Basic RAG Pipeline — Document Q&A System Build

I will build a basic RAG pipeline for your document collection — covering document ingestion, chunking, embedding generation, vector store indexing, semantic retrieval, and LLM response synthesis — delivering a working document Q&A system that answers questions from your specific content rather than the LLM's general training knowledge. A basic RAG implementation built correctly from the start avoids the most common RAG mistakes — chunk sizes that are too large or too small for your query types, retrieval without relevance threshold filtering, and synthesis prompts that don't instruct the model to stay grounded in retrieved content, all of which produce hallucinated or irrelevant answers.

The pipeline covers document loading, fixed-size chunking with overlap, embedding generation, vector store setup, similarity retrieval with threshold filtering, source citation, and a chat interface for testing.