Aspen theme for 1.x (#3473)
This commit is contained in:
Parshva Daftari
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---
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title: AWS Bedrock
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---
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To use AWS Bedrock embedding models, you need to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.
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### Setup
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- Ensure you have model access from the [AWS Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess)
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- Authenticate the boto3 client using a method described in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
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- Set up environment variables for authentication:
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```bash
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export AWS_REGION=us-east-1
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export AWS_ACCESS_KEY_ID=your-access-key
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export AWS_SECRET_ACCESS_KEY=your-secret-key
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```
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### Usage
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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# For LLM if needed
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os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
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# AWS credentials
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os.environ["AWS_REGION"] = "us-west-2"
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os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key"
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os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-key"
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config = {
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"embedder": {
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"provider": "aws_bedrock",
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"config": {
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"model": "amazon.titan-embed-text-v2:0"
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="alice")
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```
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</CodeGroup>
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### Config
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Here are the parameters available for configuring AWS Bedrock embedder:
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<Tabs>
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<Tab title="Python">
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
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</Tab>
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</Tabs>
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@@ -0,0 +1,125 @@
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---
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title: Azure OpenAI
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---
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To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure.
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### Usage
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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os.environ["EMBEDDING_AZURE_OPENAI_API_KEY"] = "your-api-key"
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os.environ["EMBEDDING_AZURE_DEPLOYMENT"] = "your-deployment-name"
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os.environ["EMBEDDING_AZURE_ENDPOINT"] = "your-api-base-url"
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os.environ["EMBEDDING_AZURE_API_VERSION"] = "version-to-use"
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os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
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config = {
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"embedder": {
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"provider": "azure_openai",
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"config": {
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"model": "text-embedding-3-large"
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"azure_kwargs": {
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"api_version": "",
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"azure_deployment": "",
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"azure_endpoint": "",
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"api_key": "",
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"default_headers": {
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"CustomHeader": "your-custom-header",
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}
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}
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="john")
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```
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```typescript TypeScript
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import { Memory } from 'mem0ai/oss';
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const config = {
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embedder: {
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provider: "azure_openai",
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config: {
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model: "text-embedding-3-large",
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modelProperties: {
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endpoint: "your-api-base-url",
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deployment: "your-deployment-name",
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apiVersion: "version-to-use",
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}
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}
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}
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}
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const memory = new Memory(config);
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const messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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await memory.add(messages, { userId: "john" });
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```
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</CodeGroup>
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As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with [Azure OpenAI role-based security](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control).
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<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>
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Below is a sample configuration for using Mem0 with Azure OpenAI and Azure Identity:
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```python
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import os
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from mem0 import Memory
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# You can set the values directly in the config dictionary or use environment variables
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os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
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os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
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os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"
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config = {
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"llm": {
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"provider": "azure_openai_structured",
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"config": {
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"model": "your-deployment-name",
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"temperature": 0.1,
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"max_tokens": 2000,
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"azure_kwargs": {
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"azure_deployment": "<your-deployment-name>",
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"api_version": "<version-to-use>",
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"azure_endpoint": "<your-api-base-url>",
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"default_headers": {
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"CustomHeader": "your-custom-header",
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}
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}
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}
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}
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}
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```
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Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/identity/azure-identity/TROUBLESHOOTING.md#troubleshoot-environmentcredential-authentication-issues) for setting up an Azure Identity credential.
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### Config
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Here are the parameters available for configuring Azure OpenAI embedder:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `model` | The name of the embedding model to use | `text-embedding-3-small` |
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| `embedding_dims` | Dimensions of the embedding model | `1536` |
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| `azure_kwargs` | The Azure OpenAI configs | `config_keys` |
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@@ -0,0 +1,69 @@
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---
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title: Google AI
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---
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To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
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### Usage
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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os.environ["GOOGLE_API_KEY"] = "key"
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os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
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config = {
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"embedder": {
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"provider": "gemini",
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"config": {
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"model": "models/text-embedding-004",
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="john")
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```
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```typescript TypeScript
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import { Memory } from 'mem0ai/oss';
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const config = {
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embedder: {
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provider: 'google',
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config: {
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apiKey: process.env.GOOGLE_API_KEY || '',
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model: 'text-embedding-004',
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// The output dimensionality is fixed at 768 for Google AI embeddings
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},
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},
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};
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const memory = new Memory(config);
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const messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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await memory.add(messages, { userId: "john" });
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```
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</CodeGroup>
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### Config
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Here are the parameters available for configuring Gemini embedder:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `model` | The name of the embedding model to use | `models/text-embedding-004` |
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| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
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| `api_key` | The Google API key | `None` |
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@@ -0,0 +1,75 @@
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---
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title: Hugging Face
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---
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You can use embedding models from Huggingface to run Mem0 locally.
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### Usage
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```python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
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config = {
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"embedder": {
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"provider": "huggingface",
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"config": {
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"model": "multi-qa-MiniLM-L6-cos-v1"
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="john")
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```
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### Using Text Embeddings Inference (TEI)
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You can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings:
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```python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
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# Using HuggingFace Text Embeddings Inference API
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config = {
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"embedder": {
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"provider": "huggingface",
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"config": {
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"huggingface_base_url": "http://localhost:3000/v1"
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}
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}
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}
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m = Memory.from_config(config)
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m.add("This text will be embedded using the TEI service.", user_id="john")
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```
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To run the TEI service, you can use Docker:
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```bash
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docker run -d -p 3000:80 -v huggingfacetei:/data --platform linux/amd64 \
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ghcr.io/huggingface/text-embeddings-inference:cpu-1.6 \
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--model-id BAAI/bge-small-en-v1.5
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```
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### Config
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Here are the parameters available for configuring Huggingface embedder:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `model` | The name of the model to use | `multi-qa-MiniLM-L6-cos-v1` |
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| `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` |
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| `model_kwargs` | Additional arguments for the model | `None` |
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| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |
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@@ -0,0 +1,196 @@
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---
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title: LangChain
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---
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Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.
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For a complete list of available embedding models supported by LangChain, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
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## Usage
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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from langchain_openai import OpenAIEmbeddings
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# Set necessary environment variables for your chosen LangChain provider
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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# Initialize a LangChain embeddings model directly
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openai_embeddings = OpenAIEmbeddings(
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model="text-embedding-3-small",
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dimensions=1536
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)
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# Pass the initialized model to the config
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config = {
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"embedder": {
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"provider": "langchain",
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"config": {
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"model": openai_embeddings
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
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]
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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|
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```typescript TypeScript
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import { Memory } from 'mem0ai/oss';
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import { OpenAIEmbeddings } from "@langchain/openai";
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// Initialize a LangChain embeddings model directly
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const openaiEmbeddings = new OpenAIEmbeddings({
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modelName: "text-embedding-3-small",
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dimensions: 1536,
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apiKey: process.env.OPENAI_API_KEY,
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});
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const config = {
|
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embedder: {
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provider: 'langchain',
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config: {
|
||||
model: openaiEmbeddings,
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},
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||||
},
|
||||
};
|
||||
|
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const memory = new Memory(config);
|
||||
const messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
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]
|
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await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
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||||
```
|
||||
</CodeGroup>
|
||||
|
||||
## Supported LangChain Embedding Providers
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LangChain supports a wide range of embedding providers, including:
|
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|
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- OpenAI (`OpenAIEmbeddings`)
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- Cohere (`CohereEmbeddings`)
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- Google (`VertexAIEmbeddings`)
|
||||
- Hugging Face (`HuggingFaceEmbeddings`)
|
||||
- Sentence Transformers (`HuggingFaceEmbeddings`)
|
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- Azure OpenAI (`AzureOpenAIEmbeddings`)
|
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- Ollama (`OllamaEmbeddings`)
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||||
- Together (`TogetherEmbeddings`)
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||||
- And many more
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||||
|
||||
You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available embedding providers, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
|
||||
|
||||
## Provider-Specific Configuration
|
||||
|
||||
When using LangChain as an embedder provider, you'll need to:
|
||||
|
||||
1. Set the appropriate environment variables for your chosen embedding provider
|
||||
2. Import and initialize the specific model class you want to use
|
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3. Pass the initialized model instance to the config
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|
||||
### Examples with Different Providers
|
||||
|
||||
<CodeGroup>
|
||||
#### HuggingFace Embeddings
|
||||
|
||||
```python Python
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||||
from langchain_huggingface import HuggingFaceEmbeddings
|
||||
|
||||
# Initialize a HuggingFace embeddings model
|
||||
hf_embeddings = HuggingFaceEmbeddings(
|
||||
model_name="BAAI/bge-small-en-v1.5",
|
||||
encode_kwargs={"normalize_embeddings": True}
|
||||
)
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "langchain",
|
||||
"config": {
|
||||
"model": hf_embeddings
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
import { HuggingFaceEmbeddings } from "@langchain/community/embeddings/hf";
|
||||
|
||||
// Initialize a HuggingFace embeddings model
|
||||
const hfEmbeddings = new HuggingFaceEmbeddings({
|
||||
modelName: "BAAI/bge-small-en-v1.5",
|
||||
encode: {
|
||||
normalize_embeddings: true,
|
||||
},
|
||||
});
|
||||
|
||||
const config = {
|
||||
embedder: {
|
||||
provider: 'langchain',
|
||||
config: {
|
||||
model: hfEmbeddings,
|
||||
},
|
||||
},
|
||||
};
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
<CodeGroup>
|
||||
#### Ollama Embeddings
|
||||
|
||||
```python Python
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
|
||||
# Initialize an Ollama embeddings model
|
||||
ollama_embeddings = OllamaEmbeddings(
|
||||
model="nomic-embed-text"
|
||||
)
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "langchain",
|
||||
"config": {
|
||||
"model": ollama_embeddings
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
|
||||
|
||||
// Initialize an Ollama embeddings model
|
||||
const ollamaEmbeddings = new OllamaEmbeddings({
|
||||
model: "nomic-embed-text",
|
||||
baseUrl: "http://localhost:11434", // Ollama server URL
|
||||
});
|
||||
|
||||
const config = {
|
||||
embedder: {
|
||||
provider: 'langchain',
|
||||
config: {
|
||||
model: ollamaEmbeddings,
|
||||
},
|
||||
},
|
||||
};
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
<Note>
|
||||
Make sure to install the necessary LangChain packages and any provider-specific dependencies.
|
||||
</Note>
|
||||
|
||||
## Config
|
||||
|
||||
All available parameters for the `langchain` embedder config are present in [Master List of All Params in Config](../config).
|
||||
@@ -0,0 +1,38 @@
|
||||
You can use embedding models from LM Studio to run Mem0 locally.
|
||||
|
||||
### Usage
|
||||
|
||||
```python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "lmstudio",
|
||||
"config": {
|
||||
"model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
m.add(messages, user_id="john")
|
||||
```
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring Ollama embedder:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `model` | The name of the OpenAI model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
|
||||
| `embedding_dims` | Dimensions of the embedding model | `1536` |
|
||||
| `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |
|
||||
@@ -0,0 +1,73 @@
|
||||
You can use embedding models from Ollama to run Mem0 locally.
|
||||
|
||||
### Usage
|
||||
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "ollama",
|
||||
"config": {
|
||||
"model": "mxbai-embed-large"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
m.add(messages, user_id="john")
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const config = {
|
||||
embedder: {
|
||||
provider: 'ollama',
|
||||
config: {
|
||||
model: 'nomic-embed-text:latest', // or any other Ollama embedding model
|
||||
url: 'http://localhost:11434', // Ollama server URL
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const memory = new Memory(config);
|
||||
const messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
await memory.add(messages, { userId: "john" });
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring Ollama embedder:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Python">
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `model` | The name of the Ollama model to use | `nomic-embed-text` |
|
||||
| `embedding_dims` | Dimensions of the embedding model | `512` |
|
||||
| `ollama_base_url` | Base URL for ollama connection | `None` |
|
||||
</Tab>
|
||||
<Tab title="TypeScript">
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `model` | The name of the Ollama model to use | `nomic-embed-text:latest` |
|
||||
| `url` | Base URL for Ollama server | `http://localhost:11434` |
|
||||
</Tab>
|
||||
</Tabs>
|
||||
@@ -0,0 +1,72 @@
|
||||
---
|
||||
title: OpenAI
|
||||
---
|
||||
|
||||
To use OpenAI embedding models, 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
|
||||
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "your_api_key"
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "text-embedding-3-large"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
m.add(messages, user_id="john")
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const config = {
|
||||
embedder: {
|
||||
provider: 'openai',
|
||||
config: {
|
||||
apiKey: 'your-openai-api-key',
|
||||
model: 'text-embedding-3-large',
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const memory = new Memory(config);
|
||||
await memory.add("I'm visiting Paris", { userId: "john" });
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring OpenAI embedder:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Python">
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `model` | The name of the embedding model to use | `text-embedding-3-small` |
|
||||
| `embedding_dims` | Dimensions of the embedding model | `1536` |
|
||||
| `api_key` | The OpenAI API key | `None` |
|
||||
</Tab>
|
||||
<Tab title="TypeScript">
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `model` | The name of the embedding model to use | `text-embedding-3-small` |
|
||||
| `embeddingDims` | Dimensions of the embedding model | `1536` |
|
||||
| `apiKey` | The OpenAI API key | `None` |
|
||||
</Tab>
|
||||
</Tabs>
|
||||
@@ -0,0 +1,45 @@
|
||||
---
|
||||
title: Together
|
||||
---
|
||||
|
||||
To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.xyz/settings/api-keys).
|
||||
|
||||
### Usage
|
||||
|
||||
<Note> The `embedding_model_dims` parameter for `vector_store` should be set to `768` for Together embedder. </Note>
|
||||
|
||||
```python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
os.environ["TOGETHER_API_KEY"] = "your_api_key"
|
||||
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "together",
|
||||
"config": {
|
||||
"model": "togethercomputer/m2-bert-80M-8k-retrieval"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
m.add(messages, user_id="john")
|
||||
```
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring Together embedder:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `model` | The name of the embedding model to use | `togethercomputer/m2-bert-80M-8k-retrieval` |
|
||||
| `embedding_dims` | Dimensions of the embedding model | `768` |
|
||||
| `api_key` | The Together API key | `None` |
|
||||
@@ -0,0 +1,55 @@
|
||||
### Vertex AI
|
||||
|
||||
To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).
|
||||
|
||||
### Usage
|
||||
|
||||
```python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
# Set the path to your Google Cloud credentials JSON file
|
||||
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/your/credentials.json"
|
||||
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "vertexai",
|
||||
"config": {
|
||||
"model": "text-embedding-004",
|
||||
"memory_add_embedding_type": "RETRIEVAL_DOCUMENT",
|
||||
"memory_update_embedding_type": "RETRIEVAL_DOCUMENT",
|
||||
"memory_search_embedding_type": "RETRIEVAL_QUERY"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
m.add(messages, user_id="john")
|
||||
```
|
||||
The embedding types can be one of the following:
|
||||
- SEMANTIC_SIMILARITY
|
||||
- CLASSIFICATION
|
||||
- CLUSTERING
|
||||
- RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION
|
||||
- CODE_RETRIEVAL_QUERY
|
||||
Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring the Vertex AI embedder:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
| ------------------------- | ------------------------------------------------ | -------------------- |
|
||||
| `model` | The name of the Vertex AI embedding model to use | `text-embedding-004` |
|
||||
| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file | `None` |
|
||||
| `embedding_dims` | Dimensions of the embedding model | `256` |
|
||||
| `memory_add_embedding_type` | The type of embedding to use for the add memory action | `RETRIEVAL_DOCUMENT` |
|
||||
| `memory_update_embedding_type` | The type of embedding to use for the update memory action | `RETRIEVAL_DOCUMENT` |
|
||||
| `memory_search_embedding_type` | The type of embedding to use for the search memory action | `RETRIEVAL_QUERY` |
|
||||
Reference in New Issue
Block a user