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Merge branch 'main' of github.com:crewAIInc/crewAI into lorenze/agent-query-for-knowledge

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lorenzejay
2025-05-06 15:32:26 -07:00
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31
docs/concepts/llms.mdx
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@@ -27,23 +27,19 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
</Card>
</CardGroup>
## Setting Up Your LLM
## Setting up your LLM
There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.
<Tabs>
<Tab title="1. Environment Variables">
The simplest way to get started. Set these variables in your environment:
The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.
```bash
# Required: Your API key for authentication
OPENAI_API_KEY=<your-api-key>
```bash .env
MODEL=model-id # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...
# Optional: Default model selection
OPENAI_MODEL_NAME=gpt-4o-mini # Default if not set
# Optional: Organization ID (if applicable)
OPENAI_ORGANIZATION_ID=<your-org-id>
# Be sure to set your API keys here too. See the Provider
# section below.
```
<Warning>
@@ -53,13 +49,13 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
<Tab title="2. YAML Configuration">
Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
```yaml
```yaml agents.yaml {6}
researcher:
role: Research Specialist
goal: Conduct comprehensive research and analysis
backstory: A dedicated research professional with years of experience
verbose: true
llm: openai/gpt-4o-mini # your model here
llm: provider/model-id # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
# (see provider configuration examples below for more)
```
@@ -74,15 +70,15 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
<Tab title="3. Direct Code">
For maximum flexibility, configure LLMs directly in your Python code:
```python
```python {4,8}
from crewai import LLM
# Basic configuration
llm = LLM(model="gpt-4")
llm = LLM(model="model-id-here") # gpt-4o, gemini-2.0-flash, anthropic/claude...
# Advanced configuration with detailed parameters
llm = LLM(
model="gpt-4o-mini",
model="model-id-here", # gpt-4o, gemini-2.0-flash, anthropic/claude...
temperature=0.7, # Higher for more creative outputs
timeout=120, # Seconds to wait for response
max_tokens=4000, # Maximum length of response
@@ -110,7 +106,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
## Provider Configuration Examples
CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
@@ -383,7 +378,7 @@ In this section, you'll find detailed examples that help you select, configure,
| microsoft/phi-3-medium-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3-medium-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3.5-mini-instruct | 128K tokens | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
| microsoft/phi-3.5-moe-instruct | 128K tokens | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
| microsoft/phi-3.5-moe-instruct | 128K tokens | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
| microsoft/kosmos-2 | 1,024 tokens | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
| microsoft/phi-3-vision-128k-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
| microsoft/phi-3.5-vision-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |

4
docs/installation.mdx
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@@ -71,6 +71,10 @@ If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on
```
</Warning>
<Warning>
If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install (Visual Studio Build Tools)[https://visualstudio.microsoft.com/downloads/] with *Desktop development with C++*.
</Warning>
- To verify that `crewai` is installed, run:
```shell
uv tool list

2
docs/tools/docxsearchtool.mdx
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@@ -22,7 +22,7 @@ streamlining the process of finding specific information within large document c
Install the crewai_tools package by running the following command in your terminal:
```shell
pip install 'crewai[tools]'
uv pip install docx2txt 'crewai[tools]'
```
## Example

20
docs/tools/ragtool.mdx
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@@ -143,12 +143,30 @@ config = {
"config": {
"model": "text-embedding-ada-002"
}
},
"vectordb": {
"provider": "elasticsearch",
"config": {
"collection_name": "my-collection",
"cloud_id": "deployment-name:xxxx",
"api_key": "your-key",
"verify_certs": False
}
},
"chunker": {
"chunk_size": 400,
"chunk_overlap": 100,
"length_function": "len",
"min_chunk_size": 0
}
}
rag_tool = RagTool(config=config, summarize=True)
```
## Conclusion
The internal RAG tool utilizes the Embedchain adapter, allowing you to pass any configuration options that are supported by Embedchain.
You can refer to the [Embedchain documentation](https://docs.embedchain.ai/components/introduction) for details.
Make sure to review the configuration options available in the .yaml file.
## Conclusion
The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.

2
src/crewai/cli/install_crew.py
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@@ -4,7 +4,7 @@ import click
# Be mindful about changing this.
# on some enviorments we don't use this command but instead uv sync directly
# on some environments we don't use this command but instead uv sync directly
# so if you expect this to support more things you will need to replicate it there
# ask @joaomdmoura if you are unsure
def install_crew(proxy_options: list[str]) -> None:

2
tests/cassettes/test_lite_agent_structured_output.yaml
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@@ -16,7 +16,7 @@ interactions:
answer MUST contain all the information requested in the following format: {\n \"summary\":
str,\n \"confidence\": int\n}\n\nIMPORTANT: Ensure the final output does not
include any code block markers like ```json or ```python."}, {"role": "user",
"content": "What is the population of Tokyo? Return your strucutred output in
"content": "What is the population of Tokyo? Return your structured output in
JSON format with the following fields: summary, confidence"}], "model": "gpt-4o-mini",
"stop": []}'
headers: