RAGWire and PythoRAG
These are competitors offering similar RAG pipelines—both ingest PDFs into Qdrant vector storage for retrieval-augmented generation—though RAGWire is more mature and feature-rich (supporting multiple document formats, hybrid search, and deduplication) while PythoRAG is a simpler, earlier-stage implementation.
About RAGWire
laxmimerit/RAGWire
Production-grade RAG toolkit — ingest PDFs, DOCX, XLSX into Qdrant with LLM metadata extraction, hybrid search, and SHA256 deduplication.
Supports multi-format ingestion via MarkItDown (PPTX, XLSX, DOCX, PDFs), markdown-aware recursive chunking, and customizable LLM-based metadata extraction through YAML configuration. Provides pluggable embedding providers (Ollama, OpenAI, HuggingFace, Google, FastEmbed) with Qdrant's dense/sparse/hybrid search, plus directory-level ingestion with file and chunk-level SHA256 deduplication. Designed as modular Python components with environment variable substitution for production deployments.
About PythoRAG
natanhp/PythoRAG
PythoRAG is a simple, open-source project designed to facilitate Retrieval-Augmented Generation (RAG) by integrating PDF document ingestion with Qdrant for vector storage and Deepseek-R1:1.5B (running on Ollama) for context-aware text generation
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