docs: improve SEO metadata across docs pages

2026-08-01 01:30:05 +00:00
parent d06ea1875c
commit 26520d4808
218 changed files with 201 additions and 232 deletions
@@ -1,6 +1,6 @@
 ---
 title: Configurations
-description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
+description: "Reference for Mem0 embedder configuration in Python and TypeScript, covering provider selection, model names, API keys, and dimension settings."
 ---


@@ -1,6 +1,6 @@
 ---
 title: AWS Bedrock
-description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
+description: "Use AWS Bedrock as a Mem0 embedder with Amazon Titan or Cohere models, boto3 or aws-sdk auth, and configurable vector output dimensions."
 ---

 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`.
@@ -1,6 +1,6 @@
 ---
 title: Azure OpenAI
-description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
+description: "Set up Azure OpenAI embeddings in Mem0 with deployment name, endpoint, API version, and optional Azure Identity role-based authentication."
 ---

 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.
@@ -1,6 +1,6 @@
 ---
 title: "FastEmbed"
-description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
+description: "Run FastEmbed inside Mem0 to generate ONNX-based text embeddings locally on CPU without API keys, using models like gte-large or bge-small."
 ---

 You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.
@@ -1,6 +1,6 @@
 ---
 title: Google AI
-description: "Configure Google AI as an embedding provider in Mem0 using Gemini models and the GOOGLE_API_KEY variable."
+description: "Use Google AI Gemini embedding models in Mem0 with the GOOGLE_API_KEY, including gemini-embedding-001 and configurable output dimensionality."
 ---

 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).
@@ -1,6 +1,6 @@
 ---
 title: Hugging Face
-description: "Configure Hugging Face as an embedding provider in Mem0 for local embedding generation with open-source models."
+description: "Use Hugging Face embeddings in Mem0 via local sentence-transformers or a Text Embeddings Inference (TEI) endpoint for fast, self-hosted vectors."
 ---

 You can use embedding models from Huggingface to run Mem0 locally.
@@ -1,6 +1,6 @@
 ---
 title: LangChain
-description: "Use LangChain as an embedding provider in Mem0 to access a wide range of models through a unified interface."
+description: "Plug LangChain embedding classes into Mem0 to reuse OpenAI, Cohere, HuggingFace, or Ollama embedding models through a single unified interface."
 ---

 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.
@@ -1,6 +1,6 @@
 ---
 title: "LM Studio"
-description: "Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text."
+description: "Run LM Studio locally as a Mem0 embedder using GGUF models like nomic-embed-text through the built-in OpenAI-compatible HTTP server."
 ---
 You can use embedding models from LM Studio to run Mem0 locally.

@@ -1,6 +1,6 @@
 ---
 title: "Ollama"
-description: "Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models."
+description: "Serve Ollama embedding models like nomic-embed-text or mxbai-embed-large inside Mem0 for private, offline vector generation on your own machine."
 ---
 You can use embedding models from Ollama to run Mem0 locally.

@@ -1,6 +1,6 @@
 ---
 title: OpenAI
-description: "Configure OpenAI as an embedding provider in Mem0 using models like text-embedding-3-large for vector generation."
+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."
 ---

 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).
@@ -1,6 +1,6 @@
 ---
 title: Together
-description: "Configure Together AI as an embedding provider in Mem0 with support for 1024-dimensional embedding models."
+description: "Use Together AI in Mem0 with the multilingual-e5-large-instruct embedding model at 1024 dimensions for cross-language semantic search."
 ---

 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).
@@ -1,6 +1,6 @@
 ---
 title: "Vertex AI"
-description: "Configure Google Cloud Vertex AI as an embedding provider in Mem0 with support for task-specific embedding types."
+description: "Use Google Cloud Vertex AI embeddings in Mem0 with ADC authentication, gemini-embedding-001, and task-specific types for add, update, and search."
 ---
 ### Vertex AI

@@ -1,6 +1,6 @@
 ---
 title: Overview
-description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
+description: "Browse Mem0 embedder providers including OpenAI, Azure, Ollama, Hugging Face, Vertex AI, Bedrock, Together, LM Studio, FastEmbed, and LangChain."
 ---

 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
@@ -1,6 +1,6 @@
 ---
 title: Configurations
-description: "Reference for LLM configuration options in Mem0 for Python and TypeScript, including value precedence rules."
+description: "Reference for Mem0 LLM configuration keys in Python and TypeScript, covering provider precedence, environment variables, and per-provider parameters."
 ---

 ## How to define configurations?
@@ -1,6 +1,6 @@
 ---
 title: Anthropic
-description: "Configure Anthropic Claude models as the LLM provider in Mem0 with API key setup and usage examples."
+description: "Use Anthropic Claude models such as claude-sonnet-4-6 as your Mem0 LLM with the ANTHROPIC_API_KEY, plus temperature and max token controls."
 ---


@@ -1,6 +1,6 @@
 ---
 title: AWS Bedrock
-description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authentication and Claude model support."
+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."
 ---

 ### Setup
@@ -1,6 +1,6 @@
 ---
 title: Azure OpenAI
-description: "Configure Azure OpenAI as an LLM provider in Mem0 with Azure Identity authentication and deployment settings."
+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."
 ---

 <Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>
@@ -1,6 +1,6 @@
 ---
 title: DeepSeek
-description: "Configure DeepSeek as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
+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."
 ---

 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").
@@ -1,6 +1,6 @@
 ---
 title: Google AI
-description: "Configure Google Gemini as an LLM provider in Mem0 using the google.genai SDK and GOOGLE_API_KEY variable."
+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."
 ---

 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).
@@ -1,6 +1,6 @@
 ---
 title: Groq
-description: "Configure Groq as an LLM provider in Mem0 for high-speed inference using LPU-powered language models."
+description: "Use Groq LPU-hosted models such as llama-3.3-70b-versatile as a Mem0 LLM for very low-latency inference with the GROQ_API_KEY environment variable."
 ---

 [Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
@@ -1,6 +1,6 @@
 ---
 title: LangChain
-description: "Use LangChain as an LLM provider in Mem0 to integrate with various chat models through a unified interface."
+description: "Wire LangChain ChatOpenAI, ChatAnthropic, and other chat model classes into Mem0 as the LLM provider to reuse existing LangChain integrations."
 ---


@@ -1,6 +1,6 @@
 ---
 title: "LiteLLM"
-description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language models through a unified interface."
+description: "Route Mem0 through LiteLLM to reach 100+ providers via one API in Python, or a LiteLLM proxy at LITELLM_API_BASE from the TypeScript SDK."
 ---
 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.

@@ -1,6 +1,6 @@
 ---
 title: LM Studio
-description: "Configure LM Studio as an LLM provider in Mem0 for running local language models via an OpenAI-compatible API."
+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."
 ---

 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.
@@ -1,6 +1,6 @@
 ---
 title: MiniMax
-description: "Configure MiniMax as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
+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").
@@ -1,6 +1,6 @@
 ---
 title: Mistral AI
-description: "Configure Mistral AI as an LLM provider in Mem0 using the litellm integration and Mixtral model family."
+description: "Use Mistral AI models such as open-mixtral-8x7b as a Mem0 LLM through the litellm provider with the MISTRAL_API_KEY environment variable."
 ---

 To use mistral's models, please obtain the Mistral AI api key from their [console](https://console.mistral.ai/). Set the `MISTRAL_API_KEY` environment variable to use the model as given below in the example.
@@ -1,6 +1,6 @@
 ---
 title: Ollama
-description: "Configure Ollama as an LLM provider in Mem0 for running local language models with tool-calling support."
+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."
 ---

 You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool calling.
@@ -1,6 +1,6 @@
 ---
 title: OpenAI
-description: "Configure OpenAI as an LLM provider in Mem0 with support for GPT models and Openrouter compatibility."
+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."
 ---

 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 +1,6 @@
 ---
 title: Sarvam AI
-description: "Configure Sarvam AI as an LLM provider in Mem0, specializing in Indian language support with the Sarvam-M model."
+description: "Use Sarvam AI models like sarvam-m as a Mem0 LLM for Indian language conversations, with reasoning_effort, frequency_penalty, and seed controls."
 ---

 **Sarvam AI** is an Indian AI company developing language models with a focus on Indian languages and cultural context. Their latest model **Sarvam-M** is designed to understand and generate content in multiple Indian languages while maintaining high performance in English.
@@ -1,6 +1,6 @@
 ---
 title: Together
-description: "Configure Together AI as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
+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."
 ---

 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 +1,6 @@
 ---
 title: vLLM
-description: "Configure vLLM as an LLM provider in Mem0 for high-performance local inference with GPU-optimized serving."
+description: "Serve local models like Qwen2.5-32B-Instruct through a vLLM server and connect Mem0 to it via VLLM_BASE_URL for fast, GPU-optimized inference."
 ---

 [vLLM](https://docs.vllm.ai/) is a high-performance inference engine for large language models that provides significant performance improvements for local inference. It's designed to maximize throughput and memory efficiency for serving LLMs.
@@ -1,6 +1,6 @@
 ---
 title: xAI
-description: "Configure xAI Grok models as an LLM provider in Mem0 with API key setup and usage examples."
+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."
 ---

 [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 +1,6 @@
 ---
 title: Overview
-description: "Overview of all supported LLM providers in Mem0, including OpenAI, Anthropic, Groq, Ollama, and more."
+description: "Compare Mem0 LLM providers including OpenAI, Anthropic, Groq, Ollama, Together, Mistral, Gemini, Bedrock, DeepSeek, xAI, Sarvam, and LangChain."
 ---

 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.
@@ -1,6 +1,6 @@
 ---
 title: Config
-description: "Reference for shared and provider-specific reranker configuration options in Mem0, including top_k and API key settings."
+description: "Reference for Mem0 reranker configuration covering Cohere, Zero Entropy, sentence-transformer, HuggingFace, and LLM-based providers with top_k limits."
 ---

 ## Common Configuration Parameters
@@ -1,6 +1,6 @@
 ---
 title: Custom Prompts
-description: "Customize the LLM reranker prompt template in Mem0 to control how search results are ranked and scored."
+description: "Write custom LLM reranker scoring prompts in Mem0 using query and document variables to tune relevance ranking for your specific domain or use case."
 ---

 When using LLM rerankers, you can customize the prompts used for ranking to better suit your specific use case and domain.
@@ -1,6 +1,6 @@
 ---
 title: Cohere
-description: "Configure Cohere as a reranker in Mem0 with support for English and multilingual reranking models."
+description: "Use Cohere rerank-v3.5 or older v3.0 models as a Mem0 reranker for English and multilingual search relevance with COHERE_API_KEY authentication."
 ---

 Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.
@@ -1,6 +1,6 @@
 ---
 title: Hugging Face Reranker
-description: 'Access thousands of reranking models from Hugging Face Hub'
+description: 'Access thousands of Hugging Face Hub reranking models in Mem0, including BAAI BGE cross-encoders, to boost memory search relevance and precision.'
 ---

 ## Overview
@@ -1,6 +1,6 @@
 ---
 title: LLM Reranker
-description: 'Use any language model as a reranker with custom prompts'
+description: 'Use any Mem0-supported LLM as a reranker with custom prompts to score and rank memories by query relevance for flexible, tunable retrieval.'
 ---

 ## Overview
@@ -1,6 +1,6 @@
 ---
 title: Sentence Transformer
-description: 'Local reranking with HuggingFace cross-encoder models'
+description: 'Run local reranking in Mem0 with Sentence Transformers cross-encoder models like ms-marco-MiniLM for privacy-focused, on-premises deployments.'
 ---

 Sentence Transformer reranker provides local reranking using HuggingFace cross-encoder models, perfect for privacy-focused deployments where you want to keep data on-premises.
@@ -1,6 +1,6 @@
 ---
 title: Zero Entropy
-description: "Configure Zero Entropy neural reranking models in Mem0 with zerank-1 and zerank-1-small support."
+description: "Configure Zero Entropy neural reranking in Mem0 with the zerank-1 and zerank-1-small models for fast, high-relevance search over stored memories."
 ---

 [Zero Entropy](https://www.zeroentropy.dev) provides neural reranking models that significantly improve search relevance with fast performance.
@@ -1,6 +1,6 @@
 ---
 title: Performance Optimization
-description: "Best practices for optimizing reranker performance in Mem0, covering candidate sizing, batching, and tuning."
+description: "Best practices for tuning reranker performance in Mem0, covering candidate set sizing, batching, model selection, and latency versus precision tradeoffs."
 ---

 Optimizing reranker performance is crucial for maintaining fast search response times while improving result quality. This guide covers best practices for different reranker types.
@@ -1,6 +1,6 @@
 ---
 title: Overview
-description: 'Pick the right reranker path to boost Mem0 search relevance.'
+description: 'Compare Mem0 reranker providers including Cohere, Hugging Face, Sentence Transformers, and LLM-based rerankers to boost search relevance for agents.'
 ---

 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.
@@ -1,6 +1,6 @@
 ---
 title: Configurations
-description: "Reference for vector database configuration options in Mem0, including provider selection and connection settings."
+description: "Reference for Mem0 vector store configuration, including provider selection, connection settings, and shared options across Qdrant, Chroma, and pgvector."
 ---

 ## How to define configurations?
@@ -1,6 +1,6 @@
 ---
 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."
+description: "Configure Azure AI Search as a managed vector store in Mem0 using your service name, API key, and index for secure, scalable memory retrieval."
 ---

 [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.
@@ -1,6 +1,6 @@
 ---
 title: Azure MySQL
-description: "Use Azure Database for MySQL as a vector store in Mem0 with JSON-based vector storage for semantic search."
+description: "Use Azure Database for MySQL as a vector store in Mem0 with JSON-based vector storage for semantic memory search on a managed MySQL backend."
 ---

 [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.
@@ -1,6 +1,6 @@
 ---
 title: Baidu VectorDB (Mochow)
-description: "Use Baidu Mochow as an enterprise vector database in Mem0 for high-performance vector storage and retrieval."
+description: "Use Baidu VectorDB (Mochow) as an enterprise-grade vector store in Mem0 for high-performance, distributed vector storage and semantic 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.
@@ -1,6 +1,6 @@
 ---
 title: Apache Cassandra
-description: "Use Apache Cassandra as a distributed vector store in Mem0 with semantic search over large-scale datasets."
+description: "Use Apache Cassandra as a distributed NoSQL vector store in Mem0 for scalable, fault-tolerant semantic memory 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.
@@ -1,6 +1,6 @@
 ---
 title: "Chroma"
-description: "Use Chroma as a vector database in Mem0 for local or cloud-hosted vector storage with built-in embedding support."
+description: "Use Chroma as an AI-native vector database in Mem0 for local or cloud-hosted embedding storage, quick prototyping, and simple LLM app development."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: "Databricks"
-description: "Use Databricks Vector Search as a serverless vector store in Mem0 with auto-updating indexes from Delta tables."
+description: "Use Databricks Vector Search as a serverless vector store in Mem0 with auto-updating indexes synced from Delta tables and Unity Catalog governance."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: "Elasticsearch"
-description: "Use Elasticsearch as a vector database in Mem0 for distributed vector search using dense vectors and k-NN queries."
+description: "Use Elasticsearch as a distributed vector database in Mem0 with dense vectors and k-NN queries for hybrid semantic and keyword memory search."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: "FAISS"
-description: "Use Facebook FAISS as a high-performance vector store in Mem0, optimized for memory usage and fast similarity search."
+description: "Use Meta FAISS as a high-performance local vector store in Mem0, optimized for memory usage and fast in-process similarity search over dense vectors."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: LangChain
-description: "Use LangChain as a unified vector store provider in Mem0 to access multiple vector databases through one interface."
+description: "Use LangChain as a unified vector store provider in Mem0 to access many vector databases like Chroma, FAISS, and Pinecone through one API."
 ---

 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.
@@ -1,6 +1,6 @@
 ---
 title: "Milvus"
-description: "Use Milvus as an open-source vector database in Mem0, scalable from local development to production workloads."
+description: "Use Milvus as an open-source vector database in Mem0, scaling from Jupyter notebook demos to production similarity search across billions of vectors."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: "MongoDB"
-description: "Use MongoDB as a vector database in Mem0 with built-in vector search for high-dimensional similarity queries."
+description: "Use MongoDB Atlas as a vector database in Mem0 with native vector search for high-dimensional similarity queries alongside your document data."
 ---

 # MongoDB
@@ -1,6 +1,6 @@
 ---
 title: "Neon"
-description: "Use Neon as a vector store in Mem0, powered by PostgreSQL and pgvector."
+description: "Use Neon serverless Postgres with the pgvector extension as a vector store in Mem0 for autoscaling, branchable memory storage with a Postgres URL."
 ---

 Use [Neon](https://neon.com/) as a vector store in Mem0, powered by PostgreSQL and the
@@ -1,6 +1,6 @@
 ---
 title: "Neptune Analytics"
-description: "Use AWS Neptune Analytics as a vector store in Mem0, combining graph analytics with vector search capabilities."
+description: "Use AWS Neptune Analytics as a memory-optimized vector store in Mem0, combining graph analytics with vector search for connected data insights."
 ---

 [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.
@@ -1,6 +1,6 @@
 ---
 title: "OpenSearch"
-description: "Use OpenSearch as a vector database in Mem0 with k-NN search support via AWS OpenSearch Service serverless collections."
+description: "Use OpenSearch as a vector database in Mem0 with k-NN search over dense embeddings, including 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.

@@ -1,6 +1,6 @@
 ---
 title: "Oracle AI Vector Search"
-description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries."
+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."
 ---

 [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.
@@ -1,6 +1,6 @@
 ---
 title: "pgvector"
-description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
+description: "Use the pgvector extension as a vector store in Mem0 for PostgreSQL-based similarity search with open-source simplicity and standard SQL tooling."
 ---

 [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.
@@ -1,6 +1,6 @@
 ---
 title: "Pinecone"
-description: "Use Pinecone as a fully managed vector database in Mem0 with serverless deployment and namespace-based multi-tenancy."
+description: "Use Pinecone as a fully managed vector database in Mem0 with serverless indexes and namespace-based multi-tenancy for low-latency semantic search."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: "Qdrant"
-description: "Use Qdrant as an open-source vector search engine in Mem0 for high-performance similarity search at scale."
+description: "Use Qdrant as an open-source vector search engine in Mem0 for high-performance, filterable similarity search at scale, self-hosted or on Qdrant Cloud."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: "Redis"
-description: "Use Redis as a real-time vector database in Mem0 for fast vector search using Redis Stack and redisvl."
+description: "Use Redis as a real-time vector database in Mem0 for fast in-memory similarity search using Redis Stack, the RediSearch module, and the redisvl client."
 ---
 [Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.

@@ -1,6 +1,6 @@
 ---
 title: Amazon S3 Vectors
-description: "Use Amazon S3 Vectors as a cost-optimized vector storage service in Mem0 with AWS credential authentication."
+description: "Use Amazon S3 Vectors as a cost-optimized vector storage service in Mem0 with AWS credential auth, sub-second queries, and S3-level durability."
 ---

 [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.
@@ -1,6 +1,6 @@
 ---
 title: "Supabase"
-description: "Use Supabase as a vector store in Mem0, powered by PostgreSQL and pgvector with HNSW indexing support."
+description: "Use Supabase as a vector store in Mem0, backed by PostgreSQL and the pgvector extension with HNSW indexing for scalable embedding search."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: "Turbopuffer"
-description: "Use Turbopuffer as a serverless vector database in Mem0 for low-latency search at scale with native metadata filtering."
+description: "Use Turbopuffer as a serverless vector database in Mem0 for low-latency search at scale with native metadata filtering and cost-effective storage."
 ---
 [Turbopuffer](https://turbopuffer.com) is a serverless vector database optimized for low-latency search at scale. It offers cost-effective vector storage with native metadata filtering.

@@ -1,6 +1,6 @@
 ---
 title: "Upstash Vector"
-description: "Use Upstash Vector as a serverless vector database in Mem0 with optional built-in embedding models."
+description: "Use Upstash Vector as a serverless vector database in Mem0 with optional built-in embedding models for zero-infrastructure memory search over HTTP."
 ---
 [Upstash Vector](https://upstash.com/docs/vector) is a serverless vector database with built-in embedding models.

@@ -1,6 +1,6 @@
 ---
 title: "Valkey"
-description: "Use Valkey as an open-source vector store in Mem0 for high-performance key-value storage with vector search."
+description: "Use Valkey as an open-source, BSD-licensed key-value datastore in Mem0 for high-performance vector search alongside rich data structures and caching."
 ---
 # Valkey Vector Store

@@ -1,6 +1,6 @@
 ---
 title: "Cloudflare Vectorize"
-description: "Use Cloudflare Vectorize as a vector database in Mem0 for building AI-powered applications at the edge."
+description: "Use Cloudflare Vectorize as a vector database in Mem0 for building AI-powered applications at the edge with globally distributed embedding search."
 ---
 [Cloudflare Vectorize](https://developers.cloudflare.com/vectorize/) is a vector database offering from Cloudflare, allowing you to build AI-powered applications with vector embeddings.

@@ -1,6 +1,6 @@
 ---
 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."
+description: "Use Google Cloud Vertex AI Vector Search as a managed vector store in Mem0, configuring your project, endpoint, and index for semantic memory search."
 ---


@@ -1,6 +1,6 @@
 ---
 title: "Weaviate"
-description: "Use Weaviate as an open-source vector search engine in Mem0 for storing and retrieving vector embeddings."
+description: "Use Weaviate as an open-source vector search engine in Mem0 for storing and retrieving high-dimensional embeddings with hybrid search support."
 ---
 [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.

@@ -1,6 +1,6 @@
 ---
 title: Overview
-description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
+description: "Explore Mem0 vector database integrations including Qdrant, Chroma, Pinecone, pgvector, Milvus, Weaviate, Redis, and Oracle for memory storage."
 ---

 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.