# MongoDB Vector Search

> Use MongoDB Vector Search to create vector indexes and perform vector search, including semantic search and hybrid search, on your vector embeddings in MongoDB.

- [Build AI Agents with MongoDB](https://www.mongodb.com/docs/vector-search/about/ai-agents.md): Learn how to build AI agents with MongoDB Vector Search, including agentic RAG, short-term and long-term memory, and integration with popular agent frameworks.
- [Generate Embeddings Automatically Using MongoDB Vector Search](https://www.mongodb.com/docs/vector-search/about/automated-embedding.md): Use MongoDB Vector Search to automatically generate vector embeddings for your data at index time and query time, without managing external embedding pipelines.
- [MongoDB Vector Search Use Cases and Design Patterns](https://www.mongodb.com/docs/vector-search/about/use-cases.md): Explore common use cases and design patterns for MongoDB Atlas Vector Search, including semantic search, retrieval-augmented generation, and AI agents.
- [Vector Quantization](https://www.mongodb.com/docs/vector-search/about/vector-quantization.md): Learn how to use MongoDB Vector Search to quantize float vector embeddings or ingest pre-quantized scalar and binary vectors to improve query performance.
- [Benchmark for MongoDB Vector Search](https://www.mongodb.com/docs/vector-search/benchmark/benchmark-tests.md): Learn how to test and improve MongoDB Atlas Vector Search performance using benchmark methodology, recall, latency, throughput results, and tuning tips.
- [MongoDB Vector Search Benchmark Overview](https://www.mongodb.com/docs/vector-search/benchmark/overview.md): Learn about the MongoDB Vector Search performance benchmark, including the methodology, optimization strategies, and cluster configuration used for testing.
- [Additional Performance Recommendations](https://www.mongodb.com/docs/vector-search/benchmark/performance-recommendations.md): Learn performance recommendations for MongoDB Atlas Vector Search, including memory allocation, index tuning, page faults monitoring, and CPU optimization.
- [MongoDB Vector Search Benchmark Results](https://www.mongodb.com/docs/vector-search/benchmark/results.md): See the results of the MongoDB Vector Search performance benchmark, including recall, latency, quantization, filtering, concurrency, and sharding metrics.
- [Automated Embedding Overview](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding.md): Configure Automated Embedding in MongoDB Vector Search to generate and manage vector embeddings at index- and query-time using a Voyage AI model.
- [Manage Billing for Automated Embedding](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/billing.md): Manage billing settings, view invoices and costs, and configure payment methods for your cluster in the Atlas UI.
- [Manage Automated Embedding](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/management.md): Learn how to manage Automated Embedding in MongoDB Atlas. Enable or disable the feature at the organization level, view embedding token usage, and manage rate limits.
- [Models for Automated Embedding](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/models.md): Learn how the Voyage AI embedding model ecosystem works with Automated Embedding.
- [How Automated Embedding Works](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/overview.md): Learn how Automated Embedding works in MongoDB Vector Search.
- [How to Create Vector Embeddings Manually](https://www.mongodb.com/docs/vector-search/crud-embeddings/create-embeddings-manual.md): Learn how to create vector embeddings manually for MongoDB Atlas Vector Search, choose an embedding model, and store embeddings for semantic search queries.
- [Review Deployment Options](https://www.mongodb.com/docs/vector-search/deployment/deployment-options.md): Explore deployment options for MongoDB Vector Search, including cluster types, cloud providers, and node architecture for testing and production environments.
- [Build a Multi-Tenant Architecture for MongoDB Vector Search](https://www.mongodb.com/docs/vector-search/deployment/multi-tenant-architecture.md): Learn design recommendations for multi-tenant architecture with MongoDB Vector Search, including strategies to isolate tenant data and optimize indexes.
- [How to Perform Hybrid Search](https://www.mongodb.com/docs/vector-search/hybrid-search/hybrid-search-overview.md): Learn how hybrid search works in MongoDB Vector Search, combining vector and full-text queries using $rankFusion or $scoreFusion with reciprocal rank fusion.
- [Perform Hybrid Vector and Full-Text Search](https://www.mongodb.com/docs/vector-search/hybrid-search/vector-search-with-full-text-search.md): Learn how to perform a hybrid search on your vector embeddings that combines full-text and semantic search to return relevant results from your MongoDB cluster.
- [How to Combine Multiple ``$vectorSearch`` Queries](https://www.mongodb.com/docs/vector-search/hybrid-search/vector-search-with-rankfusion.md): Run multiple $vectorSearch queries inside the $rankFusion aggregation stage to combine results from different vector fields, embedding models, or query terms.
- [MongoDB Vector Search Overview](https://www.mongodb.com/docs/vector-search/index.md): Use MongoDB Vector Search to create vector indexes and perform vector search, including semantic search and hybrid search, on your vector embeddings in MongoDB.
- [How to Index Fields for Vector Search](https://www.mongodb.com/docs/vector-search/index/vector-search-type.md): Use the MongoDB Vector Search type to index vector embeddings for vector search using the vectorSearch type.
- [``$rerank`` Aggregation Pipeline Stage](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/rerank.md): Learn about the MongoDB $rerank aggregation stage syntax, options, and usage for reordering search results by relevance using Voyage AI reranking models in Atlas.
- [Run Vector Search Queries](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md): Learn how to run vector search queries with the MongoDB $vectorSearch aggregation pipeline stage, including ANN and ENN search syntax and configuration.
- [Explain MongoDB Vector Search Results](https://www.mongodb.com/docs/vector-search/query/explain.md): Run your MongoDB Vector Search query with the explain method to learn about your $vectorSearch query plan, execution statistics, and vector tracing details.
- [How to Measure the Accuracy of Your Query Results](https://www.mongodb.com/docs/vector-search/query/improve-accuracy.md): Learn how to measure the accuracy of MongoDB Atlas Vector Search query results by comparing approximate nearest neighbor and exact nearest neighbor searches.
- [Manage Billing for Native Reranking](https://www.mongodb.com/docs/vector-search/query/native-reranking/billing.md): Manage billing and payments for Native Reranking in MongoDB Atlas Vector Search. View invoices and costs, and configure payment methods for Voyage AI reranker usage in the Atlas UI.
- [Manage Native Reranking](https://www.mongodb.com/docs/vector-search/query/native-reranking/managing.md): Enable or disable Native Reranking for an Atlas project, configure resource policies, and view rate limits and usage tiers for $rerank with Voyage AI reranker models.
- [Get Started With Native Reranking](https://www.mongodb.com/docs/vector-search/query/native-reranking/quickstart.md): Get started with native reranking in MongoDB Atlas Vector Search. Use the $rerank aggregation stage with Voyage AI reranker models to reorder query results by relevance.
- [Chatbot Demo Builder in Search Playground](https://www.mongodb.com/docs/vector-search/query/vector-search-playground.md): Use the Search Playground Chatbot Demo Builder to try MongoDB Vector Search and Voyage AI embeddings on your data without an Atlas account or cluster.
- [Use Views with MongoDB Vector Search](https://www.mongodb.com/docs/vector-search/query/view-support.md): Use MongoDB Vector Search with views to transform documents and collections, enabling vector search queries on aggregation pipeline results and joined data.
- [Troubleshooting](https://www.mongodb.com/docs/vector-search/troubleshooting.md): Learn how to address common issues with MongoDB Vector Search queries, index configuration, and deployment, including error messages and recommended solutions.
- [Optimize and Measure Performance](https://www.mongodb.com/docs/vector-search/tutorials/auto-quantize-with-voyage-ai.md): Learn how to automatically quantize vector embeddings to optimize memory usage and minimize Vector Search query latency.
- [Build a Local RAG Implementation with MongoDB Vector Search](https://www.mongodb.com/docs/vector-search/tutorials/local-rag.md): Build a local RAG implementation with MongoDB Vector Search using local embedding models and LLMs, without requiring external API keys or cloud services.
- [MongoDB Vector Search Quick Start Tutorial](https://www.mongodb.com/docs/vector-search/tutorials/quick-start.md): Follow this tutorial to create a vector search index on your data and run your first semantic search query in MongoDB, with examples in multiple languages.
- [Retrieval-Augmented Generation (RAG) with MongoDB](https://www.mongodb.com/docs/vector-search/tutorials/rag.md): Use MongoDB Vector Search to implement retrieval-augmented generation (RAG) in your AI applications with tutorials in Python, Node.js, Go, Java, and C#.