Build a Retrieval-Augmented Generation system. Upload documents, chunk and embed them into a vector store, and query with natural language to get AI answers grounded in your data.
0 likes0 forks0 comments
ragvector-storeembeddingsknowledge-base
Objectives
Upload and process PDF, DOCX, and text documents
Chunk documents with optimal overlap strategy
Generate embeddings and store in a vector database
Query with natural language and retrieve relevant chunks
Generate answers with source citations
Hints
Use OpenAI embeddings or sentence-transformers
Pinecone, Weaviate, or Chroma for vector storage
Chunk size of ~500 tokens with 100 token overlap works well