Open-source search engine with vector, semantic, and hybrid retrieval
Typesense documents an open-source search engine at typesense.org/docs for fast typo-tolerant keyword search, faceting, and vector retrieval. Vector search docs at typesense.org/docs/30.2/api/vector-search describe KNN search on imported embeddings or auto-generated embeddings via OpenAI, Google PaLM API, or built-in Hugging Face models in huggingface.co/typesense/models (use the `ts` namespace prefix). Features include semantic search, hybrid search with rank fusion and adjustable `alpha` weighting, similar-document queries by ID, HNSW approximate search with optional `flat_search_cutoff` brute-force mode, and cosine `vector_distance` scoring. Deploy via Typesense Cloud or self-hosted Docker/binaries with REST API and official client libraries.
Use cases
- E-commerce site search with semantic product discovery
- RAG catalogs combining keyword filters and embedding similarity
- Similar-item recommendations via document-ID vector queries
- Notebook-to-production search without separate vector DB
- Agent retrieval prototypes paired with community Typesense MCP servers
Key features
- KNN vector search with imported or auto-generated embeddings
- Hybrid keyword + semantic search via vector_query and alpha weighting
- Built-in ts/* Hugging Face models and remote OpenAI/PaLM embedders
- Typo-tolerant full-text search, faceting, and geo filters
- Self-hosted or Typesense Cloud with multi_search POST API
Who Is It For?
- Developers wanting one engine for keyword and vector search
- Teams self-hosting search before moving to Typesense Cloud
- ML engineers evaluating hybrid rank fusion for RAG quality
Frequently Asked Questions
- Is Typesense only a vector database?
- No—Typesense is a full search engine; vector/semantic/hybrid search extends its core keyword capabilities per typesense.org/docs.
- How are embeddings generated?
- Import your own vectors or enable auto-embedding via OpenAI, PaLM API, or built-in ts/* models documented in vector-search guides.
- Is there an official Typesense MCP?
- Typesense does not document a first-party MCP server; community implementations exist on GitHub for MCP clients.
Related
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3 Indexed items
Weaviate
Weaviate documents an open-source vector database at docs.weaviate.io/weaviate for storing objects and vector embeddings with semantic, keyword, and hybrid search, RAG, reranking, and agent workflows. The ecosystem includes self-hosted Docker/Kubernetes installs, Weaviate Cloud (console.weaviate.cloud), Query Agent, and Weaviate Embeddings for managed inference. Client libraries include Python (`weaviate-client` v4, requires Weaviate 1.23.7+), TypeScript, Go, and Java with REST, gRPC, and GraphQL APIs per the official documentation.
Milvus
Milvus documents a high-performance vector database at milvus.io/docs for storing, indexing, and searching embedding vectors with metadata filtering and hybrid search. Deployment options include Milvus Lite (`pip install pymilvus` for notebooks/edge), Milvus Standalone (single Docker image), and Milvus Distributed on Kubernetes per milvus.io/docs/v2.6.x/install-overview. Official SDKs include PyMilvus, Go, Java, Node.js, and C#; Zilliz Cloud offers managed Milvus. Architecture separates access, coordinator, worker, and storage layers with object storage backends (MinIO, S3, Azure Blob) per milvus.io/docs/architecture_overview.
Qdrant
Qdrant documents an AI-native vector search engine at qdrant.tech/documentation for storing, indexing, and querying high-dimensional vectors with optional payloads, supporting dense, sparse, and multi-vector configurations. Official guides cover Docker/Kubernetes self-hosting, Qdrant Cloud on AWS/GCP/Azure, Hybrid Cloud, Private Cloud, and Qdrant Edge for embedded retrieval. Client libraries include Python (`qdrant-client`), JavaScript/TypeScript (`@qdrant/js-client-rest`), Rust, Go, Java, and .NET with REST and gRPC APIs per the API reference at api.qdrant.tech.