Document Vectorization and RAG Pipeline Diagram
This diagram traces how documents are chunked, embedded, and stored in a vector database, then retrieved to ground an LLM's answers in relevant context. It's a core reference for building retrieval-augmented generation systems. Tip: clearly separate the offline indexing path from the online query path for easier debugging.
The prompt behind this diagram
Create a document vectorization and retrieval-augmented generation pipeline diagram showing: Document Source (PDFs, web pages), a Text Extraction and Chunking step, an Embedding Model converting chunks into vectors, a Vector Database storing embeddings with metadata, a User Query input, a Query Embedding step, a Similarity Search against the vector database, a Context Retrieval step assembling top matching chunks, a Prompt Construction step combining query and context, and a Large Language Model generating the final Answer output returned to the user.
Paste your own description (or Terraform / docker-compose / SQL schema) into draft1 and get a diagram like this for your exact system.
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