andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2026-01-27 17:18:13 +00:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

Updating docs

Browse Source
This commit is contained in:
João Moura
2024-04-04 13:25:04 -03:00
parent fcffc4a898
commit a7f007f475
23 changed files with 337 additions and 360 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
docs/how-to/LLM-Connections.md
View File

@@ -5,7 +5,7 @@ description: Comprehensive guide on integrating CrewAI with various Large Langua
## Connect CrewAI to LLMs
!!! note "Default LLM"
By default, CrewAI uses OpenAI's GPT-4 model for language processing. However, you can configure your agents to use a different model or API. This guide will show you how to connect your agents to different LLMs through environment variables and direct instantiation.
By default, CrewAI uses OpenAI's GPT-4 model for language processing. You can configure your agents to use a different model or API. This guide shows how to connect your agents to various LLMs through environment variables and direct instantiation.
CrewAI offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
@@ -16,15 +16,16 @@ The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. He
- `role`: Defines the agent's role within the solution.
- `goal`: Specifies the agent's objective.
- `backstory`: Provides a background story to the agent.
- `llm`: Indicates the Large Language Model the agent uses.
- `function_calling_llm` *Optinal*: Will turn the ReAct crewAI agent into a function calling agent.
- `llm`: The language model that will run the agent. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
- `function_calling_llm`: The language model that will handle the tool calling for this agent, overriding the crew function_calling_llm. Optional.
- `max_iter`: Maximum number of iterations for an agent to execute a task, default is 15.
- `memory`: Enables the agent to retain information during the execution.
- `max_rpm`: Sets the maximum number of requests per minute.
- `verbose`: Enables detailed logging of the agent's execution.
- `memory`: Enables the agent to retain information during and a across executions. Default is `False`.
- `max_rpm`: Maximum number of requests per minute the agent's execution should respect. Optional.
- `verbose`: Enables detailed logging of the agent's execution. Default is `False`.
- `allow_delegation`: Allows the agent to delegate tasks to other agents, default is `True`.
- `tools`: Specifies the tools available to the agent for task execution.
- `step_callback`: Provides a callback function to be executed after each step.
- `tools`: Specifies the tools available to the agent for task execution. Optional.
- `step_callback`: Provides a callback function to be executed after each step. Optional.
- `cache`: Determines whether the agent should use a cache for tool usage. Default is `True`.
```python
# Required
@@ -35,7 +36,8 @@ example_agent = Agent(
role='Local Expert',
goal='Provide insights about the city',
backstory="A knowledgeable local guide.",
verbose=True
verbose=True,
memory=True
)
```
@@ -51,7 +53,7 @@ OPENAI_API_KEY=''
```
## HuggingFace Integration
There are a couple different ways you can use HuggingFace to host your LLM.
There are a couple of different ways you can use HuggingFace to host your LLM.
### Your own HuggingFace endpoint
```python