save_pending_feedback`
|
||||
manually - just raise this exception and the framework handles persistence.
|
||||
|
||||
Attributes:
|
||||
context: The PendingFeedbackContext with all details needed to resume
|
||||
callback_info: Optional dict with information for external systems
|
||||
(e.g., webhook URL, ticket ID, Slack thread ID)
|
||||
|
||||
Example:
|
||||
```python
|
||||
class SlackProvider(HumanFeedbackProvider):
|
||||
def request_feedback(self, context, flow):
|
||||
# Send notification to external system
|
||||
ticket_id = self.create_slack_thread(context)
|
||||
|
||||
# Raise to pause - framework handles persistence automatically
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={
|
||||
"slack_channel": "#reviews",
|
||||
"thread_id": ticket_id,
|
||||
}
|
||||
)
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
context: PendingFeedbackContext,
|
||||
callback_info: dict[str, Any] | None = None,
|
||||
message: str | None = None,
|
||||
):
|
||||
"""Initialize the pending feedback exception.
|
||||
|
||||
Args:
|
||||
context: The pending feedback context with flow details
|
||||
callback_info: Optional information for external system callbacks
|
||||
message: Optional custom message (defaults to descriptive message)
|
||||
"""
|
||||
self.context = context
|
||||
self.callback_info = callback_info or {}
|
||||
|
||||
if message is None:
|
||||
message = (
|
||||
f"Human feedback pending for flow '{context.flow_id}' "
|
||||
f"at method '{context.method_name}'"
|
||||
)
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class HumanFeedbackProvider(Protocol):
|
||||
"""Protocol for human feedback collection strategies.
|
||||
|
||||
Implement this protocol to create custom feedback providers that integrate
|
||||
with external systems like Slack, Teams, email, or custom APIs.
|
||||
|
||||
Providers can be either:
|
||||
- **Synchronous (blocking)**: Return feedback string directly
|
||||
- **Asynchronous (non-blocking)**: Raise HumanFeedbackPending to pause
|
||||
|
||||
The default ConsoleProvider is synchronous and blocks waiting for input.
|
||||
For async workflows, implement a provider that raises HumanFeedbackPending.
|
||||
|
||||
Note:
|
||||
The flow framework automatically handles state persistence when
|
||||
HumanFeedbackPending is raised. Providers only need to:
|
||||
1. Notify the external system (Slack, email, webhook, etc.)
|
||||
2. Raise HumanFeedbackPending with the context and callback info
|
||||
|
||||
Example synchronous provider:
|
||||
```python
|
||||
class ConsoleProvider(HumanFeedbackProvider):
|
||||
def request_feedback(self, context, flow):
|
||||
print(context.method_output)
|
||||
return input("Your feedback: ")
|
||||
```
|
||||
|
||||
Example async provider:
|
||||
```python
|
||||
class SlackProvider(HumanFeedbackProvider):
|
||||
def __init__(self, channel: str):
|
||||
self.channel = channel
|
||||
|
||||
def request_feedback(self, context, flow):
|
||||
# Send notification to Slack
|
||||
thread_id = self.post_to_slack(
|
||||
channel=self.channel,
|
||||
message=context.message,
|
||||
content=context.method_output,
|
||||
)
|
||||
|
||||
# Raise to pause - framework handles persistence automatically
|
||||
raise HumanFeedbackPending(
|
||||
context=context,
|
||||
callback_info={
|
||||
"channel": self.channel,
|
||||
"thread_id": thread_id,
|
||||
}
|
||||
)
|
||||
```
|
||||
"""
|
||||
|
||||
def request_feedback(
|
||||
self,
|
||||
context: PendingFeedbackContext,
|
||||
flow: Flow,
|
||||
) -> str:
|
||||
"""Request feedback from a human.
|
||||
|
||||
For synchronous providers, block and return the feedback string.
|
||||
For async providers, notify the external system and raise
|
||||
HumanFeedbackPending to pause the flow.
|
||||
|
||||
Args:
|
||||
context: The pending feedback context containing all details
|
||||
about what feedback is needed and how to route the response.
|
||||
flow: The Flow instance, providing access to state and name.
|
||||
|
||||
Returns:
|
||||
The human's feedback as a string (synchronous providers only).
|
||||
|
||||
Raises:
|
||||
HumanFeedbackPending: To signal that the flow should pause and
|
||||
wait for external feedback. The framework will automatically
|
||||
persist state when this is raised.
|
||||
"""
|
||||
...
|
||||
@@ -7,12 +7,13 @@ for building event-driven workflows with conditional execution and routing.
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from collections.abc import Callable
|
||||
from collections.abc import Callable, Sequence
|
||||
from concurrent.futures import Future
|
||||
import copy
|
||||
import inspect
|
||||
import logging
|
||||
from typing import (
|
||||
TYPE_CHECKING,
|
||||
Any,
|
||||
ClassVar,
|
||||
Generic,
|
||||
@@ -41,10 +42,12 @@ from crewai.events.listeners.tracing.utils import (
|
||||
from crewai.events.types.flow_events import (
|
||||
FlowCreatedEvent,
|
||||
FlowFinishedEvent,
|
||||
FlowPausedEvent,
|
||||
FlowPlotEvent,
|
||||
FlowStartedEvent,
|
||||
MethodExecutionFailedEvent,
|
||||
MethodExecutionFinishedEvent,
|
||||
MethodExecutionPausedEvent,
|
||||
MethodExecutionStartedEvent,
|
||||
)
|
||||
from crewai.flow.constants import AND_CONDITION, OR_CONDITION
|
||||
@@ -69,9 +72,14 @@ from crewai.flow.utils import (
|
||||
is_flow_method_name,
|
||||
is_simple_flow_condition,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.async_feedback.types import PendingFeedbackContext
|
||||
from crewai.flow.human_feedback import HumanFeedbackResult
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
|
||||
from crewai.flow.visualization import build_flow_structure, render_interactive
|
||||
from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
|
||||
from crewai.utilities.printer import Printer, PrinterColor
|
||||
from crewai.utilities.streaming import (
|
||||
TaskInfo,
|
||||
create_async_chunk_generator,
|
||||
@@ -443,6 +451,26 @@ class FlowMeta(type):
|
||||
else:
|
||||
router_paths[attr_name] = []
|
||||
|
||||
# Handle start methods that are also routers (e.g., @human_feedback with emit)
|
||||
if (
|
||||
hasattr(attr_value, "__is_start_method__")
|
||||
and hasattr(attr_value, "__is_router__")
|
||||
and attr_value.__is_router__
|
||||
):
|
||||
routers.add(attr_name)
|
||||
# Get router paths from the decorator attribute
|
||||
if (
|
||||
hasattr(attr_value, "__router_paths__")
|
||||
and attr_value.__router_paths__
|
||||
):
|
||||
router_paths[attr_name] = attr_value.__router_paths__
|
||||
else:
|
||||
possible_returns = get_possible_return_constants(attr_value)
|
||||
if possible_returns:
|
||||
router_paths[attr_name] = possible_returns
|
||||
else:
|
||||
router_paths[attr_name] = []
|
||||
|
||||
cls._start_methods = start_methods # type: ignore[attr-defined]
|
||||
cls._listeners = listeners # type: ignore[attr-defined]
|
||||
cls._routers = routers # type: ignore[attr-defined]
|
||||
@@ -456,8 +484,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
|
||||
type parameter T must be either dict[str, Any] or a subclass of BaseModel."""
|
||||
|
||||
_printer: ClassVar[Printer] = Printer()
|
||||
|
||||
_start_methods: ClassVar[list[FlowMethodName]] = []
|
||||
_listeners: ClassVar[dict[FlowMethodName, SimpleFlowCondition | FlowCondition]] = {}
|
||||
_routers: ClassVar[set[FlowMethodName]] = set()
|
||||
@@ -500,6 +526,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
self._persistence: FlowPersistence | None = persistence
|
||||
self._is_execution_resuming: bool = False
|
||||
self._event_futures: list[Future[None]] = []
|
||||
|
||||
# Human feedback storage
|
||||
self.human_feedback_history: list[HumanFeedbackResult] = []
|
||||
self.last_human_feedback: HumanFeedbackResult | None = None
|
||||
self._pending_feedback_context: PendingFeedbackContext | None = None
|
||||
self.suppress_flow_events: bool = suppress_flow_events
|
||||
|
||||
# Initialize state with initial values
|
||||
@@ -533,6 +564,296 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
method = method.__get__(self, self.__class__)
|
||||
self._methods[method.__name__] = method
|
||||
|
||||
@classmethod
|
||||
def from_pending(
|
||||
cls,
|
||||
flow_id: str,
|
||||
persistence: FlowPersistence | None = None,
|
||||
**kwargs: Any,
|
||||
) -> "Flow[Any]":
|
||||
"""Create a Flow instance from a pending feedback state.
|
||||
|
||||
This classmethod is used to restore a flow that was paused waiting
|
||||
for async human feedback. It loads the persisted state and pending
|
||||
feedback context, then returns a flow instance ready to resume.
|
||||
|
||||
Args:
|
||||
flow_id: The unique identifier of the paused flow (from state.id)
|
||||
persistence: The persistence backend where the state was saved.
|
||||
If not provided, defaults to SQLiteFlowPersistence().
|
||||
**kwargs: Additional keyword arguments passed to the Flow constructor
|
||||
|
||||
Returns:
|
||||
A new Flow instance with restored state, ready to call resume()
|
||||
|
||||
Raises:
|
||||
ValueError: If no pending feedback exists for the given flow_id
|
||||
|
||||
Example:
|
||||
```python
|
||||
# Simple usage with default persistence:
|
||||
flow = MyFlow.from_pending("abc-123")
|
||||
result = flow.resume("looks good!")
|
||||
|
||||
# Or with custom persistence:
|
||||
persistence = SQLiteFlowPersistence("custom.db")
|
||||
flow = MyFlow.from_pending("abc-123", persistence)
|
||||
result = flow.resume("looks good!")
|
||||
```
|
||||
"""
|
||||
if persistence is None:
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
persistence = SQLiteFlowPersistence()
|
||||
|
||||
# Load pending feedback context and state
|
||||
loaded = persistence.load_pending_feedback(flow_id)
|
||||
if loaded is None:
|
||||
raise ValueError(f"No pending feedback found for flow_id: {flow_id}")
|
||||
|
||||
state_data, pending_context = loaded
|
||||
|
||||
# Create flow instance with persistence
|
||||
instance = cls(persistence=persistence, **kwargs)
|
||||
|
||||
# Restore state
|
||||
instance._initialize_state(state_data)
|
||||
|
||||
# Store pending context for resume
|
||||
instance._pending_feedback_context = pending_context
|
||||
|
||||
# Mark that we're resuming execution
|
||||
instance._is_execution_resuming = True
|
||||
|
||||
# Mark the method as completed (it ran before pausing)
|
||||
instance._completed_methods.add(FlowMethodName(pending_context.method_name))
|
||||
|
||||
return instance
|
||||
|
||||
@property
|
||||
def pending_feedback(self) -> "PendingFeedbackContext | None":
|
||||
"""Get the pending feedback context if this flow is waiting for feedback.
|
||||
|
||||
Returns:
|
||||
The PendingFeedbackContext if the flow is paused waiting for feedback,
|
||||
None otherwise.
|
||||
|
||||
Example:
|
||||
```python
|
||||
flow = MyFlow.from_pending("abc-123", persistence)
|
||||
if flow.pending_feedback:
|
||||
print(f"Waiting for feedback on: {flow.pending_feedback.method_name}")
|
||||
```
|
||||
"""
|
||||
return self._pending_feedback_context
|
||||
|
||||
def resume(self, feedback: str = "") -> Any:
|
||||
"""Resume flow execution, optionally with human feedback.
|
||||
|
||||
This method continues flow execution after a flow was paused for
|
||||
async human feedback. It processes the feedback (including LLM-based
|
||||
outcome collapsing if emit was specified), stores the result, and
|
||||
triggers downstream listeners.
|
||||
|
||||
Note:
|
||||
If called from within an async context (running event loop),
|
||||
use `await flow.resume_async(feedback)` instead.
|
||||
|
||||
Args:
|
||||
feedback: The human's feedback as a string. If empty, uses
|
||||
default_outcome or the first emit option.
|
||||
|
||||
Returns:
|
||||
The final output from the flow execution, or HumanFeedbackPending
|
||||
if another feedback point is reached.
|
||||
|
||||
Raises:
|
||||
ValueError: If no pending feedback context exists (flow wasn't paused)
|
||||
RuntimeError: If called from within a running event loop (use resume_async instead)
|
||||
|
||||
Example:
|
||||
```python
|
||||
# In a sync webhook handler:
|
||||
def handle_feedback(flow_id: str, feedback: str):
|
||||
flow = MyFlow.from_pending(flow_id)
|
||||
result = flow.resume(feedback)
|
||||
return result
|
||||
|
||||
|
||||
# In an async handler, use resume_async instead:
|
||||
async def handle_feedback_async(flow_id: str, feedback: str):
|
||||
flow = MyFlow.from_pending(flow_id)
|
||||
result = await flow.resume_async(feedback)
|
||||
return result
|
||||
```
|
||||
"""
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
except RuntimeError:
|
||||
loop = None
|
||||
|
||||
if loop is not None:
|
||||
raise RuntimeError(
|
||||
"resume() cannot be called from within an async context. "
|
||||
"Use 'await flow.resume_async(feedback)' instead."
|
||||
)
|
||||
|
||||
return asyncio.run(self.resume_async(feedback))
|
||||
|
||||
async def resume_async(self, feedback: str = "") -> Any:
|
||||
"""Async version of resume.
|
||||
|
||||
Resume flow execution, optionally with human feedback asynchronously.
|
||||
|
||||
Args:
|
||||
feedback: The human's feedback as a string. If empty, uses
|
||||
default_outcome or the first emit option.
|
||||
|
||||
Returns:
|
||||
The final output from the flow execution, or HumanFeedbackPending
|
||||
if another feedback point is reached.
|
||||
|
||||
Raises:
|
||||
ValueError: If no pending feedback context exists
|
||||
"""
|
||||
from crewai.flow.human_feedback import HumanFeedbackResult
|
||||
from datetime import datetime
|
||||
|
||||
if self._pending_feedback_context is None:
|
||||
raise ValueError(
|
||||
"No pending feedback context. Use from_pending() to restore a paused flow."
|
||||
)
|
||||
|
||||
context = self._pending_feedback_context
|
||||
emit = context.emit
|
||||
default_outcome = context.default_outcome
|
||||
llm = context.llm
|
||||
|
||||
# Determine outcome
|
||||
collapsed_outcome: str | None = None
|
||||
|
||||
if not feedback.strip():
|
||||
# Empty feedback
|
||||
if default_outcome:
|
||||
collapsed_outcome = default_outcome
|
||||
elif emit:
|
||||
# No default and no feedback - use first outcome
|
||||
collapsed_outcome = emit[0]
|
||||
elif emit:
|
||||
# Collapse feedback to outcome using LLM
|
||||
collapsed_outcome = self._collapse_to_outcome(
|
||||
feedback=feedback,
|
||||
outcomes=emit,
|
||||
llm=llm,
|
||||
)
|
||||
|
||||
# Create result
|
||||
result = HumanFeedbackResult(
|
||||
output=context.method_output,
|
||||
feedback=feedback,
|
||||
outcome=collapsed_outcome,
|
||||
timestamp=datetime.now(),
|
||||
method_name=context.method_name,
|
||||
metadata=context.metadata,
|
||||
)
|
||||
|
||||
# Store in flow instance
|
||||
self.human_feedback_history.append(result)
|
||||
self.last_human_feedback = result
|
||||
|
||||
# Clear pending context after processing
|
||||
self._pending_feedback_context = None
|
||||
|
||||
# Clear pending feedback from persistence
|
||||
if self._persistence:
|
||||
self._persistence.clear_pending_feedback(context.flow_id)
|
||||
|
||||
# Emit feedback received event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
MethodExecutionFinishedEvent(
|
||||
type="method_execution_finished",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
method_name=context.method_name,
|
||||
result=collapsed_outcome if emit else result,
|
||||
state=self._state,
|
||||
),
|
||||
)
|
||||
|
||||
# Clear resumption flag before triggering listeners
|
||||
# This allows methods to re-execute in loops (e.g., implement_changes → suggest_changes → implement_changes)
|
||||
self._is_execution_resuming = False
|
||||
|
||||
# Determine what to pass to listeners
|
||||
try:
|
||||
if emit and collapsed_outcome:
|
||||
# Router behavior - the outcome itself triggers listeners
|
||||
# First, add the outcome to method outputs as a router would
|
||||
self._method_outputs.append(collapsed_outcome)
|
||||
|
||||
# Then trigger listeners for the outcome (e.g., "approved" triggers @listen("approved"))
|
||||
final_result = await self._execute_listeners(
|
||||
FlowMethodName(collapsed_outcome), # Use outcome as trigger
|
||||
result, # Pass HumanFeedbackResult to listeners
|
||||
)
|
||||
else:
|
||||
# Normal behavior - pass the HumanFeedbackResult
|
||||
final_result = await self._execute_listeners(
|
||||
FlowMethodName(context.method_name),
|
||||
result,
|
||||
)
|
||||
except Exception as e:
|
||||
# Check if flow was paused again for human feedback (loop case)
|
||||
from crewai.flow.async_feedback.types import HumanFeedbackPending
|
||||
|
||||
if isinstance(e, HumanFeedbackPending):
|
||||
# Auto-save pending feedback (create default persistence if needed)
|
||||
if self._persistence is None:
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
self._persistence = SQLiteFlowPersistence()
|
||||
|
||||
state_data = (
|
||||
self._state
|
||||
if isinstance(self._state, dict)
|
||||
else self._state.model_dump()
|
||||
)
|
||||
self._persistence.save_pending_feedback(
|
||||
flow_uuid=e.context.flow_id,
|
||||
context=e.context,
|
||||
state_data=state_data,
|
||||
)
|
||||
|
||||
# Emit flow paused event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
FlowPausedEvent(
|
||||
type="flow_paused",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
flow_id=e.context.flow_id,
|
||||
method_name=e.context.method_name,
|
||||
state=self._copy_and_serialize_state(),
|
||||
message=e.context.message,
|
||||
emit=e.context.emit,
|
||||
),
|
||||
)
|
||||
# Return the pending exception instead of raising
|
||||
return e
|
||||
raise
|
||||
|
||||
# Emit flow finished
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
FlowFinishedEvent(
|
||||
type="flow_finished",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
result=final_result,
|
||||
state=self._state,
|
||||
),
|
||||
)
|
||||
|
||||
return final_result
|
||||
|
||||
def _create_initial_state(self) -> T:
|
||||
"""Create and initialize flow state with UUID and default values.
|
||||
|
||||
@@ -548,19 +869,21 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
state_type = self._initial_state_t
|
||||
if isinstance(state_type, type):
|
||||
if issubclass(state_type, FlowState):
|
||||
# Create instance without id, then set it
|
||||
# Create instance - FlowState auto-generates id via default_factory
|
||||
instance = state_type()
|
||||
if not hasattr(instance, "id"):
|
||||
instance.id = str(uuid4())
|
||||
# Ensure id is set - generate UUID if empty
|
||||
if not getattr(instance, "id", None):
|
||||
object.__setattr__(instance, "id", str(uuid4()))
|
||||
return cast(T, instance)
|
||||
if issubclass(state_type, BaseModel):
|
||||
# Create a new type that includes the ID field
|
||||
class StateWithId(state_type, FlowState): # type: ignore
|
||||
# Create a new type with FlowState first for proper id default
|
||||
class StateWithId(FlowState, state_type): # type: ignore
|
||||
pass
|
||||
|
||||
instance = StateWithId()
|
||||
if not hasattr(instance, "id"):
|
||||
instance.id = str(uuid4())
|
||||
# Ensure id is set - generate UUID if empty
|
||||
if not getattr(instance, "id", None):
|
||||
object.__setattr__(instance, "id", str(uuid4()))
|
||||
return cast(T, instance)
|
||||
if state_type is dict:
|
||||
return cast(T, {"id": str(uuid4())})
|
||||
@@ -578,7 +901,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
model_fields = getattr(self.initial_state, "model_fields", None)
|
||||
if not model_fields or "id" not in model_fields:
|
||||
raise ValueError("Flow state model must have an 'id' field")
|
||||
return self.initial_state() # Uses model defaults
|
||||
instance = self.initial_state()
|
||||
# Ensure id is set - generate UUID if empty
|
||||
if not getattr(instance, "id", None):
|
||||
object.__setattr__(instance, "id", str(uuid4()))
|
||||
return instance
|
||||
if self.initial_state is dict:
|
||||
return cast(T, {"id": str(uuid4())})
|
||||
|
||||
@@ -608,6 +935,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
k: v for k, v in model.__dict__.items() if not k.startswith("_")
|
||||
}
|
||||
|
||||
# Ensure id is set - generate UUID if empty
|
||||
if not state_dict.get("id"):
|
||||
state_dict["id"] = str(uuid4())
|
||||
|
||||
# Create new instance of the same class
|
||||
model_class = type(model)
|
||||
return cast(T, model_class(**state_dict))
|
||||
@@ -690,16 +1021,22 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
TypeError: If state is neither BaseModel nor dictionary
|
||||
"""
|
||||
if isinstance(self._state, dict):
|
||||
# For dict states, preserve existing fields unless overridden
|
||||
# For dict states, update with inputs
|
||||
# If inputs contains an id, use it (for restoring from persistence)
|
||||
# Otherwise preserve the current id or generate a new one
|
||||
current_id = self._state.get("id")
|
||||
# Only update specified fields
|
||||
inputs_has_id = "id" in inputs
|
||||
|
||||
# Update specified fields
|
||||
for k, v in inputs.items():
|
||||
self._state[k] = v
|
||||
# Ensure ID is preserved or generated
|
||||
if current_id:
|
||||
self._state["id"] = current_id
|
||||
elif "id" not in self._state:
|
||||
self._state["id"] = str(uuid4())
|
||||
|
||||
# Ensure ID is set: prefer inputs id, then current id, then generate
|
||||
if not inputs_has_id:
|
||||
if current_id:
|
||||
self._state["id"] = current_id
|
||||
elif "id" not in self._state:
|
||||
self._state["id"] = str(uuid4())
|
||||
elif isinstance(self._state, BaseModel):
|
||||
# For BaseModel states, preserve existing fields unless overridden
|
||||
try:
|
||||
@@ -989,15 +1326,74 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
)
|
||||
if future:
|
||||
self._event_futures.append(future)
|
||||
self._log_flow_event(
|
||||
f"Flow started with ID: {self.flow_id}", color="bold magenta"
|
||||
)
|
||||
|
||||
if inputs is not None and "id" not in inputs:
|
||||
self._initialize_state(inputs)
|
||||
|
||||
tasks = [
|
||||
self._execute_start_method(start_method)
|
||||
for start_method in self._start_methods
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
try:
|
||||
tasks = [
|
||||
self._execute_start_method(start_method)
|
||||
for start_method in self._start_methods
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
except Exception as e:
|
||||
# Check if flow was paused for human feedback
|
||||
from crewai.flow.async_feedback.types import HumanFeedbackPending
|
||||
|
||||
if isinstance(e, HumanFeedbackPending):
|
||||
# Auto-save pending feedback (create default persistence if needed)
|
||||
if self._persistence is None:
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
self._persistence = SQLiteFlowPersistence()
|
||||
|
||||
state_data = (
|
||||
self._state
|
||||
if isinstance(self._state, dict)
|
||||
else self._state.model_dump()
|
||||
)
|
||||
self._persistence.save_pending_feedback(
|
||||
flow_uuid=e.context.flow_id,
|
||||
context=e.context,
|
||||
state_data=state_data,
|
||||
)
|
||||
|
||||
# Emit flow paused event
|
||||
future = crewai_event_bus.emit(
|
||||
self,
|
||||
FlowPausedEvent(
|
||||
type="flow_paused",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
flow_id=e.context.flow_id,
|
||||
method_name=e.context.method_name,
|
||||
state=self._copy_and_serialize_state(),
|
||||
message=e.context.message,
|
||||
emit=e.context.emit,
|
||||
),
|
||||
)
|
||||
if future and isinstance(future, Future):
|
||||
self._event_futures.append(future)
|
||||
|
||||
# Wait for events to be processed
|
||||
if self._event_futures:
|
||||
await asyncio.gather(
|
||||
*[
|
||||
asyncio.wrap_future(f)
|
||||
for f in self._event_futures
|
||||
if isinstance(f, Future)
|
||||
]
|
||||
)
|
||||
self._event_futures.clear()
|
||||
|
||||
# Return the pending exception instead of raising
|
||||
# This allows the caller to handle the paused state gracefully
|
||||
return e
|
||||
|
||||
# Re-raise other exceptions
|
||||
raise
|
||||
|
||||
# Clear the resumption flag after initial execution completes
|
||||
self._is_execution_resuming = False
|
||||
@@ -1078,7 +1474,30 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
enhanced_method = self._inject_trigger_payload_for_start_method(method)
|
||||
|
||||
result = await self._execute_method(start_method_name, enhanced_method)
|
||||
await self._execute_listeners(start_method_name, result)
|
||||
|
||||
# If start method is a router, use its result as an additional trigger
|
||||
if start_method_name in self._routers and result is not None:
|
||||
# Execute listeners for the start method name first
|
||||
await self._execute_listeners(start_method_name, result)
|
||||
# Then execute listeners for the router result (e.g., "approved")
|
||||
router_result_trigger = FlowMethodName(str(result))
|
||||
listeners_for_result = self._find_triggered_methods(
|
||||
router_result_trigger, router_only=False
|
||||
)
|
||||
if listeners_for_result:
|
||||
# Pass the HumanFeedbackResult if available
|
||||
listener_result = (
|
||||
self.last_human_feedback
|
||||
if self.last_human_feedback is not None
|
||||
else result
|
||||
)
|
||||
tasks = [
|
||||
self._execute_single_listener(listener_name, listener_result)
|
||||
for listener_name in listeners_for_result
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
else:
|
||||
await self._execute_listeners(start_method_name, result)
|
||||
|
||||
def _inject_trigger_payload_for_start_method(
|
||||
self, original_method: Callable[..., Any]
|
||||
@@ -1172,17 +1591,28 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
return result
|
||||
except Exception as e:
|
||||
if not self.suppress_flow_events:
|
||||
future = crewai_event_bus.emit(
|
||||
self,
|
||||
MethodExecutionFailedEvent(
|
||||
type="method_execution_failed",
|
||||
method_name=method_name,
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
error=e,
|
||||
),
|
||||
)
|
||||
if future:
|
||||
self._event_futures.append(future)
|
||||
# Check if this is a HumanFeedbackPending exception (paused, not failed)
|
||||
from crewai.flow.async_feedback.types import HumanFeedbackPending
|
||||
|
||||
if isinstance(e, HumanFeedbackPending):
|
||||
# Auto-save pending feedback (create default persistence if needed)
|
||||
if self._persistence is None:
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
self._persistence = SQLiteFlowPersistence()
|
||||
|
||||
# Regular failure
|
||||
future = crewai_event_bus.emit(
|
||||
self,
|
||||
MethodExecutionFailedEvent(
|
||||
type="method_execution_failed",
|
||||
method_name=method_name,
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
error=e,
|
||||
),
|
||||
)
|
||||
if future:
|
||||
self._event_futures.append(future)
|
||||
raise e
|
||||
|
||||
def _copy_and_serialize_state(self) -> dict[str, Any]:
|
||||
@@ -1216,7 +1646,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
"""
|
||||
# First, handle routers repeatedly until no router triggers anymore
|
||||
router_results = []
|
||||
router_result_to_feedback: dict[
|
||||
str, Any
|
||||
] = {} # Map outcome -> HumanFeedbackResult
|
||||
current_trigger = trigger_method
|
||||
current_result = result # Track the result to pass to each router
|
||||
|
||||
while True:
|
||||
routers_triggered = self._find_triggered_methods(
|
||||
@@ -1226,13 +1660,22 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
break
|
||||
|
||||
for router_name in routers_triggered:
|
||||
await self._execute_single_listener(router_name, result)
|
||||
# For routers triggered by a router outcome, pass the HumanFeedbackResult
|
||||
router_input = router_result_to_feedback.get(
|
||||
str(current_trigger), current_result
|
||||
)
|
||||
await self._execute_single_listener(router_name, router_input)
|
||||
# After executing router, the router's result is the path
|
||||
router_result = (
|
||||
self._method_outputs[-1] if self._method_outputs else None
|
||||
)
|
||||
if router_result: # Only add non-None results
|
||||
router_results.append(router_result)
|
||||
# If this was a human_feedback router, map the outcome to the feedback
|
||||
if self.last_human_feedback is not None:
|
||||
router_result_to_feedback[str(router_result)] = (
|
||||
self.last_human_feedback
|
||||
)
|
||||
current_trigger = (
|
||||
FlowMethodName(str(router_result))
|
||||
if router_result is not None
|
||||
@@ -1248,8 +1691,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
current_trigger, router_only=False
|
||||
)
|
||||
if listeners_triggered:
|
||||
# Determine what result to pass to listeners
|
||||
# For router outcomes, pass the HumanFeedbackResult if available
|
||||
listener_result = router_result_to_feedback.get(
|
||||
str(current_trigger), result
|
||||
)
|
||||
tasks = [
|
||||
self._execute_single_listener(listener_name, result)
|
||||
self._execute_single_listener(listener_name, listener_result)
|
||||
for listener_name in listeners_triggered
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
@@ -1441,14 +1889,225 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
# Execute listeners (and possibly routers) of this listener
|
||||
await self._execute_listeners(listener_name, listener_result)
|
||||
|
||||
# If this listener is also a router (e.g., has @human_feedback with emit),
|
||||
# we need to trigger listeners for the router result as well
|
||||
if listener_name in self._routers and listener_result is not None:
|
||||
router_result_trigger = FlowMethodName(str(listener_result))
|
||||
listeners_for_result = self._find_triggered_methods(
|
||||
router_result_trigger, router_only=False
|
||||
)
|
||||
if listeners_for_result:
|
||||
# Pass the HumanFeedbackResult if available
|
||||
feedback_result = (
|
||||
self.last_human_feedback
|
||||
if self.last_human_feedback is not None
|
||||
else listener_result
|
||||
)
|
||||
tasks = [
|
||||
self._execute_single_listener(name, feedback_result)
|
||||
for name in listeners_for_result
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error executing listener {listener_name}: {e}")
|
||||
# Don't log HumanFeedbackPending as an error - it's expected control flow
|
||||
from crewai.flow.async_feedback.types import HumanFeedbackPending
|
||||
|
||||
if not isinstance(e, HumanFeedbackPending):
|
||||
logger.error(f"Error executing listener {listener_name}: {e}")
|
||||
raise
|
||||
|
||||
def _request_human_feedback(
|
||||
self,
|
||||
message: str,
|
||||
output: Any,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
emit: Sequence[str] | None = None,
|
||||
) -> str:
|
||||
"""Request feedback from a human.
|
||||
Args:
|
||||
message: The message to display when requesting feedback.
|
||||
output: The method output to show the human for review.
|
||||
metadata: Optional metadata for enterprise integrations.
|
||||
emit: Optional list of possible outcomes for routing.
|
||||
|
||||
Returns:
|
||||
The human's feedback as a string. Empty string if no feedback provided.
|
||||
"""
|
||||
from crewai.events.event_listener import event_listener
|
||||
from crewai.events.types.flow_events import (
|
||||
HumanFeedbackReceivedEvent,
|
||||
HumanFeedbackRequestedEvent,
|
||||
)
|
||||
|
||||
# Emit feedback requested event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
HumanFeedbackRequestedEvent(
|
||||
type="human_feedback_requested",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
method_name="", # Will be set by decorator if needed
|
||||
output=output,
|
||||
message=message,
|
||||
emit=list(emit) if emit else None,
|
||||
),
|
||||
)
|
||||
|
||||
# Pause live updates during human input
|
||||
formatter = event_listener.formatter
|
||||
formatter.pause_live_updates()
|
||||
|
||||
try:
|
||||
# Display output with formatting using centralized Rich console
|
||||
formatter.console.print("\n" + "═" * 50, style="bold cyan")
|
||||
formatter.console.print(" OUTPUT FOR REVIEW", style="bold cyan")
|
||||
formatter.console.print("═" * 50 + "\n", style="bold cyan")
|
||||
formatter.console.print(output)
|
||||
formatter.console.print("\n" + "═" * 50 + "\n", style="bold cyan")
|
||||
|
||||
# Show message and prompt for feedback
|
||||
formatter.console.print(message, style="yellow")
|
||||
formatter.console.print(
|
||||
"(Press Enter to skip, or type your feedback)\n", style="cyan"
|
||||
)
|
||||
|
||||
feedback = input("Your feedback: ").strip()
|
||||
|
||||
# Emit feedback received event
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
HumanFeedbackReceivedEvent(
|
||||
type="human_feedback_received",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
method_name="", # Will be set by decorator if needed
|
||||
feedback=feedback,
|
||||
outcome=None, # Will be determined after collapsing
|
||||
),
|
||||
)
|
||||
|
||||
return feedback
|
||||
finally:
|
||||
# Resume live updates
|
||||
formatter.resume_live_updates()
|
||||
|
||||
def _collapse_to_outcome(
|
||||
self,
|
||||
feedback: str,
|
||||
outcomes: Sequence[str],
|
||||
llm: str | BaseLLM,
|
||||
) -> str:
|
||||
"""Collapse free-form feedback to a predefined outcome using LLM.
|
||||
|
||||
This method uses the specified LLM to interpret the human's feedback
|
||||
and map it to one of the predefined outcomes for routing purposes.
|
||||
|
||||
Uses structured outputs (function calling) when supported by the LLM
|
||||
to guarantee the response is one of the valid outcomes. Falls back
|
||||
to simple prompting if structured outputs fail.
|
||||
|
||||
Args:
|
||||
feedback: The raw human feedback text.
|
||||
outcomes: Sequence of valid outcome strings to choose from.
|
||||
llm: The LLM model to use. Can be a model string or BaseLLM instance.
|
||||
|
||||
Returns:
|
||||
One of the outcome strings that best matches the feedback intent.
|
||||
"""
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.llm import LLM
|
||||
from crewai.llms.base_llm import BaseLLM as BaseLLMClass
|
||||
from crewai.utilities.i18n import get_i18n
|
||||
|
||||
# Get or create LLM instance
|
||||
if isinstance(llm, str):
|
||||
llm_instance = LLM(model=llm)
|
||||
elif isinstance(llm, BaseLLMClass):
|
||||
llm_instance = llm
|
||||
else:
|
||||
raise ValueError(f"Invalid llm type: {type(llm)}. Expected str or BaseLLM.")
|
||||
|
||||
# Dynamically create a Pydantic model with constrained outcomes
|
||||
outcomes_tuple = tuple(outcomes)
|
||||
|
||||
class FeedbackOutcome(BaseModel):
|
||||
"""The outcome that best matches the human's feedback intent."""
|
||||
|
||||
outcome: Literal[outcomes_tuple] = Field( # type: ignore[valid-type]
|
||||
description=f"The outcome that best matches the feedback. Must be one of: {', '.join(outcomes)}"
|
||||
)
|
||||
|
||||
# Load prompt from translations (using cached instance)
|
||||
i18n = get_i18n()
|
||||
prompt_template = i18n.slice("human_feedback_collapse")
|
||||
|
||||
prompt = prompt_template.format(
|
||||
feedback=feedback,
|
||||
outcomes=", ".join(outcomes),
|
||||
)
|
||||
|
||||
try:
|
||||
# Try structured output first (function calling)
|
||||
# Note: LLM.call with response_model returns JSON string, not Pydantic model
|
||||
response = llm_instance.call(
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
response_model=FeedbackOutcome,
|
||||
)
|
||||
|
||||
# Parse the response - LLM returns JSON string when using response_model
|
||||
if isinstance(response, str):
|
||||
import json
|
||||
|
||||
try:
|
||||
parsed = json.loads(response)
|
||||
return parsed.get("outcome", outcomes[0])
|
||||
except json.JSONDecodeError:
|
||||
# Not valid JSON, might be raw outcome string
|
||||
response_clean = response.strip()
|
||||
for outcome in outcomes:
|
||||
if outcome.lower() == response_clean.lower():
|
||||
return outcome
|
||||
return outcomes[0]
|
||||
elif isinstance(response, FeedbackOutcome):
|
||||
return response.outcome
|
||||
elif hasattr(response, "outcome"):
|
||||
return response.outcome
|
||||
else:
|
||||
# Unexpected type, fall back to first outcome
|
||||
logger.warning(f"Unexpected response type: {type(response)}")
|
||||
return outcomes[0]
|
||||
|
||||
except Exception as e:
|
||||
# Fallback to simple prompting if structured output fails
|
||||
logger.warning(
|
||||
f"Structured output failed, falling back to simple prompting: {e}"
|
||||
)
|
||||
response = llm_instance.call(messages=prompt)
|
||||
response_clean = str(response).strip()
|
||||
|
||||
# Exact match (case-insensitive)
|
||||
for outcome in outcomes:
|
||||
if outcome.lower() == response_clean.lower():
|
||||
return outcome
|
||||
|
||||
# Partial match
|
||||
for outcome in outcomes:
|
||||
if outcome.lower() in response_clean.lower():
|
||||
return outcome
|
||||
|
||||
# Fallback to first outcome
|
||||
logger.warning(
|
||||
f"Could not match LLM response '{response_clean}' to outcomes {list(outcomes)}. "
|
||||
f"Falling back to first outcome: {outcomes[0]}"
|
||||
)
|
||||
return outcomes[0]
|
||||
|
||||
def _log_flow_event(
|
||||
self,
|
||||
message: str,
|
||||
color: PrinterColor = "yellow",
|
||||
color: str = "yellow",
|
||||
level: Literal["info", "warning"] = "info",
|
||||
) -> None:
|
||||
"""Centralized logging method for flow events.
|
||||
@@ -1458,20 +2117,22 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
|
||||
Args:
|
||||
message: The message to log
|
||||
color: Color to use for console output (default: yellow)
|
||||
Available colors: purple, red, bold_green, bold_purple,
|
||||
bold_blue, yellow, yellow
|
||||
color: Rich style for console output (default: "yellow")
|
||||
Examples: "yellow", "red", "bold green", "bold magenta"
|
||||
level: Log level to use (default: info)
|
||||
Supported levels: info, warning
|
||||
|
||||
Note:
|
||||
This method uses the Printer utility for colored console output
|
||||
This method uses the centralized Rich console formatter for output
|
||||
and the standard logging module for log level support.
|
||||
"""
|
||||
self._printer.print(message, color=color)
|
||||
from crewai.events.event_listener import event_listener
|
||||
|
||||
event_listener.formatter.console.print(message, style=color)
|
||||
if level == "info":
|
||||
logger.info(message)
|
||||
logger.warning(message)
|
||||
else:
|
||||
logger.warning(message)
|
||||
|
||||
def plot(self, filename: str = "crewai_flow.html", show: bool = True) -> str:
|
||||
"""Create interactive HTML visualization of Flow structure.
|
||||
|
||||
@@ -70,6 +70,15 @@ class FlowMethod(Generic[P, R]):
|
||||
|
||||
self._is_coroutine = asyncio.coroutines._is_coroutine # type: ignore[attr-defined]
|
||||
|
||||
# Preserve flow-related attributes from wrapped method (e.g., from @human_feedback)
|
||||
for attr in [
|
||||
"__is_router__",
|
||||
"__router_paths__",
|
||||
"__human_feedback_config__",
|
||||
]:
|
||||
if hasattr(meth, attr):
|
||||
setattr(self, attr, getattr(meth, attr))
|
||||
|
||||
def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R:
|
||||
"""Call the wrapped method.
|
||||
|
||||
|
||||
400
lib/crewai/src/crewai/flow/human_feedback.py
Normal file
400
lib/crewai/src/crewai/flow/human_feedback.py
Normal file
@@ -0,0 +1,400 @@
|
||||
"""Human feedback decorator for Flow methods.
|
||||
|
||||
This module provides the @human_feedback decorator that enables human-in-the-loop
|
||||
workflows within CrewAI Flows. It allows collecting human feedback on method outputs
|
||||
and optionally routing to different listeners based on the feedback.
|
||||
|
||||
Supports both synchronous (blocking) and asynchronous (non-blocking) feedback
|
||||
collection through the provider parameter.
|
||||
|
||||
Example (synchronous, default):
|
||||
```python
|
||||
from crewai.flow import Flow, start, listen, human_feedback
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Please review this content:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
def generate_content(self):
|
||||
return {"title": "Article", "body": "Content..."}
|
||||
|
||||
@listen("approved")
|
||||
def publish(self):
|
||||
result = self.human_feedback
|
||||
print(f"Publishing: {result.output}")
|
||||
```
|
||||
|
||||
Example (asynchronous with custom provider):
|
||||
```python
|
||||
from crewai.flow import Flow, start, human_feedback
|
||||
from crewai.flow.async_feedback import HumanFeedbackProvider, HumanFeedbackPending
|
||||
|
||||
class SlackProvider(HumanFeedbackProvider):
|
||||
def request_feedback(self, context, flow):
|
||||
self.send_notification(context)
|
||||
raise HumanFeedbackPending(context=context)
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review this:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
provider=SlackProvider(),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Content..."
|
||||
```
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from collections.abc import Callable, Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from functools import wraps
|
||||
from typing import TYPE_CHECKING, Any, TypeVar
|
||||
|
||||
from crewai.flow.flow_wrappers import FlowMethod
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.async_feedback.types import HumanFeedbackProvider
|
||||
from crewai.flow.flow import Flow
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
|
||||
|
||||
F = TypeVar("F", bound=Callable[..., Any])
|
||||
|
||||
|
||||
@dataclass
|
||||
class HumanFeedbackResult:
|
||||
"""Result from a @human_feedback decorated method.
|
||||
|
||||
This dataclass captures all information about a human feedback interaction,
|
||||
including the original method output, the human's feedback, and any
|
||||
collapsed outcome for routing purposes.
|
||||
|
||||
Attributes:
|
||||
output: The original return value from the decorated method that was
|
||||
shown to the human for review.
|
||||
feedback: The raw text feedback provided by the human. Empty string
|
||||
if no feedback was provided.
|
||||
outcome: The collapsed outcome string when emit is specified.
|
||||
This is determined by the LLM based on the human's feedback.
|
||||
None if emit was not specified.
|
||||
timestamp: When the feedback was received.
|
||||
method_name: The name of the decorated method that triggered feedback.
|
||||
metadata: Optional metadata for enterprise integrations. Can be used
|
||||
to pass additional context like channel, assignee, etc.
|
||||
|
||||
Example:
|
||||
```python
|
||||
@listen("approved")
|
||||
def handle_approval(self):
|
||||
result = self.human_feedback
|
||||
print(f"Output: {result.output}")
|
||||
print(f"Feedback: {result.feedback}")
|
||||
print(f"Outcome: {result.outcome}") # "approved"
|
||||
```
|
||||
"""
|
||||
|
||||
output: Any
|
||||
feedback: str
|
||||
outcome: str | None = None
|
||||
timestamp: datetime = field(default_factory=datetime.now)
|
||||
method_name: str = ""
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
@dataclass
|
||||
class HumanFeedbackConfig:
|
||||
"""Configuration for the @human_feedback decorator.
|
||||
|
||||
Stores the parameters passed to the decorator for later use during
|
||||
method execution and for introspection by visualization tools.
|
||||
|
||||
Attributes:
|
||||
message: The message shown to the human when requesting feedback.
|
||||
emit: Optional sequence of outcome strings for routing.
|
||||
llm: The LLM model to use for collapsing feedback to outcomes.
|
||||
default_outcome: The outcome to use when no feedback is provided.
|
||||
metadata: Optional metadata for enterprise integrations.
|
||||
provider: Optional custom feedback provider for async workflows.
|
||||
"""
|
||||
|
||||
message: str
|
||||
emit: Sequence[str] | None = None
|
||||
llm: str | BaseLLM | None = None
|
||||
default_outcome: str | None = None
|
||||
metadata: dict[str, Any] | None = None
|
||||
provider: HumanFeedbackProvider | None = None
|
||||
|
||||
|
||||
class HumanFeedbackMethod(FlowMethod[Any, Any]):
|
||||
"""Wrapper for methods decorated with @human_feedback.
|
||||
|
||||
This wrapper extends FlowMethod to add human feedback specific attributes
|
||||
that are used by FlowMeta for routing and by visualization tools.
|
||||
|
||||
Attributes:
|
||||
__is_router__: True when emit is specified, enabling router behavior.
|
||||
__router_paths__: List of possible outcomes when acting as a router.
|
||||
__human_feedback_config__: The HumanFeedbackConfig for this method.
|
||||
"""
|
||||
|
||||
__is_router__: bool = False
|
||||
__router_paths__: list[str] | None = None
|
||||
__human_feedback_config__: HumanFeedbackConfig | None = None
|
||||
|
||||
|
||||
def human_feedback(
|
||||
message: str,
|
||||
emit: Sequence[str] | None = None,
|
||||
llm: str | BaseLLM | None = None,
|
||||
default_outcome: str | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
provider: HumanFeedbackProvider | None = None,
|
||||
) -> Callable[[F], F]:
|
||||
"""Decorator for Flow methods that require human feedback.
|
||||
|
||||
This decorator wraps a Flow method to:
|
||||
1. Execute the method and capture its output
|
||||
2. Display the output to the human with a feedback request
|
||||
3. Collect the human's free-form feedback
|
||||
4. Optionally collapse the feedback to a predefined outcome using an LLM
|
||||
5. Store the result for access by downstream methods
|
||||
|
||||
When `emit` is specified, the decorator acts as a router, and the
|
||||
collapsed outcome triggers the appropriate @listen decorated method.
|
||||
|
||||
Supports both synchronous (blocking) and asynchronous (non-blocking)
|
||||
feedback collection through the `provider` parameter. If no provider
|
||||
is specified, defaults to synchronous console input.
|
||||
|
||||
Args:
|
||||
message: The message shown to the human when requesting feedback.
|
||||
This should clearly explain what kind of feedback is expected.
|
||||
emit: Optional sequence of outcome strings. When provided, the
|
||||
human's feedback will be collapsed to one of these outcomes
|
||||
using the specified LLM. The outcome then triggers @listen
|
||||
methods that match.
|
||||
llm: The LLM model to use for collapsing feedback to outcomes.
|
||||
Required when emit is specified. Can be a model string
|
||||
like "gpt-4o-mini" or a BaseLLM instance.
|
||||
default_outcome: The outcome to use when the human provides no
|
||||
feedback (empty input). Must be one of the emit values
|
||||
if emit is specified.
|
||||
metadata: Optional metadata for enterprise integrations. This is
|
||||
passed through to the HumanFeedbackResult and can be used
|
||||
by enterprise forks for features like Slack/Teams integration.
|
||||
provider: Optional HumanFeedbackProvider for custom feedback
|
||||
collection. Use this for async workflows that integrate with
|
||||
external systems like Slack, Teams, or webhooks. When the
|
||||
provider raises HumanFeedbackPending, the flow pauses and
|
||||
can be resumed later with Flow.resume().
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method with human feedback
|
||||
collection logic.
|
||||
|
||||
Raises:
|
||||
ValueError: If emit is specified but llm is not provided.
|
||||
ValueError: If default_outcome is specified but emit is not.
|
||||
ValueError: If default_outcome is not in the emit list.
|
||||
HumanFeedbackPending: When an async provider pauses execution.
|
||||
|
||||
Example:
|
||||
Basic feedback without routing:
|
||||
```python
|
||||
@start()
|
||||
@human_feedback(message="Please review this output:")
|
||||
def generate_content(self):
|
||||
return "Generated content..."
|
||||
```
|
||||
|
||||
With routing based on feedback:
|
||||
```python
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review and approve or reject:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_document(self):
|
||||
return document_content
|
||||
|
||||
@listen("approved")
|
||||
def publish(self):
|
||||
print(f"Publishing: {self.last_human_feedback.output}")
|
||||
```
|
||||
|
||||
Async feedback with custom provider:
|
||||
```python
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review this content:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
provider=SlackProvider(channel="#reviews"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Content to review..."
|
||||
```
|
||||
"""
|
||||
# Validation at decoration time
|
||||
if emit is not None:
|
||||
if not llm:
|
||||
raise ValueError(
|
||||
"llm is required when emit is specified. "
|
||||
"Provide an LLM model string (e.g., 'gpt-4o-mini') or a BaseLLM instance."
|
||||
)
|
||||
if default_outcome is not None and default_outcome not in emit:
|
||||
raise ValueError(
|
||||
f"default_outcome '{default_outcome}' must be one of the "
|
||||
f"emit options: {list(emit)}"
|
||||
)
|
||||
elif default_outcome is not None:
|
||||
raise ValueError("default_outcome requires emit to be specified.")
|
||||
|
||||
def decorator(func: F) -> F:
|
||||
"""Inner decorator that wraps the function."""
|
||||
|
||||
def _request_feedback(flow_instance: Flow, method_output: Any) -> str:
|
||||
"""Request feedback using provider or default console."""
|
||||
from crewai.flow.async_feedback.types import PendingFeedbackContext
|
||||
|
||||
# Build context for provider
|
||||
# Use flow_id property which handles both dict and BaseModel states
|
||||
context = PendingFeedbackContext(
|
||||
flow_id=flow_instance.flow_id or "unknown",
|
||||
flow_class=f"{flow_instance.__class__.__module__}.{flow_instance.__class__.__name__}",
|
||||
method_name=func.__name__,
|
||||
method_output=method_output,
|
||||
message=message,
|
||||
emit=list(emit) if emit else None,
|
||||
default_outcome=default_outcome,
|
||||
metadata=metadata or {},
|
||||
llm=llm if isinstance(llm, str) else None,
|
||||
)
|
||||
|
||||
if provider is not None:
|
||||
# Use custom provider (may raise HumanFeedbackPending)
|
||||
return provider.request_feedback(context, flow_instance)
|
||||
else:
|
||||
# Use default console input
|
||||
return flow_instance._request_human_feedback(
|
||||
message=message,
|
||||
output=method_output,
|
||||
metadata=metadata,
|
||||
emit=emit,
|
||||
)
|
||||
|
||||
def _process_feedback(
|
||||
flow_instance: Flow,
|
||||
method_output: Any,
|
||||
raw_feedback: str,
|
||||
) -> HumanFeedbackResult | str:
|
||||
"""Process feedback and return result or outcome."""
|
||||
# Determine outcome
|
||||
collapsed_outcome: str | None = None
|
||||
|
||||
if not raw_feedback.strip():
|
||||
# Empty feedback
|
||||
if default_outcome:
|
||||
collapsed_outcome = default_outcome
|
||||
elif emit:
|
||||
# No default and no feedback - use first outcome
|
||||
collapsed_outcome = emit[0]
|
||||
elif emit:
|
||||
# Collapse feedback to outcome using LLM
|
||||
collapsed_outcome = flow_instance._collapse_to_outcome(
|
||||
feedback=raw_feedback,
|
||||
outcomes=emit,
|
||||
llm=llm,
|
||||
)
|
||||
|
||||
# Create result
|
||||
result = HumanFeedbackResult(
|
||||
output=method_output,
|
||||
feedback=raw_feedback,
|
||||
outcome=collapsed_outcome,
|
||||
timestamp=datetime.now(),
|
||||
method_name=func.__name__,
|
||||
metadata=metadata or {},
|
||||
)
|
||||
|
||||
# Store in flow instance
|
||||
flow_instance.human_feedback_history.append(result)
|
||||
flow_instance.last_human_feedback = result
|
||||
|
||||
# Return based on mode
|
||||
if emit:
|
||||
# Return outcome for routing
|
||||
return collapsed_outcome # type: ignore[return-value]
|
||||
return result
|
||||
|
||||
if asyncio.iscoroutinefunction(func):
|
||||
# Async wrapper
|
||||
@wraps(func)
|
||||
async def async_wrapper(self: Flow, *args: Any, **kwargs: Any) -> Any:
|
||||
# Execute the original method
|
||||
method_output = await func(self, *args, **kwargs)
|
||||
|
||||
# Request human feedback (may raise HumanFeedbackPending)
|
||||
raw_feedback = _request_feedback(self, method_output)
|
||||
|
||||
# Process and return
|
||||
return _process_feedback(self, method_output, raw_feedback)
|
||||
|
||||
wrapper: Any = async_wrapper
|
||||
else:
|
||||
# Sync wrapper
|
||||
@wraps(func)
|
||||
def sync_wrapper(self: Flow, *args: Any, **kwargs: Any) -> Any:
|
||||
# Execute the original method
|
||||
method_output = func(self, *args, **kwargs)
|
||||
|
||||
# Request human feedback (may raise HumanFeedbackPending)
|
||||
raw_feedback = _request_feedback(self, method_output)
|
||||
|
||||
# Process and return
|
||||
return _process_feedback(self, method_output, raw_feedback)
|
||||
|
||||
wrapper = sync_wrapper
|
||||
|
||||
# Preserve existing Flow decorator attributes
|
||||
for attr in [
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__trigger_condition__",
|
||||
"__is_flow_method__",
|
||||
]:
|
||||
if hasattr(func, attr):
|
||||
setattr(wrapper, attr, getattr(func, attr))
|
||||
|
||||
# Add human feedback specific attributes (create config inline to avoid race conditions)
|
||||
wrapper.__human_feedback_config__ = HumanFeedbackConfig(
|
||||
message=message,
|
||||
emit=emit,
|
||||
llm=llm,
|
||||
default_outcome=default_outcome,
|
||||
metadata=metadata,
|
||||
provider=provider,
|
||||
)
|
||||
wrapper.__is_flow_method__ = True
|
||||
|
||||
# Make it a router if emit specified
|
||||
if emit:
|
||||
wrapper.__is_router__ = True
|
||||
wrapper.__router_paths__ = list(emit)
|
||||
|
||||
return wrapper # type: ignore[return-value]
|
||||
|
||||
return decorator
|
||||
@@ -1,16 +1,26 @@
|
||||
"""Base class for flow state persistence."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.async_feedback.types import PendingFeedbackContext
|
||||
|
||||
|
||||
class FlowPersistence(ABC):
|
||||
"""Abstract base class for flow state persistence.
|
||||
|
||||
This class defines the interface that all persistence implementations must follow.
|
||||
It supports both structured (Pydantic BaseModel) and unstructured (dict) states.
|
||||
|
||||
For async human feedback support, implementations can optionally override:
|
||||
- save_pending_feedback(): Saves state with pending feedback context
|
||||
- load_pending_feedback(): Loads state and pending feedback context
|
||||
- clear_pending_feedback(): Clears pending feedback after resume
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
@@ -45,3 +55,52 @@ class FlowPersistence(ABC):
|
||||
Returns:
|
||||
The most recent state as a dictionary, or None if no state exists
|
||||
"""
|
||||
|
||||
def save_pending_feedback(
|
||||
self,
|
||||
flow_uuid: str,
|
||||
context: PendingFeedbackContext,
|
||||
state_data: dict[str, Any] | BaseModel,
|
||||
) -> None:
|
||||
"""Save state with a pending feedback marker.
|
||||
|
||||
This method is called when a flow is paused waiting for async human
|
||||
feedback. The default implementation just saves the state without
|
||||
the pending feedback context. Override to store the context.
|
||||
|
||||
Args:
|
||||
flow_uuid: Unique identifier for the flow instance
|
||||
context: The pending feedback context with all resume information
|
||||
state_data: Current state data
|
||||
"""
|
||||
# Default: just save the state without pending context
|
||||
self.save_state(flow_uuid, context.method_name, state_data)
|
||||
|
||||
def load_pending_feedback(
|
||||
self,
|
||||
flow_uuid: str,
|
||||
) -> tuple[dict[str, Any], PendingFeedbackContext] | None:
|
||||
"""Load state and pending feedback context.
|
||||
|
||||
This method is called when resuming a paused flow. Override to
|
||||
load both the state and the pending feedback context.
|
||||
|
||||
Args:
|
||||
flow_uuid: Unique identifier for the flow instance
|
||||
|
||||
Returns:
|
||||
Tuple of (state_data, pending_context) if pending feedback exists,
|
||||
None otherwise.
|
||||
"""
|
||||
return None
|
||||
|
||||
def clear_pending_feedback(self, flow_uuid: str) -> None: # noqa: B027
|
||||
"""Clear the pending feedback marker after successful resume.
|
||||
|
||||
This is called after feedback is received and the flow resumes.
|
||||
Optional override to remove the pending feedback marker.
|
||||
|
||||
Args:
|
||||
flow_uuid: Unique identifier for the flow instance
|
||||
"""
|
||||
pass
|
||||
|
||||
@@ -2,17 +2,22 @@
|
||||
SQLite-based implementation of flow state persistence.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime, timezone
|
||||
import json
|
||||
from pathlib import Path
|
||||
import sqlite3
|
||||
from typing import Any
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.flow.persistence.base import FlowPersistence
|
||||
from crewai.utilities.paths import db_storage_path
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.async_feedback.types import PendingFeedbackContext
|
||||
|
||||
|
||||
class SQLiteFlowPersistence(FlowPersistence):
|
||||
"""SQLite-based implementation of flow state persistence.
|
||||
@@ -20,6 +25,28 @@ class SQLiteFlowPersistence(FlowPersistence):
|
||||
This class provides a simple, file-based persistence implementation using SQLite.
|
||||
It's suitable for development and testing, or for production use cases with
|
||||
moderate performance requirements.
|
||||
|
||||
This implementation supports async human feedback by storing pending feedback
|
||||
context in a separate table. When a flow is paused waiting for feedback,
|
||||
use save_pending_feedback() to persist the context. Later, use
|
||||
load_pending_feedback() to retrieve it when resuming.
|
||||
|
||||
Example:
|
||||
```python
|
||||
persistence = SQLiteFlowPersistence("flows.db")
|
||||
|
||||
# Start a flow with async feedback
|
||||
try:
|
||||
flow = MyFlow(persistence=persistence)
|
||||
result = flow.kickoff()
|
||||
except HumanFeedbackPending as e:
|
||||
# Flow is paused, state is already persisted
|
||||
print(f"Waiting for feedback: {e.context.flow_id}")
|
||||
|
||||
# Later, resume with feedback
|
||||
flow = MyFlow.from_pending("abc-123", persistence)
|
||||
result = flow.resume("looks good!")
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: str | None = None) -> None:
|
||||
@@ -45,6 +72,7 @@ class SQLiteFlowPersistence(FlowPersistence):
|
||||
def init_db(self) -> None:
|
||||
"""Create the necessary tables if they don't exist."""
|
||||
with sqlite3.connect(self.db_path) as conn:
|
||||
# Main state table
|
||||
conn.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS flow_states (
|
||||
@@ -64,6 +92,26 @@ class SQLiteFlowPersistence(FlowPersistence):
|
||||
"""
|
||||
)
|
||||
|
||||
# Pending feedback table for async HITL
|
||||
conn.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS pending_feedback (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
flow_uuid TEXT NOT NULL UNIQUE,
|
||||
context_json TEXT NOT NULL,
|
||||
state_json TEXT NOT NULL,
|
||||
created_at DATETIME NOT NULL
|
||||
)
|
||||
"""
|
||||
)
|
||||
# Add index for faster UUID lookups on pending feedback
|
||||
conn.execute(
|
||||
"""
|
||||
CREATE INDEX IF NOT EXISTS idx_pending_feedback_uuid
|
||||
ON pending_feedback(flow_uuid)
|
||||
"""
|
||||
)
|
||||
|
||||
def save_state(
|
||||
self,
|
||||
flow_uuid: str,
|
||||
@@ -130,3 +178,104 @@ class SQLiteFlowPersistence(FlowPersistence):
|
||||
if row:
|
||||
return json.loads(row[0])
|
||||
return None
|
||||
|
||||
def save_pending_feedback(
|
||||
self,
|
||||
flow_uuid: str,
|
||||
context: PendingFeedbackContext,
|
||||
state_data: dict[str, Any] | BaseModel,
|
||||
) -> None:
|
||||
"""Save state with a pending feedback marker.
|
||||
|
||||
This method stores both the flow state and the pending feedback context,
|
||||
allowing the flow to be resumed later when feedback is received.
|
||||
|
||||
Args:
|
||||
flow_uuid: Unique identifier for the flow instance
|
||||
context: The pending feedback context with all resume information
|
||||
state_data: Current state data
|
||||
"""
|
||||
# Import here to avoid circular imports
|
||||
from crewai.flow.async_feedback.types import PendingFeedbackContext
|
||||
|
||||
# Convert state_data to dict
|
||||
if isinstance(state_data, BaseModel):
|
||||
state_dict = state_data.model_dump()
|
||||
elif isinstance(state_data, dict):
|
||||
state_dict = state_data
|
||||
else:
|
||||
raise ValueError(
|
||||
f"state_data must be either a Pydantic BaseModel or dict, got {type(state_data)}"
|
||||
)
|
||||
|
||||
# Also save to regular state table for consistency
|
||||
self.save_state(flow_uuid, context.method_name, state_data)
|
||||
|
||||
# Save pending feedback context
|
||||
with sqlite3.connect(self.db_path) as conn:
|
||||
# Use INSERT OR REPLACE to handle re-triggering feedback on same flow
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT OR REPLACE INTO pending_feedback (
|
||||
flow_uuid,
|
||||
context_json,
|
||||
state_json,
|
||||
created_at
|
||||
) VALUES (?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
flow_uuid,
|
||||
json.dumps(context.to_dict()),
|
||||
json.dumps(state_dict),
|
||||
datetime.now(timezone.utc).isoformat(),
|
||||
),
|
||||
)
|
||||
|
||||
def load_pending_feedback(
|
||||
self,
|
||||
flow_uuid: str,
|
||||
) -> tuple[dict[str, Any], PendingFeedbackContext] | None:
|
||||
"""Load state and pending feedback context.
|
||||
|
||||
Args:
|
||||
flow_uuid: Unique identifier for the flow instance
|
||||
|
||||
Returns:
|
||||
Tuple of (state_data, pending_context) if pending feedback exists,
|
||||
None otherwise.
|
||||
"""
|
||||
# Import here to avoid circular imports
|
||||
from crewai.flow.async_feedback.types import PendingFeedbackContext
|
||||
|
||||
with sqlite3.connect(self.db_path) as conn:
|
||||
cursor = conn.execute(
|
||||
"""
|
||||
SELECT state_json, context_json
|
||||
FROM pending_feedback
|
||||
WHERE flow_uuid = ?
|
||||
""",
|
||||
(flow_uuid,),
|
||||
)
|
||||
row = cursor.fetchone()
|
||||
|
||||
if row:
|
||||
state_dict = json.loads(row[0])
|
||||
context_dict = json.loads(row[1])
|
||||
context = PendingFeedbackContext.from_dict(context_dict)
|
||||
return (state_dict, context)
|
||||
return None
|
||||
|
||||
def clear_pending_feedback(self, flow_uuid: str) -> None:
|
||||
"""Clear the pending feedback marker after successful resume.
|
||||
|
||||
Args:
|
||||
flow_uuid: Unique identifier for the flow instance
|
||||
"""
|
||||
with sqlite3.connect(self.db_path) as conn:
|
||||
conn.execute(
|
||||
"""
|
||||
DELETE FROM pending_feedback
|
||||
WHERE flow_uuid = ?
|
||||
""",
|
||||
(flow_uuid,),
|
||||
)
|
||||
|
||||
@@ -679,6 +679,49 @@ class AnthropicCompletion(BaseLLM):
|
||||
params["messages"], full_response, from_agent
|
||||
)
|
||||
|
||||
def _execute_tools_and_collect_results(
|
||||
self,
|
||||
tool_uses: list[ToolUseBlock],
|
||||
available_functions: dict[str, Any],
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Execute tools and collect results in Anthropic format.
|
||||
|
||||
Args:
|
||||
tool_uses: List of tool use blocks from Claude's response
|
||||
available_functions: Available functions for tool calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
List of tool result dictionaries in Anthropic format
|
||||
"""
|
||||
tool_results = []
|
||||
|
||||
for tool_use in tool_uses:
|
||||
function_name = tool_use.name
|
||||
function_args = tool_use.input
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=cast(dict[str, Any], function_args),
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
tool_result = {
|
||||
"type": "tool_result",
|
||||
"tool_use_id": tool_use.id,
|
||||
"content": str(result)
|
||||
if result is not None
|
||||
else "Tool execution completed",
|
||||
}
|
||||
tool_results.append(tool_result)
|
||||
|
||||
return tool_results
|
||||
|
||||
def _handle_tool_use_conversation(
|
||||
self,
|
||||
initial_response: Message,
|
||||
@@ -696,33 +739,10 @@ class AnthropicCompletion(BaseLLM):
|
||||
3. We send tool results back to Claude
|
||||
4. Claude processes results and generates final response
|
||||
"""
|
||||
# Execute all requested tools and collect results
|
||||
tool_results = []
|
||||
tool_results = self._execute_tools_and_collect_results(
|
||||
tool_uses, available_functions, from_task, from_agent
|
||||
)
|
||||
|
||||
for tool_use in tool_uses:
|
||||
function_name = tool_use.name
|
||||
function_args = tool_use.input
|
||||
|
||||
# Execute the tool
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
# Create tool result in Anthropic format
|
||||
tool_result = {
|
||||
"type": "tool_result",
|
||||
"tool_use_id": tool_use.id,
|
||||
"content": str(result)
|
||||
if result is not None
|
||||
else "Tool execution completed",
|
||||
}
|
||||
tool_results.append(tool_result)
|
||||
|
||||
# Prepare follow-up conversation with tool results
|
||||
follow_up_params = params.copy()
|
||||
|
||||
# Add Claude's tool use response to conversation
|
||||
@@ -810,7 +830,7 @@ class AnthropicCompletion(BaseLLM):
|
||||
logging.error(f"Tool follow-up conversation failed: {e}")
|
||||
# Fallback: return the first tool result if follow-up fails
|
||||
if tool_results:
|
||||
return tool_results[0]["content"]
|
||||
return cast(str, tool_results[0]["content"])
|
||||
raise e
|
||||
|
||||
async def _ahandle_completion(
|
||||
@@ -1003,28 +1023,9 @@ class AnthropicCompletion(BaseLLM):
|
||||
3. We send tool results back to Claude
|
||||
4. Claude processes results and generates final response
|
||||
"""
|
||||
tool_results = []
|
||||
|
||||
for tool_use in tool_uses:
|
||||
function_name = tool_use.name
|
||||
function_args = tool_use.input
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
tool_result = {
|
||||
"type": "tool_result",
|
||||
"tool_use_id": tool_use.id,
|
||||
"content": str(result)
|
||||
if result is not None
|
||||
else "Tool execution completed",
|
||||
}
|
||||
tool_results.append(tool_result)
|
||||
tool_results = self._execute_tools_and_collect_results(
|
||||
tool_uses, available_functions, from_task, from_agent
|
||||
)
|
||||
|
||||
follow_up_params = params.copy()
|
||||
|
||||
@@ -1079,7 +1080,7 @@ class AnthropicCompletion(BaseLLM):
|
||||
|
||||
logging.error(f"Tool follow-up conversation failed: {e}")
|
||||
if tool_results:
|
||||
return tool_results[0]["content"]
|
||||
return cast(str, tool_results[0]["content"])
|
||||
raise e
|
||||
|
||||
def supports_function_calling(self) -> bool:
|
||||
@@ -1115,7 +1116,8 @@ class AnthropicCompletion(BaseLLM):
|
||||
# Default context window size for Claude models
|
||||
return int(200000 * CONTEXT_WINDOW_USAGE_RATIO)
|
||||
|
||||
def _extract_anthropic_token_usage(self, response: Message) -> dict[str, Any]:
|
||||
@staticmethod
|
||||
def _extract_anthropic_token_usage(response: Message) -> dict[str, Any]:
|
||||
"""Extract token usage from Anthropic response."""
|
||||
if hasattr(response, "usage") and response.usage:
|
||||
usage = response.usage
|
||||
|
||||
@@ -3,22 +3,21 @@ from __future__ import annotations
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING, Any, TypedDict
|
||||
|
||||
from pydantic import BaseModel
|
||||
from typing_extensions import Self
|
||||
|
||||
from crewai.utilities.agent_utils import is_context_length_exceeded
|
||||
from crewai.utilities.converter import generate_model_description
|
||||
from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
LLMContextLengthExceededError,
|
||||
)
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.llms.hooks.base import BaseInterceptor
|
||||
from crewai.tools.base_tool import BaseTool
|
||||
|
||||
|
||||
try:
|
||||
@@ -31,6 +30,8 @@ try:
|
||||
from azure.ai.inference.models import (
|
||||
ChatCompletions,
|
||||
ChatCompletionsToolCall,
|
||||
ChatCompletionsToolDefinition,
|
||||
FunctionDefinition,
|
||||
JsonSchemaFormat,
|
||||
StreamingChatCompletionsUpdate,
|
||||
)
|
||||
@@ -50,6 +51,24 @@ except ImportError:
|
||||
) from None
|
||||
|
||||
|
||||
class AzureCompletionParams(TypedDict, total=False):
|
||||
"""Type definition for Azure chat completion parameters."""
|
||||
|
||||
messages: list[LLMMessage]
|
||||
stream: bool
|
||||
model_extras: dict[str, Any]
|
||||
response_format: JsonSchemaFormat
|
||||
model: str
|
||||
temperature: float
|
||||
top_p: float
|
||||
frequency_penalty: float
|
||||
presence_penalty: float
|
||||
max_tokens: int
|
||||
stop: list[str]
|
||||
tools: list[ChatCompletionsToolDefinition]
|
||||
tool_choice: str
|
||||
|
||||
|
||||
class AzureCompletion(BaseLLM):
|
||||
"""Azure AI Inference native completion implementation.
|
||||
|
||||
@@ -156,7 +175,8 @@ class AzureCompletion(BaseLLM):
|
||||
and "/openai/deployments/" in self.endpoint
|
||||
)
|
||||
|
||||
def _validate_and_fix_endpoint(self, endpoint: str, model: str) -> str:
|
||||
@staticmethod
|
||||
def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
|
||||
"""Validate and fix Azure endpoint URL format.
|
||||
|
||||
Azure OpenAI endpoints should be in the format:
|
||||
@@ -179,10 +199,75 @@ class AzureCompletion(BaseLLM):
|
||||
|
||||
return endpoint
|
||||
|
||||
def _handle_api_error(
|
||||
self,
|
||||
error: Exception,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> None:
|
||||
"""Handle API errors with appropriate logging and events.
|
||||
|
||||
Args:
|
||||
error: The exception that occurred
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Raises:
|
||||
The original exception after logging and emitting events
|
||||
"""
|
||||
if isinstance(error, HttpResponseError):
|
||||
if error.status_code == 401:
|
||||
error_msg = "Azure authentication failed. Check your API key."
|
||||
elif error.status_code == 404:
|
||||
error_msg = (
|
||||
f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
|
||||
)
|
||||
elif error.status_code == 429:
|
||||
error_msg = "Azure API rate limit exceeded. Please retry later."
|
||||
else:
|
||||
error_msg = (
|
||||
f"Azure API HTTP error: {error.status_code} - {error.message}"
|
||||
)
|
||||
else:
|
||||
error_msg = f"Azure API call failed: {error!s}"
|
||||
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise error
|
||||
|
||||
def _handle_completion_error(
|
||||
self,
|
||||
error: Exception,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> None:
|
||||
"""Handle completion-specific errors including context length checks.
|
||||
|
||||
Args:
|
||||
error: The exception that occurred
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Raises:
|
||||
LLMContextLengthExceededError if context window exceeded, otherwise the original exception
|
||||
"""
|
||||
if is_context_length_exceeded(error):
|
||||
logging.error(f"Context window exceeded: {error}")
|
||||
raise LLMContextLengthExceededError(str(error)) from error
|
||||
|
||||
error_msg = f"Azure API call failed: {error!s}"
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise error
|
||||
|
||||
def call(
|
||||
self,
|
||||
messages: str | list[LLMMessage],
|
||||
tools: list[dict[str, BaseTool]] | None = None,
|
||||
tools: list[dict[str, Any]] | None = None,
|
||||
callbacks: list[Any] | None = None,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
@@ -198,6 +283,7 @@ class AzureCompletion(BaseLLM):
|
||||
available_functions: Available functions for tool calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Response model
|
||||
|
||||
Returns:
|
||||
Chat completion response or tool call result
|
||||
@@ -242,35 +328,13 @@ class AzureCompletion(BaseLLM):
|
||||
response_model,
|
||||
)
|
||||
|
||||
except HttpResponseError as e:
|
||||
if e.status_code == 401:
|
||||
error_msg = "Azure authentication failed. Check your API key."
|
||||
elif e.status_code == 404:
|
||||
error_msg = (
|
||||
f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
|
||||
)
|
||||
elif e.status_code == 429:
|
||||
error_msg = "Azure API rate limit exceeded. Please retry later."
|
||||
else:
|
||||
error_msg = f"Azure API HTTP error: {e.status_code} - {e.message}"
|
||||
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise
|
||||
except Exception as e:
|
||||
error_msg = f"Azure API call failed: {e!s}"
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise
|
||||
return self._handle_api_error(e, from_task, from_agent) # type: ignore[func-returns-value]
|
||||
|
||||
async def acall(
|
||||
async def acall( # type: ignore[return]
|
||||
self,
|
||||
messages: str | list[LLMMessage],
|
||||
tools: list[dict[str, BaseTool]] | None = None,
|
||||
tools: list[dict[str, Any]] | None = None,
|
||||
callbacks: list[Any] | None = None,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
@@ -324,37 +388,15 @@ class AzureCompletion(BaseLLM):
|
||||
response_model,
|
||||
)
|
||||
|
||||
except HttpResponseError as e:
|
||||
if e.status_code == 401:
|
||||
error_msg = "Azure authentication failed. Check your API key."
|
||||
elif e.status_code == 404:
|
||||
error_msg = (
|
||||
f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
|
||||
)
|
||||
elif e.status_code == 429:
|
||||
error_msg = "Azure API rate limit exceeded. Please retry later."
|
||||
else:
|
||||
error_msg = f"Azure API HTTP error: {e.status_code} - {e.message}"
|
||||
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise
|
||||
except Exception as e:
|
||||
error_msg = f"Azure API call failed: {e!s}"
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise
|
||||
self._handle_api_error(e, from_task, from_agent)
|
||||
|
||||
def _prepare_completion_params(
|
||||
self,
|
||||
messages: list[LLMMessage],
|
||||
tools: list[dict[str, Any]] | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
) -> AzureCompletionParams:
|
||||
"""Prepare parameters for Azure AI Inference chat completion.
|
||||
|
||||
Args:
|
||||
@@ -365,11 +407,14 @@ class AzureCompletion(BaseLLM):
|
||||
Returns:
|
||||
Parameters dictionary for Azure API
|
||||
"""
|
||||
params = {
|
||||
params: AzureCompletionParams = {
|
||||
"messages": messages,
|
||||
"stream": self.stream,
|
||||
}
|
||||
|
||||
if self.stream:
|
||||
params["model_extras"] = {"stream_options": {"include_usage": True}}
|
||||
|
||||
if response_model and self.is_openai_model:
|
||||
model_description = generate_model_description(response_model)
|
||||
json_schema_info = model_description["json_schema"]
|
||||
@@ -412,37 +457,42 @@ class AzureCompletion(BaseLLM):
|
||||
|
||||
if drop_params and isinstance(additional_drop_params, list):
|
||||
for drop_param in additional_drop_params:
|
||||
params.pop(drop_param, None)
|
||||
if isinstance(drop_param, str):
|
||||
params.pop(drop_param, None) # type: ignore[misc]
|
||||
|
||||
return params
|
||||
|
||||
def _convert_tools_for_interference(
|
||||
def _convert_tools_for_interference( # type: ignore[override]
|
||||
self, tools: list[dict[str, Any]]
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Convert CrewAI tool format to Azure OpenAI function calling format."""
|
||||
) -> list[ChatCompletionsToolDefinition]:
|
||||
"""Convert CrewAI tool format to Azure OpenAI function calling format.
|
||||
|
||||
Args:
|
||||
tools: List of CrewAI tool definitions
|
||||
|
||||
Returns:
|
||||
List of Azure ChatCompletionsToolDefinition objects
|
||||
"""
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
|
||||
azure_tools = []
|
||||
azure_tools: list[ChatCompletionsToolDefinition] = []
|
||||
|
||||
for tool in tools:
|
||||
name, description, parameters = safe_tool_conversion(tool, "Azure")
|
||||
|
||||
azure_tool = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": name,
|
||||
"description": description,
|
||||
},
|
||||
}
|
||||
function_def = FunctionDefinition(
|
||||
name=name,
|
||||
description=description,
|
||||
parameters=parameters
|
||||
if isinstance(parameters, dict)
|
||||
else dict(parameters)
|
||||
if parameters
|
||||
else None,
|
||||
)
|
||||
|
||||
if parameters:
|
||||
if isinstance(parameters, dict):
|
||||
azure_tool["function"]["parameters"] = parameters # type: ignore
|
||||
else:
|
||||
azure_tool["function"]["parameters"] = dict(parameters)
|
||||
tool_def = ChatCompletionsToolDefinition(function=function_def)
|
||||
|
||||
azure_tools.append(azure_tool)
|
||||
azure_tools.append(tool_def)
|
||||
|
||||
return azure_tools
|
||||
|
||||
@@ -471,148 +521,239 @@ class AzureCompletion(BaseLLM):
|
||||
|
||||
return azure_messages
|
||||
|
||||
def _handle_completion(
|
||||
def _validate_and_emit_structured_output(
|
||||
self,
|
||||
params: dict[str, Any],
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
content: str,
|
||||
response_model: type[BaseModel],
|
||||
params: AzureCompletionParams,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Handle non-streaming chat completion."""
|
||||
# Make API call
|
||||
) -> str:
|
||||
"""Validate content against response model and emit completion event.
|
||||
|
||||
Args:
|
||||
content: Response content to validate
|
||||
response_model: Pydantic model for validation
|
||||
params: Completion parameters containing messages
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
Validated and serialized JSON string
|
||||
|
||||
Raises:
|
||||
ValueError: If validation fails
|
||||
"""
|
||||
try:
|
||||
response: ChatCompletions = self.client.complete(**params)
|
||||
structured_data = response_model.model_validate_json(content)
|
||||
structured_json = structured_data.model_dump_json()
|
||||
|
||||
if not response.choices:
|
||||
raise ValueError("No choices returned from Azure API")
|
||||
|
||||
choice = response.choices[0]
|
||||
message = choice.message
|
||||
|
||||
# Extract and track token usage
|
||||
usage = self._extract_azure_token_usage(response)
|
||||
self._track_token_usage_internal(usage)
|
||||
|
||||
if response_model and self.is_openai_model:
|
||||
content = message.content or ""
|
||||
try:
|
||||
structured_data = response_model.model_validate_json(content)
|
||||
structured_json = structured_data.model_dump_json()
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
|
||||
return structured_json
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
|
||||
logging.error(error_msg)
|
||||
raise ValueError(error_msg) from e
|
||||
|
||||
# Handle tool calls
|
||||
if message.tool_calls and available_functions:
|
||||
tool_call = message.tool_calls[0] # Handle first tool call
|
||||
if isinstance(tool_call, ChatCompletionsToolCall):
|
||||
function_name = tool_call.function.name
|
||||
|
||||
try:
|
||||
function_args = json.loads(tool_call.function.arguments)
|
||||
except json.JSONDecodeError as e:
|
||||
logging.error(f"Failed to parse tool arguments: {e}")
|
||||
function_args = {}
|
||||
|
||||
# Execute tool
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
# Extract content
|
||||
content = message.content or ""
|
||||
|
||||
# Apply stop words
|
||||
content = self._apply_stop_words(content)
|
||||
|
||||
# Emit completion event and return content
|
||||
self._emit_call_completed_event(
|
||||
response=content,
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
|
||||
content = self._invoke_after_llm_call_hooks(
|
||||
params["messages"], content, from_agent
|
||||
)
|
||||
|
||||
return structured_json
|
||||
except Exception as e:
|
||||
if is_context_length_exceeded(e):
|
||||
logging.error(f"Context window exceeded: {e}")
|
||||
raise LLMContextLengthExceededError(str(e)) from e
|
||||
|
||||
error_msg = f"Azure API call failed: {e!s}"
|
||||
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise e
|
||||
raise ValueError(error_msg) from e
|
||||
|
||||
return content
|
||||
|
||||
def _handle_streaming_completion(
|
||||
def _process_completion_response(
|
||||
self,
|
||||
params: dict[str, Any],
|
||||
response: ChatCompletions,
|
||||
params: AzureCompletionParams,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Process completion response with usage tracking, tool execution, and events.
|
||||
|
||||
Args:
|
||||
response: Chat completion response from Azure API
|
||||
params: Completion parameters containing messages
|
||||
available_functions: Available functions for tool calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Pydantic model for structured output
|
||||
|
||||
Returns:
|
||||
Response content or structured output
|
||||
"""
|
||||
if not response.choices:
|
||||
raise ValueError("No choices returned from Azure API")
|
||||
|
||||
choice = response.choices[0]
|
||||
message = choice.message
|
||||
|
||||
# Extract and track token usage
|
||||
usage = self._extract_azure_token_usage(response)
|
||||
self._track_token_usage_internal(usage)
|
||||
|
||||
if response_model and self.is_openai_model:
|
||||
content = message.content or ""
|
||||
return self._validate_and_emit_structured_output(
|
||||
content=content,
|
||||
response_model=response_model,
|
||||
params=params,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
# Handle tool calls
|
||||
if message.tool_calls and available_functions:
|
||||
tool_call = message.tool_calls[0] # Handle first tool call
|
||||
if isinstance(tool_call, ChatCompletionsToolCall):
|
||||
function_name = tool_call.function.name
|
||||
|
||||
try:
|
||||
function_args = json.loads(tool_call.function.arguments)
|
||||
except json.JSONDecodeError as e:
|
||||
logging.error(f"Failed to parse tool arguments: {e}")
|
||||
function_args = {}
|
||||
|
||||
# Execute tool
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
# Extract content
|
||||
content = message.content or ""
|
||||
|
||||
# Apply stop words
|
||||
content = self._apply_stop_words(content)
|
||||
|
||||
# Emit completion event and return content
|
||||
self._emit_call_completed_event(
|
||||
response=content,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
|
||||
return self._invoke_after_llm_call_hooks(
|
||||
params["messages"], content, from_agent
|
||||
)
|
||||
|
||||
def _handle_completion(
|
||||
self,
|
||||
params: AzureCompletionParams,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Handle non-streaming chat completion."""
|
||||
try:
|
||||
# Cast params to Any to avoid type checking issues with TypedDict unpacking
|
||||
response: ChatCompletions = self.client.complete(**params) # type: ignore[assignment,arg-type]
|
||||
return self._process_completion_response(
|
||||
response=response,
|
||||
params=params,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_model=response_model,
|
||||
)
|
||||
except Exception as e:
|
||||
return self._handle_completion_error(e, from_task, from_agent) # type: ignore[func-returns-value]
|
||||
|
||||
def _process_streaming_update(
|
||||
self,
|
||||
update: StreamingChatCompletionsUpdate,
|
||||
full_response: str,
|
||||
tool_calls: dict[str, dict[str, str]],
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> str:
|
||||
"""Handle streaming chat completion."""
|
||||
full_response = ""
|
||||
tool_calls = {}
|
||||
"""Process a single streaming update chunk.
|
||||
|
||||
# Make streaming API call
|
||||
for update in self.client.complete(**params):
|
||||
if isinstance(update, StreamingChatCompletionsUpdate):
|
||||
if update.choices:
|
||||
choice = update.choices[0]
|
||||
if choice.delta and choice.delta.content:
|
||||
content_delta = choice.delta.content
|
||||
full_response += content_delta
|
||||
self._emit_stream_chunk_event(
|
||||
chunk=content_delta,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
Args:
|
||||
update: Streaming update from Azure API
|
||||
full_response: Accumulated response content
|
||||
tool_calls: Dictionary of accumulated tool calls
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
# Handle tool call streaming
|
||||
if choice.delta and choice.delta.tool_calls:
|
||||
for tool_call in choice.delta.tool_calls:
|
||||
call_id = tool_call.id or "default"
|
||||
if call_id not in tool_calls:
|
||||
tool_calls[call_id] = {
|
||||
"name": "",
|
||||
"arguments": "",
|
||||
}
|
||||
Returns:
|
||||
Updated full_response string
|
||||
"""
|
||||
if update.choices:
|
||||
choice = update.choices[0]
|
||||
if choice.delta and choice.delta.content:
|
||||
content_delta = choice.delta.content
|
||||
full_response += content_delta
|
||||
self._emit_stream_chunk_event(
|
||||
chunk=content_delta,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if tool_call.function and tool_call.function.name:
|
||||
tool_calls[call_id]["name"] = tool_call.function.name
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
tool_calls[call_id]["arguments"] += (
|
||||
tool_call.function.arguments
|
||||
)
|
||||
if choice.delta and choice.delta.tool_calls:
|
||||
for tool_call in choice.delta.tool_calls:
|
||||
call_id = tool_call.id or "default"
|
||||
if call_id not in tool_calls:
|
||||
tool_calls[call_id] = {
|
||||
"name": "",
|
||||
"arguments": "",
|
||||
}
|
||||
|
||||
if tool_call.function and tool_call.function.name:
|
||||
tool_calls[call_id]["name"] = tool_call.function.name
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
tool_calls[call_id]["arguments"] += tool_call.function.arguments
|
||||
|
||||
return full_response
|
||||
|
||||
def _finalize_streaming_response(
|
||||
self,
|
||||
full_response: str,
|
||||
tool_calls: dict[str, dict[str, str]],
|
||||
usage_data: dict[str, int],
|
||||
params: AzureCompletionParams,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Finalize streaming response with usage tracking, tool execution, and events.
|
||||
|
||||
Args:
|
||||
full_response: The complete streamed response content
|
||||
tool_calls: Dictionary of tool calls accumulated during streaming
|
||||
usage_data: Token usage data from the stream
|
||||
params: Completion parameters containing messages
|
||||
available_functions: Available functions for tool calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Pydantic model for structured output validation
|
||||
|
||||
Returns:
|
||||
Final response content after processing, or structured output
|
||||
"""
|
||||
self._track_token_usage_internal(usage_data)
|
||||
|
||||
# Handle structured output validation
|
||||
if response_model and self.is_openai_model:
|
||||
return self._validate_and_emit_structured_output(
|
||||
content=full_response,
|
||||
response_model=response_model,
|
||||
params=params,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
# Handle completed tool calls
|
||||
if tool_calls and available_functions:
|
||||
@@ -653,9 +794,52 @@ class AzureCompletion(BaseLLM):
|
||||
params["messages"], full_response, from_agent
|
||||
)
|
||||
|
||||
def _handle_streaming_completion(
|
||||
self,
|
||||
params: AzureCompletionParams,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Handle streaming chat completion."""
|
||||
full_response = ""
|
||||
tool_calls: dict[str, dict[str, Any]] = {}
|
||||
|
||||
usage_data = {"total_tokens": 0}
|
||||
for update in self.client.complete(**params): # type: ignore[arg-type]
|
||||
if isinstance(update, StreamingChatCompletionsUpdate):
|
||||
if update.usage:
|
||||
usage = update.usage
|
||||
usage_data = {
|
||||
"prompt_tokens": usage.prompt_tokens,
|
||||
"completion_tokens": usage.completion_tokens,
|
||||
"total_tokens": usage.total_tokens,
|
||||
}
|
||||
continue
|
||||
|
||||
full_response = self._process_streaming_update(
|
||||
update=update,
|
||||
full_response=full_response,
|
||||
tool_calls=tool_calls,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
return self._finalize_streaming_response(
|
||||
full_response=full_response,
|
||||
tool_calls=tool_calls,
|
||||
usage_data=usage_data,
|
||||
params=params,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_model=response_model,
|
||||
)
|
||||
|
||||
async def _ahandle_completion(
|
||||
self,
|
||||
params: dict[str, Any],
|
||||
params: AzureCompletionParams,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
@@ -663,160 +847,64 @@ class AzureCompletion(BaseLLM):
|
||||
) -> str | Any:
|
||||
"""Handle non-streaming chat completion asynchronously."""
|
||||
try:
|
||||
response: ChatCompletions = await self.async_client.complete(**params)
|
||||
|
||||
if not response.choices:
|
||||
raise ValueError("No choices returned from Azure API")
|
||||
|
||||
choice = response.choices[0]
|
||||
message = choice.message
|
||||
|
||||
usage = self._extract_azure_token_usage(response)
|
||||
self._track_token_usage_internal(usage)
|
||||
|
||||
if response_model and self.is_openai_model:
|
||||
content = message.content or ""
|
||||
try:
|
||||
structured_data = response_model.model_validate_json(content)
|
||||
structured_json = structured_data.model_dump_json()
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
|
||||
return structured_json
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
|
||||
logging.error(error_msg)
|
||||
raise ValueError(error_msg) from e
|
||||
|
||||
if message.tool_calls and available_functions:
|
||||
tool_call = message.tool_calls[0] # Handle first tool call
|
||||
if isinstance(tool_call, ChatCompletionsToolCall):
|
||||
function_name = tool_call.function.name
|
||||
|
||||
try:
|
||||
function_args = json.loads(tool_call.function.arguments)
|
||||
except json.JSONDecodeError as e:
|
||||
logging.error(f"Failed to parse tool arguments: {e}")
|
||||
function_args = {}
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
content = message.content or ""
|
||||
|
||||
content = self._apply_stop_words(content)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=content,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
# Cast params to Any to avoid type checking issues with TypedDict unpacking
|
||||
response: ChatCompletions = await self.async_client.complete(**params) # type: ignore[assignment,arg-type]
|
||||
return self._process_completion_response(
|
||||
response=response,
|
||||
params=params,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
response_model=response_model,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
if is_context_length_exceeded(e):
|
||||
logging.error(f"Context window exceeded: {e}")
|
||||
raise LLMContextLengthExceededError(str(e)) from e
|
||||
|
||||
error_msg = f"Azure API call failed: {e!s}"
|
||||
logging.error(error_msg)
|
||||
self._emit_call_failed_event(
|
||||
error=error_msg, from_task=from_task, from_agent=from_agent
|
||||
)
|
||||
raise e
|
||||
|
||||
return content
|
||||
return self._handle_completion_error(e, from_task, from_agent) # type: ignore[func-returns-value]
|
||||
|
||||
async def _ahandle_streaming_completion(
|
||||
self,
|
||||
params: dict[str, Any],
|
||||
params: AzureCompletionParams,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str:
|
||||
) -> str | Any:
|
||||
"""Handle streaming chat completion asynchronously."""
|
||||
full_response = ""
|
||||
tool_calls = {}
|
||||
tool_calls: dict[str, dict[str, Any]] = {}
|
||||
|
||||
stream = await self.async_client.complete(**params)
|
||||
async for update in stream:
|
||||
usage_data = {"total_tokens": 0}
|
||||
|
||||
stream = await self.async_client.complete(**params) # type: ignore[arg-type]
|
||||
async for update in stream: # type: ignore[union-attr]
|
||||
if isinstance(update, StreamingChatCompletionsUpdate):
|
||||
if update.choices:
|
||||
choice = update.choices[0]
|
||||
if choice.delta and choice.delta.content:
|
||||
content_delta = choice.delta.content
|
||||
full_response += content_delta
|
||||
self._emit_stream_chunk_event(
|
||||
chunk=content_delta,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if choice.delta and choice.delta.tool_calls:
|
||||
for tool_call in choice.delta.tool_calls:
|
||||
call_id = tool_call.id or "default"
|
||||
if call_id not in tool_calls:
|
||||
tool_calls[call_id] = {
|
||||
"name": "",
|
||||
"arguments": "",
|
||||
}
|
||||
|
||||
if tool_call.function and tool_call.function.name:
|
||||
tool_calls[call_id]["name"] = tool_call.function.name
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
tool_calls[call_id]["arguments"] += (
|
||||
tool_call.function.arguments
|
||||
)
|
||||
|
||||
if tool_calls and available_functions:
|
||||
for call_data in tool_calls.values():
|
||||
function_name = call_data["name"]
|
||||
|
||||
try:
|
||||
function_args = json.loads(call_data["arguments"])
|
||||
except json.JSONDecodeError as e:
|
||||
logging.error(f"Failed to parse streamed tool arguments: {e}")
|
||||
if hasattr(update, "usage") and update.usage:
|
||||
usage = update.usage
|
||||
usage_data = {
|
||||
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
|
||||
"completion_tokens": getattr(usage, "completion_tokens", 0),
|
||||
"total_tokens": getattr(usage, "total_tokens", 0),
|
||||
}
|
||||
continue
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
full_response = self._process_streaming_update(
|
||||
update=update,
|
||||
full_response=full_response,
|
||||
tool_calls=tool_calls,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
full_response = self._apply_stop_words(full_response)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=full_response,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
return self._finalize_streaming_response(
|
||||
full_response=full_response,
|
||||
tool_calls=tool_calls,
|
||||
usage_data=usage_data,
|
||||
params=params,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
response_model=response_model,
|
||||
)
|
||||
|
||||
return full_response
|
||||
|
||||
def supports_function_calling(self) -> bool:
|
||||
"""Check if the model supports function calling."""
|
||||
# Azure OpenAI models support function calling
|
||||
@@ -860,7 +948,8 @@ class AzureCompletion(BaseLLM):
|
||||
# Default context window size
|
||||
return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
|
||||
|
||||
def _extract_azure_token_usage(self, response: ChatCompletions) -> dict[str, Any]:
|
||||
@staticmethod
|
||||
def _extract_azure_token_usage(response: ChatCompletions) -> dict[str, Any]:
|
||||
"""Extract token usage from Azure response."""
|
||||
if hasattr(response, "usage") and response.usage:
|
||||
usage = response.usage
|
||||
|
||||
@@ -3,7 +3,7 @@ from __future__ import annotations
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING, Any, Literal, cast
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
@@ -105,6 +105,7 @@ class GeminiCompletion(BaseLLM):
|
||||
self.stream = stream
|
||||
self.safety_settings = safety_settings or {}
|
||||
self.stop_sequences = stop_sequences or []
|
||||
self.tools: list[dict[str, Any]] | None = None
|
||||
|
||||
# Model-specific settings
|
||||
version_match = re.search(r"gemini-(\d+(?:\.\d+)?)", model.lower())
|
||||
@@ -223,10 +224,11 @@ class GeminiCompletion(BaseLLM):
|
||||
Args:
|
||||
messages: Input messages for the chat completion
|
||||
tools: List of tool/function definitions
|
||||
callbacks: Callback functions (not used as token counts are handled by the reponse)
|
||||
callbacks: Callback functions (not used as token counts are handled by the response)
|
||||
available_functions: Available functions for tool calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Response model to use.
|
||||
|
||||
Returns:
|
||||
Chat completion response or tool call result
|
||||
@@ -267,7 +269,6 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
return self._handle_completion(
|
||||
formatted_content,
|
||||
system_instruction,
|
||||
config,
|
||||
available_functions,
|
||||
from_task,
|
||||
@@ -309,6 +310,7 @@ class GeminiCompletion(BaseLLM):
|
||||
available_functions: Available functions for tool calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Response model to use.
|
||||
|
||||
Returns:
|
||||
Chat completion response or tool call result
|
||||
@@ -344,7 +346,6 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
return await self._ahandle_completion(
|
||||
formatted_content,
|
||||
system_instruction,
|
||||
config,
|
||||
available_functions,
|
||||
from_task,
|
||||
@@ -497,35 +498,113 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
return contents, system_instruction
|
||||
|
||||
def _handle_completion(
|
||||
def _validate_and_emit_structured_output(
|
||||
self,
|
||||
content: str,
|
||||
response_model: type[BaseModel],
|
||||
messages_for_event: list[LLMMessage],
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> str:
|
||||
"""Validate content against response model and emit completion event.
|
||||
|
||||
Args:
|
||||
content: Response content to validate
|
||||
response_model: Pydantic model for validation
|
||||
messages_for_event: Messages to include in event
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
Validated and serialized JSON string
|
||||
|
||||
Raises:
|
||||
ValueError: If validation fails
|
||||
"""
|
||||
try:
|
||||
structured_data = response_model.model_validate_json(content)
|
||||
structured_json = structured_data.model_dump_json()
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return structured_json
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
|
||||
logging.error(error_msg)
|
||||
raise ValueError(error_msg) from e
|
||||
|
||||
def _finalize_completion_response(
|
||||
self,
|
||||
content: str,
|
||||
contents: list[types.Content],
|
||||
response_model: type[BaseModel] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> str:
|
||||
"""Finalize completion response with validation and event emission.
|
||||
|
||||
Args:
|
||||
content: The response content
|
||||
contents: Original contents for event conversion
|
||||
response_model: Pydantic model for structured output validation
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
Final response content after processing
|
||||
"""
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
# Handle structured output validation
|
||||
if response_model:
|
||||
return self._validate_and_emit_structured_output(
|
||||
content=content,
|
||||
response_model=response_model,
|
||||
messages_for_event=messages_for_event,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=content,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return self._invoke_after_llm_call_hooks(
|
||||
messages_for_event, content, from_agent
|
||||
)
|
||||
|
||||
def _process_response_with_tools(
|
||||
self,
|
||||
response: GenerateContentResponse,
|
||||
contents: list[types.Content],
|
||||
system_instruction: str | None,
|
||||
config: types.GenerateContentConfig,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Handle non-streaming content generation."""
|
||||
try:
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
response = self.client.models.generate_content(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
)
|
||||
"""Process response, execute function calls, and finalize completion.
|
||||
|
||||
usage = self._extract_token_usage(response)
|
||||
except Exception as e:
|
||||
if is_context_length_exceeded(e):
|
||||
logging.error(f"Context window exceeded: {e}")
|
||||
raise LLMContextLengthExceededError(str(e)) from e
|
||||
raise e from e
|
||||
|
||||
self._track_token_usage_internal(usage)
|
||||
Args:
|
||||
response: The completion response
|
||||
contents: Original contents for event conversion
|
||||
available_functions: Available functions for function calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Pydantic model for structured output validation
|
||||
|
||||
Returns:
|
||||
Final response content or function call result
|
||||
"""
|
||||
if response.candidates and (self.tools or available_functions):
|
||||
candidate = response.candidates[0]
|
||||
if candidate.content and candidate.content.parts:
|
||||
@@ -554,61 +633,90 @@ class GeminiCompletion(BaseLLM):
|
||||
content = response.text or ""
|
||||
content = self._apply_stop_words(content)
|
||||
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=content,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
return self._finalize_completion_response(
|
||||
content=content,
|
||||
contents=contents,
|
||||
response_model=response_model,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return self._invoke_after_llm_call_hooks(
|
||||
messages_for_event, content, from_agent
|
||||
)
|
||||
|
||||
def _handle_streaming_completion(
|
||||
def _process_stream_chunk(
|
||||
self,
|
||||
chunk: GenerateContentResponse,
|
||||
full_response: str,
|
||||
function_calls: dict[str, dict[str, Any]],
|
||||
usage_data: dict[str, int],
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
) -> tuple[str, dict[str, dict[str, Any]], dict[str, int]]:
|
||||
"""Process a single streaming chunk.
|
||||
|
||||
Args:
|
||||
chunk: The streaming chunk response
|
||||
full_response: Accumulated response text
|
||||
function_calls: Accumulated function calls
|
||||
usage_data: Accumulated usage data
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
|
||||
Returns:
|
||||
Tuple of (updated full_response, updated function_calls, updated usage_data)
|
||||
"""
|
||||
if chunk.usage_metadata:
|
||||
usage_data = self._extract_token_usage(chunk)
|
||||
|
||||
if chunk.text:
|
||||
full_response += chunk.text
|
||||
self._emit_stream_chunk_event(
|
||||
chunk=chunk.text,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if chunk.candidates:
|
||||
candidate = chunk.candidates[0]
|
||||
if candidate.content and candidate.content.parts:
|
||||
for part in candidate.content.parts:
|
||||
if hasattr(part, "function_call") and part.function_call:
|
||||
call_id = part.function_call.name or "default"
|
||||
if call_id not in function_calls:
|
||||
function_calls[call_id] = {
|
||||
"name": part.function_call.name,
|
||||
"args": dict(part.function_call.args)
|
||||
if part.function_call.args
|
||||
else {},
|
||||
}
|
||||
|
||||
return full_response, function_calls, usage_data
|
||||
|
||||
def _finalize_streaming_response(
|
||||
self,
|
||||
full_response: str,
|
||||
function_calls: dict[str, dict[str, Any]],
|
||||
usage_data: dict[str, int],
|
||||
contents: list[types.Content],
|
||||
config: types.GenerateContentConfig,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str:
|
||||
"""Handle streaming content generation."""
|
||||
full_response = ""
|
||||
function_calls: dict[str, dict[str, Any]] = {}
|
||||
"""Finalize streaming response with usage tracking, function execution, and events.
|
||||
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
for chunk in self.client.models.generate_content_stream(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
):
|
||||
if chunk.text:
|
||||
full_response += chunk.text
|
||||
self._emit_stream_chunk_event(
|
||||
chunk=chunk.text,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
Args:
|
||||
full_response: The complete streamed response content
|
||||
function_calls: Dictionary of function calls accumulated during streaming
|
||||
usage_data: Token usage data from the stream
|
||||
contents: Original contents for event conversion
|
||||
available_functions: Available functions for function calling
|
||||
from_task: Task that initiated the call
|
||||
from_agent: Agent that initiated the call
|
||||
response_model: Pydantic model for structured output validation
|
||||
|
||||
if chunk.candidates:
|
||||
candidate = chunk.candidates[0]
|
||||
if candidate.content and candidate.content.parts:
|
||||
for part in candidate.content.parts:
|
||||
if hasattr(part, "function_call") and part.function_call:
|
||||
call_id = part.function_call.name or "default"
|
||||
if call_id not in function_calls:
|
||||
function_calls[call_id] = {
|
||||
"name": part.function_call.name,
|
||||
"args": dict(part.function_call.args)
|
||||
if part.function_call.args
|
||||
else {},
|
||||
}
|
||||
Returns:
|
||||
Final response content after processing
|
||||
"""
|
||||
self._track_token_usage_internal(usage_data)
|
||||
|
||||
# Handle completed function calls
|
||||
if function_calls and available_functions:
|
||||
@@ -636,24 +744,95 @@ class GeminiCompletion(BaseLLM):
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=full_response,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
return self._finalize_completion_response(
|
||||
content=full_response,
|
||||
contents=contents,
|
||||
response_model=response_model,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return self._invoke_after_llm_call_hooks(
|
||||
messages_for_event, full_response, from_agent
|
||||
def _handle_completion(
|
||||
self,
|
||||
contents: list[types.Content],
|
||||
config: types.GenerateContentConfig,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Handle non-streaming content generation."""
|
||||
try:
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
response = self.client.models.generate_content(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
)
|
||||
|
||||
usage = self._extract_token_usage(response)
|
||||
except Exception as e:
|
||||
if is_context_length_exceeded(e):
|
||||
logging.error(f"Context window exceeded: {e}")
|
||||
raise LLMContextLengthExceededError(str(e)) from e
|
||||
raise e from e
|
||||
|
||||
self._track_token_usage_internal(usage)
|
||||
|
||||
return self._process_response_with_tools(
|
||||
response=response,
|
||||
contents=contents,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_model=response_model,
|
||||
)
|
||||
|
||||
def _handle_streaming_completion(
|
||||
self,
|
||||
contents: list[types.Content],
|
||||
config: types.GenerateContentConfig,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str:
|
||||
"""Handle streaming content generation."""
|
||||
full_response = ""
|
||||
function_calls: dict[str, dict[str, Any]] = {}
|
||||
usage_data = {"total_tokens": 0}
|
||||
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
for chunk in self.client.models.generate_content_stream(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
):
|
||||
full_response, function_calls, usage_data = self._process_stream_chunk(
|
||||
chunk=chunk,
|
||||
full_response=full_response,
|
||||
function_calls=function_calls,
|
||||
usage_data=usage_data,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
return self._finalize_streaming_response(
|
||||
full_response=full_response,
|
||||
function_calls=function_calls,
|
||||
usage_data=usage_data,
|
||||
contents=contents,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
response_model=response_model,
|
||||
)
|
||||
|
||||
async def _ahandle_completion(
|
||||
self,
|
||||
contents: list[types.Content],
|
||||
system_instruction: str | None,
|
||||
config: types.GenerateContentConfig,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
@@ -679,46 +858,15 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
self._track_token_usage_internal(usage)
|
||||
|
||||
if response.candidates and (self.tools or available_functions):
|
||||
candidate = response.candidates[0]
|
||||
if candidate.content and candidate.content.parts:
|
||||
for part in candidate.content.parts:
|
||||
if hasattr(part, "function_call") and part.function_call:
|
||||
function_name = part.function_call.name
|
||||
if function_name is None:
|
||||
continue
|
||||
function_args = (
|
||||
dict(part.function_call.args)
|
||||
if part.function_call.args
|
||||
else {}
|
||||
)
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions or {},
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
content = response.text or ""
|
||||
content = self._apply_stop_words(content)
|
||||
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=content,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
return self._process_response_with_tools(
|
||||
response=response,
|
||||
contents=contents,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
response_model=response_model,
|
||||
)
|
||||
|
||||
return content
|
||||
|
||||
async def _ahandle_streaming_completion(
|
||||
self,
|
||||
contents: list[types.Content],
|
||||
@@ -731,6 +879,7 @@ class GeminiCompletion(BaseLLM):
|
||||
"""Handle async streaming content generation."""
|
||||
full_response = ""
|
||||
function_calls: dict[str, dict[str, Any]] = {}
|
||||
usage_data = {"total_tokens": 0}
|
||||
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
@@ -740,214 +889,24 @@ class GeminiCompletion(BaseLLM):
|
||||
config=config,
|
||||
)
|
||||
async for chunk in stream:
|
||||
if chunk.text:
|
||||
full_response += chunk.text
|
||||
self._emit_stream_chunk_event(
|
||||
chunk=chunk.text,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if chunk.candidates:
|
||||
candidate = chunk.candidates[0]
|
||||
if candidate.content and candidate.content.parts:
|
||||
for part in candidate.content.parts:
|
||||
if hasattr(part, "function_call") and part.function_call:
|
||||
call_id = part.function_call.name or "default"
|
||||
if call_id not in function_calls:
|
||||
function_calls[call_id] = {
|
||||
"name": part.function_call.name,
|
||||
"args": dict(part.function_call.args)
|
||||
if part.function_call.args
|
||||
else {},
|
||||
}
|
||||
|
||||
if function_calls and available_functions:
|
||||
for call_data in function_calls.values():
|
||||
function_name = call_data["name"]
|
||||
function_args = call_data["args"]
|
||||
|
||||
# Skip if function_name is None
|
||||
if not isinstance(function_name, str):
|
||||
continue
|
||||
|
||||
# Ensure function_args is a dict
|
||||
if not isinstance(function_args, dict):
|
||||
function_args = {}
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=full_response,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return self._invoke_after_llm_call_hooks(
|
||||
messages_for_event, full_response, from_agent
|
||||
)
|
||||
|
||||
async def _ahandle_completion(
|
||||
self,
|
||||
contents: list[types.Content],
|
||||
system_instruction: str | None,
|
||||
config: types.GenerateContentConfig,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str | Any:
|
||||
"""Handle async non-streaming content generation."""
|
||||
try:
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
response = await self.client.aio.models.generate_content(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
full_response, function_calls, usage_data = self._process_stream_chunk(
|
||||
chunk=chunk,
|
||||
full_response=full_response,
|
||||
function_calls=function_calls,
|
||||
usage_data=usage_data,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
usage = self._extract_token_usage(response)
|
||||
except Exception as e:
|
||||
if is_context_length_exceeded(e):
|
||||
logging.error(f"Context window exceeded: {e}")
|
||||
raise LLMContextLengthExceededError(str(e)) from e
|
||||
raise e from e
|
||||
|
||||
self._track_token_usage_internal(usage)
|
||||
|
||||
if response.candidates and (self.tools or available_functions):
|
||||
candidate = response.candidates[0]
|
||||
if candidate.content and candidate.content.parts:
|
||||
for part in candidate.content.parts:
|
||||
if hasattr(part, "function_call") and part.function_call:
|
||||
function_name = part.function_call.name
|
||||
if function_name is None:
|
||||
continue
|
||||
function_args = (
|
||||
dict(part.function_call.args)
|
||||
if part.function_call.args
|
||||
else {}
|
||||
)
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions or {},
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
content = response.text or ""
|
||||
content = self._apply_stop_words(content)
|
||||
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=content,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
return self._finalize_streaming_response(
|
||||
full_response=full_response,
|
||||
function_calls=function_calls,
|
||||
usage_data=usage_data,
|
||||
contents=contents,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return content
|
||||
|
||||
async def _ahandle_streaming_completion(
|
||||
self,
|
||||
contents: list[types.Content],
|
||||
config: types.GenerateContentConfig,
|
||||
available_functions: dict[str, Any] | None = None,
|
||||
from_task: Any | None = None,
|
||||
from_agent: Any | None = None,
|
||||
response_model: type[BaseModel] | None = None,
|
||||
) -> str:
|
||||
"""Handle async streaming content generation."""
|
||||
full_response = ""
|
||||
function_calls: dict[str, dict[str, Any]] = {}
|
||||
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
stream = await self.client.aio.models.generate_content_stream(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
)
|
||||
async for chunk in stream:
|
||||
if chunk.text:
|
||||
full_response += chunk.text
|
||||
self._emit_stream_chunk_event(
|
||||
chunk=chunk.text,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if chunk.candidates:
|
||||
candidate = chunk.candidates[0]
|
||||
if candidate.content and candidate.content.parts:
|
||||
for part in candidate.content.parts:
|
||||
if hasattr(part, "function_call") and part.function_call:
|
||||
call_id = part.function_call.name or "default"
|
||||
if call_id not in function_calls:
|
||||
function_calls[call_id] = {
|
||||
"name": part.function_call.name,
|
||||
"args": dict(part.function_call.args)
|
||||
if part.function_call.args
|
||||
else {},
|
||||
}
|
||||
|
||||
if function_calls and available_functions:
|
||||
for call_data in function_calls.values():
|
||||
function_name = call_data["name"]
|
||||
function_args = call_data["args"]
|
||||
|
||||
# Skip if function_name is None
|
||||
if not isinstance(function_name, str):
|
||||
continue
|
||||
|
||||
# Ensure function_args is a dict
|
||||
if not isinstance(function_args, dict):
|
||||
function_args = {}
|
||||
|
||||
result = self._handle_tool_execution(
|
||||
function_name=function_name,
|
||||
function_args=function_args,
|
||||
available_functions=available_functions,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
if result is not None:
|
||||
return result
|
||||
|
||||
messages_for_event = self._convert_contents_to_dict(contents)
|
||||
|
||||
self._emit_call_completed_event(
|
||||
response=full_response,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=messages_for_event,
|
||||
)
|
||||
|
||||
return self._invoke_after_llm_call_hooks(
|
||||
messages_for_event, full_response, from_agent
|
||||
response_model=response_model,
|
||||
)
|
||||
|
||||
def supports_function_calling(self) -> bool:
|
||||
@@ -1009,12 +968,12 @@ class GeminiCompletion(BaseLLM):
|
||||
}
|
||||
return {"total_tokens": 0}
|
||||
|
||||
@staticmethod
|
||||
def _convert_contents_to_dict(
|
||||
self,
|
||||
contents: list[types.Content],
|
||||
) -> list[LLMMessage]:
|
||||
"""Convert contents to dict format."""
|
||||
result: list[dict[str, str]] = []
|
||||
result: list[LLMMessage] = []
|
||||
for content_obj in contents:
|
||||
role = content_obj.role
|
||||
if role == "model":
|
||||
@@ -1027,5 +986,10 @@ class GeminiCompletion(BaseLLM):
|
||||
part.text for part in parts if hasattr(part, "text") and part.text
|
||||
)
|
||||
|
||||
result.append({"role": role, "content": content})
|
||||
result.append(
|
||||
LLMMessage(
|
||||
role=cast(Literal["user", "assistant", "system"], role),
|
||||
content=content,
|
||||
)
|
||||
)
|
||||
return result
|
||||
|
||||
@@ -18,10 +18,10 @@ from crewai.events.types.llm_events import LLMCallType
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
|
||||
from crewai.utilities.agent_utils import is_context_length_exceeded
|
||||
from crewai.utilities.converter import generate_model_description
|
||||
from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
LLMContextLengthExceededError,
|
||||
)
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
@@ -297,6 +297,7 @@ class OpenAICompletion(BaseLLM):
|
||||
}
|
||||
if self.stream:
|
||||
params["stream"] = self.stream
|
||||
params["stream_options"] = {"include_usage": True}
|
||||
|
||||
params.update(self.additional_params)
|
||||
|
||||
@@ -544,18 +545,21 @@ class OpenAICompletion(BaseLLM):
|
||||
)
|
||||
|
||||
final_completion = stream.get_final_completion()
|
||||
if final_completion and final_completion.choices:
|
||||
parsed_result = final_completion.choices[0].message.parsed
|
||||
if parsed_result:
|
||||
structured_json = parsed_result.model_dump_json()
|
||||
self._emit_call_completed_event(
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_json
|
||||
if final_completion:
|
||||
usage = self._extract_openai_token_usage(final_completion)
|
||||
self._track_token_usage_internal(usage)
|
||||
if final_completion.choices:
|
||||
parsed_result = final_completion.choices[0].message.parsed
|
||||
if parsed_result:
|
||||
structured_json = parsed_result.model_dump_json()
|
||||
self._emit_call_completed_event(
|
||||
response=structured_json,
|
||||
call_type=LLMCallType.LLM_CALL,
|
||||
from_task=from_task,
|
||||
from_agent=from_agent,
|
||||
messages=params["messages"],
|
||||
)
|
||||
return structured_json
|
||||
|
||||
logging.error("Failed to get parsed result from stream")
|
||||
return ""
|
||||
@@ -564,7 +568,13 @@ class OpenAICompletion(BaseLLM):
|
||||
self.client.chat.completions.create(**params)
|
||||
)
|
||||
|
||||
usage_data = {"total_tokens": 0}
|
||||
|
||||
for completion_chunk in completion_stream:
|
||||
if hasattr(completion_chunk, "usage") and completion_chunk.usage:
|
||||
usage_data = self._extract_openai_token_usage(completion_chunk)
|
||||
continue
|
||||
|
||||
if not completion_chunk.choices:
|
||||
continue
|
||||
|
||||
@@ -593,6 +603,8 @@ class OpenAICompletion(BaseLLM):
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
tool_calls[call_id]["arguments"] += tool_call.function.arguments
|
||||
|
||||
self._track_token_usage_internal(usage_data)
|
||||
|
||||
if tool_calls and available_functions:
|
||||
for call_data in tool_calls.values():
|
||||
function_name = call_data["name"]
|
||||
@@ -785,7 +797,12 @@ class OpenAICompletion(BaseLLM):
|
||||
] = await self.async_client.chat.completions.create(**params)
|
||||
|
||||
accumulated_content = ""
|
||||
usage_data = {"total_tokens": 0}
|
||||
async for chunk in completion_stream:
|
||||
if hasattr(chunk, "usage") and chunk.usage:
|
||||
usage_data = self._extract_openai_token_usage(chunk)
|
||||
continue
|
||||
|
||||
if not chunk.choices:
|
||||
continue
|
||||
|
||||
@@ -800,6 +817,8 @@ class OpenAICompletion(BaseLLM):
|
||||
from_agent=from_agent,
|
||||
)
|
||||
|
||||
self._track_token_usage_internal(usage_data)
|
||||
|
||||
try:
|
||||
parsed_object = response_model.model_validate_json(accumulated_content)
|
||||
structured_json = parsed_object.model_dump_json()
|
||||
@@ -828,7 +847,13 @@ class OpenAICompletion(BaseLLM):
|
||||
ChatCompletionChunk
|
||||
] = await self.async_client.chat.completions.create(**params)
|
||||
|
||||
usage_data = {"total_tokens": 0}
|
||||
|
||||
async for chunk in stream:
|
||||
if hasattr(chunk, "usage") and chunk.usage:
|
||||
usage_data = self._extract_openai_token_usage(chunk)
|
||||
continue
|
||||
|
||||
if not chunk.choices:
|
||||
continue
|
||||
|
||||
@@ -857,6 +882,8 @@ class OpenAICompletion(BaseLLM):
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
tool_calls[call_id]["arguments"] += tool_call.function.arguments
|
||||
|
||||
self._track_token_usage_internal(usage_data)
|
||||
|
||||
if tool_calls and available_functions:
|
||||
for call_data in tool_calls.values():
|
||||
function_name = call_data["name"]
|
||||
@@ -944,8 +971,10 @@ class OpenAICompletion(BaseLLM):
|
||||
# Default context window size
|
||||
return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
|
||||
|
||||
def _extract_openai_token_usage(self, response: ChatCompletion) -> dict[str, Any]:
|
||||
"""Extract token usage from OpenAI ChatCompletion response."""
|
||||
def _extract_openai_token_usage(
|
||||
self, response: ChatCompletion | ChatCompletionChunk
|
||||
) -> dict[str, Any]:
|
||||
"""Extract token usage from OpenAI ChatCompletion or ChatCompletionChunk response."""
|
||||
if hasattr(response, "usage") and response.usage:
|
||||
usage = response.usage
|
||||
return {
|
||||
|
||||
@@ -494,8 +494,11 @@ class Task(BaseModel):
|
||||
future: Future[TaskOutput],
|
||||
) -> None:
|
||||
"""Execute the task asynchronously with context handling."""
|
||||
result = self._execute_core(agent, context, tools)
|
||||
future.set_result(result)
|
||||
try:
|
||||
result = self._execute_core(agent, context, tools)
|
||||
future.set_result(result)
|
||||
except Exception as e:
|
||||
future.set_exception(e)
|
||||
|
||||
async def aexecute_sync(
|
||||
self,
|
||||
|
||||
@@ -174,9 +174,12 @@ class Telemetry:
|
||||
|
||||
self._register_signal_handler(signal.SIGTERM, SigTermEvent, shutdown=True)
|
||||
self._register_signal_handler(signal.SIGINT, SigIntEvent, shutdown=True)
|
||||
self._register_signal_handler(signal.SIGHUP, SigHupEvent, shutdown=False)
|
||||
self._register_signal_handler(signal.SIGTSTP, SigTStpEvent, shutdown=False)
|
||||
self._register_signal_handler(signal.SIGCONT, SigContEvent, shutdown=False)
|
||||
if hasattr(signal, "SIGHUP"):
|
||||
self._register_signal_handler(signal.SIGHUP, SigHupEvent, shutdown=False)
|
||||
if hasattr(signal, "SIGTSTP"):
|
||||
self._register_signal_handler(signal.SIGTSTP, SigTStpEvent, shutdown=False)
|
||||
if hasattr(signal, "SIGCONT"):
|
||||
self._register_signal_handler(signal.SIGCONT, SigContEvent, shutdown=False)
|
||||
|
||||
def _register_signal_handler(
|
||||
self,
|
||||
|
||||
@@ -3,15 +3,13 @@ from __future__ import annotations
|
||||
from abc import ABC, abstractmethod
|
||||
import asyncio
|
||||
from collections.abc import Awaitable, Callable
|
||||
from inspect import signature
|
||||
from inspect import Parameter, signature
|
||||
import json
|
||||
from typing import (
|
||||
Any,
|
||||
Generic,
|
||||
ParamSpec,
|
||||
TypeVar,
|
||||
cast,
|
||||
get_args,
|
||||
get_origin,
|
||||
overload,
|
||||
)
|
||||
|
||||
@@ -27,6 +25,7 @@ from typing_extensions import TypeIs
|
||||
|
||||
from crewai.tools.structured_tool import CrewStructuredTool
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
|
||||
|
||||
_printer = Printer()
|
||||
@@ -103,20 +102,40 @@ class BaseTool(BaseModel, ABC):
|
||||
if v != cls._ArgsSchemaPlaceholder:
|
||||
return v
|
||||
|
||||
return cast(
|
||||
type[PydanticBaseModel],
|
||||
type(
|
||||
f"{cls.__name__}Schema",
|
||||
(PydanticBaseModel,),
|
||||
{
|
||||
"__annotations__": {
|
||||
k: v
|
||||
for k, v in cls._run.__annotations__.items()
|
||||
if k != "return"
|
||||
},
|
||||
},
|
||||
),
|
||||
)
|
||||
run_sig = signature(cls._run)
|
||||
fields: dict[str, Any] = {}
|
||||
|
||||
for param_name, param in run_sig.parameters.items():
|
||||
if param_name in ("self", "return"):
|
||||
continue
|
||||
if param.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
|
||||
continue
|
||||
|
||||
annotation = param.annotation if param.annotation != param.empty else Any
|
||||
|
||||
if param.default is param.empty:
|
||||
fields[param_name] = (annotation, ...)
|
||||
else:
|
||||
fields[param_name] = (annotation, param.default)
|
||||
|
||||
if not fields:
|
||||
arun_sig = signature(cls._arun)
|
||||
for param_name, param in arun_sig.parameters.items():
|
||||
if param_name in ("self", "return"):
|
||||
continue
|
||||
if param.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
|
||||
continue
|
||||
|
||||
annotation = (
|
||||
param.annotation if param.annotation != param.empty else Any
|
||||
)
|
||||
|
||||
if param.default is param.empty:
|
||||
fields[param_name] = (annotation, ...)
|
||||
else:
|
||||
fields[param_name] = (annotation, param.default)
|
||||
|
||||
return create_model(f"{cls.__name__}Schema", **fields)
|
||||
|
||||
@field_validator("max_usage_count", mode="before")
|
||||
@classmethod
|
||||
@@ -226,24 +245,23 @@ class BaseTool(BaseModel, ABC):
|
||||
args_schema = getattr(tool, "args_schema", None)
|
||||
|
||||
if args_schema is None:
|
||||
# Infer args_schema from the function signature if not provided
|
||||
func_signature = signature(tool.func)
|
||||
annotations = func_signature.parameters
|
||||
args_fields: dict[str, Any] = {}
|
||||
for name, param in annotations.items():
|
||||
if name != "self":
|
||||
param_annotation = (
|
||||
param.annotation if param.annotation != param.empty else Any
|
||||
)
|
||||
field_info = Field(
|
||||
default=...,
|
||||
description="",
|
||||
)
|
||||
args_fields[name] = (param_annotation, field_info)
|
||||
if args_fields:
|
||||
args_schema = create_model(f"{tool.name}Input", **args_fields)
|
||||
fields: dict[str, Any] = {}
|
||||
for name, param in func_signature.parameters.items():
|
||||
if name == "self":
|
||||
continue
|
||||
if param.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
|
||||
continue
|
||||
param_annotation = (
|
||||
param.annotation if param.annotation != param.empty else Any
|
||||
)
|
||||
if param.default is param.empty:
|
||||
fields[name] = (param_annotation, ...)
|
||||
else:
|
||||
fields[name] = (param_annotation, param.default)
|
||||
if fields:
|
||||
args_schema = create_model(f"{tool.name}Input", **fields)
|
||||
else:
|
||||
# Create a default schema with no fields if no parameters are found
|
||||
args_schema = create_model(
|
||||
f"{tool.name}Input", __base__=PydanticBaseModel
|
||||
)
|
||||
@@ -257,53 +275,37 @@ class BaseTool(BaseModel, ABC):
|
||||
|
||||
def _set_args_schema(self) -> None:
|
||||
if self.args_schema is None:
|
||||
class_name = f"{self.__class__.__name__}Schema"
|
||||
self.args_schema = cast(
|
||||
type[PydanticBaseModel],
|
||||
type(
|
||||
class_name,
|
||||
(PydanticBaseModel,),
|
||||
{
|
||||
"__annotations__": {
|
||||
k: v
|
||||
for k, v in self._run.__annotations__.items()
|
||||
if k != "return"
|
||||
},
|
||||
},
|
||||
),
|
||||
run_sig = signature(self._run)
|
||||
fields: dict[str, Any] = {}
|
||||
|
||||
for param_name, param in run_sig.parameters.items():
|
||||
if param_name in ("self", "return"):
|
||||
continue
|
||||
if param.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
|
||||
continue
|
||||
|
||||
annotation = (
|
||||
param.annotation if param.annotation != param.empty else Any
|
||||
)
|
||||
|
||||
if param.default is param.empty:
|
||||
fields[param_name] = (annotation, ...)
|
||||
else:
|
||||
fields[param_name] = (annotation, param.default)
|
||||
|
||||
self.args_schema = create_model(
|
||||
f"{self.__class__.__name__}Schema", **fields
|
||||
)
|
||||
|
||||
def _generate_description(self) -> None:
|
||||
args_schema = {
|
||||
name: {
|
||||
"description": field.description,
|
||||
"type": BaseTool._get_arg_annotations(field.annotation),
|
||||
}
|
||||
for name, field in self.args_schema.model_fields.items()
|
||||
}
|
||||
|
||||
self.description = f"Tool Name: {self.name}\nTool Arguments: {args_schema}\nTool Description: {self.description}"
|
||||
|
||||
@staticmethod
|
||||
def _get_arg_annotations(annotation: type[Any] | None) -> str:
|
||||
if annotation is None:
|
||||
return "None"
|
||||
|
||||
origin = get_origin(annotation)
|
||||
args = get_args(annotation)
|
||||
|
||||
if origin is None:
|
||||
return (
|
||||
annotation.__name__
|
||||
if hasattr(annotation, "__name__")
|
||||
else str(annotation)
|
||||
)
|
||||
|
||||
if args:
|
||||
args_str = ", ".join(BaseTool._get_arg_annotations(arg) for arg in args)
|
||||
return str(f"{origin.__name__}[{args_str}]")
|
||||
|
||||
return str(origin.__name__)
|
||||
"""Generate the tool description with a JSON schema for arguments."""
|
||||
schema = generate_model_description(self.args_schema)
|
||||
args_json = json.dumps(schema["json_schema"]["schema"], indent=2)
|
||||
self.description = (
|
||||
f"Tool Name: {self.name}\n"
|
||||
f"Tool Arguments: {args_json}\n"
|
||||
f"Tool Description: {self.description}"
|
||||
)
|
||||
|
||||
|
||||
class Tool(BaseTool, Generic[P, R]):
|
||||
@@ -406,24 +408,23 @@ class Tool(BaseTool, Generic[P, R]):
|
||||
args_schema = getattr(tool, "args_schema", None)
|
||||
|
||||
if args_schema is None:
|
||||
# Infer args_schema from the function signature if not provided
|
||||
func_signature = signature(tool.func)
|
||||
annotations = func_signature.parameters
|
||||
args_fields: dict[str, Any] = {}
|
||||
for name, param in annotations.items():
|
||||
if name != "self":
|
||||
param_annotation = (
|
||||
param.annotation if param.annotation != param.empty else Any
|
||||
)
|
||||
field_info = Field(
|
||||
default=...,
|
||||
description="",
|
||||
)
|
||||
args_fields[name] = (param_annotation, field_info)
|
||||
if args_fields:
|
||||
args_schema = create_model(f"{tool.name}Input", **args_fields)
|
||||
fields: dict[str, Any] = {}
|
||||
for name, param in func_signature.parameters.items():
|
||||
if name == "self":
|
||||
continue
|
||||
if param.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
|
||||
continue
|
||||
param_annotation = (
|
||||
param.annotation if param.annotation != param.empty else Any
|
||||
)
|
||||
if param.default is param.empty:
|
||||
fields[name] = (param_annotation, ...)
|
||||
else:
|
||||
fields[name] = (param_annotation, param.default)
|
||||
if fields:
|
||||
args_schema = create_model(f"{tool.name}Input", **fields)
|
||||
else:
|
||||
# Create a default schema with no fields if no parameters are found
|
||||
args_schema = create_model(
|
||||
f"{tool.name}Input", __base__=PydanticBaseModel
|
||||
)
|
||||
@@ -502,32 +503,38 @@ def tool(
|
||||
def _make_tool(f: Callable[P2, R2]) -> Tool[P2, R2]:
|
||||
if f.__doc__ is None:
|
||||
raise ValueError("Function must have a docstring")
|
||||
|
||||
func_annotations = getattr(f, "__annotations__", None)
|
||||
if func_annotations is None:
|
||||
if f.__annotations__ is None:
|
||||
raise ValueError("Function must have type annotations")
|
||||
|
||||
func_sig = signature(f)
|
||||
fields: dict[str, Any] = {}
|
||||
|
||||
for param_name, param in func_sig.parameters.items():
|
||||
if param_name == "return":
|
||||
continue
|
||||
if param.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
|
||||
continue
|
||||
|
||||
annotation = (
|
||||
param.annotation if param.annotation != param.empty else Any
|
||||
)
|
||||
|
||||
if param.default is param.empty:
|
||||
fields[param_name] = (annotation, ...)
|
||||
else:
|
||||
fields[param_name] = (annotation, param.default)
|
||||
|
||||
class_name = "".join(tool_name.split()).title()
|
||||
tool_args_schema = cast(
|
||||
type[PydanticBaseModel],
|
||||
type(
|
||||
class_name,
|
||||
(PydanticBaseModel,),
|
||||
{
|
||||
"__annotations__": {
|
||||
k: v for k, v in func_annotations.items() if k != "return"
|
||||
},
|
||||
},
|
||||
),
|
||||
)
|
||||
args_schema = create_model(class_name, **fields)
|
||||
|
||||
return Tool(
|
||||
name=tool_name,
|
||||
description=f.__doc__,
|
||||
func=f,
|
||||
args_schema=tool_args_schema,
|
||||
args_schema=args_schema,
|
||||
result_as_answer=result_as_answer,
|
||||
max_usage_count=max_usage_count,
|
||||
current_usage_count=0,
|
||||
)
|
||||
|
||||
return _make_tool
|
||||
|
||||
@@ -29,7 +29,8 @@
|
||||
"lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
|
||||
"lite_agent_response_format": "Ensure your final answer strictly adheres to the following OpenAPI schema: {response_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
|
||||
"knowledge_search_query": "The original query is: {task_prompt}.",
|
||||
"knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
|
||||
"knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions.",
|
||||
"human_feedback_collapse": "Based on the following human feedback, determine which outcome best matches their intent.\n\nFeedback: {feedback}\n\nPossible outcomes: {outcomes}\n\nRespond with ONLY one of the exact outcome values listed above, nothing else."
|
||||
},
|
||||
"errors": {
|
||||
"force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
|
||||
|
||||
@@ -30,4 +30,3 @@ NOT_SPECIFIED: Final[
|
||||
"allows us to distinguish between 'not passed at all' and 'explicitly passed None' or '[]'.",
|
||||
]
|
||||
] = _NotSpecified()
|
||||
CREWAI_BASE_URL: Final[str] = "https://app.crewai.com"
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from copy import deepcopy
|
||||
import json
|
||||
import re
|
||||
from typing import TYPE_CHECKING, Any, Final, TypedDict
|
||||
@@ -13,6 +11,7 @@ from crewai.agents.agent_builder.utilities.base_output_converter import OutputCo
|
||||
from crewai.utilities.i18n import get_i18n
|
||||
from crewai.utilities.internal_instructor import InternalInstructor
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@@ -421,221 +420,3 @@ def create_converter(
|
||||
raise Exception("No output converter found or set.")
|
||||
|
||||
return converter # type: ignore[no-any-return]
|
||||
|
||||
|
||||
def resolve_refs(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Recursively resolve all local $refs in the given JSON Schema using $defs as the source.
|
||||
|
||||
This is needed because Pydantic generates $ref-based schemas that
|
||||
some consumers (e.g. LLMs, tool frameworks) don't handle well.
|
||||
|
||||
Args:
|
||||
schema: JSON Schema dict that may contain "$refs" and "$defs".
|
||||
|
||||
Returns:
|
||||
A new schema dictionary with all local $refs replaced by their definitions.
|
||||
"""
|
||||
defs = schema.get("$defs", {})
|
||||
schema_copy = deepcopy(schema)
|
||||
|
||||
def _resolve(node: Any) -> Any:
|
||||
if isinstance(node, dict):
|
||||
ref = node.get("$ref")
|
||||
if isinstance(ref, str) and ref.startswith("#/$defs/"):
|
||||
def_name = ref.replace("#/$defs/", "")
|
||||
if def_name in defs:
|
||||
return _resolve(deepcopy(defs[def_name]))
|
||||
raise KeyError(f"Definition '{def_name}' not found in $defs.")
|
||||
return {k: _resolve(v) for k, v in node.items()}
|
||||
|
||||
if isinstance(node, list):
|
||||
return [_resolve(i) for i in node]
|
||||
|
||||
return node
|
||||
|
||||
return _resolve(schema_copy) # type: ignore[no-any-return]
|
||||
|
||||
|
||||
def add_key_in_dict_recursively(
|
||||
d: dict[str, Any], key: str, value: Any, criteria: Callable[[dict[str, Any]], bool]
|
||||
) -> dict[str, Any]:
|
||||
"""Recursively adds a key/value pair to all nested dicts matching `criteria`."""
|
||||
if isinstance(d, dict):
|
||||
if criteria(d) and key not in d:
|
||||
d[key] = value
|
||||
for v in d.values():
|
||||
add_key_in_dict_recursively(v, key, value, criteria)
|
||||
elif isinstance(d, list):
|
||||
for i in d:
|
||||
add_key_in_dict_recursively(i, key, value, criteria)
|
||||
return d
|
||||
|
||||
|
||||
def fix_discriminator_mappings(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Replace '#/$defs/...' references in discriminator.mapping with just the model name."""
|
||||
output = schema.get("properties", {}).get("output")
|
||||
if not output:
|
||||
return schema
|
||||
|
||||
disc = output.get("discriminator")
|
||||
if not disc or "mapping" not in disc:
|
||||
return schema
|
||||
|
||||
disc["mapping"] = {k: v.split("/")[-1] for k, v in disc["mapping"].items()}
|
||||
return schema
|
||||
|
||||
|
||||
def add_const_to_oneof_variants(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Add const fields to oneOf variants for discriminated unions.
|
||||
|
||||
The json_schema_to_pydantic library requires each oneOf variant to have
|
||||
a const field for the discriminator property. This function adds those
|
||||
const fields based on the discriminator mapping.
|
||||
|
||||
Args:
|
||||
schema: JSON Schema dict that may contain discriminated unions
|
||||
|
||||
Returns:
|
||||
Modified schema with const fields added to oneOf variants
|
||||
"""
|
||||
|
||||
def _process_oneof(node: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Process a single node that might contain a oneOf with discriminator."""
|
||||
if not isinstance(node, dict):
|
||||
return node
|
||||
|
||||
if "oneOf" in node and "discriminator" in node:
|
||||
discriminator = node["discriminator"]
|
||||
property_name = discriminator.get("propertyName")
|
||||
mapping = discriminator.get("mapping", {})
|
||||
|
||||
if property_name and mapping:
|
||||
one_of_variants = node.get("oneOf", [])
|
||||
|
||||
for variant in one_of_variants:
|
||||
if isinstance(variant, dict) and "properties" in variant:
|
||||
variant_title = variant.get("title", "")
|
||||
|
||||
matched_disc_value = None
|
||||
for disc_value, schema_name in mapping.items():
|
||||
if variant_title == schema_name or variant_title.endswith(
|
||||
schema_name
|
||||
):
|
||||
matched_disc_value = disc_value
|
||||
break
|
||||
|
||||
if matched_disc_value is not None:
|
||||
props = variant["properties"]
|
||||
if property_name in props:
|
||||
props[property_name]["const"] = matched_disc_value
|
||||
|
||||
for key, value in node.items():
|
||||
if isinstance(value, dict):
|
||||
node[key] = _process_oneof(value)
|
||||
elif isinstance(value, list):
|
||||
node[key] = [
|
||||
_process_oneof(item) if isinstance(item, dict) else item
|
||||
for item in value
|
||||
]
|
||||
|
||||
return node
|
||||
|
||||
return _process_oneof(deepcopy(schema))
|
||||
|
||||
|
||||
def convert_oneof_to_anyof(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Convert oneOf to anyOf for OpenAI compatibility.
|
||||
|
||||
OpenAI's Structured Outputs support anyOf better than oneOf.
|
||||
This recursively converts all oneOf occurrences to anyOf.
|
||||
|
||||
Args:
|
||||
schema: JSON schema dictionary.
|
||||
|
||||
Returns:
|
||||
Modified schema with anyOf instead of oneOf.
|
||||
"""
|
||||
if isinstance(schema, dict):
|
||||
if "oneOf" in schema:
|
||||
schema["anyOf"] = schema.pop("oneOf")
|
||||
|
||||
for value in schema.values():
|
||||
if isinstance(value, dict):
|
||||
convert_oneof_to_anyof(value)
|
||||
elif isinstance(value, list):
|
||||
for item in value:
|
||||
if isinstance(item, dict):
|
||||
convert_oneof_to_anyof(item)
|
||||
|
||||
return schema
|
||||
|
||||
|
||||
def ensure_all_properties_required(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Ensure all properties are in the required array for OpenAI strict mode.
|
||||
|
||||
OpenAI's strict structured outputs require all properties to be listed
|
||||
in the required array. This recursively updates all objects to include
|
||||
all their properties in required.
|
||||
|
||||
Args:
|
||||
schema: JSON schema dictionary.
|
||||
|
||||
Returns:
|
||||
Modified schema with all properties marked as required.
|
||||
"""
|
||||
if isinstance(schema, dict):
|
||||
if schema.get("type") == "object" and "properties" in schema:
|
||||
properties = schema["properties"]
|
||||
if properties:
|
||||
schema["required"] = list(properties.keys())
|
||||
|
||||
for value in schema.values():
|
||||
if isinstance(value, dict):
|
||||
ensure_all_properties_required(value)
|
||||
elif isinstance(value, list):
|
||||
for item in value:
|
||||
if isinstance(item, dict):
|
||||
ensure_all_properties_required(item)
|
||||
|
||||
return schema
|
||||
|
||||
|
||||
def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
|
||||
"""Generate JSON schema description of a Pydantic model.
|
||||
|
||||
This function takes a Pydantic model class and returns its JSON schema,
|
||||
which includes full type information, discriminators, and all metadata.
|
||||
The schema is dereferenced to inline all $ref references for better LLM understanding.
|
||||
|
||||
Args:
|
||||
model: A Pydantic model class.
|
||||
|
||||
Returns:
|
||||
A JSON schema dictionary representation of the model.
|
||||
"""
|
||||
|
||||
json_schema = model.model_json_schema(ref_template="#/$defs/{model}")
|
||||
|
||||
json_schema = add_key_in_dict_recursively(
|
||||
json_schema,
|
||||
key="additionalProperties",
|
||||
value=False,
|
||||
criteria=lambda d: d.get("type") == "object"
|
||||
and "additionalProperties" not in d,
|
||||
)
|
||||
|
||||
json_schema = resolve_refs(json_schema)
|
||||
|
||||
json_schema.pop("$defs", None)
|
||||
json_schema = fix_discriminator_mappings(json_schema)
|
||||
json_schema = convert_oneof_to_anyof(json_schema)
|
||||
json_schema = ensure_all_properties_required(json_schema)
|
||||
|
||||
return {
|
||||
"type": "json_schema",
|
||||
"json_schema": {
|
||||
"name": model.__name__,
|
||||
"strict": True,
|
||||
"schema": json_schema,
|
||||
},
|
||||
}
|
||||
|
||||
@@ -1,14 +1,15 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, cast
|
||||
import json
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.types.task_events import TaskEvaluationEvent
|
||||
from crewai.llm import LLM
|
||||
from crewai.utilities.converter import Converter
|
||||
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
|
||||
from crewai.utilities.i18n import get_i18n
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.training_converter import TrainingConverter
|
||||
|
||||
|
||||
@@ -62,7 +63,7 @@ class TaskEvaluator:
|
||||
Args:
|
||||
original_agent: The agent to evaluate.
|
||||
"""
|
||||
self.llm = cast(LLM, original_agent.llm)
|
||||
self.llm = original_agent.llm
|
||||
self.original_agent = original_agent
|
||||
|
||||
def evaluate(self, task: Task, output: str) -> TaskEvaluation:
|
||||
@@ -79,7 +80,8 @@ class TaskEvaluator:
|
||||
- Investigate the Converter.to_pydantic signature, returns BaseModel strictly?
|
||||
"""
|
||||
crewai_event_bus.emit(
|
||||
self, TaskEvaluationEvent(evaluation_type="task_evaluation", task=task)
|
||||
self,
|
||||
TaskEvaluationEvent(evaluation_type="task_evaluation", task=task), # type: ignore[no-untyped-call]
|
||||
)
|
||||
evaluation_query = (
|
||||
f"Assess the quality of the task completed based on the description, expected output, and actual results.\n\n"
|
||||
@@ -94,9 +96,14 @@ class TaskEvaluator:
|
||||
|
||||
instructions = "Convert all responses into valid JSON output."
|
||||
|
||||
if not self.llm.supports_function_calling():
|
||||
model_schema = PydanticSchemaParser(model=TaskEvaluation).get_schema()
|
||||
instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
|
||||
if not self.llm.supports_function_calling(): # type: ignore[union-attr]
|
||||
schema_dict = generate_model_description(TaskEvaluation)
|
||||
output_schema: str = (
|
||||
get_i18n()
|
||||
.slice("formatted_task_instructions")
|
||||
.format(output_format=json.dumps(schema_dict, indent=2))
|
||||
)
|
||||
instructions = f"{instructions}\n\n{output_schema}"
|
||||
|
||||
converter = Converter(
|
||||
llm=self.llm,
|
||||
@@ -108,7 +115,7 @@ class TaskEvaluator:
|
||||
return cast(TaskEvaluation, converter.to_pydantic())
|
||||
|
||||
def evaluate_training_data(
|
||||
self, training_data: dict, agent_id: str
|
||||
self, training_data: dict[str, Any], agent_id: str
|
||||
) -> TrainingTaskEvaluation:
|
||||
"""
|
||||
Evaluate the training data based on the llm output, human feedback, and improved output.
|
||||
@@ -121,7 +128,8 @@ class TaskEvaluator:
|
||||
- Investigate the Converter.to_pydantic signature, returns BaseModel strictly?
|
||||
"""
|
||||
crewai_event_bus.emit(
|
||||
self, TaskEvaluationEvent(evaluation_type="training_data_evaluation")
|
||||
self,
|
||||
TaskEvaluationEvent(evaluation_type="training_data_evaluation"), # type: ignore[no-untyped-call]
|
||||
)
|
||||
|
||||
output_training_data = training_data[agent_id]
|
||||
@@ -164,11 +172,14 @@ class TaskEvaluator:
|
||||
)
|
||||
instructions = "I'm gonna convert this raw text into valid JSON."
|
||||
|
||||
if not self.llm.supports_function_calling():
|
||||
model_schema = PydanticSchemaParser(
|
||||
model=TrainingTaskEvaluation
|
||||
).get_schema()
|
||||
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
|
||||
if not self.llm.supports_function_calling(): # type: ignore[union-attr]
|
||||
schema_dict = generate_model_description(TrainingTaskEvaluation)
|
||||
output_schema: str = (
|
||||
get_i18n()
|
||||
.slice("formatted_task_instructions")
|
||||
.format(output_format=json.dumps(schema_dict, indent=2))
|
||||
)
|
||||
instructions = f"{instructions}\n\n{output_schema}"
|
||||
|
||||
converter = TrainingConverter(
|
||||
llm=self.llm,
|
||||
|
||||
@@ -15,9 +15,12 @@ logger = logging.getLogger(__name__)
|
||||
class PlanPerTask(BaseModel):
|
||||
"""Represents a plan for a specific task."""
|
||||
|
||||
task: str = Field(..., description="The task for which the plan is created")
|
||||
task_number: int = Field(
|
||||
description="The 1-indexed task number this plan corresponds to",
|
||||
ge=1,
|
||||
)
|
||||
task: str = Field(description="The task for which the plan is created")
|
||||
plan: str = Field(
|
||||
...,
|
||||
description="The step by step plan on how the agents can execute their tasks using the available tools with mastery",
|
||||
)
|
||||
|
||||
|
||||
@@ -1,103 +0,0 @@
|
||||
from typing import Any, Union, get_args, get_origin
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class PydanticSchemaParser(BaseModel):
|
||||
model: type[BaseModel] = Field(..., description="The Pydantic model to parse.")
|
||||
|
||||
def get_schema(self) -> str:
|
||||
"""Public method to get the schema of a Pydantic model.
|
||||
|
||||
Returns:
|
||||
String representation of the model schema.
|
||||
"""
|
||||
return "{\n" + self._get_model_schema(self.model) + "\n}"
|
||||
|
||||
def _get_model_schema(self, model: type[BaseModel], depth: int = 0) -> str:
|
||||
"""Recursively get the schema of a Pydantic model, handling nested models and lists.
|
||||
|
||||
Args:
|
||||
model: The Pydantic model to process.
|
||||
depth: The current depth of recursion for indentation purposes.
|
||||
|
||||
Returns:
|
||||
A string representation of the model schema.
|
||||
"""
|
||||
indent: str = " " * 4 * depth
|
||||
lines: list[str] = [
|
||||
f"{indent} {field_name}: {self._get_field_type_for_annotation(field.annotation, depth + 1)}"
|
||||
for field_name, field in model.model_fields.items()
|
||||
]
|
||||
return ",\n".join(lines)
|
||||
|
||||
def _format_list_type(self, list_item_type: Any, depth: int) -> str:
|
||||
"""Format a List type, handling nested models if necessary.
|
||||
|
||||
Args:
|
||||
list_item_type: The type of items in the list.
|
||||
depth: The current depth of recursion for indentation purposes.
|
||||
|
||||
Returns:
|
||||
A string representation of the List type.
|
||||
"""
|
||||
if isinstance(list_item_type, type) and issubclass(list_item_type, BaseModel):
|
||||
nested_schema = self._get_model_schema(list_item_type, depth + 1)
|
||||
nested_indent = " " * 4 * depth
|
||||
return f"List[\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}\n{nested_indent}]"
|
||||
return f"List[{list_item_type.__name__}]"
|
||||
|
||||
def _format_union_type(self, field_type: Any, depth: int) -> str:
|
||||
"""Format a Union type, handling Optional and nested types.
|
||||
|
||||
Args:
|
||||
field_type: The Union type to format.
|
||||
depth: The current depth of recursion for indentation purposes.
|
||||
|
||||
Returns:
|
||||
A string representation of the Union type.
|
||||
"""
|
||||
args = get_args(field_type)
|
||||
if type(None) in args:
|
||||
# It's an Optional type
|
||||
non_none_args = [arg for arg in args if arg is not type(None)]
|
||||
if len(non_none_args) == 1:
|
||||
inner_type = self._get_field_type_for_annotation(
|
||||
non_none_args[0], depth
|
||||
)
|
||||
return f"Optional[{inner_type}]"
|
||||
# Union with None and multiple other types
|
||||
inner_types = ", ".join(
|
||||
self._get_field_type_for_annotation(arg, depth) for arg in non_none_args
|
||||
)
|
||||
return f"Optional[Union[{inner_types}]]"
|
||||
# General Union type
|
||||
inner_types = ", ".join(
|
||||
self._get_field_type_for_annotation(arg, depth) for arg in args
|
||||
)
|
||||
return f"Union[{inner_types}]"
|
||||
|
||||
def _get_field_type_for_annotation(self, annotation: Any, depth: int) -> str:
|
||||
"""Recursively get the string representation of a field's type annotation.
|
||||
|
||||
Args:
|
||||
annotation: The type annotation to process.
|
||||
depth: The current depth of recursion for indentation purposes.
|
||||
|
||||
Returns:
|
||||
A string representation of the type annotation.
|
||||
"""
|
||||
origin: Any = get_origin(annotation)
|
||||
if origin is list:
|
||||
list_item_type = get_args(annotation)[0]
|
||||
return self._format_list_type(list_item_type, depth)
|
||||
if origin is dict:
|
||||
key_type, value_type = get_args(annotation)
|
||||
return f"Dict[{key_type.__name__}, {value_type.__name__}]"
|
||||
if origin is Union:
|
||||
return self._format_union_type(annotation, depth)
|
||||
if isinstance(annotation, type) and issubclass(annotation, BaseModel):
|
||||
nested_schema = self._get_model_schema(annotation, depth)
|
||||
nested_indent = " " * 4 * depth
|
||||
return f"{annotation.__name__}\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}"
|
||||
return annotation.__name__
|
||||
245
lib/crewai/src/crewai/utilities/pydantic_schema_utils.py
Normal file
245
lib/crewai/src/crewai/utilities/pydantic_schema_utils.py
Normal file
@@ -0,0 +1,245 @@
|
||||
"""Utilities for generating JSON schemas from Pydantic models.
|
||||
|
||||
This module provides functions for converting Pydantic models to JSON schemas
|
||||
suitable for use with LLMs and tool definitions.
|
||||
"""
|
||||
|
||||
from collections.abc import Callable
|
||||
from copy import deepcopy
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
def resolve_refs(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Recursively resolve all local $refs in the given JSON Schema using $defs as the source.
|
||||
|
||||
This is needed because Pydantic generates $ref-based schemas that
|
||||
some consumers (e.g. LLMs, tool frameworks) don't handle well.
|
||||
|
||||
Args:
|
||||
schema: JSON Schema dict that may contain "$refs" and "$defs".
|
||||
|
||||
Returns:
|
||||
A new schema dictionary with all local $refs replaced by their definitions.
|
||||
"""
|
||||
defs = schema.get("$defs", {})
|
||||
schema_copy = deepcopy(schema)
|
||||
|
||||
def _resolve(node: Any) -> Any:
|
||||
if isinstance(node, dict):
|
||||
ref = node.get("$ref")
|
||||
if isinstance(ref, str) and ref.startswith("#/$defs/"):
|
||||
def_name = ref.replace("#/$defs/", "")
|
||||
if def_name in defs:
|
||||
return _resolve(deepcopy(defs[def_name]))
|
||||
raise KeyError(f"Definition '{def_name}' not found in $defs.")
|
||||
return {k: _resolve(v) for k, v in node.items()}
|
||||
|
||||
if isinstance(node, list):
|
||||
return [_resolve(i) for i in node]
|
||||
|
||||
return node
|
||||
|
||||
return _resolve(schema_copy) # type: ignore[no-any-return]
|
||||
|
||||
|
||||
def add_key_in_dict_recursively(
|
||||
d: dict[str, Any], key: str, value: Any, criteria: Callable[[dict[str, Any]], bool]
|
||||
) -> dict[str, Any]:
|
||||
"""Recursively adds a key/value pair to all nested dicts matching `criteria`.
|
||||
|
||||
Args:
|
||||
d: The dictionary to modify.
|
||||
key: The key to add.
|
||||
value: The value to add.
|
||||
criteria: A function that returns True for dicts that should receive the key.
|
||||
|
||||
Returns:
|
||||
The modified dictionary.
|
||||
"""
|
||||
if isinstance(d, dict):
|
||||
if criteria(d) and key not in d:
|
||||
d[key] = value
|
||||
for v in d.values():
|
||||
add_key_in_dict_recursively(v, key, value, criteria)
|
||||
elif isinstance(d, list):
|
||||
for i in d:
|
||||
add_key_in_dict_recursively(i, key, value, criteria)
|
||||
return d
|
||||
|
||||
|
||||
def fix_discriminator_mappings(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Replace '#/$defs/...' references in discriminator.mapping with just the model name.
|
||||
|
||||
Args:
|
||||
schema: JSON schema dictionary.
|
||||
|
||||
Returns:
|
||||
Modified schema with fixed discriminator mappings.
|
||||
"""
|
||||
output = schema.get("properties", {}).get("output")
|
||||
if not output:
|
||||
return schema
|
||||
|
||||
disc = output.get("discriminator")
|
||||
if not disc or "mapping" not in disc:
|
||||
return schema
|
||||
|
||||
disc["mapping"] = {k: v.split("/")[-1] for k, v in disc["mapping"].items()}
|
||||
return schema
|
||||
|
||||
|
||||
def add_const_to_oneof_variants(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Add const fields to oneOf variants for discriminated unions.
|
||||
|
||||
The json_schema_to_pydantic library requires each oneOf variant to have
|
||||
a const field for the discriminator property. This function adds those
|
||||
const fields based on the discriminator mapping.
|
||||
|
||||
Args:
|
||||
schema: JSON Schema dict that may contain discriminated unions
|
||||
|
||||
Returns:
|
||||
Modified schema with const fields added to oneOf variants
|
||||
"""
|
||||
|
||||
def _process_oneof(node: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Process a single node that might contain a oneOf with discriminator."""
|
||||
if not isinstance(node, dict):
|
||||
return node
|
||||
|
||||
if "oneOf" in node and "discriminator" in node:
|
||||
discriminator = node["discriminator"]
|
||||
property_name = discriminator.get("propertyName")
|
||||
mapping = discriminator.get("mapping", {})
|
||||
|
||||
if property_name and mapping:
|
||||
one_of_variants = node.get("oneOf", [])
|
||||
|
||||
for variant in one_of_variants:
|
||||
if isinstance(variant, dict) and "properties" in variant:
|
||||
variant_title = variant.get("title", "")
|
||||
|
||||
matched_disc_value = None
|
||||
for disc_value, schema_name in mapping.items():
|
||||
if variant_title == schema_name or variant_title.endswith(
|
||||
schema_name
|
||||
):
|
||||
matched_disc_value = disc_value
|
||||
break
|
||||
|
||||
if matched_disc_value is not None:
|
||||
props = variant["properties"]
|
||||
if property_name in props:
|
||||
props[property_name]["const"] = matched_disc_value
|
||||
|
||||
for key, value in node.items():
|
||||
if isinstance(value, dict):
|
||||
node[key] = _process_oneof(value)
|
||||
elif isinstance(value, list):
|
||||
node[key] = [
|
||||
_process_oneof(item) if isinstance(item, dict) else item
|
||||
for item in value
|
||||
]
|
||||
|
||||
return node
|
||||
|
||||
return _process_oneof(deepcopy(schema))
|
||||
|
||||
|
||||
def convert_oneof_to_anyof(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Convert oneOf to anyOf for OpenAI compatibility.
|
||||
|
||||
OpenAI's Structured Outputs support anyOf better than oneOf.
|
||||
This recursively converts all oneOf occurrences to anyOf.
|
||||
|
||||
Args:
|
||||
schema: JSON schema dictionary.
|
||||
|
||||
Returns:
|
||||
Modified schema with anyOf instead of oneOf.
|
||||
"""
|
||||
if isinstance(schema, dict):
|
||||
if "oneOf" in schema:
|
||||
schema["anyOf"] = schema.pop("oneOf")
|
||||
|
||||
for value in schema.values():
|
||||
if isinstance(value, dict):
|
||||
convert_oneof_to_anyof(value)
|
||||
elif isinstance(value, list):
|
||||
for item in value:
|
||||
if isinstance(item, dict):
|
||||
convert_oneof_to_anyof(item)
|
||||
|
||||
return schema
|
||||
|
||||
|
||||
def ensure_all_properties_required(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Ensure all properties are in the required array for OpenAI strict mode.
|
||||
|
||||
OpenAI's strict structured outputs require all properties to be listed
|
||||
in the required array. This recursively updates all objects to include
|
||||
all their properties in required.
|
||||
|
||||
Args:
|
||||
schema: JSON schema dictionary.
|
||||
|
||||
Returns:
|
||||
Modified schema with all properties marked as required.
|
||||
"""
|
||||
if isinstance(schema, dict):
|
||||
if schema.get("type") == "object" and "properties" in schema:
|
||||
properties = schema["properties"]
|
||||
if properties:
|
||||
schema["required"] = list(properties.keys())
|
||||
|
||||
for value in schema.values():
|
||||
if isinstance(value, dict):
|
||||
ensure_all_properties_required(value)
|
||||
elif isinstance(value, list):
|
||||
for item in value:
|
||||
if isinstance(item, dict):
|
||||
ensure_all_properties_required(item)
|
||||
|
||||
return schema
|
||||
|
||||
|
||||
def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
|
||||
"""Generate JSON schema description of a Pydantic model.
|
||||
|
||||
This function takes a Pydantic model class and returns its JSON schema,
|
||||
which includes full type information, discriminators, and all metadata.
|
||||
The schema is dereferenced to inline all $ref references for better LLM understanding.
|
||||
|
||||
Args:
|
||||
model: A Pydantic model class.
|
||||
|
||||
Returns:
|
||||
A JSON schema dictionary representation of the model.
|
||||
"""
|
||||
json_schema = model.model_json_schema(ref_template="#/$defs/{model}")
|
||||
|
||||
json_schema = add_key_in_dict_recursively(
|
||||
json_schema,
|
||||
key="additionalProperties",
|
||||
value=False,
|
||||
criteria=lambda d: d.get("type") == "object"
|
||||
and "additionalProperties" not in d,
|
||||
)
|
||||
|
||||
json_schema = resolve_refs(json_schema)
|
||||
|
||||
json_schema.pop("$defs", None)
|
||||
json_schema = fix_discriminator_mappings(json_schema)
|
||||
json_schema = convert_oneof_to_anyof(json_schema)
|
||||
json_schema = ensure_all_properties_required(json_schema)
|
||||
|
||||
return {
|
||||
"type": "json_schema",
|
||||
"json_schema": {
|
||||
"name": model.__name__,
|
||||
"strict": True,
|
||||
"schema": json_schema,
|
||||
},
|
||||
}
|
||||
@@ -79,6 +79,7 @@ class RPMController(BaseModel):
|
||||
self._current_rpm = 0
|
||||
if not self._shutdown_flag:
|
||||
self._timer = threading.Timer(60.0, self._reset_request_count)
|
||||
self._timer.daemon = True
|
||||
self._timer.start()
|
||||
|
||||
if self._lock:
|
||||
|
||||
@@ -40,20 +40,10 @@ interactions:
|
||||
uri: https://api.openai.com/v1/chat/completions
|
||||
response:
|
||||
body:
|
||||
string: !!binary |
|
||||
H4sIAAAAAAAAAwAAAP//jFJNb9QwEL3nVww+b9Am7Ee7FyT2wCIQ0CJxqarItSdZg+Ox7AmwVPvf
|
||||
KyftJv1A4uLDvHnP783MbQYgjBYbEGovWbXe5tvm6/bv5ZeDu5AmlubTzr///G778fKi+O6/iVli
|
||||
0M0PVPzAeq2o9RbZkBtgFVAyJtVivVqUZbku3vRASxptojWe8wXlrXEmL+flIp+v8+Lsnr0nozCK
|
||||
DVxlAAC3/Zt8Oo1/xAbms4dKizHKBsXm1AQgAtlUETJGE1k6FrMRVOQYXW99h9bSK9jRb1DSwQcY
|
||||
CHCgDpi0PLydEgPWXZTJvOusnQDSOWKZwveWr++R48mkpcYHuolPqKI2zsR9FVBGcslQZPKiR48Z
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Reference in New Issue
Block a user