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6d0039b117 | ||
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311a078ca6 |
@@ -4,6 +4,36 @@ description: View the latest updates and changes to CrewAI
|
||||
icon: timeline
|
||||
---
|
||||
|
||||
<Update label="2025-04-07" description="v0.114.0">
|
||||
## Release Highlights
|
||||
<Frame>
|
||||
<img src="/images/v01140.png" />
|
||||
</Frame>
|
||||
|
||||
**New Features & Enhancements**
|
||||
- Agents as an atomic unit. (`Agent(...).kickoff()`)
|
||||
- Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
|
||||
- Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
|
||||
- Enhanced YAML extraction.
|
||||
- Multimodal agent validation.
|
||||
- Added Secure fingerprints for agents and crews.
|
||||
|
||||
**Core Improvements & Fixes**
|
||||
- Improved serialization, agent copying, and Python compatibility.
|
||||
- Added wildcard support to `emit()`
|
||||
- Added support for additional router calls and context window adjustments.
|
||||
- Fixed typing issues, validation, and import statements.
|
||||
- Improved method performance.
|
||||
- Enhanced agent task handling, event emissions, and memory management.
|
||||
- Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
|
||||
|
||||
**Documentation & Guides**
|
||||
- Improved documentation structure, theme, and organization.
|
||||
- Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
|
||||
- Updated tool configuration examples, prompts, and observability docs.
|
||||
- Guide on using singular agents within Flows.
|
||||
</Update>
|
||||
|
||||
<Update label="2025-03-17" description="v0.108.0">
|
||||
**Features**
|
||||
- Converted tabs to spaces in `crew.py` template
|
||||
|
||||
@@ -179,7 +179,78 @@ def crew(self) -> Crew:
|
||||
```
|
||||
</Note>
|
||||
|
||||
### 10. API Keys
|
||||
### 10. Deploy
|
||||
|
||||
Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
|
||||
|
||||
- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
|
||||
```shell Terminal
|
||||
crewai signup
|
||||
```
|
||||
If you already have an account, you can login with:
|
||||
```shell Terminal
|
||||
crewai login
|
||||
```
|
||||
|
||||
- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
|
||||
```shell Terminal
|
||||
crewai deploy create
|
||||
```
|
||||
- Reads your local project configuration.
|
||||
- Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
|
||||
- Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
|
||||
|
||||
- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
|
||||
```shell Terminal
|
||||
crewai deploy push
|
||||
```
|
||||
- Initiates the deployment process on the CrewAI Enterprise platform.
|
||||
- Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
|
||||
|
||||
- **Deployment Status**: You can check the status of your deployment with:
|
||||
```shell Terminal
|
||||
crewai deploy status
|
||||
```
|
||||
This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
|
||||
|
||||
- **Deployment Logs**: You can check the logs of your deployment with:
|
||||
```shell Terminal
|
||||
crewai deploy logs
|
||||
```
|
||||
This streams the deployment logs to your terminal.
|
||||
|
||||
- **List deployments**: You can list all your deployments with:
|
||||
```shell Terminal
|
||||
crewai deploy list
|
||||
```
|
||||
This lists all your deployments.
|
||||
|
||||
- **Delete a deployment**: You can delete a deployment with:
|
||||
```shell Terminal
|
||||
crewai deploy remove
|
||||
```
|
||||
This deletes the deployment from the CrewAI Enterprise platform.
|
||||
|
||||
- **Help Command**: You can get help with the CLI with:
|
||||
```shell Terminal
|
||||
crewai deploy --help
|
||||
```
|
||||
This shows the help message for the CrewAI Deploy CLI.
|
||||
|
||||
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
|
||||
|
||||
<iframe
|
||||
width="100%"
|
||||
height="400"
|
||||
src="https://www.youtube.com/embed/3EqSV-CYDZA"
|
||||
title="CrewAI Deployment Guide"
|
||||
frameborder="0"
|
||||
style={{ borderRadius: '10px' }}
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
|
||||
allowfullscreen
|
||||
></iframe>
|
||||
|
||||
### 11. API Keys
|
||||
|
||||
When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
|
||||
|
||||
|
||||
@@ -1,71 +0,0 @@
|
||||
---
|
||||
title: Using LlamaIndex Tools
|
||||
description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
|
||||
icon: toolbox
|
||||
---
|
||||
|
||||
## Using LlamaIndex Tools
|
||||
|
||||
<Info>
|
||||
CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more.
|
||||
</Info>
|
||||
|
||||
Here are the available built-in tools offered by LlamaIndex.
|
||||
|
||||
```python Code
|
||||
from crewai import Agent
|
||||
from crewai_tools import LlamaIndexTool
|
||||
|
||||
# Example 1: Initialize from FunctionTool
|
||||
from llama_index.core.tools import FunctionTool
|
||||
|
||||
your_python_function = lambda ...: ...
|
||||
og_tool = FunctionTool.from_defaults(
|
||||
your_python_function,
|
||||
name="<name>",
|
||||
description='<description>'
|
||||
)
|
||||
tool = LlamaIndexTool.from_tool(og_tool)
|
||||
|
||||
# Example 2: Initialize from LlamaHub Tools
|
||||
from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
|
||||
wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
|
||||
wolfram_tools = wolfram_spec.to_tool_list()
|
||||
tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
|
||||
|
||||
# Example 3: Initialize Tool from a LlamaIndex Query Engine
|
||||
query_engine = index.as_query_engine()
|
||||
query_tool = LlamaIndexTool.from_query_engine(
|
||||
query_engine,
|
||||
name="Uber 2019 10K Query Tool",
|
||||
description="Use this tool to lookup the 2019 Uber 10K Annual Report"
|
||||
)
|
||||
|
||||
# Create and assign the tools to an agent
|
||||
agent = Agent(
|
||||
role='Research Analyst',
|
||||
goal='Provide up-to-date market analysis',
|
||||
backstory='An expert analyst with a keen eye for market trends.',
|
||||
tools=[tool, *tools, query_tool]
|
||||
)
|
||||
|
||||
# rest of the code ...
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the LlamaIndexTool, follow these steps:
|
||||
|
||||
<Steps>
|
||||
<Step title="Package Installation">
|
||||
Make sure that `crewai[tools]` package is installed in your Python environment:
|
||||
<CodeGroup>
|
||||
```shell Terminal
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
</CodeGroup>
|
||||
</Step>
|
||||
<Step title="Install and Use LlamaIndex">
|
||||
Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
|
||||
</Step>
|
||||
</Steps>
|
||||
117
docs/docs.json
117
docs/docs.json
@@ -8,25 +8,27 @@
|
||||
"dark": "#C94C3C"
|
||||
},
|
||||
"favicon": "favicon.svg",
|
||||
"contextual": {
|
||||
"options": ["copy", "view", "chatgpt", "claude"]
|
||||
},
|
||||
"navigation": {
|
||||
"tabs": [
|
||||
{
|
||||
"tab": "Get Started",
|
||||
"tab": "Documentation",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"introduction",
|
||||
"installation",
|
||||
"quickstart",
|
||||
"changelog"
|
||||
"quickstart"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Guides",
|
||||
"pages": [
|
||||
{
|
||||
"group": "Concepts",
|
||||
"group": "Strategy",
|
||||
"pages": [
|
||||
"guides/concepts/evaluating-use-cases"
|
||||
]
|
||||
@@ -79,41 +81,6 @@
|
||||
"concepts/event-listener"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "How to Guides",
|
||||
"pages": [
|
||||
"how-to/create-custom-tools",
|
||||
"how-to/sequential-process",
|
||||
"how-to/hierarchical-process",
|
||||
"how-to/custom-manager-agent",
|
||||
"how-to/llm-connections",
|
||||
"how-to/customizing-agents",
|
||||
"how-to/multimodal-agents",
|
||||
"how-to/coding-agents",
|
||||
"how-to/force-tool-output-as-result",
|
||||
"how-to/human-input-on-execution",
|
||||
"how-to/kickoff-async",
|
||||
"how-to/kickoff-for-each",
|
||||
"how-to/replay-tasks-from-latest-crew-kickoff",
|
||||
"how-to/conditional-tasks",
|
||||
"how-to/langchain-tools",
|
||||
"how-to/llamaindex-tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Agent Monitoring & Observability",
|
||||
"pages": [
|
||||
"how-to/agentops-observability",
|
||||
"how-to/arize-phoenix-observability",
|
||||
"how-to/langfuse-observability",
|
||||
"how-to/langtrace-observability",
|
||||
"how-to/mlflow-observability",
|
||||
"how-to/openlit-observability",
|
||||
"how-to/opik-observability",
|
||||
"how-to/portkey-observability",
|
||||
"how-to/weave-integration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Tools",
|
||||
"pages": [
|
||||
@@ -141,6 +108,7 @@
|
||||
"tools/hyperbrowserloadtool",
|
||||
"tools/linkupsearchtool",
|
||||
"tools/llamaindextool",
|
||||
"tools/langchaintool",
|
||||
"tools/serperdevtool",
|
||||
"tools/s3readertool",
|
||||
"tools/s3writertool",
|
||||
@@ -170,6 +138,40 @@
|
||||
"tools/youtubevideosearchtool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Agent Monitoring & Observability",
|
||||
"pages": [
|
||||
"how-to/agentops-observability",
|
||||
"how-to/arize-phoenix-observability",
|
||||
"how-to/langfuse-observability",
|
||||
"how-to/langtrace-observability",
|
||||
"how-to/mlflow-observability",
|
||||
"how-to/openlit-observability",
|
||||
"how-to/opik-observability",
|
||||
"how-to/portkey-observability",
|
||||
"how-to/weave-integration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Learn",
|
||||
"pages": [
|
||||
"how-to/conditional-tasks",
|
||||
"how-to/coding-agents",
|
||||
"how-to/create-custom-tools",
|
||||
"how-to/custom-llm",
|
||||
"how-to/custom-manager-agent",
|
||||
"how-to/customizing-agents",
|
||||
"how-to/force-tool-output-as-result",
|
||||
"how-to/hierarchical-process",
|
||||
"how-to/human-input-on-execution",
|
||||
"how-to/kickoff-async",
|
||||
"how-to/kickoff-for-each",
|
||||
"how-to/llm-connections",
|
||||
"how-to/multimodal-agents",
|
||||
"how-to/replay-tasks-from-latest-crew-kickoff",
|
||||
"how-to/sequential-process"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Telemetry",
|
||||
"pages": [
|
||||
@@ -188,19 +190,35 @@
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Releases",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Releases",
|
||||
"pages": [
|
||||
"changelog"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"global": {
|
||||
"anchors": [
|
||||
{
|
||||
"anchor": "Community",
|
||||
"anchor": "Website",
|
||||
"href": "https://crewai.com",
|
||||
"icon": "globe"
|
||||
},
|
||||
{
|
||||
"anchor": "Forum",
|
||||
"href": "https://community.crewai.com",
|
||||
"icon": "discourse"
|
||||
},
|
||||
{
|
||||
"anchor": "Tutorials",
|
||||
"href": "https://www.youtube.com/@crewAIInc",
|
||||
"icon": "youtube"
|
||||
"anchor": "Get Help",
|
||||
"href": "mailto:support@crewai.com",
|
||||
"icon": "headset"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -214,6 +232,12 @@
|
||||
"strict": false
|
||||
},
|
||||
"navbar": {
|
||||
"links": [
|
||||
{
|
||||
"label": "Start Free Trial",
|
||||
"href": "https://app.crewai.com"
|
||||
}
|
||||
],
|
||||
"primary": {
|
||||
"type": "github",
|
||||
"href": "https://github.com/crewAIInc/crewAI"
|
||||
@@ -223,7 +247,12 @@
|
||||
"prompt": "Search CrewAI docs"
|
||||
},
|
||||
"seo": {
|
||||
"indexing": "navigable"
|
||||
"indexing": "all"
|
||||
},
|
||||
"errors": {
|
||||
"404": {
|
||||
"redirect": true
|
||||
}
|
||||
},
|
||||
"footer": {
|
||||
"socials": {
|
||||
|
||||
@@ -1,9 +1,13 @@
|
||||
# Custom LLM Implementations
|
||||
---
|
||||
title: Custom LLM Implementation
|
||||
description: Learn how to create custom LLM implementations in CrewAI.
|
||||
icon: code
|
||||
---
|
||||
|
||||
## Custom LLM Implementations
|
||||
|
||||
CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
|
||||
|
||||
## Using Custom LLM Implementations
|
||||
|
||||
To create a custom LLM implementation, you need to:
|
||||
|
||||
1. Inherit from the `BaseLLM` abstract base class
|
||||
@@ -1,5 +1,5 @@
|
||||
---
|
||||
title: Create Your Own Manager Agent
|
||||
title: Custom Manager Agent
|
||||
description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
|
||||
icon: user-shield
|
||||
---
|
||||
|
||||
@@ -20,10 +20,8 @@ Here's an example of how to replay from a task:
|
||||
To use the replay feature, follow these steps:
|
||||
|
||||
<Steps>
|
||||
<Step title="Open your terminal or command prompt.">
|
||||
</Step>
|
||||
<Step title="Navigate to the directory where your CrewAI project is located.">
|
||||
</Step>
|
||||
<Step title="Open your terminal or command prompt."></Step>
|
||||
<Step title="Navigate to the directory where your CrewAI project is located."></Step>
|
||||
<Step title="Run the following commands:">
|
||||
To view the latest kickoff task_ids use:
|
||||
|
||||
|
||||
BIN
docs/images/v01140.png
Normal file
BIN
docs/images/v01140.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 2.4 MiB |
@@ -336,9 +336,22 @@ email_summarizer_task:
|
||||
- research_task
|
||||
```
|
||||
|
||||
## Deploying Your Project
|
||||
## Deploying Your Crew
|
||||
|
||||
The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com), where you can deploy your crew in a few clicks.
|
||||
The easiest way to deploy your crew to production is through [CrewAI Enterprise](http://app.crewai.com).
|
||||
|
||||
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
|
||||
|
||||
<iframe
|
||||
width="100%"
|
||||
height="400"
|
||||
src="https://www.youtube.com/embed/3EqSV-CYDZA"
|
||||
title="CrewAI Deployment Guide"
|
||||
frameborder="0"
|
||||
style={{ borderRadius: '10px' }}
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
|
||||
allowfullscreen
|
||||
></iframe>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
|
||||
@@ -8,11 +8,29 @@ icon: code-simple
|
||||
|
||||
## Description
|
||||
|
||||
The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. The code is run in a secure, isolated Docker container, ensuring safety regardless of the content. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
|
||||
The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
|
||||
|
||||
## Requirements
|
||||
There are several ways to use this tool:
|
||||
|
||||
### Docker Container (Recommended)
|
||||
|
||||
This is the primary option. The code runs in a secure, isolated Docker container, ensuring safety regardless of its content.
|
||||
Make sure Docker is installed and running on your system. If you don’t have it, you can install it from [here](https://docs.docker.com/get-docker/).
|
||||
|
||||
### Sandbox environment
|
||||
|
||||
If Docker is unavailable — either not installed or not accessible for any reason — the code will be executed in a restricted Python environment - called sandbox.
|
||||
This environment is very limited, with strict restrictions on many modules and built-in functions.
|
||||
|
||||
### Unsafe Execution
|
||||
|
||||
**NOT RECOMMENDED FOR PRODUCTION**
|
||||
This mode allows execution of any Python code, including dangerous calls to `sys, os..` and similar modules. [Check out](/tools/codeinterpretertool#enabling-unsafe-mode) how to enable this mode
|
||||
|
||||
## Logging
|
||||
|
||||
The `CodeInterpreterTool` logs the selected execution strategy to STDOUT
|
||||
|
||||
- Docker must be installed and running on your system. If you don't have it, you can install it from [here](https://docs.docker.com/get-docker/).
|
||||
|
||||
## Installation
|
||||
|
||||
@@ -74,18 +92,32 @@ programmer_agent = Agent(
|
||||
)
|
||||
```
|
||||
|
||||
### Enabling `unsafe_mode`
|
||||
|
||||
```python Code
|
||||
from crewai_tools import CodeInterpreterTool
|
||||
|
||||
code = """
|
||||
import os
|
||||
os.system("ls -la")
|
||||
"""
|
||||
|
||||
CodeInterpreterTool(unsafe_mode=True).run(code=code)
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `CodeInterpreterTool` accepts the following parameters during initialization:
|
||||
|
||||
- **user_dockerfile_path**: Optional. Path to a custom Dockerfile to use for the code interpreter container.
|
||||
- **user_docker_base_url**: Optional. URL to the Docker daemon to use for running the container.
|
||||
- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container. Default is `False`. Use with caution!
|
||||
- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container or sandbox. Default is `False`. Use with caution!
|
||||
- **default_image_tag**: Optional. Default Docker image tag. Default is `code-interpreter:latest`
|
||||
|
||||
When using the tool with an agent, the agent will need to provide:
|
||||
|
||||
- **code**: Required. The Python 3 code to execute.
|
||||
- **libraries_used**: Required. A list of libraries used in the code that need to be installed.
|
||||
- **libraries_used**: Optional. A list of libraries used in the code that need to be installed. Default is `[]`
|
||||
|
||||
## Agent Integration Example
|
||||
|
||||
@@ -152,7 +184,7 @@ class CodeInterpreterTool(BaseTool):
|
||||
if self.unsafe_mode:
|
||||
return self.run_code_unsafe(code, libraries_used)
|
||||
else:
|
||||
return self.run_code_in_docker(code, libraries_used)
|
||||
return self.run_code_safety(code, libraries_used)
|
||||
```
|
||||
|
||||
The tool performs the following steps:
|
||||
@@ -168,8 +200,9 @@ The tool performs the following steps:
|
||||
By default, the `CodeInterpreterTool` runs code in an isolated Docker container, which provides a layer of security. However, there are still some security considerations to keep in mind:
|
||||
|
||||
1. The Docker container has access to the current working directory, so sensitive files could potentially be accessed.
|
||||
2. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
|
||||
3. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
|
||||
2. If the Docker container is unavailable and the code needs to run safely, it will be executed in a sandbox environment. For security reasons, installing arbitrary libraries is not allowed
|
||||
3. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
|
||||
4. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
|
||||
|
||||
## Conclusion
|
||||
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
---
|
||||
title: Using LangChain Tools
|
||||
description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
|
||||
title: LangChain Tool
|
||||
description: The `LangChainTool` is a wrapper for LangChain tools and query engines.
|
||||
icon: link
|
||||
---
|
||||
|
||||
## Using LangChain Tools
|
||||
## `LangChainTool`
|
||||
|
||||
<Info>
|
||||
CrewAI seamlessly integrates with LangChain's comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.
|
||||
@@ -122,7 +122,16 @@ PROVIDERS = [
|
||||
]
|
||||
|
||||
MODELS = {
|
||||
"openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
|
||||
"openai": [
|
||||
"gpt-4",
|
||||
"gpt-4.1",
|
||||
"gpt-4.1-mini-2025-04-14",
|
||||
"gpt-4.1-nano-2025-04-14",
|
||||
"gpt-4o",
|
||||
"gpt-4o-mini",
|
||||
"o1-mini",
|
||||
"o1-preview",
|
||||
],
|
||||
"anthropic": [
|
||||
"claude-3-5-sonnet-20240620",
|
||||
"claude-3-sonnet-20240229",
|
||||
@@ -132,8 +141,17 @@ MODELS = {
|
||||
"gemini": [
|
||||
"gemini/gemini-1.5-flash",
|
||||
"gemini/gemini-1.5-pro",
|
||||
"gemini/gemini-2.0-flash-lite-001",
|
||||
"gemini/gemini-2.0-flash-001",
|
||||
"gemini/gemini-2.0-flash-thinking-exp-01-21",
|
||||
"gemini/gemini-2.5-flash-preview-04-17",
|
||||
"gemini/gemini-2.5-pro-exp-03-25",
|
||||
"gemini/gemini-gemma-2-9b-it",
|
||||
"gemini/gemini-gemma-2-27b-it",
|
||||
"gemini/gemma-3-1b-it",
|
||||
"gemini/gemma-3-4b-it",
|
||||
"gemini/gemma-3-12b-it",
|
||||
"gemini/gemma-3-27b-it",
|
||||
],
|
||||
"nvidia_nim": [
|
||||
"nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",
|
||||
|
||||
@@ -117,7 +117,9 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
|
||||
published_handle = publish_response.json()["handle"]
|
||||
console.print(
|
||||
f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
|
||||
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}",
|
||||
style="bold green",
|
||||
)
|
||||
|
||||
@@ -153,8 +155,12 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
login_response_json = login_response.json()
|
||||
|
||||
settings = Settings()
|
||||
settings.tool_repository_username = login_response_json["credential"]["username"]
|
||||
settings.tool_repository_password = login_response_json["credential"]["password"]
|
||||
settings.tool_repository_username = login_response_json["credential"][
|
||||
"username"
|
||||
]
|
||||
settings.tool_repository_password = login_response_json["credential"][
|
||||
"password"
|
||||
]
|
||||
settings.dump()
|
||||
|
||||
console.print(
|
||||
@@ -179,7 +185,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
capture_output=False,
|
||||
env=self._build_env_with_credentials(repository_handle),
|
||||
text=True,
|
||||
check=True
|
||||
check=True,
|
||||
)
|
||||
|
||||
if add_package_result.stderr:
|
||||
@@ -204,7 +210,11 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
settings = Settings()
|
||||
|
||||
env = os.environ.copy()
|
||||
env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
|
||||
env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
|
||||
env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
|
||||
settings.tool_repository_username or ""
|
||||
)
|
||||
env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
|
||||
settings.tool_repository_password or ""
|
||||
)
|
||||
|
||||
return env
|
||||
|
||||
@@ -37,6 +37,7 @@ with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore", UserWarning)
|
||||
import litellm
|
||||
from litellm import Choices
|
||||
from litellm.exceptions import ContextWindowExceededError
|
||||
from litellm.litellm_core_utils.get_supported_openai_params import (
|
||||
get_supported_openai_params,
|
||||
)
|
||||
@@ -81,14 +82,26 @@ LLM_CONTEXT_WINDOW_SIZES = {
|
||||
"gpt-4o": 128000,
|
||||
"gpt-4o-mini": 128000,
|
||||
"gpt-4-turbo": 128000,
|
||||
"gpt-4.1": 1047576, # Based on official docs
|
||||
"gpt-4.1-mini-2025-04-14": 1047576,
|
||||
"gpt-4.1-nano-2025-04-14": 1047576,
|
||||
"o1-preview": 128000,
|
||||
"o1-mini": 128000,
|
||||
"o3-mini": 200000, # Based on official o3-mini specifications
|
||||
# gemini
|
||||
"gemini-2.0-flash": 1048576,
|
||||
"gemini-2.0-flash-thinking-exp-01-21": 32768,
|
||||
"gemini-2.0-flash-lite-001": 1048576,
|
||||
"gemini-2.0-flash-001": 1048576,
|
||||
"gemini-2.5-flash-preview-04-17": 1048576,
|
||||
"gemini-2.5-pro-exp-03-25": 1048576,
|
||||
"gemini-1.5-pro": 2097152,
|
||||
"gemini-1.5-flash": 1048576,
|
||||
"gemini-1.5-flash-8b": 1048576,
|
||||
"gemini/gemma-3-1b-it": 32000,
|
||||
"gemini/gemma-3-4b-it": 128000,
|
||||
"gemini/gemma-3-12b-it": 128000,
|
||||
"gemini/gemma-3-27b-it": 128000,
|
||||
# deepseek
|
||||
"deepseek-chat": 128000,
|
||||
# groq
|
||||
@@ -585,6 +598,11 @@ class LLM(BaseLLM):
|
||||
self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
|
||||
return full_response
|
||||
|
||||
except ContextWindowExceededError as e:
|
||||
# Catch context window errors from litellm and convert them to our own exception type.
|
||||
# This exception is handled by CrewAgentExecutor._invoke_loop() which can then
|
||||
# decide whether to summarize the content or abort based on the respect_context_window flag.
|
||||
raise LLMContextLengthExceededException(str(e))
|
||||
except Exception as e:
|
||||
logging.error(f"Error in streaming response: {str(e)}")
|
||||
if full_response.strip():
|
||||
@@ -699,7 +717,16 @@ class LLM(BaseLLM):
|
||||
str: The response text
|
||||
"""
|
||||
# --- 1) Make the completion call
|
||||
response = litellm.completion(**params)
|
||||
try:
|
||||
# Attempt to make the completion call, but catch context window errors
|
||||
# and convert them to our own exception type for consistent handling
|
||||
# across the codebase. This allows CrewAgentExecutor to handle context
|
||||
# length issues appropriately.
|
||||
response = litellm.completion(**params)
|
||||
except ContextWindowExceededError as e:
|
||||
# Convert litellm's context window error to our own exception type
|
||||
# for consistent handling in the rest of the codebase
|
||||
raise LLMContextLengthExceededException(str(e))
|
||||
|
||||
# --- 2) Extract response message and content
|
||||
response_message = cast(Choices, cast(ModelResponse, response).choices)[
|
||||
@@ -858,15 +885,17 @@ class LLM(BaseLLM):
|
||||
params, callbacks, available_functions
|
||||
)
|
||||
|
||||
except LLMContextLengthExceededException:
|
||||
# Re-raise LLMContextLengthExceededException as it should be handled
|
||||
# by the CrewAgentExecutor._invoke_loop method, which can then decide
|
||||
# whether to summarize the content or abort based on the respect_context_window flag
|
||||
raise
|
||||
except Exception as e:
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=LLMCallFailedEvent(error=str(e)),
|
||||
)
|
||||
if not LLMContextLengthExceededException(
|
||||
str(e)
|
||||
)._is_context_limit_error(str(e)):
|
||||
logging.error(f"LiteLLM call failed: {str(e)}")
|
||||
logging.error(f"LiteLLM call failed: {str(e)}")
|
||||
raise
|
||||
|
||||
def _handle_emit_call_events(self, response: Any, call_type: LLMCallType):
|
||||
|
||||
@@ -0,0 +1,59 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
|
||||
of France?"}]}], "generationConfig": {"stop_sequences": []}}'
|
||||
headers:
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accept:
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- '*/*'
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accept-encoding:
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- gzip, deflate
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connection:
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- keep-alive
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content-length:
|
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- '131'
|
||||
content-type:
|
||||
- application/json
|
||||
host:
|
||||
- generativelanguage.googleapis.com
|
||||
user-agent:
|
||||
- litellm/1.60.2
|
||||
method: POST
|
||||
uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-001:generateContent
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body:
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string: !!binary |
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Content-Type:
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- application/json; charset=UTF-8
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Date:
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- Tue, 22 Apr 2025 14:25:05 GMT
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Server:
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- scaffolding on HTTPServer2
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Server-Timing:
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- gfet4t7; dur=1219
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Transfer-Encoding:
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||||
- chunked
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Vary:
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||||
- Origin
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- X-Origin
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- Referer
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X-Content-Type-Options:
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- nosniff
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X-Frame-Options:
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- SAMEORIGIN
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X-XSS-Protection:
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||||
- '0'
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||||
status:
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code: 200
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message: OK
|
||||
version: 1
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@@ -0,0 +1,59 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
|
||||
of France?"}]}], "generationConfig": {"stop_sequences": []}}'
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headers:
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accept:
|
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- '*/*'
|
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accept-encoding:
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- gzip, deflate
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connection:
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||||
- keep-alive
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||||
content-length:
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- '131'
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content-type:
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- application/json
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host:
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- generativelanguage.googleapis.com
|
||||
user-agent:
|
||||
- litellm/1.60.2
|
||||
method: POST
|
||||
uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-lite-001:generateContent
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response:
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body:
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string: !!binary |
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Server:
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Server-Timing:
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- gfet4t7; dur=1090
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Transfer-Encoding:
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- chunked
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Vary:
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- Origin
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- X-Origin
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X-Content-Type-Options:
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- nosniff
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X-Frame-Options:
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- SAMEORIGIN
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X-XSS-Protection:
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- '0'
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status:
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code: 200
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message: OK
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version: 1
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@@ -0,0 +1,58 @@
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interactions:
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- request:
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body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
|
||||
of France?"}]}], "generationConfig": {"stop_sequences": []}}'
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headers:
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accept:
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- '*/*'
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accept-encoding:
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- gzip, deflate
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connection:
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- keep-alive
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content-length:
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- '131'
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content-type:
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- application/json
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host:
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- generativelanguage.googleapis.com
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user-agent:
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- litellm/1.60.2
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method: POST
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uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-thinking-exp-01-21:generateContent
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60
tests/cassettes/test_gemma3[gemini-gemma-3-1b-it].yaml
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101
tests/cassettes/test_gpt_4_1[gpt-4.1-mini-2025-04-14].yaml
Normal file
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@pytest.mark.vcr(filter_headers=["authorization"], filter_query_parameters=["key"])
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@pytest.mark.parametrize(
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|
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|
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llm = LLM(model=model)
|
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result = llm.call("What is the capital of France?")
|
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|
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|
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|
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|
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|
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llm = LLM(model=model)
|
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result = llm.call("What is the capital of France?")
|
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|
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|
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)
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llm = LLM(model=model)
|
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result = llm.call("What is the capital of France?")
|
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assert isinstance(result, str)
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|
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|
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|
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def test_o3_mini_reasoning_effort_high():
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llm = LLM(
|
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@@ -327,6 +373,45 @@ def get_weather_tool_schema():
|
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},
|
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}
|
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|
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def test_context_window_exceeded_error_handling():
|
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|
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from litellm.exceptions import ContextWindowExceededError
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from crewai.utilities.exceptions.context_window_exceeding_exception import (
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LLMContextLengthExceededException,
|
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)
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llm = LLM(model="gpt-4")
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|
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# Test non-streaming response
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with patch("litellm.completion") as mock_completion:
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mock_completion.side_effect = ContextWindowExceededError(
|
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"This model's maximum context length is 8192 tokens. However, your messages resulted in 10000 tokens.",
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model="gpt-4",
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llm_provider="openai"
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with pytest.raises(LLMContextLengthExceededException) as excinfo:
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llm.call("This is a test message")
|
||||
|
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assert "context length exceeded" in str(excinfo.value).lower()
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assert "8192 tokens" in str(excinfo.value)
|
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|
||||
# Test streaming response
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llm = LLM(model="gpt-4", stream=True)
|
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with patch("litellm.completion") as mock_completion:
|
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model="gpt-4",
|
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llm_provider="openai"
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)
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with pytest.raises(LLMContextLengthExceededException) as excinfo:
|
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llm.call("This is a test message")
|
||||
|
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assert "context length exceeded" in str(excinfo.value).lower()
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assert "8192 tokens" in str(excinfo.value)
|
||||
|
||||
|
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@pytest.mark.vcr(filter_headers=["authorization"])
|
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@pytest.fixture
|
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|
||||
Reference in New Issue
Block a user