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RAG pipeline over PDFs — chunking, embeddings, vector search and grounded LLM answers.
Built bySadia- approach
- Retrieval-Augmented Generation
- source
- PDF documents
- storage
- Vector database
- language
- Python
Overview
Retrieval-Augmented Generation pipeline: extracted text from PDF documents, split it into overlapping chunks, generated vector embeddings, and indexed them in a vector database for semantic search. Implemented retrieval and context assembly, formatting retrieved documents into a structured prompt so the LLM answers from source material rather than parametric memory. Chained retriever, prompt template, and LLM into a single invocable pipeline; tuned chunk size, overlap, and top-k retrieval to reduce irrelevant context.
What it delivers
- Answers grounded in source documents
- Tuned chunk size, overlap and top-k to cut irrelevant context
- Single invocable retriever → prompt → LLM pipeline
Features
Document Ingestion
- Text extraction from PDF documents
- Splitting into overlapping chunks
- Chunk size and overlap tuned for relevance
Embeddings & Vector Search
- Vector embeddings generated per chunk
- Indexed in a vector database
- Semantic top-k retrieval
Grounded Answering
- Retrieved context assembled into a structured prompt
- LLM answers from source material, not parametric memory
- Retriever, prompt template and LLM chained into one pipeline
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