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pinecone-research

Implements agent RAG pipelines and namespace-based long-term memory using Pinecone vector indexes.

Updated Aug 22, 2026
Search Tags:#semantic search#embeddings#langchain#rag#vector database#agent memory#Pinecone
One-click install
npx skills add https://github.com/vivekgoquest/hermes-agent-stable --skill pinecone-research-vivekgoquest
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone-research
Source: https://github.com/vivekgoquest/hermes-agent-stable/tree/main/optional-skills/research/pinecone-research
Command: npx skills add https://github.com/vivekgoquest/hermes-agent-stable --skill pinecone-research-vivekgoquest

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Agents lose context between sessions, making it impossible to recall past conversations or retrieve relevant knowledge. This Skill provides a Pinecone-backed retrieval-augmented generation pipeline that persists embeddings, retrieves relevant context, and builds long-term memory across agent sessions. ## Core Features & Use Cases - RAG Pipeline: Index documents into a Pinecone serverless index and query them with embedding-based similarity search via LangChain. - Session Memory Management: Store, recall, and clean up per-session memory using Pinecone namespaces for multi-tenant isolation. - Cross-Session Recall: Query across all namespaces to surface relevant context from any past conversation. - Use Case: An agent handling ongoing research conversations stores each session's key findings in its own namespace, then recalls related decisions from earlier sessions when the user asks about prior architecture discussions. ## Quick Start Set your PINECONE_API_KEY and OPENAI_API_KEY environment variables, then ask the agent to store this conversation's key points in Pinecone memory and recall what was discussed about a given topic.

Frequently Asked Questions about pinecone-research

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

FAQPage Schema
How do I build a RAG pipeline with Pinecone and LangChain?▼

Create a Pinecone serverless index, then use PineconeVectorStore from langchain-pinecone with OpenAIEmbeddings to embed and upsert documents. Query it with as_retriever or similarity_search to retrieve the top-k most relevant chunks for your prompt.

How to store agent conversation memory in Pinecone?▼

Embed each memory entry with OpenAIEmbeddings and upsert it into a Pinecone index using the session ID as the namespace. This isolates each session's vectors while still allowing cross-session search by querying without a namespace filter.

What are Pinecone namespaces used for in agent memory?▼

Namespaces partition vectors within a single index, letting you isolate data per session or per user for multi-tenant agents. You can query one namespace for scoped recall, query globally for cross-session memory, or delete an entire namespace to prune stale data.

Does Pinecone have a free tier for prototyping RAG?▼

Yes, Pinecone's free tier includes one index with up to 100K vectors at 1536 dimensions, which is enough for prototyping agent memory and semantic search workflows. Serverless indexes provide auto-scaling and pay-per-use pricing beyond that.

Why is my Pinecone query returning no results?▼

Common causes include querying the wrong namespace, an embedding dimension mismatch with the index (must be 1536 for OpenAI embeddings), or an empty index. Check index stats with describe_index_stats to verify vector counts per namespace.

When should I use a general Pinecone skill instead of this one?▼

Use a general Pinecone reference when you need index management, CRUD operations, or hybrid search on production infrastructure without agent integration. This Skill is specifically for agent RAG pipelines and session-based long-term memory.

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