ABOUT THE SYSTEM
DataTalk is an intelligent document analysis system built using Retrieval-Augmented Generation (RAG). It enables users to interact with documents through natural language while ensuring context-aware and accurate responses.
SYSTEM ARCHITECTURE
1
Document Ingestion
Supports PDF and text files using PyPDFLoader and TextLoader for structured data extraction.
2
Preprocessing Pipeline
Cleans and normalizes text while preserving semantic structure for downstream processing.
3
Chunking Strategy
Uses RecursiveCharacterTextSplitter (chunk size 300, overlap 30) for optimal context retention.
4
Embedding Generation
Generates dense vector embeddings using all-MiniLM-L6-v2 (384-dimensional semantic vectors).
5
Vector Database
Stores embeddings in ChromaDB with efficient similarity-based indexing.
6
Semantic Retrieval
Uses Max Marginal Relevance (MMR) to retrieve diverse and relevant document chunks.
7
Context Optimization
Refines queries and ensures high-quality contextual input before response generation.
8
Answer Generation
Generates responses using LLaMA 3.1 (via Groq) with context-aware prompting.
TECH STACK
Python
Reflex
LangChain
Groq
ChromaDB
Transformers
RETRIEVAL-AUGMENTED GENERATION
RAG enhances traditional language models by grounding responses in external knowledge sources. Instead of relying solely on model memory, it retrieves relevant document context and combines it with generation, resulting in more accurate and reliable outputs.
KEY FEATURES
Multi-Format
PDF and TXT support
Semantic Search
Context-aware retrieval
Data Privacy
Local processing only
Fast Response
Low-latency queries
Built with Reflex