IlyaRice/RAG-Challenge-2

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2431 stars · 493 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python implementation of the winning RAG system from the Enterprise RAG Challenge 2 competition, answering questions about company annual reports. It combines PDF parsing with Docling, parent document vector retrieval, LLM reranking, chain-of-thought structured prompting, and query routing.

Use cases

• build a rag pipeline over pdf documents
• answer questions about company annual reports
• learn rag techniques like parent document retrieval and llm reranking
• parse pdfs with tables into structured data
• study a winning rag competition solution
• experiment with query routing for multi-document comparison

When to choose

• you want to study or replicate state-of-the-art RAG techniques
• you need a reference implementation for question answering over PDF reports
• you're exploring vector search, reranking, and structured LLM prompting patterns

When to avoid

• you need production-ready, tested code with error handling
• you want a maintained library or framework rather than competition code
• you can't provide your own OpenAI/Gemini API keys or GPU for parsing

Facets

application · maturity experimental

rag llm-inference pdf search-engine prompt-engineering large-language-models pdf machine-learning python cli rag-challenge competition-code pdf-parsing docling vector-search reranking annual-reports question-answering retrieval-augmented-generation natural-language-processing gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="IlyaRice/RAG-Challenge-2")