# RAG Chatbot - Complete Implementation Build a production-ready **RAG (Retrieval-Augmented Generation) chatbot** with a ChatGPT-like frontend and Python FastAPI backend. ## Frontend Requirements ### Technology Stack - **Framework**: React with Type...

[Copilot]

RAG Chatbot - Complete Implementation

• Project Restructuring

  • Move existing frontend files to frontend/ directory
  • Create backend/ directory structure

• Frontend Implementation (React + TypeScript)

  • Install required dependencies (react-markdown, react-syntax-highlighter, remark-math, rehype-katex, katex)
  • Create TypeScript types (types/index.ts)
  • Create API service (services/api.ts)
  • Create components:
    • ChatMessage component with CSS
    • ChatInput component with CSS
    • ChatWindow component with CSS
    • CodeBlock component with CSS
    • LoadingIndicator component with CSS
  • Update App.tsx and App.css for chat interface
  • Update index.css and index.html
  • Create frontend README.md

• Backend Implementation (Python FastAPI)

  • Create main.py (FastAPI entry point)
  • Create config.py (environment variables)
  • Create models.py (Pydantic models)
  • Create RAG modules:
    • vectorstore.py (Chroma DB)
    • ingestion.py (document loading)
    • retrieval.py (RAG query logic)
  • Create LLM modules:
    • longcat_client.py (LongCat API wrapper)
  • Create utils:
    • logger.py (logging utilities)
  • Create requirements.txt
  • Create .env.example
  • Create details.txt (setup instructions)
  • Create backend README.md
  • Create data/ folder with README and .gitkeep
  • Create chroma_db/ folder with .gitignore

• Configuration Files

  • Update root .gitignore
  • Create root README.md with full documentation

• Testing & Validation

  • Test frontend build
  • Test backend startup
  • Run code review
  • Run CodeQL security check

Original prompt

RAG Chatbot - Complete Implementation

Build a production-ready RAG (Retrieval-Augmented Generation) chatbot with a ChatGPT-like frontend and Python FastAPI backend.

Frontend Requirements

Technology Stack

• Framework: React with TypeScript
• Styling: Pure CSS (NO Tailwind) - each component has its own CSS file
• Build Tool: Vite or Create React App

Core Features

1. Chat Interface

   • Clean, ChatGPT-inspired UI
   • Message input box at bottom with send button
   • Scrollable chat history with auto-scroll to latest message
   • User messages aligned right, bot messages aligned left
   • Avatar/icons for user and bot
   • Loading indicator (typing animation) while waiting for responses
   • Timestamp for each message
   • Clear chat button

2. Rendering Capabilities (CRITICAL - Must Support)

   • Markdown: Full markdown support (headers, lists, links, bold, italic, blockquotes, tables)
   • Code Blocks: Syntax-highlighted code with language detection, line numbers, and copy-to-clipboard button
   • LaTeX: Render mathematical formulas (inline with $.. .$ and display mode with $$...$$)

3. Required Libraries

   • react-markdown - Markdown rendering
   • react-syntax-highlighter - Code syntax highlighting
   • remark-math and rehype-katex - Math support in markdown
   • katex - LaTeX rendering
   • Standard React hooks for state management

File Structure

frontend/
├── public/
│   └── index. html
├── src/
│   ├── components/
│   │   ├── ChatMessage/
│   │   │   ├── ChatMessage.tsx
│   │   │   └── ChatMessage.css
│   │   ├── ChatInput/
│   │   │   ├── ChatInput.tsx
│   │   │   └── ChatInput.css
│   │   ├── ChatWindow/
│   │   │   ├── ChatWindow.tsx
│   │   │   └── ChatWindow.css
│   │   ├── CodeBlock/
│   │   │   ├── CodeBlock.tsx
│   │   │   └── CodeBlock.css
│   │   └── LoadingIndicator/
│   │       ├── LoadingIndicator.tsx
│   │       └── LoadingIndicator.css
│   ├── services/
│   │   └── api.ts
│   ├── types/
│   │   └── index.ts
│   ├── App.tsx
│   ├── App.css
│   ├── index.tsx
│   └── index.css
├── package.json
├── tsconfig.json
└── README.md

---

Backend Requirements

Technology Stack

• Framework: Python FastAPI with CORS middleware
• LLM Provider: LongCat API (using OpenAI Python client)
• RAG Framework: LangChain
• Vector Database: Chroma (free, embedded, no external setup)
• Embeddings: OpenAI text-embedding-3-small (via OpenAI API)
• Document Processing: PyPDF for PDFs, LangChain text loaders for Markdown

LongCat API Integration

Use the OpenAI-compatible client (best option):

from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("LONGCAT_API_KEY"),
    base_url="https://api.longcat.chat/openai"
)

response = client.chat.completions. create(
    model="LongCat-Flash-Chat",
    messages=[... ],
    max_tokens=2000,
    temperature=0.7
)

Core Features

1. Document Ingestion System

   • Automatically scan backend/data/ folder on startup
   • Load all PDF and Markdown (. md) files
   • Chunk documents: 1000 characters per chunk, 200 character overlap
   • Generate embeddings using OpenAI's text-embedding-3-small
   • Store in Chroma vector database (persistent storage in backend/chroma_db/)
   • Provide /reindex endpoint to manually re-index documents

2. RAG Pipeline

   • User sends query → Retrieve k=5 most similar document chunks
   • Build context from retrieved chunks
   • Construct final prompt:

     System: You are a helpful AI assistant. Answer questions based on the provided context documents.  If the answer isn't in the context, say so clearly.  Be concise and accurate. 
     
     Context:  {retrieved_chunks}
     
     User Question: {user_input}
     

   • Send to LongCat LLM
   • Return response with sources/metadata

3. Comprehensive Server Logging (CRITICAL)
   Display ALL of the following in console logs:

   ============================================
   [2025-12-31 10:30:45] NEW QUERY RECEIVED
   ============================================
   USER INPUT: "What is the capital of France?"
   
   --------------------------------------------
   RAG RETRIEVAL PROCESS
   --------------------------------------------
   Similarity Search: k=5
   Retrieved Chunks:  5
   
   Similarity Scores:
     1. Score: 0.8734 | Source: documents/geography. pdf (page 12)
     2. Score: 0.8521 | Source: documents/europe.md
     3. Score: 0.7892 | Source: documents/geography.pdf (page 45)
     4. Score: 0.7654 | Source: documents/cities.md
     5. Score: 0.7341 | Source: documents/world_facts.pdf (page 3)
   
   Chunk Previews:
     [1] "France is a country in Western Europe.  Its capital and largest city is Paris..."
     [2] "European capitals include:  Paris (France), Berlin (Germany)..."
     [3] "Major European cities by population: Paris - 2.1M..."
     [4] "Paris, the capital of France, is known for...
   
   

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