rapid-rag 0.1.0

Fast local RAG - search your documents with AI, no cloud needed

rapid-rag

Fast local RAG - search your documents with AI, no cloud needed.

Installation

pip install rapid-rag

For PDF support:

pip install rapid-rag[pdf]

Quick Start

from rapid_rag import RapidRAG

# Create a RAG instance
rag = RapidRAG("my_documents")

# Add documents
rag.add("doc1", "The quick brown fox jumps over the lazy dog.")
rag.add_file("report.pdf")
rag.add_directory("./docs/")

# Semantic search
results = rag.search("fox jumping")
for r in results:
    print(f"{r['score']:.3f}: {r['content'][:100]}")

# RAG query with LLM (requires Ollama)
answer = rag.query("What does the fox do?", model="qwen2.5:7b")
print(answer["answer"])

CLI Usage

# Initialize a collection
rapid-rag init my_docs

# Add documents
rapid-rag add ./documents/ -c my_docs -r

# Search
rapid-rag search "query here" -c my_docs

# RAG query (requires Ollama)
rapid-rag query "What is X?" -c my_docs -m qwen2.5:7b

# Info
rapid-rag info -c my_docs

Features

• Local-first: Everything runs on your machine
• Fast: ChromaDB + sentence-transformers
• Simple API: Add, search, query in 3 lines
• File support: .txt, .md, .pdf
• Chunking: Automatic with overlap
• LLM integration: Works with Ollama

Requirements

• Python 3.10+
• For LLM queries: Ollama running locally

License

MIT - Humotica