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pgvector - PostgreSQL extension for vector similarity search. Use for embedding storage, cosine similarity, IVFFlat indexes, and HNSW indexes.
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Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains.
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. **This project:** We use OpenAI text-embedding-3-small (1536) and store as vector(1536) in knowledge_chunks. halfvec is an optional future optimization; apply this skill's tuning (ef_search, iterative_scan
Supabase pgvector setup, RAG pipeline, and vector search for StartupAI. Covers knowledge_chunks schema, HNSW/IVFFlat indexes, search_knowledge + hybrid_search_knowledge RPCs, embedding generation via OpenAI, Edge Function integration, and Gemini web search grounding. **Trigger when user asks to:** - Set up or modify vector search, embeddings, or knowledge base - Ingest documents into knowledge_chunks - Debug search quality or missing results - Tune HNSW parameters or search performance - Wire RAG into edge functions or AI chat - Use Google Search grounding or URL Context with Gemini **This project:** OpenAI text-embedding-3-small (1536 dims), stored in knowledge_chunks, HNSW index, search via search_knowledge() and hybrid_search_knowledge() RPCs.
Channel the strategic thinking of fastest-growing AI startup founders. Use when asked to analyze current state, brainstorm strategy, set OKRs, or create execution plans. Provides founder personas, strategic frameworks, and battle-tested patterns from Anthropic, OpenAI, Mistral, Scale AI, and others.
Validate startup ideas using Hexa's Opportunity Memo framework and Perceived Created Value (PCV) methodology. Assess problem-solution fit, market opportunity, and determine if an idea is worth pursuing.
| name | pgvector |
| description | pgvector - PostgreSQL extension for vector similarity search. Use for embedding storage, cosine similarity, IVFFlat indexes, and HNSW indexes. |
Comprehensive assistance with pgvector development, generated from official documentation.
This skill should be triggered when:
Pattern 1: To run an example:
cd examples/openai
createdb pgvector_example
dart pub get
dart run example.dart
Pattern 2: To run an example:
cd examples/loading
createdb pgvector_example
cmake -S . -B build
cmake --build build
build/example
Pattern 3: To run an example:
cd examples/openai
createdb pgvector_example
dub run
Pattern 4: To run an example:
cd examples/openai
createdb pgvector_example
sbcl --noinform --non-interactive --load example.lisp
Pattern 5: To run an example:
cd examples/openai
createdb pgvector_example
cmake -S . -B build
cmake --build build
build/example
Pattern 6: To run an example:
cd examples/loading
mix deps.get
createdb pgvector_example
mix run example.exs
Pattern 7: To run an example:
cd examples/openai
createdb pgvector_example
crystal examples/openai/example.cr
Pattern 8: To run an example:
cd examples/Loading
createdb pgvector_example
dotnet run
This skill includes comprehensive documentation in references/:
Use view to read specific reference files when detailed information is needed.
Start with the getting_started or tutorials reference files for foundational concepts.
Use the appropriate category reference file (api, guides, etc.) for detailed information.
The quick reference section above contains common patterns extracted from the official docs.
Organized documentation extracted from official sources. These files contain:
Add helper scripts here for common automation tasks.
Add templates, boilerplate, or example projects here.
To refresh this skill with updated documentation: