cortexdb

Solid

Use CortexDB for vector search, hybrid search, knowledge graphs, and RAG. Use when working with embeddings, similarity search, semantic search, AI knowledge bases, or when the user mentions vectors, embeddings, RAG, knowledge graph, or CortexDB.

AI & Automation 32 stars 3 forks Updated 3 weeks ago MIT

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Skill Content

# CortexDB CortexDB is a lightweight SQLite-based vector database for Go AI projects. It supports vector similarity search, hybrid search (vector + keyword), full-text search (FTS5/BM25), knowledge graphs, and memory APIs. ## Core Concepts ### Vector Search Store and search embeddings (vector representations of data). CortexDB supports multiple index types (HNSW, IVF, Flat) and similarity functions (cosine, dot product, euclidean). ### Hybrid Search Combines vector search with FTS5 keyword search using Reciprocal Rank Fusion (RRF). ### Lexical Fallback (v2.13.0+) When no embedder is configured, `SearchText` and `HybridSearchText` automatically fall back to FTS5/BM25 full-text search. ### LLM-Assisted Retrieval (v2.13.0+) Query expansion using LLM-generated keywords and alternate query phrasings for improved FTS5 recall. ### External Vector Support (v2.13.0+) APIs for ingesting pre-computed vectors from external pipelines without requiring an embedder. ## Installation ```go import "github.com/liliang-cn/cortexdb/v2" ``` ## Quick Start ```go import "github.com/liliang-cn/cortexdb/v2/pkg/cortexdb" // Open database db, err := cortexdb.Open(cortexdb.DefaultConfig("/path/to/db")) if err != nil { log.Fatal(err) } defer db.Close() // Quick operations q := db.Quick() // Add vector with auto ID id, err := q.Add(ctx, []float32{0.1, 0.2, 0.3}, "document content") // Search results, err := q.Search(ctx, []float32{0.1, 0.2, 0.3}, 10) // Text search (requires embedder) r...

Details

Author
liliang-cn
Repository
liliang-cn/cortexdb
Created
9 months ago
Last Updated
3 weeks ago
Language
Go
License
MIT

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