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Key Takeaways

  • RAG is most valuable when connected to a workflow and a schema.

  • Store page level provenance and citations from day one.

  • Use MongoDB Atlas Vector Search with tenant scoped filters.

  • Add evaluation and monitoring so quality does not drift silently.

Retrieval augmented generation (RAG) is most valuable when it is attached to a real workflow: intake, search, extraction, and decision support. This post outlines a practical architecture that pairs AWS storage and compute with MongoDB Atlas Vector Search to power grounded Q and A over documents, with auditable citations.

What teams usually get wrong about RAG

Common failure modes:

  • Treating RAG as a chatbot, not a workflow component

  • Indexing messy text without page level provenance

  • No evaluation plan, which makes quality feel random

  • No access control model, which becomes a security problem

The core requirement: citations and traceability

If a user asks "why" or "where did this come from", your system should point to:

  • Document ID

  • Page number

  • Bounding box or paragraph span

  • Extraction timestamp and model versions

That is how you keep RAG usable for operations, not just demos.

Reference architecture (AWS plus MongoDB)

Ingestion

  1. Upload PDF to S3

  2. Extract text using Textract or an OCR pipeline

  3. Normalize into page chunks with stable IDs

References:

Storage and indexing

Use MongoDB as the system of record:

  • Store chunk text and metadata

  • Store embeddings per chunk

  • Use Atlas Vector Search for kNN retrieval

References:

Query flow

  1. User question enters API

  2. Retrieve top k chunks with tenant scoped filter

  3. Assemble prompt with citations

  4. Call LLM

  5. Return answer plus sources

Chunking strategy that works in practice

Chunking is a product decision. Recommended baseline:

  • Chunk per page, then split by headings or paragraph length

  • Keep chunk size consistent, for example 300 to 800 tokens

  • Add overlap when sections run long

Metadata you should store per chunk:

  • tenantId

  • documentId

  • pageNumber

  • chunkIndex

  • sourceType (OCR, native PDF text)

  • hash for dedupe

Embeddings choices

Pick an embedding model that fits your latency and cost constraints. Two practical rules:

  • Use one embedding model consistently so similarity scores are comparable

  • Re embed only when you change the model or chunking

You also need an evaluation set. See:

Security model: tenant isolation and least privilege

At minimum:

  • Use per tenant filters in your vector query

  • Enforce authorization server side, never in the UI

  • Encrypt documents at rest, and use private networking where possible

References:

Evaluation: make RAG measurable

Add simple, repeatable tests:

  • Retrieval quality: does the correct page appear in top k

  • Groundedness: do answers cite retrieved chunks

  • Task success: did a user reach the next workflow step

Useful overview:

Where Lid Vizion fits

We typically help teams:

  • Build ingestion pipelines on AWS

  • Store structured outputs and workflow state in MongoDB

  • Add RAG safely, with citations and tenant scoped retrieval

zation in the API layer for every request.

FAQs

Q: Can we do RAG without embeddings? You can start with keyword search, but embeddings usually improve semantic matching. Many teams use both, keyword search for precision and vector for recall.

Q: Does Vector Search replace a full search product? Not always. Atlas Search provides rich filtering and ranking. Vector Search can be combined with it for hybrid retrieval.

Q: How do we avoid leaking data across tenants? Enforce tenant filtering inside the database query, and validate authori

Q: What should we link to internally? A: Link to relevant solution pages like Computer Vision or Document Intelligence, and only link to published blog URLs on the main domain. Avoid staging links.

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