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RAGWire

Production-grade RAG toolkit for document ingestion and retrieval with hybrid search support.

RAGWire handles the full RAG pipeline — from loading raw documents to storing and retrieving them from a vector database — so you can focus on building your application.

RAGWire High-Level Overview

Features

  • Document Loading — PDF, DOCX, XLSX, PPTX and more via MarkItDown
  • LLM Metadata Extraction — extracts company, doc type, and fiscal period automatically
  • Smart Text Splitting — markdown-aware and recursive chunking strategies
  • Multiple Embedding Providers — Ollama, OpenAI, HuggingFace, Google, FastEmbed
  • Qdrant Vector Store — dense, sparse, and hybrid search
  • Advanced Retrieval — similarity, MMR, and hybrid search
  • SHA256 Deduplication — at both file and chunk level — no duplicate ingestion

Architecture

RAGWire Architecture

Installation

pip install ragwire

# With Ollama support (local, no API key)
pip install "ragwire[ollama]"

# With all providers
pip install "ragwire[all]"

Quick Start

from ragwire import RAGWire

rag = RAGWire("config.yaml")

# Ingest documents
stats = rag.ingest_documents(["data/Apple_10k_2025.pdf"])
print(f"Chunks created: {stats['chunks_created']}")

# Retrieve
results = rag.retrieve("What is Apple's total revenue?", top_k=5)
for doc in results:
    print(doc.metadata.get("company_name"), doc.page_content[:200])

Supported Providers

Provider Embeddings LLM Free
Ollama Yes Yes Yes (local)
OpenAI Yes Yes No
Google Gemini Yes Yes Free tier
Groq No Yes Free tier
Anthropic No Yes No
HuggingFace Yes No Yes (local)
FastEmbed Yes No Yes (local)