chore: merge origin/main into mintlify/af82bda4 and resolve conflicts
This commit is contained in:
mintlify[bot]
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Configurations
|
||||
description: "Reference for Mem0 embedder configuration in Python and TypeScript, covering provider selection, model names, API keys, and dimension settings."
|
||||
title: "Embedder Configuration Reference"
|
||||
sidebarTitle: "Configurations"
|
||||
description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
|
||||
---
|
||||
|
||||
|
||||
@@ -97,4 +98,4 @@ Here's a comprehensive list of all parameters that can be used across different
|
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|
||||
## Supported Embedding Models
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|
||||
For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
|
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For detailed information on configuring specific embedders, please visit the [Embedding Models](./overview) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
|
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+3
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@@ -1,6 +1,7 @@
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---
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title: AWS Bedrock
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description: "Use AWS Bedrock as a Mem0 embedder with Amazon Titan or Cohere models, boto3 or aws-sdk auth, and configurable vector output dimensions."
|
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title: "AWS Bedrock as Embedding Provider"
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sidebarTitle: "AWS Bedrock"
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||||
description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
|
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---
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|
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To use AWS Bedrock embedding models, you need the appropriate AWS credentials and permissions. Python uses `boto3`, and TypeScript uses `@aws-sdk/client-bedrock-runtime`.
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+3
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@@ -1,6 +1,7 @@
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---
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title: Azure OpenAI
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description: "Set up Azure OpenAI embeddings in Mem0 with deployment name, endpoint, API version, and optional Azure Identity role-based authentication."
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title: "Azure OpenAI as Embedding Provider"
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sidebarTitle: "Azure OpenAI"
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description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
|
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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 Portal.
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+3
-2
@@ -1,6 +1,7 @@
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---
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title: Google AI
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description: "Use Google AI Gemini embedding models in Mem0 with the GOOGLE_API_KEY, including gemini-embedding-001 and configurable output dimensionality."
|
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title: "Google AI as Embedding Provider"
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sidebarTitle: "Google AI"
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description: "Configure Google AI as an embedding provider in Mem0 using Gemini models and the GOOGLE_API_KEY variable."
|
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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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@@ -99,6 +99,7 @@ Here are the parameters available for configuring the Hugging Face embedder:
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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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| `api_key` | API key for the endpoint; falls back to the `HUGGINGFACE_API_KEY` env var. Only used on the `huggingface_base_url` path | `"hf"` |
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</Tab>
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<Tab title="TypeScript">
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| Parameter | Description | Default Value |
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@@ -1,6 +1,7 @@
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---
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title: LangChain
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description: "Plug LangChain embedding classes into Mem0 to reuse OpenAI, Cohere, HuggingFace, or Ollama embedding models through a single unified interface."
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title: "LangChain as Embedding Provider"
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sidebarTitle: "LangChain"
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description: "Use LangChain as an embedding provider in Mem0 to access a wide range of models through a unified interface."
|
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---
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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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@@ -1,6 +1,7 @@
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---
|
||||
title: "LM Studio"
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description: "Run LM Studio locally as a Mem0 embedder using GGUF models like nomic-embed-text through the built-in OpenAI-compatible HTTP server."
|
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title: "LM Studio as Embedding Provider"
|
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sidebarTitle: "LM Studio"
|
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description: "Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text."
|
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---
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You can use embedding models from LM Studio to run Mem0 locally.
|
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@@ -1,6 +1,7 @@
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---
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||||
title: "Ollama"
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description: "Serve Ollama embedding models like nomic-embed-text or mxbai-embed-large inside Mem0 for private, offline vector generation on your own machine."
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title: "Ollama as Embedding Provider"
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sidebarTitle: "Ollama"
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||||
description: "Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models."
|
||||
---
|
||||
You can use embedding models from Ollama to run Mem0 locally.
|
||||
|
||||
|
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@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: OpenAI
|
||||
description: "Use OpenAI embedding models such as text-embedding-3-small and text-embedding-3-large inside Mem0 with the OPENAI_API_KEY environment variable."
|
||||
title: "OpenAI as Embedding Provider"
|
||||
sidebarTitle: "OpenAI"
|
||||
description: "Configure OpenAI as an embedding provider in Mem0 using models like text-embedding-3-large for vector generation."
|
||||
---
|
||||
|
||||
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).
|
||||
|
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@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Together
|
||||
description: "Use Together AI in Mem0 with the multilingual-e5-large-instruct embedding model at 1024 dimensions for cross-language semantic search."
|
||||
title: "Together AI as Embedding Provider"
|
||||
sidebarTitle: "Together"
|
||||
description: "Configure Together AI as an embedding provider in Mem0 with support for 1024-dimensional embedding models."
|
||||
---
|
||||
|
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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.ai/settings/projects/~current/api-keys).
|
||||
|
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@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Overview
|
||||
description: "Browse Mem0 embedder providers including OpenAI, Azure, Ollama, Hugging Face, Vertex AI, Bedrock, Together, LM Studio, FastEmbed, and LangChain."
|
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title: "Embedding Providers Overview"
|
||||
sidebarTitle: Overview
|
||||
description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
|
||||
---
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|
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Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
|
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@@ -15,15 +16,15 @@ See the list of supported embedders below.
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<CardGroup cols={4}>
|
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<Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/embedders/models/openai"></Card>
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||||
<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure_openai"></Card>
|
||||
<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure-openai"></Card>
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<Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/embedders/models/ollama"></Card>
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<Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/embedders/models/huggingface"></Card>
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<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google_AI"></Card>
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<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google-ai"></Card>
|
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<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/embedders/models/vertexai"></Card>
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<Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/embedders/models/together"></Card>
|
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<Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/embedders/models/lmstudio"></Card>
|
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<Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/embedders/models/langchain"></Card>
|
||||
<Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws_bedrock"></Card>
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||||
<Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws-bedrock"></Card>
|
||||
<Card title="FastEmbed" icon="/images/provider-icons/qdrant.svg" href="/components/embedders/models/fastembed"></Card>
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||||
</CardGroup>
|
||||
|
||||
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@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Configurations
|
||||
description: "Reference for Mem0 LLM configuration keys in Python and TypeScript, covering provider precedence, environment variables, and per-provider parameters."
|
||||
title: "LLM Configuration Reference"
|
||||
sidebarTitle: "Configurations"
|
||||
description: "Reference for LLM configuration options in Mem0 for Python and TypeScript, including value precedence rules."
|
||||
---
|
||||
|
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## How to define configurations?
|
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@@ -133,4 +134,4 @@ Here's a comprehensive list of all parameters that can be used across different
|
||||
|
||||
## Supported LLMs
|
||||
|
||||
For detailed information on configuring specific LLMs, please visit the [LLMs](./models) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.
|
||||
For detailed information on configuring specific LLMs, please visit the [LLMs](./overview) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.
|
||||
|
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---
|
||||
title: AWS Bedrock
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||||
description: "Run Claude and other Bedrock LLMs in Mem0 using boto3 or the AWS SDK Converse API, with IAM credentials and cross-region inference profile support."
|
||||
title: "AWS Bedrock as LLM Provider"
|
||||
sidebarTitle: "AWS Bedrock"
|
||||
description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authentication and Claude model support."
|
||||
---
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||||
|
||||
### Setup
|
||||
@@ -78,6 +79,38 @@ await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } }
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The TypeScript provider calls the Bedrock [Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html), a single uniform interface across the current Bedrock model families. Streaming and `InvokeModel`-only models are not supported yet.
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</Note>
|
||||
|
||||
### Application inference profiles
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||||
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||||
Bedrock resolves the model family from the model identifier. An application inference profile ARN ends in an opaque ID, so there is nothing to resolve from. Set `provider_override` (Python) / `providerOverride` (TypeScript) when your model is one:
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<CodeGroup>
|
||||
```python Python
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config = {
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"llm": {
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"provider": "aws_bedrock",
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"config": {
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"model": "arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/abc123xyz",
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"provider_override": "anthropic",
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}
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}
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}
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```
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||||
```typescript TypeScript
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const config = {
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llm: {
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provider: 'aws_bedrock',
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config: {
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||||
model: 'arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/abc123xyz',
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providerOverride: 'anthropic',
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},
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},
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};
|
||||
```
|
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</CodeGroup>
|
||||
|
||||
Without it, initialization raises `Unknown provider in model` (Python: `ValueError`; TypeScript: `Error`). Plain model IDs and cross-region inference profiles such as `us.anthropic.claude-sonnet-4-20250514-v1:0` still resolve automatically and need no override.
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### Config
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||||
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All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).
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---
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||||
title: Azure OpenAI
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||||
description: "Use Azure OpenAI deployments as a Mem0 LLM with API key or Azure Identity auth, including the azure_openai_structured provider for structured output."
|
||||
title: "Azure OpenAI as LLM Provider"
|
||||
sidebarTitle: "Azure OpenAI"
|
||||
description: "Configure Azure OpenAI as an LLM provider in Mem0 with Azure Identity authentication and deployment settings."
|
||||
---
|
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<Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>
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@@ -3,7 +3,7 @@ title: DeepSeek
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description: "Use DeepSeek chat models such as deepseek-chat as a Mem0 LLM with the DEEPSEEK_API_KEY and an optional DEEPSEEK_API_BASE custom endpoint URL."
|
||||
---
|
||||
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||||
To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to "https://api.deepseek.com").
|
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To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to `https://api.deepseek.com`).
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|
||||
## Usage
|
||||
|
||||
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|
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---
|
||||
title: Google AI
|
||||
description: "Use Google Gemini models like gemini-2.0-flash-001 as a Mem0 LLM through the new google.genai SDK with the GOOGLE_API_KEY environment variable."
|
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title: "Google AI as LLM Provider"
|
||||
sidebarTitle: "Google AI"
|
||||
description: "Configure Google Gemini as an LLM provider in Mem0 using the google.genai SDK and GOOGLE_API_KEY variable."
|
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---
|
||||
|
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To use the Gemini model, set the `GOOGLE_API_KEY` environment variable. You can obtain the Google/Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
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@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: LangChain
|
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description: "Wire LangChain ChatOpenAI, ChatAnthropic, and other chat model classes into Mem0 as the LLM provider to reuse existing LangChain integrations."
|
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title: "LangChain as LLM Provider"
|
||||
sidebarTitle: "LangChain"
|
||||
description: "Use LangChain as an LLM provider in Mem0 to integrate with various chat models through a unified interface."
|
||||
---
|
||||
|
||||
|
||||
|
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@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: LM Studio
|
||||
description: "Run local GGUF models in Mem0 via LM Studio server on localhost:1234 using its OpenAI-compatible API, with optional lmstudio_response_format schema."
|
||||
title: "LM Studio as LLM Provider"
|
||||
sidebarTitle: "LM Studio"
|
||||
description: "Configure LM Studio as an LLM provider in Mem0 for running local language models via an OpenAI-compatible API."
|
||||
---
|
||||
|
||||
To use LM Studio with Mem0, you'll need to have LM Studio running locally with its server enabled. LM Studio provides a way to run local LLMs with an OpenAI-compatible API.
|
||||
@@ -76,7 +77,7 @@ m.add(messages, user_id="alice123", metadata={"category": "movies"})
|
||||
To use LM Studio, you need to:
|
||||
1. Download and install [LM Studio](https://lmstudio.ai/)
|
||||
2. Start a local server from the "Server" tab
|
||||
3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually http://localhost:1234/v1)
|
||||
3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually `http://localhost:1234/v1`)
|
||||
</Note>
|
||||
|
||||
## Config
|
||||
|
||||
@@ -3,7 +3,7 @@ title: MiniMax
|
||||
description: "Use MiniMax chat models like MiniMax-M2.7 as a Mem0 LLM with the MINIMAX_API_KEY and an optional MINIMAX_API_BASE custom endpoint override."
|
||||
---
|
||||
|
||||
To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to "https://api.minimax.io/v1").
|
||||
To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to `https://api.minimax.io/v1`).
|
||||
|
||||
## Usage
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Ollama
|
||||
description: "Run local Ollama LLMs such as mixtral:8x7b or llama3.1:8b as a Mem0 provider with tool calling support and a configurable Ollama server URL."
|
||||
title: "Ollama as LLM Provider"
|
||||
sidebarTitle: "Ollama"
|
||||
description: "Configure Ollama as an LLM provider in Mem0 for running local language models with tool-calling support."
|
||||
---
|
||||
|
||||
You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool calling.
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: OpenAI
|
||||
description: "Use OpenAI GPT models like gpt-5-mini as a Mem0 LLM with OPENAI_API_KEY, plus optional Openrouter routing and the openai_structured output mode."
|
||||
title: "OpenAI as LLM Provider"
|
||||
sidebarTitle: "OpenAI"
|
||||
description: "Configure OpenAI as an LLM provider in Mem0 with support for GPT models and Openrouter compatibility."
|
||||
---
|
||||
|
||||
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).
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Together
|
||||
description: "Use Together AI hosted models such as MiniMaxAI/MiniMax-M3 as a Mem0 LLM with the TOGETHER_API_KEY and an optional TOGETHER_API_BASE endpoint."
|
||||
title: "Together AI as LLM Provider"
|
||||
sidebarTitle: "Together"
|
||||
description: "Configure Together AI as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
|
||||
---
|
||||
|
||||
To use Together LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from their [Account settings page](https://api.together.ai/settings/projects/~current/api-keys).
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: xAI
|
||||
description: "Use xAI Grok models such as grok-4.3 as a Mem0 LLM with the XAI_API_KEY, plus an optional XAI_API_BASE for custom endpoint routing."
|
||||
title: "xAI Grok as LLM Provider"
|
||||
sidebarTitle: "xAI"
|
||||
description: "Configure xAI Grok models as an LLM provider in Mem0 with API key setup and usage examples."
|
||||
---
|
||||
|
||||
[xAI](https://x.ai/) is a new AI company founded by Elon Musk that develops large language models, including Grok. Grok is trained on real-time data from X (formerly Twitter) and aims to provide accurate, up-to-date responses with a touch of wit and humor.
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Overview
|
||||
description: "Compare Mem0 LLM providers including OpenAI, Anthropic, Groq, Ollama, Together, Mistral, Gemini, Bedrock, DeepSeek, xAI, Sarvam, and LangChain."
|
||||
title: "LLM Providers Overview"
|
||||
sidebarTitle: Overview
|
||||
description: "Overview of all supported LLM providers in Mem0, including OpenAI, Anthropic, Groq, Ollama, and more."
|
||||
---
|
||||
|
||||
Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
|
||||
@@ -22,14 +23,14 @@ See the list of supported LLMs below.
|
||||
<CardGroup cols={4}>
|
||||
<Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/llms/models/openai" />
|
||||
<Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/llms/models/ollama" />
|
||||
<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/llms/models/azure_openai" />
|
||||
<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/llms/models/azure-openai" />
|
||||
<Card title="Anthropic" icon="/images/provider-icons/anthropic.svg" href="/components/llms/models/anthropic" />
|
||||
<Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/llms/models/together" />
|
||||
<Card title="Groq" icon="/images/provider-icons/groq.svg" href="/components/llms/models/groq" />
|
||||
<Card title="Litellm" icon="shuffle" href="/components/llms/models/litellm" />
|
||||
<Card title="Mistral AI" icon="/images/provider-icons/mistral-color.svg" href="/components/llms/models/mistral_AI" />
|
||||
<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/llms/models/google_AI" />
|
||||
<Card title="AWS bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/llms/models/aws_bedrock" />
|
||||
<Card title="Mistral AI" icon="/images/provider-icons/mistral-color.svg" href="/components/llms/models/mistral-ai" />
|
||||
<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/llms/models/google-ai" />
|
||||
<Card title="AWS bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/llms/models/aws-bedrock" />
|
||||
<Card title="DeepSeek" icon="/images/provider-icons/deepseek-color.svg" href="/components/llms/models/deepseek" />
|
||||
<Card title="MiniMax" icon="/images/provider-icons/minimax-color.svg" href="/components/llms/models/minimax" />
|
||||
<Card title="xAI" icon="/images/provider-icons/xai.svg" href="/components/llms/models/xAI" />
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Overview
|
||||
description: 'Compare Mem0 reranker providers including Cohere, Hugging Face, Sentence Transformers, and LLM-based rerankers to boost search relevance for agents.'
|
||||
title: "Reranker Providers Overview"
|
||||
sidebarTitle: "Overview"
|
||||
description: 'Pick the right reranker path to boost Mem0 search relevance.'
|
||||
---
|
||||
|
||||
Mem0 rerankers rescore vector search hits so your agents surface the most relevant memories. Use this hub to decide when reranking helps, configure a provider, and fine-tune performance.
|
||||
@@ -13,10 +14,10 @@ Reranking trades extra latency for better precision. Start once you have baselin
|
||||
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Cohere" icon="/images/provider-icons/cohere.svg" href="/components/rerankers/models/cohere" />
|
||||
<Card title="Sentence Transformers" icon="vector-square" href="/components/rerankers/models/sentence_transformer" />
|
||||
<Card title="Sentence Transformers" icon="vector-square" href="/components/rerankers/models/sentence-transformer" />
|
||||
<Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/rerankers/models/huggingface" />
|
||||
<Card title="LLM Reranker" icon="wand-magic-sparkles" href="/components/rerankers/models/llm_reranker" />
|
||||
<Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero_entropy" />
|
||||
<Card title="LLM Reranker" icon="wand-magic-sparkles" href="/components/rerankers/models/llm-reranker" />
|
||||
<Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero-entropy" />
|
||||
</CardGroup>
|
||||
|
||||
<Note>
|
||||
@@ -54,13 +55,13 @@ All five rerankers are available in both the Python and the [TypeScript](/open-s
|
||||
title="Zero Entropy Guide"
|
||||
description="Adopt the managed neural reranker for production workloads."
|
||||
icon="sparkles"
|
||||
href="/components/rerankers/models/zero_entropy"
|
||||
href="/components/rerankers/models/zero-entropy"
|
||||
/>
|
||||
<Card
|
||||
title="Sentence Transformers"
|
||||
description="Keep reranking on-device with cross-encoder models."
|
||||
icon="microchip"
|
||||
href="/components/rerankers/models/sentence_transformer"
|
||||
href="/components/rerankers/models/sentence-transformer"
|
||||
/>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Configurations
|
||||
description: "Reference for Mem0 vector store configuration, including provider selection, connection settings, and shared options across Qdrant, Chroma, and pgvector."
|
||||
title: "Vector Store Configuration Reference"
|
||||
sidebarTitle: "Configurations"
|
||||
description: "Reference for vector database configuration options in Mem0, including provider selection and connection settings."
|
||||
---
|
||||
|
||||
## How to define configurations?
|
||||
@@ -123,10 +124,10 @@ Here's a comprehensive list of all parameters that can be used across different
|
||||
|
||||
Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:
|
||||
|
||||
1. Identify the vector database you want to use from [supported vector databases](./dbs).
|
||||
1. Identify the vector database you want to use from [supported vector databases](./overview).
|
||||
2. Refer to the `Config` section in the respective vector database's documentation.
|
||||
3. Include only the relevant parameters for your chosen database in the `config` dictionary.
|
||||
|
||||
## Supported Vector Databases
|
||||
|
||||
For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
|
||||
For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./overview) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: LangChain
|
||||
description: "Use LangChain as a unified vector store provider in Mem0 to access many vector databases like Chroma, FAISS, and Pinecone through one API."
|
||||
title: "LangChain as Vector Store Provider"
|
||||
sidebarTitle: "LangChain"
|
||||
description: "Use LangChain as a unified vector store provider in Mem0 to access multiple vector databases through one interface."
|
||||
---
|
||||
|
||||
Mem0 supports LangChain as a provider for vector store integration. LangChain provides a unified interface to various vector databases, making it easy to integrate different vector store providers through a consistent API.
|
||||
|
||||
@@ -3,17 +3,25 @@ title: "Oracle AI Vector Search"
|
||||
description: "Use Oracle Database AI Vector Search as a vector store in Mem0 with the native VECTOR type to combine semantic memory search and relational queries."
|
||||
---
|
||||
|
||||
{/* Copyright (c) 2026, Oracle and/or its affiliates. */}
|
||||
|
||||
[Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) stores embeddings in an Oracle table using the native `VECTOR` data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.
|
||||
|
||||
### Requirements
|
||||
|
||||
- Oracle Database 23.4 or later, with a user that can create tables and vector indexes
|
||||
- The `python-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
|
||||
- The `python-oracledb` or `node-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
|
||||
|
||||
```bash
|
||||
pip install oracledb
|
||||
<CodeGroup>
|
||||
```bash Python
|
||||
pip install mem0ai
|
||||
```
|
||||
|
||||
```bash TypeScript
|
||||
npm install oracledb
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
### Usage
|
||||
|
||||
<CodeGroup>
|
||||
@@ -47,11 +55,58 @@ messages = [
|
||||
]
|
||||
m.add(messages, user_id="alice", metadata={"category": "movies"})
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const config = {
|
||||
vectorStore: {
|
||||
provider: "oracledb",
|
||||
config: {
|
||||
collectionName: "mem0",
|
||||
embeddingModelDims: 1536,
|
||||
connectionParams: {
|
||||
user: "mem0_user",
|
||||
password: "your-password",
|
||||
connectString: "localhost:1521/FREEPDB1",
|
||||
},
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
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 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: "alice",
|
||||
metadata: { category: "movies" },
|
||||
});
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:
|
||||
To reuse a connection or pool you already manage, pass it as `client` instead of the connection parameters:
|
||||
|
||||
```python
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
import oracledb
|
||||
|
||||
pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")
|
||||
@@ -64,33 +119,54 @@ config = {
|
||||
}
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import oracledb from "oracledb";
|
||||
|
||||
const pool = await oracledb.createPool({
|
||||
user: "mem0_user",
|
||||
password: "your-password",
|
||||
connectString: "localhost:1521/FREEPDB1",
|
||||
});
|
||||
|
||||
const config = {
|
||||
vectorStore: {
|
||||
provider: "oracledb",
|
||||
config: { client: pool },
|
||||
},
|
||||
};
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring Oracle AI Vector Search:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `connection_params` | Connection settings passed to `python-oracledb`, such as `user`, `password` and `dsn`. See the [connection handling guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html). | `None` |
|
||||
| `use_connection_pool` | Create a connection pool from `connection_params` instead of a single connection | `True` |
|
||||
| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` |
|
||||
| `collection_name` | Name of the Oracle table that stores vectors and payloads | `mem0` |
|
||||
| `embedding_model_dims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
|
||||
| `distance_metric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
|
||||
| `do_create_index` | Whether to create a vector index on the collection | `True` |
|
||||
| `index_type` | Vector index type: `HNSW` or `IVF` | `HNSW` |
|
||||
| `index_name` | Name of the vector index | `<collection_name>_VEC_IDX` |
|
||||
| `index_parameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
|
||||
| `index_accuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
|
||||
Provide either `connection_params`/`connectionParams` or an existing connection or pool as `client`.
|
||||
|
||||
| Python | TypeScript | Description | Default Value |
|
||||
| --- | --- | --- | --- |
|
||||
| `connection_params` | `connectionParams` | Connection settings passed to the Oracle driver, such as `user`, `password` and `dsn` (`connectString` in TypeScript). Required unless `client` is provided. See the [Python](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html) or [Node.js](https://node-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html) connection handling guide. | `None` |
|
||||
| `use_connection_pool` | `useConnectionPool` | Create a connection pool from the connection parameters instead of a single connection | `True` |
|
||||
| `client` | `client` | An existing Oracle connection or pool to use instead of building one from the connection parameters. Required unless connection parameters are provided. | `None` |
|
||||
| `collection_name` | `collectionName` | Name of the Oracle table that stores vectors and payloads | `mem0` |
|
||||
| `embedding_model_dims` | `embeddingModelDims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
|
||||
| `distance_metric` | `distanceMetric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
|
||||
| `do_create_index` | `doCreateIndex` | Whether to create a vector index on the collection | `True` |
|
||||
| `index_type` | `indexType` | Vector index type: `HNSW` or `IVF` | `HNSW` |
|
||||
| `index_name` | `indexName` | Name of the vector index | `<collection_name>_VEC_IDX` |
|
||||
| `index_parameters` | `indexParameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
|
||||
| `index_accuracy` | `indexAccuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
|
||||
|
||||
<Note>
|
||||
When you pass a pre-built `client`, Mem0 uses it as-is and ignores `connection_params` and `use_connection_pool`. Mem0 does not close a client it did not create.
|
||||
When you pass a pre-built `client`, Mem0 uses it as-is and ignores the connection parameters and pooling options. Mem0 does not close a client it did not create.
|
||||
</Note>
|
||||
|
||||
### Vector indexes
|
||||
|
||||
Set the index type with `index_type` and tune it with `index_parameters`:
|
||||
|
||||
```python
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
config = {
|
||||
"vector_store": {
|
||||
"provider": "oracledb",
|
||||
@@ -104,6 +180,25 @@ config = {
|
||||
}
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
const config = {
|
||||
vectorStore: {
|
||||
provider: "oracledb",
|
||||
config: {
|
||||
connectionParams: {
|
||||
user: "mem0_user",
|
||||
password: "your-password",
|
||||
connectString: "localhost:1521/FREEPDB1",
|
||||
},
|
||||
indexType: "HNSW",
|
||||
indexParameters: { neighbors: 32, efconstruction: 200 },
|
||||
indexAccuracy: 95,
|
||||
},
|
||||
},
|
||||
};
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
For the full list of supported options, see the Oracle [`CREATE VECTOR INDEX`](https://docs.oracle.com/en/database/oracle/oracle-database/26/sqlrf/create-vector-index.html) reference.
|
||||
|
||||
### Search scores
|
||||
@@ -121,14 +216,29 @@ Filters run against the JSON `payload` column and support:
|
||||
| Comparison | `{"score": {"gte": 0.5}}`, also `eq`, `ne`, `gt`, `lt`, `lte` |
|
||||
| Membership | `{"category": {"in": ["movies", "books"]}}`, also `nin` |
|
||||
| String matching | `{"title": {"contains": "sci-fi"}}`, also `icontains` for case-insensitive |
|
||||
| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}` |
|
||||
| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}`, also `$and`, `$or`, `$not` |
|
||||
|
||||
Multiple fields at the top level are combined with `AND`:
|
||||
|
||||
```python
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
m.search(
|
||||
"movie recommendations",
|
||||
user_id="alice",
|
||||
filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
|
||||
filters={
|
||||
"user_id": "alice",
|
||||
"category": {"in": ["movies", "books"]},
|
||||
"rating": {"gte": 4},
|
||||
},
|
||||
)
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
await memory.search("movie recommendations", {
|
||||
filters: {
|
||||
user_id: "alice",
|
||||
category: { in: ["movies", "books"] },
|
||||
rating: { gte: 4 },
|
||||
},
|
||||
});
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
@@ -102,7 +102,7 @@ Here are the parameters available for configuring Upstash Vector:
|
||||
| `url` | URL for the Upstash Vector index | `None` |
|
||||
| `token` | Token for the Upstash Vector index | `None` |
|
||||
| `client` | An `upstash_vector.Index` instance | `None` |
|
||||
| `collection_name` | The default namespace used | `""` |
|
||||
| `collection_name` | The default namespace used | `"mem0"` |
|
||||
| `enable_embeddings` | Whether to use Upstash embeddings | `False` |
|
||||
|
||||
<Note>
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Overview
|
||||
description: "Explore Mem0 vector database integrations including Qdrant, Chroma, Pinecone, pgvector, Milvus, Weaviate, Redis, and Oracle for memory storage."
|
||||
title: "Vector Store Providers Overview"
|
||||
sidebarTitle: "Overview"
|
||||
description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
|
||||
---
|
||||
|
||||
Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
|
||||
@@ -10,7 +11,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
|
||||
See the list of supported vector databases below.
|
||||
|
||||
<Note>
|
||||
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
|
||||
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Oracle AI Vector Search, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
|
||||
</Note>
|
||||
|
||||
<CardGroup cols={3}>
|
||||
@@ -28,12 +29,12 @@ See the list of supported vector databases below.
|
||||
<Card title="Elasticsearch" icon="/images/provider-icons/elasticsearch.svg" href="/components/vectordbs/dbs/elasticsearch"></Card>
|
||||
<Card title="OpenSearch" icon="/images/provider-icons/opensearch.svg" href="/components/vectordbs/dbs/opensearch"></Card>
|
||||
<Card title="Supabase" icon="/images/provider-icons/supabase.svg" href="/components/vectordbs/dbs/supabase"></Card>
|
||||
<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex_ai"></Card>
|
||||
<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex-ai"></Card>
|
||||
<Card title="Weaviate" icon="circle-nodes" href="/components/vectordbs/dbs/weaviate"></Card>
|
||||
<Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
|
||||
<Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
|
||||
<Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
|
||||
<Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune_analytics"></Card>
|
||||
<Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3-vectors"></Card>
|
||||
<Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune-analytics"></Card>
|
||||
<Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
|
||||
<Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
|
||||
</CardGroup>
|
||||
|
||||
Reference in New Issue
Block a user