Project goals were to build a Retrieval-Augmented Generation (RAG) chatbot that leverages ChatGPT-4 and Pinecone for fast, accurate research on 2024 blockchain and crypto regulations across multiple jurisdictions.
Project Details
- Designed a LangChain-based backend that embeds legal and regulatory texts (PDFs, CSVs, HTML, plain text) into vector representations and indexes them in Pinecone for sub-second retrieval.
- Integrated ChatGPT-4 as the LLM backbone, orchestrating prompts to combine retrieved context with generative responses tailored to user queries about crypto laws.
- Developed a Streamlit frontend, delivering an intuitive conversational UI where users can ask jurisdiction-specific legal questions and view source citations inline.
- Built flexible ingestion pipelines to handle a variety of document formats—automating OCR for scanned PDFs, parsing CSV tables, and scraping HTML from the Global Legal Insights site.
- Optimized vector store performance, tuning embedding dimensions and Pinecone index parameters for cost-effective storage and rapid similarity searches at scale.
- Packaged the solution with deployment scripts, Docker configuration, and clear README instructions so other teams can adapt the RAG chatbot to their own data sources.
- Ensured extensibility, allowing future connectors (e.g., databases, APIs, streaming sources) to be added with minimal code changes.