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pinecone-research

Integrate Pinecone vector databases into agent workflows for persistent memory and semantic search.

1|Updated Jul 31, 2026
Search Tags:#rag#vector database#agent#retrieval#memory#pinecone
One-click install
npx skills add https://github.com/icyzh/hermes-web --skill pinecone-research-icyzh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone-research
Source: https://github.com/icyzh/hermes-web/tree/main/optional-skills/research/pinecone-research
Command: npx skills add https://github.com/icyzh/hermes-web --skill pinecone-research-icyzh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pinecone-client, langchain-pinecone, langchain-openai, and includes scripts (resource) components.

What problem does it solve?

This skill solves the challenge of maintaining long-term context and memory for AI agents by providing a structured way to store and retrieve conversation history and document knowledge using vector databases.

Core Features & Use Cases

  • Namespace-based Memory: Isolate agent sessions using Pinecone namespaces to ensure data privacy and context relevance.
  • RAG Pipeline Integration: Index local text documents and perform semantic searches to provide agents with up-to-date information.
  • Use Case: An agent can recall specific project decisions made in a previous session by querying the Pinecone index for relevant past interactions.

Quick Start

Use the pinecone-research skill to index all text files in the documents directory into the agent-memory index.

Frequently Asked Questions about pinecone-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add persistent long-term memory to an AI agent?▼

Persistent long-term memory for an AI agent is achieved by integrating a vector database like Pinecone to store and retrieve conversation history and document knowledge across sessions.

Can I isolate agent session data using Pinecone namespaces?▼

Yes, Pinecone namespaces isolate agent sessions to ensure data privacy and context relevance, preventing cross-session data contamination during retrieval-augmented generation.

What is the best way to index local text documents for RAG?▼

Indexing local text documents for retrieval-augmented generation involves batch embedding operations and storing the vectors in Pinecone for semantic search and up-to-date agent information.

Do I need langchain-pinecone to perform metadata-filtered retrieval?▼

Using langchain-pinecone along with pinecone-client and langchain-openai provides the necessary framework integration for scalable vector storage and efficient metadata-filtered retrieval.

How does semantic search across session-scoped namespaces work?▼

Semantic search across session-scoped namespaces works by querying the Pinecone index for relevant past interactions, allowing an agent to recall specific project decisions from previous sessions.

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