Improve SEO metadata across documentation pages (#4447)

2026-03-20 02:52:39 -07:00
parent 401754ca65
commit f05e50d940
134 changed files with 197 additions and 14 deletions
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 ---
 title: Configurations
+description: "Reference for vector database configuration options in Mem0, including provider selection and connection settings."
 ---

 ## How to define configurations?
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 ---
 title: Azure AI Search
+description: "Use Azure AI Search as a vector store in Mem0 for managed vector search with service name and API key setup."
 ---

 [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.
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 ---
 title: Azure MySQL
+description: "Use Azure Database for MySQL as a vector store in Mem0 with JSON-based vector storage for semantic search."
 ---

 [Azure Database for MySQL](https://azure.microsoft.com/products/mysql) is a fully managed relational database service that provides enterprise-grade reliability and security. It supports JSON-based vector storage for semantic search capabilities in AI applications.
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 ---
 title: Baidu VectorDB (Mochow)
+description: "Use Baidu Mochow as an enterprise vector database in Mem0 for high-performance vector storage and retrieval."
 ---

 [Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.
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 ---
 title: Apache Cassandra
+description: "Use Apache Cassandra as a distributed vector store in Mem0 with semantic search over large-scale datasets."
 ---

 [Apache Cassandra](https://cassandra.apache.org/) is a highly scalable, distributed NoSQL database designed for handling large amounts of data across many commodity servers with no single point of failure. It supports vector storage for semantic search capabilities in AI applications and can scale to massive datasets with linear performance improvements.
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+---
+title: "Chroma"
+description: "Use Chroma as a vector database in Mem0 for local or cloud-hosted vector storage with built-in embedding support."
+---
 [Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed. It supports both local deployment and cloud hosting through ChromaDB Cloud.

 ### Usage
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+---
+title: "Databricks"
+description: "Use Databricks Vector Search as a serverless vector store in Mem0 with auto-updating indexes from Delta tables."
+---
 [Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html) is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.

 ### Usage
@@ -1,3 +1,7 @@
+---
+title: "Elasticsearch"
+description: "Use Elasticsearch as a vector database in Mem0 for distributed vector search using dense vectors and k-NN queries."
+---
 [Elasticsearch](https://www.elastic.co/) is a distributed, RESTful search and analytics engine that can efficiently store and search vector data using dense vectors and k-NN search.

 ### Installation
@@ -1,3 +1,7 @@
+---
+title: "FAISS"
+description: "Use Facebook FAISS as a high-performance vector store in Mem0, optimized for memory usage and fast similarity search."
+---
 [FAISS](https://github.com/facebookresearch/faiss) is a library for efficient similarity search and clustering of dense vectors. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data. FAISS is optimized for memory usage and search speed, making it an excellent choice for production environments.

 ### Usage
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 ---
 title: 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.
@@ -1,3 +1,7 @@
+---
+title: "Milvus"
+description: "Use Milvus as an open-source vector database in Mem0, scalable from local development to production workloads."
+---
 [Milvus](https://milvus.io/) is an open-source vector database that suits AI applications of every size, from running a demo chatbot in a Jupyter notebook to building web-scale search that serves billions of users.

 ### Usage
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+---
+title: "MongoDB"
+description: "Use MongoDB as a vector database in Mem0 with built-in vector search for high-dimensional similarity queries."
+---
 # MongoDB

 [MongoDB](https://www.mongodb.com/) is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.
@@ -1,3 +1,7 @@
+---
+title: "Neptune Analytics"
+description: "Use AWS Neptune Analytics as a vector store in Mem0, combining graph analytics with vector search capabilities."
+---
 # Neptune Analytics Vector Store

 [Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html/) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.
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+---
+title: "OpenSearch"
+description: "Use OpenSearch as a vector database in Mem0 with k-NN search support via AWS OpenSearch Service serverless collections."
+---
 [OpenSearch](https://opensearch.org/) is an enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.

 ### Installation
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+---
+title: "pgvector"
+description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
+---
 [pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.

 ### Usage
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+---
+title: "Pinecone"
+description: "Use Pinecone as a fully managed vector database in Mem0 with serverless deployment and namespace-based multi-tenancy."
+---
 [Pinecone](https://www.pinecone.io/) is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It's particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.

 > **New**: Pinecone integration now supports custom namespaces! Use the `namespace` parameter to logically separate data within the same index. This is especially useful for multi-tenant or multi-user applications.
@@ -1,3 +1,7 @@
+---
+title: "Qdrant"
+description: "Use Qdrant as an open-source vector search engine in Mem0 for high-performance similarity search at scale."
+---
 [Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.

 ### Usage
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+---
+title: "Redis"
+description: "Use Redis as a real-time vector database in Mem0 for fast vector search using Redis Stack and redisvl."
+---
 [Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.

 ### Installation
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 ---
 title: Amazon S3 Vectors
+description: "Use Amazon S3 Vectors as a cost-optimized vector storage service in Mem0 with AWS credential authentication."
 ---

 [Amazon S3 Vectors](https://aws.amazon.com/s3/features/vectors/) is a purpose-built, cost-optimized vector storage and query service for semantic search and AI applications. It provides S3-level elasticity and durability with sub-second query performance.
@@ -1,3 +1,7 @@
+---
+title: "Supabase"
+description: "Use Supabase as a vector store in Mem0, powered by PostgreSQL and pgvector with HNSW indexing support."
+---
 [Supabase](https://supabase.com/) is an open-source Firebase alternative that provides a PostgreSQL database with pgvector extension for vector similarity search. It offers a powerful and scalable solution for storing and querying vector embeddings.

 Create a [Supabase](https://supabase.com/dashboard/projects) account and project, then get your connection string from Project Settings > Database. See the [docs](https://supabase.github.io/vecs/hosting/) for details.
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+---
+title: "Upstash Vector"
+description: "Use Upstash Vector as a serverless vector database in Mem0 with optional built-in embedding models."
+---
 [Upstash Vector](https://upstash.com/docs/vector) is a serverless vector database with built-in embedding models.

 ### Usage with Upstash embeddings
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+---
+title: "Valkey"
+description: "Use Valkey as an open-source vector store in Mem0 for high-performance key-value storage with vector search."
+---
 # Valkey Vector Store

 [Valkey](https://valkey.io/) is an open source (BSD) high-performance key/value datastore that supports a variety of workloads and rich datastructures including vector search.
@@ -1,3 +1,7 @@
+---
+title: "Cloudflare Vectorize"
+description: "Use Cloudflare Vectorize as a vector database in Mem0 for building AI-powered applications at the edge."
+---
 [Cloudflare Vectorize](https://developers.cloudflare.com/vectorize/) is a vector database offering from Cloudflare, allowing you to build AI-powered applications with vector embeddings.

 ### Usage
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 ---
 title: Vertex AI Vector Search
+description: "Use Google Cloud Vertex AI Vector Search as a managed vector store in Mem0 with endpoint and index configuration."
 ---


@@ -1,3 +1,7 @@
+---
+title: "Weaviate"
+description: "Use Weaviate as an open-source vector search engine in Mem0 for storing and retrieving vector embeddings."
+---
 [Weaviate](https://weaviate.io/) is an open-source vector search engine. It allows efficient storage and retrieval of high-dimensional vector embeddings, enabling powerful search and retrieval capabilities.


@@ -1,5 +1,6 @@
 ---
 title: Overview
+description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, 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.