Agent Switchboard
Q

Qdrant

by Qdrant

Verified

High-performance open-source vector database. Supports dense, sparse, and multi-vector search. On-premise or cloud. Rust-built for production-scale RAG pipelines.

Access
APICLI
Auth
API Key
Skills
5

Skills

  • Index and query dense vectors with HNSW for high-recall approximate search

    Index and query dense vectors with HNSW for high-recall approximate search

  • Support sparse vectors for keyword-style retrieval alongside semantic dense search

    Support sparse vectors for keyword-style retrieval alongside semantic dense search

  • Apply complex payload filters to narrow search results without re-ranking overhead

    Apply complex payload filters to narrow search results without re-ranking overhead

  • Use named multi-vector collections for image, text, and code embedding co-search

    Use named multi-vector collections for image, text, and code embedding co-search

  • Quantize vectors to reduce memory footprint by up to 32x with minimal recall loss

    Quantize vectors to reduce memory footprint by up to 32x with minimal recall loss

Vector Databasesvector-databaseopen-sourceragembeddingsrustself-hostedsemantic-search
Visit Agent

Related Agents

Pinecone

API

Fully managed vector database for AI applications. Store and search billions of high-dimensional embeddings with low…

Verifiedby Pinecone · Vector Databases · 5 skills · API Key

ChromaDB

API · CLI

Open-source embedding database for local and cloud AI apps. Simple API for storing, querying, and filtering embeddings.…

Verifiedby Chroma · Vector Databases · 5 skills · No auth

Milvus

API · MCP · CLI

High-performance open-source vector database with REST API, gRPC, and an official MCP server. Stores billions of…

Verifiedby Zilliz · Vector Databases · 3 skills · API Key

Zvec

API

Embeds an in-process vector database into applications for dense, sparse, full-text, and hybrid search with durable…

VerifiedOfficialby Alibaba · Vector Databases · 4 skills · No auth
qdrant
High-performance open-source vector database. Supports dense, sparse, and multi-vector search. On-premise or cloud. Rust-built for production-scale RAG pipelines.
fields
nameQdrant
providerQdrant
urlhttps://qdrant.tech/documentation
categoriesvector-databases
accessapi · cli
authapiKey
streamingfalse
pushfalse
verifiedtrue
officialfalse
tagsvector-database, open-source, rag, embeddings, rust, self-hosted, semantic-search
skills
index-and-query-dense-vectors-with-hnsw-for-high-recall-approximate-searchIndex and query dense vectors with HNSW for high-recall approximate searchIndex and query dense vectors with HNSW for high-recall…
support-sparse-vectors-for-keyword-style-retrieval-alongside-semantic-dense-searchSupport sparse vectors for keyword-style retrieval alongside semantic dense searchSupport sparse vectors for keyword-style retrieval alon…
apply-complex-payload-filters-to-narrow-search-results-without-re-ranking-overheadApply complex payload filters to narrow search results without re-ranking overheadApply complex payload filters to narrow search results …
use-named-multi-vector-collections-for-image-text-and-code-embedding-co-searchUse named multi-vector collections for image, text, and code embedding co-searchUse named multi-vector collections for image, text, and…
quantize-vectors-to-reduce-memory-footprint-by-up-to-32x-with-minimal-recall-lossQuantize vectors to reduce memory footprint by up to 32x with minimal recall lossQuantize vectors to reduce memory footprint by up to 32…
→ https://qdrant.tech/documentation