Bitcoin Question-Answering RAG System
Published:
A production-ready Retrieval Augmented Generation (RAG) system designed to provide accurate, context-aware answers about Bitcoin and cryptocurrency by combining vector search with large language models.
Overview
This system demonstrates modern RAG architecture patterns, combining semantic search over a knowledge base of Bitcoin articles with GPT-4’s reasoning capabilities. The integration of Tavily’s search agent allows the system to augment responses with up-to-date information from the web when needed.
Technical Stack
- Vector Database: Chroma for efficient semantic similarity search
- Embeddings: Sentence Transformers for converting text to dense vector representations
- LLM Framework: LangChain for orchestrating the RAG pipeline
- Language Model: OpenAI GPT-4 for answer generation and reasoning
- Search Enhancement: Tavily LLM Search agent for real-time web information
Key Features
- Semantic Search: Uses dense embeddings to retrieve the most relevant Bitcoin articles based on meaning, not just keywords
- Context-Aware Responses: Combines retrieved documents with LLM reasoning for comprehensive answers
- Hybrid Information Retrieval: Falls back to web search for questions outside the knowledge base
- Conversation Memory: Maintains context across multiple questions for natural dialogue
Implementation Highlights
The system follows a standard RAG pipeline:
- Document ingestion and chunking of Bitcoin articles
- Embedding generation using Sentence Transformers
- Vector storage and indexing in Chroma
- Query processing with semantic similarity search
- Context injection and answer generation via GPT-4
- Optional web search augmentation for current events
This project showcases practical experience with modern LLM application patterns including vector databases, embedding models, and prompt engineering for reliable question-answering systems.