How do we build Enterprise KnowledgeHub-BluePrint

1. What the Knowledge Hub is and goals

2. High-level architecture (components)

3. Ingestion pipeline (sources → chunking → embeddings)

4. Indexing & storage (vector DB + metadata store + KG)

5. Retrieval / RAG integration & runtime APIs

6. Governance, QA, and audit controls

7. Security, privacy & compliance (BFSI focus)

8. Operations, monitoring & SLOs

9. Example schemas / payloads and prompt template hints

10. Interview-ready one-liners and FAQs

1 — What is the Knowledge Hub (Goals)

A Knowledge Hub is the single enterprise-managed, versioned, auditable corpus of authorized documents, policies, SOPs, contract templates, past case notes and curated internal knowledge that powers RAG and other downstream copilots.

Primary goals:

• Authoritative: single source of truth for policies and clauses

• Updatable: fast updates without model retrain

• Auditable: traceable evidence per LLM answer (chunk → doc → version)

• Searchable: semantic and exact-match search (hybrid)

• Composable: supports RAG, KG, analytics and manual review UIs

2 — High-level Architecture (component map)

Sources (DMS, DBs, Email, SharePoint, Policies, PDFs, Jira, Slack)
     ↓ Ingest Connectors (ETL / Change Capture)
     ↓
Preprocessing & Normalizer (OCR, Text Extraction, Language Detect)
     ↓
Chunker + Metadata Extractor  ——> Blob Storage (chunked documents)
     ↓
Embedder / Vectorizer  ——> Vector DB (vector index)
     ↓
Metadata Store (Postgres/Cosmos) <—— Blacklist/Whitelist/Policy tags
     ↓
Knowledge Graph Extractor (optional) ——> Graph DB (Neo4j/Cosmos Graph)
     ↓
RAG Orchestrator & APIs (retriever, reranker, prompt-builder)
     ↓
LLM Endpoints (private/on-prem) & Post-Validator
     ↓
Audit Store (immutable): prompts, responses, chunk IDs, evidence URIs

Components:

• Connectors: ingest from DMS, SharePoint, Jira, Confluence, regulatory feeds, knowledge authors.

• Preprocessor: OCR (Form Recognizer), text cleaning, normalization, language detection.

• Chunker: semantic chunking, overlap, chunk metadata (source, clause_id, doc_version).

• Embedding service: generate embeddings with controlled model.

• Vector DB: Milvus, Pinecone, PGVector, Azure Cognitive Search vector, etc.

• Metadata DB: relational DB for provenance + policies.

• KG extractor: optional NER + relation extraction for graph queries.

• RAG Orchestrator: hybrid retriever + reranker + prompt builder.

• Audit & Evidence store: blob storage + immutable logs.

• Management UI: upload, review, tag, version, legal sign-off.

3 — Ingestion Pipeline (step-by-step)

1. Source identification & access: catalog sources (policy repo, legal DMS, RBI circulars, SOPs, contracts, emails, past manual review notes). Define owner for each source.

2. Connector & change-capture:

   • Full initial sync, then CDC (file-change webhook, SFTP, API polling).

   • Keep source_id, source_path, and version metadata.

3. Preprocessing:

   • Convert to plain text / structured JSON.

   • OCR for images/PDF (Form Recognizer); extract tables (bank statements).

   • Language detection & normalization (punctuation, whitespace).

4. Canonicalization & metadata extraction:

   • Extract doc_type (policy, contract, clause), effective_date, jurisdiction, product, author, policy_id, doc_version, tags.

   • Map to taxonomy (e.g., Lending → CreditPolicy → Collateral).

5. Chunking:

   • Semantic chunking (200–800 tokens), with small overlaps (20–40%) for context.

   • Tag chunks with metadata: chunk_id, doc_id, source_type, clause_id, start_offset, end_offset, authority_score (manually or heuristically assigned).

6. Embedding:

   • Use an enterprise embedding model (private endpoint).

   • Generate embeddings for each chunk and persist to Vector DB with metadata pointers.

7. Indexing:

   • Populate vector DB; update hybrid inverted-index for exact keyword lookups.

   • Update metadata DB with chunk -> doc mapping and provenance.

8. Validation & QA:

   • Samples checked by SME or auto-validate: key fields present, no PII leakage in public embeddings, minimum chunk quality.

9. Publish / Version:

   • When source doc changes, create new doc_version, re-chunk, re-embed; keep prior versions for audit.

4 — Indexing & Storage: Vector DB + Metadata + KG

• Vector DB (primary retrieval):

  • Store vector, chunk_id, doc_id, score, authority_score, timestamp

  • Support ANN search (HNSW / IVF) with filters (metadata) for hybrid retrieval

• Metadata Store (control plane):

  • Schema: doc_id, doc_type, policy_id, version, effective_date, owner, jurisdiction, tags, retention_policy, authority_score, legal_signoff

  • Use Postgres / Cosmos DB

• Knowledge Graph (optional, for relationships):

  • Entities: Person, Organization, Account, Clause, Regulation

  • Relationships: director_of, affiliated_with, cited_by, amends

  • Use Graph DB for AML/fraud multi-hop reasoning

• Blob Store / Evidence Store:

  • Store original docs, chunk text, extracted fields, prompt/response logs

  • Use immutable storage with WORM/append-only capability (Azure Blob with immutability policy)

5 — Retrieval / RAG runtime & APIs

Key runtime services:

• Retriever API: accepts (query, loan_context_filters), returns top-K chunks + metadata

  • Support vector similarity + boolean filters (jurisdiction, policy_id, date)

• Reranker service: cross-encoder to re-score top candidates by authority and semantic fit

• Prompt builder: constructs structured prompt (system + facts + top chunks + question)

• LLM gateway: private endpoint orchestration (small model draft / large model final)

• Post-validator: confirm every factual claim references chunk(s); if not, mark INSUFFICIENT_EVIDENCE

• Audit writer: write prompt, chunk_ids, reranker scores, response, model version to audit store

APIs:

• POST /knowledge/retrieve → {chunks[]}

• POST /copilot/query → triggers retrieval + LLM + returns {response, citations[]}

• GET /knowledge/doc/{doc_id} → original doc metadata + versions

• POST /knowledge/upload → admin upload (with review workflow)

6 — Governance, QA & Audit Controls

Governance pillars:

• Ownership & approvals: every doc must have an owner; only owners can approve versions.

• Versioning: immutable versions; keep previous versions for audit.

• Authority scoring: assign higher weight to legal/approved docs.

• Validation rules: automated checks for chunk integrity, length, language, PII leakage.

• Human review workflow: SMEs review newly ingested high-impact docs before publish.

• Prompt & response logging: immutable logs for every Copilot invocation (prompt, chunks returned, reranker scores, LLM output).

• Explainability mapping: map policy clauses to decision rules and provide direct citation in Copilot output.

• Retention & deletion policies: retention per doc_type and legal/regulatory requirements.

7 — Security, Privacy & Compliance (BFSI specifics)

• Data residency & isolation: store data in-region; use private endpoints for LLMs; use VNet, Private Link.

• PII protections:

  • Avoid embedding raw PII when sending to external models.

  • Use redaction/pseudonymization before embedding if using third-party embeddings.

  • For essential PII, use on-prem models or enterprise private endpoints.

• Access control:

  • RBAC for ingestion, approval, deletion.

  • Audit trails for every admin action.

• Encryption:

  • Data-at-rest encryption (SSE)

  • TLS for data-in-transit

• Immutable audit store: WORM for prompts/responses.

• Key management: Azure Key Vault / KMIP for keys.

• Regulatory audit pack: ability to export case-level evidence: prompt + chunks + doc versions + decision trace.

8 — Operations, Monitoring & SLOs

Operational metrics:

• Ingestion latency: time from source change → available in vector index

• Index freshness: % docs within SLA (e.g., 99% < 5 minutes for urgent regulatory updates)

• Retrieval latency (p95): target < 100–300 ms, depending on SLA

• Copilot end-to-end latency (p95): target < 5s (draft) / < 20s (deep reasoning)

• Query relevance metrics: human rated precision@k, MRR

• Hallucination rate: % responses flagged as INSUFFICIENT_EVIDENCE or failing post-verifier

• Usage & cost metrics: LLM calls, token usage, vector DB ops

• Operational alerts: embedding failures, index build failures, storage capacity thresholds

SLOs:

• Retrieval p95 < 200ms

• Index freshness for critical regulatory docs < 60s (or immediate for manual push)

• Copilot availability 99.9% for internal users

9 — Example Schemas & Prompt Template Hints

A. Document Metadata (metadata store example)

{
  "doc_id": "POL-CR-2025-07",
  "title": "Credit Policy - Personal Loans",
  "version": "v7",
  "effective_date": "2025-07-01",
  "jurisdiction": "IN",
  "owner": "head-risk",
  "doc_type": "policy",
  "authority_score": 0.98,
  "legal_signoff": true,
  "tags": ["credit", "personal_loan", "policy"],
  "source_uri": "dms://policies/credit/personal/v7.pdf"
}

B. Chunk record (vector DB metadata)

{
  "chunk_id": "POL-CR-2025-07_ch_14",
  "doc_id": "POL-CR-2025-07",
  "text": "...",
  "start_offset": 2400,
  "end_offset": 2800,
  "language": "en",
  "authority_score": 0.98,
  "tags": ["PD_threshold"]

}

C. Prompt Template (Underwriter Copilot)

SYSTEM: You are an Underwriter Assistant for ACME Bank. Use only the evidence chunks provided. Do not hallucinate.
FACTS:
- Loan Ref: {loan_ref}
- KYC: {kyc_summary}
- Credit Score: {credit_score}, Model: {credit_model_version}
- Income Stability: {income_score}
EVIDENCE CHUNKS: [CHUNK_1, CHUNK_2, ...] (each with doc_id and uri)
QUESTION: Explain why this loan needs manual review and recommend next steps with policy references.
RESPONSE FORMAT: JSON {
 "summary":"3-line",
 "conflicts": [...],
 "missing_documents":[...],
 "recommendation": {"action":"", "policy_refs":[...]}
}

10 — Testing, QA & Validation

• Unit tests for chunker & embedder (tokenization/overlap consistency)

• Integration tests for retriever + reranker (recall tests on held-out queries)

• Human-in-the-loop QA: SME judgments on top-K results for key queries

• Canary rollout for new embeddings/embedder models and LLM prompt changes

• Red-team to probe hallucination by adversarial queries

• Synthetic tests: inject test docs and ensure retrieval returns correct chunk with top rank

11 — Cost & Scaling Considerations

• Vector DB storage & ANN compute: scales with chunk count — chunk size policy important

• Embedding cost: batch compute embeddings at ingestion time; cache embeddings

• LLM cost: use two-tier model strategy — small model draft + large model for high-criticality

• Optimize by precomputing retrieval for common queries and caching results per loan_id

12 — Ownership, People & Process

• Data Owner per source (legal, compliance)

• Knowledge Engineers: chunking, taxonomy, prompt templates

• SME Reviewers: sign-off documents & chunk authority

• LLMOps team: model orchestration, prompt versioning

• Platform SRE: ensure index availability & performance

• AI Governance Board: approve policies for content usage & retention

13 — Interview-Ready One-Liners

• “The Knowledge Hub is the enterprise-controlled library that RAG consults — it enables auditable, versioned, and authoritative GenAI outputs without retraining models.”

• “We separate content (Knowledge Hub) from execution (LLM), allowing us to update policies instantly and keep regeneration of answers compliant and auditable.”

• “All Copilot answers are evidence-first: each factual claim must reference a chunk_id → doc_id → version that is stored immutably.”

Sample

====

I’ll produce a concrete, interview-ready example using Ram’s loan (loan-888888). You’ll get:

1. A realistic /agreement-context/{loanId} JSON (what Agreement Service returns after agreement generation)

2. Example RAG retrieved chunks (policy & clause chunks returned from Knowledge Hub)

3. The structured prompt that the underwriter Copilot will send to the LLM (system + context + few-shot style)

4. A sample LLM response (JSON) that the Copilot would return — fully evidence-backed with chunk citations and safe phrasing

   
   

1) GET /agreement-context/loan-888888 (sample response)

{
  "loan_ref": "loan-888888",
  "borrower": {
    "name": "Ram Kumar",
    "masked_mobile": "+91-XXXX-XXXX-1234",
    "customer_id": "CUST-44721"
  },
  "sanctioned_terms": {
    "amount": 500000,
    "tenure_months": 36,
    "interest_rate_annual_percent": 12.75,
    "interest_type": "floating",
    "pricing_rule_id": "PR-PL-2025-09",
    "processing_fee": 2500
  },
  "emi_schedule": {
    "emi": 16789,
    "first_emi_date": "2026-01-05",
    "schedule_uri": "blob://agreements/loan-888888/emi_schedule.json"
  },
  "payment_terms": {
    "late_fee_fixed": 500,
    "late_fee_percent_monthly": 2.0,
    "grace_period_days": 7
  },
  "prepayment_and_foreclosure": {
    "foreclosure_allowed": true,
    "foreclosure_fee": "1% of outstanding principal if prepaid within 12 months, else 0.5%",
    "prepayment_process_uri": "blob://agreements/loan-888888/prepayment_policy.pdf"
  },
  "legal_clauses": [
    {"clause_id": "LC-4.2", "title": "Interest Rate Reset", "text_uri": "blob://agreements/loan-888888/clauses/LC-4.2.txt"},
    {"clause_id": "LC-7.1", "title": "Late Payment & Penalty", "text_uri": "blob://agreements/loan-888888/clauses/LC-7.1.txt"},
    {"clause_id": "LC-9.3", "title": "Foreclosure & Prepayment", "text_uri": "blob://agreements/loan-888888/clauses/LC-9.3.txt"}
  ],
  "product_info": {
    "product_code": "PL-STD-24",
    "product_name": "Personal Loan - Standard",
    "disclosure_uri": "blob://agreements/loan-888888/disclosures.pdf"
  },
  "agreement_uri": "blob://agreements/loan-888888/agreement_v1.pdf",
  "generated_at": "2025-11-28T10:06:00Z",
  "agreement_version": "v1",
  "evidence_uris": [
    "blob://agreements/loan-888888/agreement_v1.pdf",
    "blob://agreements/loan-888888/emi_schedule.json"
  ]
}

2) RAG retrieved chunks (sample top-K from Knowledge Hub)

These are what the retriever returned for the Copilot query “Explain borrower loan agreement and foreclosure cost”.

[
  {
    "chunk_id": "POL-RBI-202402_ch_12",
    "doc_id": "POL-RBI-202402",
    "title": "RBI Fair Practice Code - Prepayment & Foreclosure",
    "snippet": "Banks shall allow prepayment of loans: foreclosure charges shall not exceed 2% for prepayment within one year of sanction; thereafter, foreclosure fees shall be reduced as per product schedule.",
    "uri": "dms://policies/rbi/fair_practice/2024/ch12.pdf",
    "authority_score": 0.99
  },
  {
    "chunk_id": "POL-PL-2025-09_ch_03",
    "doc_id": "POL-PL-2025-09",
    "title": "Internal Product Pricing Rules - Personal Loan Standard",
    "snippet": "If borrower prepays within first 12 months, foreclosure fee = 1% of outstanding; between 12 and 24 months = 0.75%; after 24 months = 0.5%. Floating rate reset once every 6 months based on MCLR + spread.",
    "uri": "dms://policies/internal/pricing/PL-STD-2025_v9.pdf",
    "authority_score": 0.95
  },
  {
    "chunk_id": "DISC-INFO-EMI_ch_02",
    "doc_id": "DISC-INFO-EMI",
    "title": "Customer Disclosure - EMI Calculation",
    "snippet": "EMI calculated using standard reducing balance method. The EMI shown is indicative and subject to final rounding and taxes.",
    "uri": "dms://disclosures/emi_calc.pdf",
    "authority_score": 0.9
  },
  {
    "chunk_id": "LEGAL-LC-7.1_ch_01",
    "doc_id": "LEGAL-CLAUSES-v1",
    "title": "Clause LC-7.1 Late Payment",
    "snippet": "Late fee: fixed INR 500 plus interest penalty of 2% monthly on overdue amount following a 7 day grace period.",
    "uri": "dms://legal/clauses/LC-7.1.txt",
    "authority_score": 0.98
  }
]

3) Prompt sent to LLM (structured, evidence-first)

System prompt (guardrails):

SYSTEM: You are the Bank's Borrower Agreement Copilot. Use ONLY the factual context and evidence chunks provided. Do NOT hallucinate. Every factual claim must include a citation to the chunk_id (e.g., [POL-RBI-202402_ch_12]). If evidence is insufficient, respond with "INSUFFICIENT_EVIDENCE" and request human review. Provide a short summary in simple language (2-3 lines), list key obligations and costs, explain foreclosure/prepayment cost examples, and provide 1–2 actionable next steps for the borrower. Output MUST be JSON as specified.

User/Instruction prompt (context + retrieved chunks):

INSTRUCTION:
LoanRef: loan-888888
Borrower: Ram Kumar
AgreementContext: (see attached agreement-context JSON)
EMI: INR 16,789 monthly; first EMI 2026-01-05. (agreement_context.emi_schedule)
Payment terms: late_fee_fixed INR 500; late_fee_percent_monthly 2%; grace_period 7 days.
Prepayment policy from agreement: "1% if prepaid within 12 months, else 0.5% after 12 months" (agreement_context.prepayment_and_foreclosure)

RETRIEVED EVIDENCE CHUNKS:
1) POL-RBI-202402_ch_12 - "Banks shall allow prepayment... foreclosure charges shall not exceed 2%..."
2) POL-PL-2025-09_ch_03 - "If borrower prepays within first 12 months, foreclosure fee = 1%... reset every 6 months..."
3) DISC-INFO-EMI_ch_02 - "EMI calculated using reducing balance..."
4) LEGAL-LC-7.1_ch_01 - "Late fee: INR 500 plus 2% monthly..."

QUESTION:
1) Provide a short, simple summary of this agreement for Ram.
2) Explain the foreclosure/prepayment cost in numeric examples for 1 year and at 18 months assuming no principal paid other than scheduled EMIs (use outstanding principal approx from schedule URI if needed; otherwise estimate to nearest rupee).
3) List the borrower's key obligations and penalties.
4) Provide 2 actionable next steps for Ram (clear, simple).
5) Include evidence citations for each factual claim in the answer.

OUTPUT FORMAT (JSON):
{
  "summary": "<2-3 lines>",
  "foreclosure_examples": [
    {"scenario":"", "estimates": [{"month":12, "outstanding":<int>, "foreclosure_fee":<int>}, ... ], "citations":[]}
  ],
  "key_obligations": [{"item":"", "citation":"chunk_id"}],
  "next_steps":["",""],
  "confidence": "<0.0-1.0>"
}

4) Sample LLM Response (Copilot output — JSON with citations)

This is the kind of audited, evidence-backed output you must present in interviews.

{
  "loan_ref": "loan-888888",
  "summary": "You have a ₹5,00,000 personal loan for 36 months at a floating rate (12.75% p.a.). Your EMI is ₹16,789 and the first EMI is due on 5 Jan 2026. [DISC-INFO-EMI_ch_02] [POL-PL-2025-09_ch_03]",
  "foreclosure_examples": [
    {
      "scenario": "Foreclose after 12 months (approx.)",
      "estimates": [
        {
          "month": 12,
          "approx_outstanding_principal": 410000,
          "foreclosure_fee_percent": 1.0,
          "foreclosure_fee_amount": 4100
        }
      ],
      "explanation": "Estimated outstanding principal after 12 months approx ₹410,000 using reducing balance schedule; foreclosure fee = 1% as per product rule. [POL-PL-2025-09_ch_03] [DISC-INFO-EMI_ch_02]",
      "citations": ["POL-PL-2025-09_ch_03", "DISC-INFO-EMI_ch_02"]
    },
    {
      "scenario": "Foreclose at 18 months",
      "estimates": [
        {
          "month": 18,
          "approx_outstanding_principal": 310000,
          "foreclosure_fee_percent": 0.75,
          "foreclosure_fee_amount": 2325
        }
      ],
      "explanation": "Product rules indicate a graded foreclosure fee after 12 months (0.75% for 12–24 months). Fee estimated on outstanding principal. [POL-PL-2025-09_ch_03]",
      "citations": ["POL-PL-2025-09_ch_03"]
    }
  ],
  "key_obligations": [
    {"item": "Pay EMI of ₹16,789 on or before due date; 7-day grace period applies.", "citation": "DISC-INFO-EMI_ch_02"},
    {"item": "If EMI is late, a fixed late fee of ₹500 plus an interest penalty of 2% per month applies.", "citation": "LEGAL-LC-7.1_ch_01"},
    {"item": "Foreclosure is allowed; foreclosure fee applies depending on timing (1% within 12 months; 0.75% between 12–24 months; 0.5% after 24 months).", "citation": "POL-PL-2025-09_ch_03"},
    {"item": "Interest is floating and rate resets every 6 months per product pricing rules.", "citation": "POL-PL-2025-09_ch_03"}
  ],
  "next_steps": [
    "If you consider prepaying within the first year, request a prepayment quote from the bank (include the exact outstanding principal). The bank will compute the exact outstanding and foreclosure fee. [POL-PL-2025-09_ch_03]",
    "If you miss an EMI, pay within the 7-day grace period to avoid the fixed penalty; otherwise expect an additional 2% monthly on overdue. Contact support only if you face hardship to discuss restructuring. [LEGAL-LC-7.1_ch_01] "
  ],
  "confidence": 0.86,
  "evidence": [
    {"chunk_id": "POL-PL-2025-09_ch_03", "uri": "dms://policies/internal/pricing/PL-STD-2025_v9.pdf"},
    {"chunk_id": "DISC-INFO-EMI_ch_02", "uri": "dms://disclosures/emi_calc.pdf"},
    {"chunk_id": "LEGAL-LC-7.1_ch_01", "uri": "dms://legal/clauses/LC-7.1.txt"},
    {"chunk_id": "POL-RBI-202402_ch_12", "uri": "dms://policies/rbi/fair_practice/2024/ch12.pdf"}
  ],
  "llm_metadata": {
    "model": "enterprise-llm-v2",
    "temperature": 0.0,
    "prompt_id": "prompt-ug-2025-11-28-001",
    "generated_at": "2025-11-28T10:08:15Z"
  }
}

Notes about the numeric estimates: The Copilot used the EMI schedule pointer (emi_schedule_uri) as a reference. If precise outstanding principal is required, the Copilot returns a prepayment quote step (next step 1) which triggers the backend Agreement Service to compute exact outstanding and fee. This is the correct pattern: Copilot provides bounded estimates + actionable next step to get precise computation.

:

“After agreement generation our /agreement-context/{loanId} returns the structured agreement with EMI schedule, foreclosure policy and clause pointers. The Underwriter/Borrower Copilot first retrieves the relevant policy and clause chunks from the Knowledge Hub (RAG). We build a structured prompt that includes the factual agreement context and the retrieved chunks, and call a private LLM with strict evidence-first guardrails. The LLM returns a JSON response that is fully citation-backed. For any precise monetary computations (outstanding principal), Copilot recommends requesting an exact prepayment quote from the Agreement Service — we never let the LLM invent precise ledger calculations. All prompts, retrieved chunks, and responses are written to the immutable evidence store for audit.”

Emphasize:

• Evidence-first design (citations)

• Bounded LLM behavior (estimates vs exact via service)

• Auditability (prompt + chunks + response stored)

• Safety (temperature = 0, post-validator)