add-postgres-native-vector-retrieval-to-agent-and-rag-workflows-
SolidStore embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.
Install
Quality Score: 83/100
Skill Content
Details
- Author
- agentskillexchange
- Repository
- agentskillexchange/skills
- Created
- 5 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
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pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
postgres-semantic-search
PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector indexing, hybrid FTS/BM25 + RRF, ParadeDB, reranking, halfvec, multilingual search, query translation, and domain evals. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW, IVFFlat, ParadeDB, pg_search, reranking, iterative_scan, filtered HNSW, halfvec, websearch_to_tsquery, unaccent, multilingual FTS, pg_trgm, trigram, fuzzy search, ILIKE, autocomplete, typo tolerance, fuzzystrmatch, Hit@K, MRR, retrieval evals, cross-lingual retrieval, non-English corpus, per-language indexing, query translation For general Postgres schema, index, RLS or query tuning unrelated to retrieval, use supabase-postgres-best-practices instead.
vector-databases
Vector database integration for embeddings and similarity search. Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Index management, metadata filtering, hybrid search, and production optimization. USE WHEN: user mentions "vector database", "embeddings", "similarity search", "Pinecone", "Weaviate", "Qdrant", "ChromaDB", "pgvector", "HNSW", "ANN" DO NOT USE FOR: LangChain integration - use `langchain`; RAG architecture - use `rag-patterns`; traditional databases - use database skills