Databases · Pinecone · For data scientists

Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.

Orchestra-Research/AI-Research-SKILLs

Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.

wshobson/agents

Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from…

elementalsouls/Claude-BugHunter

Vector database implementation for AI/ML applications, semantic search, and RAG systems.

ancoleman/ai-design-components

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

github/awesome-copilot

Agent RAG and long-term memory with Pinecone. An agent skill from Luciole-Studio/Misaka-Agent.

Luciole-Studio/Misaka-Agent

Managed vector DB for production RAG and search. An agent skill from Luciole-Studio/Misaka-Agent.

Luciole-Studio/Misaka-Agent

A skill your agent uses when operating a vector store as a data layer — choosing or migrating between Pinecone, Qdrant, Weaviate and pgvector; designing a collection or index (distance metric…

ericrisco/rsc-harness

Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.

AlexAI-MCP/hermes-CCC

Every Pinecone API feature, plus local sync, snapshot history, and text-first search no other Pinecone tool has.

mvanhorn/printing-press-library