pdf-rag-knowledge
CommunitySearch datasheets instantly with local RAG.
Software Engineering#ollama#semantic-search#retrieval-augmented-generation#vector-store#datasheets#pdf-rag#hardware-docs
AuthorGherkin
Version1.0.0
Installs0
System Documentation
What problem does it solve?
It solves the problem of getting accurate answers from hardware manuals and datasheets without manually reading and searching through PDFs.
Core Features & Use Cases
- Semantic search over indexed PDFs: Retrieves the most relevant chunks using embeddings and cosine similarity.
- Local, repo-scoped knowledge base: Stores embeddings in a local
vector_store.jsonfor portability and privacy. - Ollama-based embedding generation: Uses a locally running Ollama model to embed text chunks, then supports quick question answering with citations (source and page).
Quick Start
Index your PDFs by running python3 rag_search.py --index /path/to/your/datasheets/*.pdf.
Dependency Matrix
Required Modules
requestsPyPDF2pycryptodome
Components
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: pdf-rag-knowledge Download link: https://github.com/Gherkin/vscode-pdf-rag-skill/archive/main.zip#pdf-rag-knowledge Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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