PaperLM

PaperLM was born out of the need to efficiently extract and interact with information from large sets of research papers. Traditional search methods fall short when you need specific answers buried in hundreds of pages of technical documentation.

Key Features

• Intelligent RAG Pipeline: Leverages LangChain to orchestrate document ingestion and retrieval.
• Vector Search: Uses Pinecone as a high-performance vector database for semantic search.
• Secure Storage: Integrated with AWS S3 for reliable and secure document management.
• Context-Aware Responses: Utilizes OpenAI embeddings to understand the nuances of technical text.

Technical Deep Dive

The system processes PDF files by chunking them into manageable segments, generating embeddings, and storing them in Pinecone. When a user asks a question, the most relevant chunks are retrieved and passed to the LLM to generate a precise answer with citations.

Outcome

The project resulted in a significant improvement in the speed of information retrieval for researchers, reducing the time spent searching through documents by approximately 60%.