RagWiser
Enables users to upload PDF documents, process them, and ask questions about their content using natural language, powered by Retrieval Augmented Generation (RAG).
Acerca de
RagWiser is a Spring Boot-based Retrieval Augmented Generation (RAG) system designed for document question-answering. It allows users to upload PDF documents, which are then processed to extract text, split into chunks, and have their vectorized representations stored in a PostgreSQL database using the pgvector extension. Users can then ask questions about the document content using natural language, and the system retrieves relevant context to generate accurate answers using OpenAI's GPT models. RagWiser leverages Spring AI for vector store and LLM integration, making it an advanced solution for knowledge retrieval and question answering from documents.
Características Principales
- Automatically extract text from PDFs, split it into chunks, and store embeddings
- Generate accurate answers based on document content using RAG
- Leverages Spring AI for vector stores and LLM integration
- Query documents using natural language with semantic search
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- Upload and process PDF documents via REST API
Casos de Uso
- Integrate RAG capabilities into other AI systems using Spring AI's Tool Callbacks
- Quickly find answers within large PDF document collections
- Build a question-answering system on top of a knowledge base of PDF documents
