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updating LLM docs

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João Moura
2024-11-10 11:36:03 -03:00
parent 5bb1a3b257
commit 80dcbc7e46
1 changed files with 124 additions and 8 deletions
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docs/concepts/llms.mdx
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@@ -25,7 +25,100 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
- `OPENAI_API_BASE`
- `OPENAI_API_KEY`
### 2. Custom LLM Objects
### 2. Updating YAML files
You can update the `agents.yml` file to refer to the LLM you want to use:
```yaml Code
researcher:
role: Research Specialist
goal: Conduct comprehensive research and analysis to gather relevant information,
synthesize findings, and produce well-documented insights.
backstory: A dedicated research professional with years of experience in academic
investigation, literature review, and data analysis, known for thorough and
methodical approaches to complex research questions.
verbose: true
llm: openai/gpt-4o
# llm: azure/gpt-4o-mini
# llm: gemini/gemini-pro
# llm: anthropic/claude-3-5-sonnet-20240620
# llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
# llm: mistral/mistral-large-latest
# llm: ollama/llama3:70b
# llm: groq/llama-3.2-90b-vision-preview
# llm: watsonx/meta-llama/llama-3-1-70b-instruct
# ...
```
Keep in mind that you will need to set certain ENV vars depending on the model you are
using to account for the credentials or set a custom LLM object like described below.
Here are some of the required ENV vars for some of the LLM integrations:
<AccordionGroup>
<Accordion title="OpenAI">
```python Code
OPENAI_API_KEY=<your-api-key>
OPENAI_API_BASE=<optional-custom-base-url>
OPENAI_MODEL_NAME=<openai-model-name>
OPENAI_ORGANIZATION=<your-org-id> # OPTIONAL
OPENAI_API_BASE=<openaiai-api-base> # OPTIONAL
```
</Accordion>
<Accordion title="Anthropic">
```python Code
ANTHROPIC_API_KEY=<your-api-key>
```
</Accordion>
<Accordion title="Google">
```python Code
GEMINI_API_KEY=<your-api-key>
```
</Accordion>
<Accordion title="Azure">
```python Code
AZURE_API_KEY=<your-api-key> # "my-azure-api-key"
AZURE_API_BASE=<your-resource-url> # "https://example-endpoint.openai.azure.com"
AZURE_API_VERSION=<api-version> # "2023-05-15"
AZURE_AD_TOKEN=<your-azure-ad-token> # Optional
AZURE_API_TYPE=<your-azure-api-type> # Optional
```
</Accordion>
<Accordion title="AWS Bedrock">
```python Code
AWS_ACCESS_KEY_ID=<your-access-key>
AWS_SECRET_ACCESS_KEY=<your-secret-key>
AWS_DEFAULT_REGION=<your-region>
```
</Accordion>
<Accordion title="Mistral">
```python Code
MISTRAL_API_KEY=<your-api-key>
```
</Accordion>
<Accordion title="Groq">
```python Code
GROQ_API_KEY=<your-api-key>
```
</Accordion>
<Accordion title="IBM watsonx.ai">
```python Code
WATSONX_URL=<your-url> # (required) Base URL of your WatsonX instance
WATSONX_APIKEY=<your-apikey> # (required) IBM cloud API key
WATSONX_TOKEN=<your-token> # (required) IAM auth token (alternative to APIKEY)
WATSONX_PROJECT_ID=<your-project-id> # (optional) Project ID of your WatsonX instance
WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id> # (optional) ID of deployment space for deployed models
```
</Accordion>
</AccordionGroup>
### 3. Custom LLM Objects
Pass a custom LLM implementation or object from another library.
@@ -102,7 +195,7 @@ When configuring an LLM for your agent, you have access to a wide range of param
These are examples of how to configure LLMs for your agent.
<AccordionGroup>
<AccordionGroup>
<Accordion title="OpenAI">
```python Code
@@ -133,10 +226,10 @@ These are examples of how to configure LLMs for your agent.
model="cerebras/llama-3.1-70b",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="Ollama (Local LLMs)">
CrewAI supports using Ollama for running open-source models locally:
@@ -150,7 +243,7 @@ These are examples of how to configure LLMs for your agent.
agent = Agent(
llm=LLM(
model="ollama/llama3.1",
model="ollama/llama3.1",
base_url="http://localhost:11434"
),
...
@@ -164,7 +257,7 @@ These are examples of how to configure LLMs for your agent.
from crewai import LLM
llm = LLM(
model="groq/llama3-8b-8192",
model="groq/llama3-8b-8192",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
@@ -189,7 +282,7 @@ These are examples of how to configure LLMs for your agent.
from crewai import LLM
llm = LLM(
model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
@@ -224,6 +317,29 @@ These are examples of how to configure LLMs for your agent.
</Accordion>
<Accordion title="IBM watsonx.ai">
You can use IBM Watson by seeting the following ENV vars:
```python Code
WATSONX_URL=<your-url>
WATSONX_APIKEY=<your-apikey>
WATSONX_PROJECT_ID=<your-project-id>
```
You can then define your agents llms by updating the `agents.yml`
```yaml Code
researcher:
role: Research Specialist
goal: Conduct comprehensive research and analysis to gather relevant information,
synthesize findings, and produce well-documented insights.
backstory: A dedicated research professional with years of experience in academic
investigation, literature review, and data analysis, known for thorough and
methodical approaches to complex research questions.
verbose: true
llm: watsonx/meta-llama/llama-3-1-70b-instruct
```
You can also set up agents more dynamically as a base level LLM instance, like bellow:
```python Code
from crewai import LLM
@@ -247,7 +363,7 @@ These are examples of how to configure LLMs for your agent.
api_key="your-api-key-here",
base_url="your_api_endpoint"
)
agent = Agent(llm=llm, ...)
agent = Agent(llm=llm, ...)
```
</Accordion>
</AccordionGroup>