Does this skill require coding to set up RAG?

No, it automates chunking, embedding, and indexing so you can create a fully functional knowledge base using simple natural language commands.

How does the sync feature work?

The sync command uses mtime and size detection to identify new or changed local files and updates the Pinecone Assistant accordingly, with an optional flag to delete missing files.

What file formats are supported?

The Pinecone Assistant skill supports PDF, DOCX, TXT, MD, and JSON files, making it ideal for natural language documentation.

Does it provide source citations?

Yes, every response generated through the assistant includes citations pointing to the specific source file and page numbers for PDF documents.

Can I use this for source code search?

No, this skill is optimized for document-based content and natural language. For codebase search, we recommend Pinecone's vector database with specialized code embeddings.

Pinecone Document Assistant

Name: Pinecone Document Assistant
Author: pinecone-io

bypinecone-io

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Content Management

Manages Pinecone Assistants to enable natural language Q&A and document retrieval with automated RAG workflows.

About

The Pinecone Assistant skill provides a seamless interface for Claude to manage a fully automated Retrieval Augmented Generation (RAG) service. It allows users to create AI-powered knowledge bases from local documentation, research papers, and guides without writing complex embedding pipelines. By recognizing natural language requests, the skill handles file chunking, indexing, and synchronization, enabling Claude to answer questions with precise citations and page-level references from your uploaded files.

Key Features

Interactive Q&A with verifiable source citations and page numbers
36 GitHub stars
Proactive natural language intent recognition for assistant operations
Context retrieval for extracting specific knowledge snippets without full chat
Smart directory synchronization with local file change detection
Automated document processing, chunking, and indexing for RAG

Use Cases

Analyzing large sets of research papers or whitepapers with cited responses
Building an instant-expert Q&A system from product manuals or API documentation
Managing internal company knowledge bases through simple terminal commands

About

Key Features

Interactive Q&A with verifiable source citations and page numbers
36 GitHub stars
Proactive natural language intent recognition for assistant operations
Context retrieval for extracting specific knowledge snippets without full chat
Smart directory synchronization with local file change detection
Automated document processing, chunking, and indexing for RAG

Use Cases

Analyzing large sets of research papers or whitepapers with cited responses
Building an instant-expert Q&A system from product manuals or API documentation
Managing internal company knowledge bases through simple terminal commands