RAGWire and pdf-process-rag
These are competitors offering overlapping RAG-based PDF processing solutions, with RAGWire being the mature, production-ready choice (supporting multiple document formats, hybrid search, and deduplication) while pdf-process-rag appears to be an earlier-stage alternative focused on vector embeddings for PDF querying.
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 pdf-process-rag
salameaz/pdf-process-rag
A Python-based application that extracts and processes PDF content using a Retrieval-Augmented Generation (RAG) approach. Leverage vector embeddings to enable efficient querying of both text-based and scanned PDFs, and interact with your documents using a large language model.
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