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Pinecone Vector Database

Teaches the agent to build and query Pinecone managed vector search indexes for RAG and semantic search.

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
2 files

What it does

This skill gives an AI agent working knowledge of Pinecone, a hosted service that stores numerical 'embeddings' of text or other content so an app can find items by meaning rather than exact keywords. With it installed, the agent can write code to create an index, load vectors with metadata, run similarity and filtered searches, use namespaces to separate users or tenants, and wire Pinecone into LangChain or LlamaIndex. The skill itself is instructional text and code examples; it does not run anything on its own.

Great for

  • Python developers building production retrieval-augmented generation (RAG) features who want a managed database
  • Teams that need semantic search or recommendations and do not want to run their own vector infrastructure
  • Developers already using LangChain or LlamaIndex who want a hosted vector store

Skip it if

  • Projects that must keep data fully self-hosted or offline; the skill is built around a cloud service that requires sending data to Pinecone
  • Small local prototypes where a free local library such as FAISS or Chroma is simpler and avoids account setup and costs
  • Non-developers looking for a finished application; this only helps the agent write code that uses Pinecone

Try asking

  • “Create a Pinecone serverless index for 1536-dimension OpenAI embeddings and write code to upsert my document chunks with metadata.”
  • “Write a query function that searches my Pinecone index and filters results to category 'tutorial'.”
  • “Set up namespaces so each customer's documents are kept separate in one Pinecone index.”
  • “Add hybrid dense plus sparse search to my existing Pinecone RAG pipeline.”
Step-by-step guide: how to use Pinecone Vector DatabaseInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 2 original files, the MIT licence and an attribution note.

    pinecone.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip pinecone.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip pinecone.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Make sure the dimension value in create_index matches your embedding model's output size, or upserts will fail.
  • Keep API keys in environment variables rather than pasting them into code; the examples show literal placeholder keys.
  • Check current Pinecone pricing and free-tier limits yourself; the skill's numbers (such as the free tier and 99.9% uptime SLA) are claims in the file and were not verified.

What's inside

  • references/deployment.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.