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Retrieval-Augmented Conversational Agents for Literary Discussion

A sophisticated literary discussion platform that combines the power of Large Language Models (LLMs) with retrieval-augmented generation to provide intelligent, context-aware discussions about books.

Backend Architecture

Technology Stack

  • FastAPI: Modern, fast web framework for building APIs
  • ChromaDB: Vector database for efficient similarity search and storage
  • Sentence Transformers: For generating high-quality text embeddings
  • Google Gemini API: For generating contextual responses
  • Wikipedia API: For fetching book-related information
  • Google Books API: For book metadata and search

Core Components

1. Book Data Preparation Pipeline

The system follows a three-step process to prepare books for discussion:

  1. Book Search & Selection

    • Uses Google Books API to search and retrieve book metadata
    • Endpoint: /search-books
    • Returns book details including title, authors, description, and thumbnail

  2. Wikipedia Data Fetching

    • Retrieves comprehensive book information from Wikipedia
    • Endpoint: /books/fetch-wiki
    • Stores book summaries and content in JSON format
    • Handles disambiguation and not-found cases gracefully

  3. Content Embedding

    • Processes book content into vector embeddings
    • Uses SentenceTransformer ("all-MiniLM-L6-v2") for embedding generation
    • Chunks content into 500-character segments for optimal processing
    • Stores embeddings in ChromaDB for efficient retrieval
    • Endpoint: /books/embed

2. Vector Storage & Retrieval

  • ChromaDB Integration

    • Persistent vector storage in vectorstore directory
    • Each book gets its own collection
    • Efficient similarity search for context retrieval
    • Automatic collection management and persistence

  • Embedding Process

    • Content chunking for manageable segments
    • High-quality embeddings using SentenceTransformer
    • Unique IDs for each chunk
    • Automatic collection creation and management

3. Query Processing System

The system implements a sophisticated query processing pipeline:

  1. Context Retrieval

    • Converts user questions into embeddings
    • Retrieves most relevant book chunks using similarity search
    • Combines retrieved context with conversation history

  2. Response Generation

    • Uses Google Gemini API for response generation
    • Incorporates:
      • Retrieved book context
      • Conversation history
      • Book metadata
      • Wikipedia information
    • Generates concise, contextually relevant responses

  3. Response Management

    • Maintains conversation history
    • Compresses responses for efficient storage
    • Handles error cases gracefully

API Endpoints

Book Management

  • GET /search-books: Search for books using Google Books API
  • POST /books/fetch-wiki: Fetch Wikipedia data for a book
  • POST /books/embed: Generate and store embeddings for a book
  • GET /books/check: Check if a book is prepared for discussion
  • POST /books/prepare: Prepare a book for discussion (combines wiki fetch and embedding)

Discussion

  • POST /chat/query: Process user questions and generate responses
    • Input: book_id, question, conversation history
    • Output: AI response with updated history

Data Flow

  1. Book Preparation

    User Request → Book Search → Wikipedia Fetch → Content Embedding → Vector Storage

  2. Query Processing

    User Question → Embedding → Context Retrieval → Response Generation → User Response

Security & Environment

  • Environment variables for API keys
  • CORS middleware for secure cross-origin requests
  • Error handling and validation at each step
  • Persistent storage with proper directory management

Frontend Overview

The frontend is a modern web application that provides:

  • Book search and selection interface
  • Real-time chat interface for book discussions
  • Progress tracking for book preparation
  • Responsive design for various devices

Getting Started

Prerequisites

  • Python 3.10+
  • Required API keys:
    • Google Books API
    • Google Gemini API
    • (Optional) Wikipedia API

Environment Setup

  1. Create a .env file with required API keys:

    GOOGLE_API_KEY=your_google_books_api_key
GEMINI_API_KEY=your_gemini_api_key

  2. Install dependencies:

    pip install -r requirements.txt

  3. Start the backend server:

    uvicorn backend.main:app --reload

Technical Approach

Retrieval-Augmented Generation (RAG)

The system implements RAG to enhance LLM responses with relevant book context:

  1. Retrieval: Uses vector similarity search to find relevant book passages
  2. Augmentation: Combines retrieved context with user query
  3. Generation: Uses Gemini to generate contextually relevant responses

Vector Search Optimization

  • Chunk size optimization (500 characters)
  • Efficient embedding storage and retrieval
  • Persistent vector database for quick access
  • Automatic collection management

Error Handling & Resilience

  • Graceful handling of API failures
  • Proper error messages for users
  • Automatic retry mechanisms
  • Data validation at each step

Future Improvements

  1. Enhanced Context Retrieval

    • Implement more sophisticated chunking strategies
    • Add metadata-aware retrieval
    • Implement hybrid search (keyword + semantic)

  2. Performance Optimization

    • Implement caching for frequent queries
    • Optimize vector search parameters
    • Add batch processing for embeddings

  3. Additional Features

    • Support for multiple languages
    • Enhanced book metadata integration
    • Advanced conversation management
    • User feedback integration
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