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João Moura
2024-06-20 02:19:20 -03:00
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docs/how-to/LLM-Connections.md
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@@ -16,16 +16,20 @@ 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. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
- `function_calling_llm` *Optional*: Will turn the ReAct crewAI agent into a function calling agent.
- `max_iter`: Maximum number of iterations for an agent to execute a task, default is 15.
- `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`.
- `cache` *Optional*: Determines whether the agent should use a cache for tool usage. Default is `True`.
- `max_rpm` *Optional*: Maximum number of requests per minute the agent's execution should respect. Optional.
- `verbose` *Optional*: Enables detailed logging of the agent's execution. Default is `False`.
- `allow_delegation` *Optional*: Allows the agent to delegate tasks to other agents, default is `True`.
- `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`.
- `max_iter` *Optional*: Maximum number of iterations for an agent to execute a task, default is 25.
- `max_execution_time` *Optional*: Maximum execution time for an agent to execute a task. Optional.
- `step_callback` *Optional*: Provides a callback function to be executed after each step. Optional.
- `llm` *Optional*: Indicates the Large Language Model the agent uses. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
- `function_calling_llm` *Optional* : Will turn the ReAct CrewAI agent into a function-calling agent.
- `callbacks` *Optional*: A list of callback functions from the LangChain library that are triggered during the agent's execution process.
- `system_template` *Optional*: Optional string to define the system format for the agent.
- `prompt_template` *Optional*: Optional string to define the prompt format for the agent.
- `response_template` *Optional*: Optional string to define the response format for the agent.
```python
# Required
@@ -36,13 +40,12 @@ example_agent = Agent(
role='Local Expert',
goal='Provide insights about the city',
backstory="A knowledgeable local guide.",
verbose=True,
memory=True
verbose=True
)
```
## Ollama Integration
Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below.
Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below.
### Setting Up Ollama
- **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
@@ -53,8 +56,8 @@ OPENAI_API_KEY=''
```
## Ollama Integration (ex. for using Llama 2 locally)
1. [Download Ollama](https://ollama.com/download).
2. After setting up the Ollama, Pull the Llama2 by typing following lines into the terminal ```ollama pull llama2```.
1. [Download Ollama](https://ollama.com/download).
2. After setting up the Ollama, Pull the Llama2 by typing following lines into the terminal ```ollama pull llama2```.
3. Create a ModelFile similar the one below in your project directory.
```
FROM llama2
@@ -70,7 +73,7 @@ PARAMETER stop Result
SYSTEM """"""
```
4. Create a script to get the base model, which in our case is llama2, and create a model on top of that with ModelFile above. PS: this will be ".sh" file.
4. Create a script to get the base model, which in our case is llama2, and create a model on top of that with ModelFile above. PS: this will be ".sh" file.
```
#!/bin/zsh
@@ -84,9 +87,9 @@ ollama pull $model_name
#create the model file
ollama create $custom_model_name -f ./Llama2ModelFile
```
5. Go into the directory where the script file and ModelFile is located and run the script.
6. Enjoy your free Llama2 model that powered up by excellent agents from crewai.
```
5. Go into the directory where the script file and ModelFile is located and run the script.
6. Enjoy your free Llama2 model that is powered up by excellent agents from CrewAI.
```python
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI
import os
@@ -102,7 +105,8 @@ general_agent = Agent(role = "Math Professor",
allow_delegation = False,
verbose = True,
llm = llm)
task = Task (description="""what is 3 + 5""",
task = Task(description="""what is 3 + 5""",
agent = general_agent,
expected_output="A numerical answer.")
@@ -162,7 +166,7 @@ OPENAI_API_KEY=NA
```
#### LM Studio
Launch [LM Studio](https://lmstudio.ai) and go to the Server tab. Then select a model from the dropdown menu then wait for it to load. Once it's loaded, click the green Start Server button and use the URL, port, and API key that's shown (you can modify them). Below is an example of the default settings as of LM Studio 0.2.19:
Launch [LM Studio](https://lmstudio.ai) and go to the Server tab. Then select a model from the dropdown menu and wait for it to load. Once it's loaded, click the green Start Server button and use the URL, port, and API key that's shown (you can modify them). Below is an example of the default settings as of LM Studio 0.2.19:
```sh
OPENAI_API_BASE="http://localhost:1234/v1"
OPENAI_API_KEY="lm-studio"
@@ -176,15 +180,16 @@ OPENAI_MODEL_NAME="mistral-small"
```
### Solar
```sh
```python
from langchain_community.chat_models.solar import SolarChat
# Initialize language model
os.environ["SOLAR_API_KEY"] = "your-solar-api-key"
llm = SolarChat(max_tokens=1024)
Free developer API key available here: https://console.upstage.ai/services/solar
Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
# Free developer API key available here: https://console.upstage.ai/services/solar
# Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
```
### text-gen-web-ui
```sh
OPENAI_API_BASE=http://localhost:5000/v1
@@ -193,17 +198,16 @@ OPENAI_API_KEY=NA
```
### Cohere
```sh
```python
from langchain_cohere import ChatCohere
# Initialize language model
os.environ["COHERE_API_KEY"] = "your-cohere-api-key"
llm = ChatCohere()
Free developer API key available here: https://cohere.com/
Langchain Documentation: https://python.langchain.com/docs/integrations/chat/cohere
# Free developer API key available here: https://cohere.com/
# Langchain Documentation: https://python.langchain.com/docs/integrations/chat/cohere
```
### Azure Open AI Configuration
For Azure OpenAI API integration, set the following environment variables:
```sh
@@ -235,4 +239,4 @@ azure_agent = Agent(
```
## Conclusion
Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.
Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.