Document Vectorization and RAG Pipeline Diagram

What this diagram shows

This diagram shows how unstructured documents become queryable through a retrieval-augmented generation (RAG) pipeline. Documents flow through text splitting and embedding to create vector representations, which are stored in a vector database indexed for similarity search. When a user query arrives, it is embedded using the same model, matched against stored vectors to retrieve relevant chunks, and passed alongside the original query to a language model for final answer generation. The complete workflow connects data ingestion through retrieval to response synthesis.

Key components

• Document Source — Ingestion point where raw documents (PDFs, web pages, markdown files) enter the pipeline for processing.
• Text Splitter — Divides large documents into overlapping chunks of fixed size to create manageable units for embedding.
• Embedding Model — Converts text chunks and user queries into dense numerical vectors using a transformer model like OpenAI's text-embedding-3-small or Sentence-BERT.
• Vector Database — Stores embedded vectors with indexed metadata, enabling fast similarity search using approximate nearest neighbor algorithms (HNSW, IVF).
• Retrieval Engine — Accepts an embedded query and returns the k most similar document chunks using cosine similarity or dot product scoring.
• Language Model — Receives retrieved context chunks plus the original user question and generates a grounded answer through autoregressive decoding.
• Final Response — The synthesized answer delivered to the user, augmented with relevant document context and citation references.

When to use it

Use this diagram when implementing a question-answering system over proprietary documents, building internal knowledge base search, or designing chatbots grounded in specific content collections. It suits scenarios where you need to explain the data flow from raw documents to final answers, particularly when stakeholders must understand why retrieval quality and embedding consistency matter. Ideal for documentation, architecture reviews, and implementation planning for RAG systems.

Common mistakes

• Treating the embedding model as generic interchangeable software rather than confirming the same model version is used for both document and query embedding, which breaks retrieval accuracy.
• Omitting the text splitting stage and attempting to embed entire documents at once, resulting in lost context, slow retrieval, and violated token limits in language model context windows.
• Failing to show the retrieval step explicitly, instead showing a direct path from queries to the language model, which obscures the critical dependency on vector database quality and chunk relevance.

Adapting it to your system

Identify your document sources (internal wikis, PDF repositories, APIs) and replace the generic ingestion box. Specify your actual embedding model by vendor and dimension count (e.g. text-embedding-ada-002, 1536D). Name the vector database you will use: Pinecone, Weaviate, Milvus, or pgvector. Document your chunk size and overlap strategy based on your content structure. Add a metrics node if tracking retrieval success rates or generation accuracy. Include any reranking or filtering layers between retrieval and generation if your system requires them.