The product features include chat-based document Q&A, citation of source data, PDF viewer with citation highlighting, and use of API-based tools for answering quantitative questions. The tech stack includes React, Next.js, FastAPI, Docker, SQLAlchemy, OpenAI, PGVector, and LlamaIndex. The infrastructure is provided by Render.com, Vercel, and AWS. However, the frontend currently doesn't support mobile and there's room for improvement in terms of RAG performance. Contributions from the LlamaIndex community are welcomed.
Key takeaways:
- SEC Insights is a Retrieval Augmented Generation (RAG) application that uses LlamaIndex to answer questions about SEC 10-K & 10-Q documents.
- The application serves as a complete example of a working real-world RAG application, providing a solid foundation for developers to build their own RAG applications.
- SEC Insights offers features such as chat-based document Q&A, citation of source data, PDF viewer with highlighting of citations, and use of API-based tools for answering quantitative questions.
- The tech stack for SEC Insights includes React/Next.js, FastAPI, Docker, SQLAlchemy, OpenAI, PGVector, LlamaIndex, Render.com, Vercel, and AWS.