We need a chatbot that only answers from approved internal sources. What should I use for retrieval, indexing, and access control?

If the chatbot must only answer from approved internal sources, the safest pattern is:

Recommended architecture

Use a RAG system with a controlled document store:

1. Ingestion / indexing

   • Pull only approved sources from your internal systems.
   • Normalize documents, chunk them, and store metadata:
     • source system
     • document ID
     • owner
     • classification
     • ACLs / groups allowed
     • timestamp / version
   • Create embeddings for semantic retrieval.

2. Retrieval

   • Use hybrid search:
     • keyword / BM25 for exact matches
     • vector search for semantic matches
   • Apply metadata filtering before results reach the model.
   • Retrieve only chunks the user is authorized to see.

3. Answer generation

   • The model should answer only using retrieved context.
   • If the context is insufficient, it should say it cannot answer from approved sources.

4. Access control

   • Enforce authorization in the retrieval layer, not just in the prompt.
   • Use document-level and chunk-level ACLs.
   • Filter by user identity, groups, department, clearance, etc.
   • Log all retrievals for auditing.

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What to use for retrieval

Choose a vector database or search platform that supports filtering and hybrid search:

• Elasticsearch / OpenSearch
  Good for hybrid keyword + vector search and metadata filters.
• Pinecone Good managed vector search with metadata filtering.
• Weaviate Good vector search and hybrid retrieval.
• Milvus Good for large-scale vector search, usually paired with another search layer for BM25.
• Azure AI Search Strong choice if you’re in Microsoft/Azure and need enterprise auth patterns.
• Postgres + pgvector Fine for smaller setups, but less ideal for sophisticated enterprise search.

If you need strict enterprise controls, OpenSearch/Elasticsearch or Azure AI Search are often the best starting points because they combine search, filters, and governance better than a pure vector store.

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What to use for indexing

For indexing, use a pipeline that does:

• document extraction: PDFs, Office docs, wiki pages, tickets
• cleaning and chunking
• deduplication
• metadata enrichment
• embedding generation
• ACL attachment

Tools commonly used:

• LlamaIndex or LangChain for ingestion pipelines
• Haystack for end-to-end retrieval workflows
• Custom ETL jobs if governance is strict

Important:

• keep chunks small enough for precision
• store source citations for every chunk
• version documents so stale content can be removed
• reindex on source changes

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What to use for access control

Access control should be enforced at multiple layers:

1. Source approval layer

Only ingest from approved repositories.

2. Identity layer

Integrate with SSO / IdP:

• Okta
• Azure AD / Entra ID
• Google Workspace
• Ping

3. Retrieval authorization layer

Before retrieval, resolve the user’s:

• roles
• groups
• departments
• clearance level

Then filter search results by ACL metadata.

4. Output guardrails

Require citations from retrieved documents only. If no approved evidence exists, return:

• “I don’t have enough approved internal sources to answer that.”

5. Auditing

Log:

• who asked
• what was retrieved
• what sources were used
• what answer was returned

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Practical implementation pattern

A strong setup looks like:

• Source systems: SharePoint, Confluence, Drive, internal wiki, ticketing system
• ETL/indexing: custom pipeline or LlamaIndex/Haystack
• Search layer: OpenSearch / Azure AI Search
• Auth: Entra ID / Okta
• LLM: any model behind a strict RAG wrapper
• Policy enforcement: in retrieval + response validation

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Best-practice recommendation

If your main requirement is “only approved internal sources,” I’d recommend:

• Azure AI Search + Entra ID if you’re Microsoft-centric
• OpenSearch/Elasticsearch + SSO + ACL filtering for flexible enterprise control
• Add hybrid retrieval, metadata-based authorization, and citation-only generation

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Key caution

Do not rely on prompting alone to enforce access control.
The model must never even see unauthorized documents.

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If you want, I can also give you:

1. a reference architecture diagram, or
2. a vendor comparison table for OpenSearch vs Azure AI Search vs Pinecone vs Weaviate.