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Add configs to llm docs (#1707)

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Dev Khant
2024-08-15 21:13:00 +05:30
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29
docs/components/llms/models/anthropic.mdx Normal file
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To use anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "litellm",
"config": {
"model": "claude-3-opus-20240229",
"temperature": 0.1,
"max_tokens": 2000,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `anthropic` config are present in [Master List of All Params in Config](../config).

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### Setup
- Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
### Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ['AWS_REGION'] = 'us-east-1'
os.environ["AWS_ACCESS_KEY"] = "xx"
os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"
config = {
"llm": {
"provider": "aws_bedrock",
"config": {
"model": "arn:aws:bedrock:us-east-1:123456789012:model/your-model-name",
"temperature": 0.2,
"max_tokens": 1500,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
### Config
All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).

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docs/components/llms/models/azure_openai.mdx Normal file
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To use Azure OpenAI models, you have to set the `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, and `OPENAI_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure](https://azure.microsoft.com/).
## Usage
```python
import os
from mem0 import Memory
os.environ["AZURE_OPENAI_API_KEY"] = "your-api-key"
os.environ["AZURE_OPENAI_ENDPOINT"] = "your-api-base-url"
os.environ["OPENAI_API_VERSION"] = "version-to-use"
config = {
"llm": {
"provider": "azure_openai",
"config": {
"model": "your-deployment-name",
"temperature": 0.1,
"max_tokens": 2000,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `azure_openai` config are present in [Master List of All Params in Config](../config).

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docs/components/llms/models/google_AI.mdx Normal file
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To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["GEMINI_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "litellm",
"config": {
"model": "gemini/gemini-pro",
"temperature": 0.2,
"max_tokens": 1500,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

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docs/components/llms/models/groq.mdx Normal file
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[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["GROQ_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "groq",
"config": {
"model": "mixtral-8x7b-32768",
"temperature": 0.1,
"max_tokens": 1000,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `groq` config are present in [Master List of All Params in Config](../config).

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docs/components/llms/models/litellm.mdx Normal file
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[Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models]((https://litellm.vercel.app/docs/providers)) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "litellm",
"config": {
"model": "gpt-3.5-turbo",
"temperature": 0.2,
"max_tokens": 1500,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

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docs/components/llms/models/mistral_AI.mdx Normal file
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To use mistral's models, please Obtain the Mistral AI api key from their [console](https://console.mistral.ai/). Set the `MISTRAL_API_KEY` environment variable to use the model as given below in the example.
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["MISTRAL_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "litellm",
"config": {
"model": "open-mixtral-8x7b",
"temperature": 0.1,
"max_tokens": 2000,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

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You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool support.
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # for embedder
config = {
"llm": {
"provider": "ollama",
"config": {
"model": "mixtral:8x7b",
"temperature": 0.1,
"max_tokens": 2000,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `ollama` config are present in [Master List of All Params in Config](../config).

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To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4o",
"temperature": 0.2,
"max_tokens": 1500,
}
}
}
# Use Openrouter by passing it's api key
# os.environ["OPENROUTER_API_KEY"] = "your-api-key"
# config = {
# "llm": {
# "provider": "openai",
# "config": {
# "model": "meta-llama/llama-3.1-70b-instruct",
# }
# }
# }
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `openai` config are present in [Master List of All Params in Config](../config).

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To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["TOGETHER_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "togetherai",
"config": {
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"temperature": 0.2,
"max_tokens": 1500,
}
}
}
m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```
## Config
All available parameters for the `togetherai` config are present in [Master List of All Params in Config](../config).