OP Vault is an open-source system designed to give large language models long-term memory by enabling them to interact with a custom knowledge base built from user-provided documents. It combines a backend written in Go with a React frontend, allowing users to upload files such as PDFs, text documents, and books to create a searchable repository of information. The system uses vector databases like Pinecone alongside OpenAI models to index and retrieve relevant content, enabling precise question-answering grounded in the uploaded materials. Users can query the system in natural language and receive answers that include references to specific files and sections, improving transparency and trust in the responses. The project is designed to handle large volumes of data, making it suitable for personal knowledge management, research archives, or enterprise documentation systems.
Features
- Upload and index documents to create a custom knowledge base
- Semantic search using vector databases for accurate retrieval
- Question answering grounded in user-provided content
- Displays source files and contextual snippets for transparency
- Frontend interface built with React for ease of use
- Backend integration with OpenAI and vector database technologies