
This project implements a Retrieval-Augmented Generation (RAG) chat assistant that allows users to upload documents and interact with them conversationally through a professional interface. Each document maintains its own persistent chat history, ensuring context isolation and accurate retrieval. The system combines vector search, metadata-filtered retrieval, and LLM-based reasoning to deliver grounded, document-specific answers through a clean web UI.
๐ Introduction
1.1 Purpose and Scope
The purpose of this project is to demonstrate a clean, reliable, and user-centric RAG system that allows users to:
- Upload documents (PDF, text-based formats)
- Select a document explicitly
- Ask natural language questions about it
- Receive answers strictly grounded in the document content
The system is intentionally scoped to single-document conversations to maximize correctness and interpretability.
Techniques and Tech Stack
Core Techniques:
- Retrieval-Augmented Generation (RAG)
- Vector similarity search with metadata filtering
- Context-controlled prompting
- Stack: FastAPI ยท ChromaDB ยท HuggingFace ยท LangChain ยท Groq LLM
1.2 Importance of Effective Presentation
Many RAG demos fail in real usage due to:
- Mixing context across multiple documents
- Unclear retrieval boundaries
- UI designs that hide system state
This project addresses those issues by aligning backend architecture, retrieval logic, and UI design around clarity and determinism.
๐งฉ Foundations of Effective Project Design
2.1 Core Tenets of a Strong RAG System
- Context Isolation โ each document has its own chat history
- Grounded Answers โ LLM responses are strictly limited to retrieved chunks
- Persistent Memory โ chat history is stored on disk per document
- Explicit Retrieval โ no hidden or implicit context mixing
- User Transparency โ UI always shows which document is active
2.2 Project Type on Ready Tensor
Category:
๐น AI Application / System Design
This is a full-stack AI system, not just a model demo. It showcases practical integration of LLMs into a real user-facing workflow.
2.3 Selecting the Right Scope
Instead of supporting unrestricted multi-file chat, this project deliberately limits conversations to one document at a time.
Benefits:
- Higher retrieval accuracy
- No ambiguity about answer source
- Simpler and more intuitive UX
- Reduced hallucinations
This mirrors how professional document assistants are often designed in enterprise settings.
๐๏ธ Architecture Overview
System Flow
- User uploads a document
- Document is chunked and embedded
- Chunks are stored in ChromaDB with
file metadata
- User selects a document from the sidebar
- User asks a question
- Relevant chunks are retrieved using:
- Semantic similarity
- Metadata filtering (
file = selected_file)
- LLM generates an answer using only retrieved context
- Conversation is stored in a file-scoped history
๐งช Assessing the Technical Content
Retrieval Layer
- Dense embeddings using SentenceTransformers (MiniLM)
- Vector search via ChromaDB
- Metadata-based filtering to ensure file isolation
- Controlled chunk limits to avoid context overflow
This ensures answers remain relevant, concise, and grounded.
Prompt Engineering Strategy
The system prompt enforces:
- Use of retrieved context only
- Explicit refusal when the answer is not present
- Clear separation between conversation history and document knowledge
This significantly reduces hallucinations and improves reliability.
Chat History Management
- Each document has its own
FileChatMessageHistory
- Stored as JSON on disk
- Reloaded automatically when switching documents
- Preserved across server restarts
This design mirrors scoped memory in professional AI tools.
๐จ Enhancing Readability and User Experience
Interface Highlights
- professional-style layout
- Sidebar for document navigation
- Active document indicator
- Disabled input until a document is selected
- Scrollable chat window with clear message roles
Every UI element reflects the systemโs internal state.
Error Handling & Safety
- Invalid or empty documents are rejected
- Failed uploads are clearly reported
- Users cannot query without selecting a document
These checks improve robustness and user trust.
โ ๏ธ Known Limitations
- Single-document chat only
- No token-level streaming
- No inline citation highlighting
These are intentional design trade-offs to maintain clarity and correctness.
๐ฎ Future Improvements
- Streaming responses (typing effect)
- Per-answer citations with chunk references
- Document preview pane
- Optional analytical multi-document mode
๐งพ Summary
This project demonstrates how to build a production-minded RAG assistant by:
- Prioritizing correctness over feature sprawl
- Aligning backend design with UX clarity
- Using metadata and history scoping to avoid confusion
A good RAG system is defined not by how much it answers, but by how well it knows its limits.