Document Cortex: A Full-Stack RAG Application for Smarter, More Traceable Document Dialogue

Document Cortex is an open-source full-stack Retrieval-Augmented Generation (RAG) application that supports uploading PDF, DOCX, TXT documents. It enables intelligent Q&A via semantic search and Chroma vector database, and provides LLM-driven answers with citations. Key tech stack includes FastAPI, Streamlit, LangChain, HuggingFace Inference, and Chroma. This app addresses LLM limitations like context window constraints and hallucinations, emphasizing answer traceability.

With the development of LLMs, users expect natural language dialogue with documents, but direct use of LLMs has issues: context window limitations, imprecise retrieval, and hallucinations. Retrieval-Augmented Generation (RAG) solves these problems by retrieving relevant text fragments from a knowledge base before generating answers. Document Cortex is a complete RAG implementation that focuses on answer traceability.

Document Cortex is a full-stack application covering everything from data ingestion to UI. Its tech stack:

• FastAPI: Backend framework for high-performance APIs (supports async, auto-generated documentation).
• Streamlit: Frontend tool for quickly building data application UIs (ideal for document upload and dialogue features).
• LangChain: LLM application framework that encapsulates document loading, text splitting, embedding, vector retrieval, and prompt construction.
• HuggingFace Inference: Backend for LLM and embedding model inference (provides access to open-source models).
• Chroma: Lightweight vector database for storing embeddings and performing semantic search.

1. Multi-format support: Handles PDF, DOCX, and TXT (covers enterprise and research scenarios).
2. Semantic search with Chroma: Converts text into vectors for meaning-based search (as opposed to keyword matching), using the same embedding model for both documents and queries.
3. Cited answers: The LLM generates answers with explicit source references, enhancing verifiability and transparency, reducing hallucinations, and building trust.

Document Cortex addresses classic RAG challenges:

• Text splitting: Choosing chunk sizes (fixed characters, paragraphs, or semantic boundaries) to balance context completeness and relevance.
• Retrieval balance: Adjusting similarity thresholds, the number of retrieved fragments, or reranking to balance precision (avoiding irrelevant information) and recall (not missing key information).
• Prompt engineering: Organizing retrieved fragments into prompts that instruct the LLM to use the provided context, admit unknowns, and cite sources.
• Multi-round context: Managing dialogue history to consider prior interactions in subsequent queries.

Document Cortex applies to:

• Enterprise knowledge bases: Employees can quickly query policies, technical specifications, and reports.
• Academic research: Researchers retrieve paper methods and results for literature reviews.
• Legal analysis: Lawyers locate contract clauses, precedents, and regulations.
• Customer support: Teams query product manuals and FAQs for accurate information.

Compared to commercial services (OpenAI GPTs, Claude Projects) or open-source solutions:

• Fully open-source: Code is reviewable, customizable, and supports private deployment.
• Clear tech stack: Uses mainstream open-source components for easy understanding and extension.
• Cited answers: A standout feature among open-source RAG implementations.
• Lightweight: Low deployment threshold (Chroma + Streamlit).

Document Cortex is a well-structured RAG application built with a mainstream tech stack. It demonstrates how to build a multi-format, semantic search, and cited-answer Q&A system using FastAPI, Streamlit, LangChain, Chroma, and HuggingFace. It is an excellent reference for developers who want to understand RAG or customize their own systems. As RAG technology matures, such applications will play an increasingly important role in enterprise and personal knowledge management.