AI- Knowledge Hub

❗ Loan agreement does NOT have to go into the permanent “Knowledge Hub” to be explained by GenAI.It can be processed via a temporary, isolated vector index (ephemeral RAG).

1️⃣ What is Your “Knowledge Hub” in Banking GenAI?

You defined it correctly:

Knowledge Hub (Permanent Vector Store) contains:

• RBI circulars

• Bank lending policies

• Credit risk rules

• AML/Sanctions SOPs

• Legal templates and compliance rules

• Product brochures and pricing rules

LLMOps Pipeline for Knowledge Hub (Permanent)

Document Upload
 → OCR
 → Chunking
 → Embedding
 → Indexing
 → Vector Store (pgvector / Pinecone / Azure AI Search)
 → RAG Layer

✅ This is long-lived

✅ Used across all customers

✅ Updated only when policies change

✅ Fully governed & audited

2️⃣ Loan Agreement is NOT Knowledge Hub Content

A loan agreement is:

• Customer-specific

• Confidential (PII + financial data)

• Used only by:

  • That borrower

  • That underwriter

  • That relationship manager

So it should NOT be mixed with RBI policies or SOPs in the same permanent vector store.

Instead, it goes into a:

✅ Temporary / Ephemeral Vector Index

3️⃣ Correct End-to-End Flow for Loan Agreement + GenAI Explanation

Step A — Agreement is Generated (Traditional System)

Loan Agreement is generated via:

• Rule engine + templates

• Product configuration

• Legal clauses

• CBS + LOS APIs

This is NOT GenAI yet.

It is stored in:

• Secure Object Store (Blob / S3 / MinIO)

loan-agreement-ramesh-12345.pdf

Step B — Event Triggers GenAI Processing

An event is fired:

Event: LoanAgreement.Generated
Payload:
{
  applicationId: "LN-12345",
  documentPath: "/agreements/loan-agreement-ramesh-12345.pdf",
  customerId: "CUST-9912",
  ttl: "48h"
}

This event starts the LLMOps pipeline for this document only.

Step C — Temporary LLMOps Pipeline for This Agreement

PDF from Blob
 → OCR (if scanned)
 → Chunking (clause-wise)
 → Embedding
 → TEMP Vector Index (Isolated Namespace)
 → TTL = 48 hours

This is NOT your permanent knowledge hub.This is called:

✅ Ephemeral / Session-Based Vector Index

Used only for:

• This one customer

• This one agreement

• For limited time

Step D — How GenAI Explains the Agreement

Now when Ram asks:

“Summarize my loan agreement in simple terms”

GenAI does:

1. Query the TEMP vector index

2. Retrieve only Ram’s agreement clauses

3. Combine with permanent Knowledge Hub (for interpretation rules)

4. Generate explanation

Example prompt to LLM:

You are a banking legal assistant.
Use the retrieved clauses from the agreement.
Explain risks in simple language for a retail borrower.

Output:

• EMI structure

• Prepayment penalty

• Variable interest risk

• Default consequences

• Cooling-off period

✅ This explanation is real-time✅ No data pollution✅ Fully compliant

Step E — Automatic Expiry (TTL Control)

After 24–48 hours:

TEMP Vector Index → Auto Deleted
Original PDF → Only in core LMS document store
Embeddings → Destroyed

This ensures:

• ✅ Data privacy

• ✅ RBI compliance

• ✅ No accidental reuse

• ✅ No model contamination

4️⃣

“Loan agreement also goes to Knowledge Hub with TTL”

🔴 Correction:It should NOT go to the permanent Knowledge Hub.

✅ Correct Architecture is:

Store Type	Purpose	Retention
Permanent Knowledge Hub	RBI, policies, SOPs	Years
Temporary Vector Index	One customer’s agreement	24–48 hrs

This is the industry-grade BFSI setup.

5️⃣Summary

“Loan agreements are not stored in the permanent knowledge hub. Once the agreement is generated, an event triggers a temporary LLMOps pipeline where the document is OCRed, chunked, embedded, and stored in an isolated, short-lived vector index with strict TTL. The GenAI assistant uses this ephemeral index combined with the bank’s permanent policy knowledge hub via RAG to explain clauses, risks, and EMI terms to the customer. After expiry, all embeddings are purged to meet RBI data-retention and privacy guidelines.”

This answer shows:

✅ LLMOps maturity

✅ BFSI compliance

✅ Architectural depth

✅ AI-First thinking

1️⃣ What is a Permanent Knowledge Hub (Long-Term Vector Store)?

This is your enterprise memory.

It contains:

• RBI circulars

• Bank lending policies

• Credit risk frameworks

• AML / Fraud SOPs

• Product brochures

• Underwriting manuals

• Regulatory FAQs

Pipeline (One-time or Periodic)

Policy PDF → OCR → Chunking → Embedding → Indexing → Stored in PGVector / Pinecone

Characteristics

✅ Long-lived✅ Versioned✅ Audited✅ Used by RAG for months/years✅ Supports compliance✅ Not deleted automatically

Examples of Tech

• PGVector (Postgres)

• Pinecone

• Weaviate

• Azure AI Search (vector mode)

This is what you correctly called the Knowledge Hub ✅

2️⃣ What is an Ephemeral (Temporary) Vector Index?

This is short-lived, request-scoped memory created only for a specific transaction or document, NOT for enterprise reuse.

Typical use cases:

• Generated loan agreement

• One customer’s KYC packet

• One uploaded bank statement

• One email thread

• One support ticket document

Characteristics

Property	Ephemeral Vector Index
Lifetime	Minutes / Hours / 1–2 Days
Scope	Per customer / per transaction
Storage	In-memory / temp Redis / temp vector DB
Compliance	Auto-expiry (TTL)
Reuse	❌ Not reused globally
Purpose	Only for contextual understanding
Cost	Low
Risk	Minimal data residency

Typical Tech

• In-memory FAISS

• Redis Vector

• Temporary PGVector schema

• Temp Pinecone namespace with TTL

3️⃣ Your Key Doubt – “If Loan Agreement is NOT in Knowledge Hub, how does GenAI read it?”

✅ Correct: The loan agreement should NOT go to the permanent Knowledge Hub✅ Correct: It must go to a temporary / ephemeral vector index

Why?

Because:

• It contains PII + financial data

• It is user-specific

• It does not apply to other customers

• Storing it permanently violates data minimization (RBI/DPDP Act)

4️⃣ Correct End-to-End Flow for Loan Agreement + GenAI Explanation

This is the exact production-grade flow you should explain in interview:

🔹 Step 1: Loan Agreement Generated (Template-Based)

This is NOT GenAI — you are right.

Loan Service → Fills Template → Generates PDF → Stores in Secure Object Store

Example:

• Azure Blob (private container)

• S3 Private Bucket

Event Fired:

LoanAgreement.Generated
{
  loanId,
  customerId,
  documentUrl
}

🔹 Step 2: Ephemeral LLMOps Pipeline Triggered

Event triggers a temporary GenAI pipeline:

LoanAgreement PDF
   ↓
OCR (if scanned)
   ↓
Chunking
   ↓
Embedding
   ↓
Ephemeral Vector Index (TTL = 24–48 hours)

⚠️ This does NOT go into your permanent Knowledge Hub

It goes into a temporary vector space scoped to:

sessionId + loanId + customerId

🔹 Step 3: Borrower Assistant Uses RAG with TWO Data Sources

When customer asks:

“Explain my loan agreement risks”

The GenAI does:

A) From Permanent Knowledge Hub

• RBI rules

• Prepayment penalty norms

• Foreclosure regulations

• Fair practice code

B) From Ephemeral Vector Index

• Customer’s specific loan agreement

• EMIs

• Clauses

• Interest rate

• Penalties

Then it performs:

RAG (Policy Context + Agreement Context)
→ LLM Reasoning
→ Customer-friendly explanation

🔹 Step 4: TTL Expiry and Auto-Delete

After 24 or 48 hours:

✅ The ephemeral vector index is deleted automatically✅ The original PDF remains in secure storage (for legal reasons)✅ No embeddings of customer data remain in vector DB✅ You remain RBI + DPDP compliant

5️⃣ So Your Understanding Is 100% Correct ✅

You said:

“Once loan agreement is generated and uploaded, based on event it will kick LLMOps → chunk → embed → index → then GenAI explains it”

✅ Correct✅ Just one refinement:

• It is indexed into a TEMPORARY vector index, not the permanent knowledge hub.

6️⃣

“Our Knowledge Hub is a permanent vector store containing RBI policies, lending rules, and SOPs. However, customer-specific documents like loan agreements are never stored there. Instead, when a loan agreement is generated, an event triggers an ephemeral LLMOps pipeline which creates a temporary vector index with a strict TTL. GenAI uses this temporary index together with the permanent knowledge hub for RAG-based explanation of clauses, risks, and EMIs. After TTL expiry, the temporary embeddings are auto-deleted to ensure regulatory compliance and data minimization.”

1️⃣ What is your Knowledge Hub (Permanent Vector Store)?

You are 100% correct:

Knowledge Hub = All RBI docs, lending policies, credit rules, SOPs, risk frameworks, product brochures, compliance norms→ OCR → Chunk → Embed → Index → Store in Permanent Vector DB (pgvector / Pinecone / Weaviate)

This is:

✅ Long-term memory✅ Used by RAG for:

• Policy explanations

• Eligibility rules

• Compliance justifications

• Customer FAQs

• Underwriter reasoning

This data:

• Never expires

• Is versioned

• Is governed

• Is auditable

2️⃣ When Loan Agreement is Generated — Why It Is NOT in Knowledge Hub

You are also correct here:

Loan agreement is customer-specific, transaction-specific & short-lived

So it must NOT be mixed with permanent Knowledge Hub, because:

• It is PII + Financial Contract

• It is not reusable for other customers

• It is legally sensitive

• It must follow data minimization & retention policy

So it goes into a:

✅ Temporary / Ephemeral Vector Index✅ With TTL (time-to-live), e.g., 24–72 hours

3️⃣ What is an Ephemeral Vector Index (Temporary Vector Store)?

Think of it as:

A short-term memory for GenAI, created only for one customer session or one document.

Examples:

• In-memory FAISS

• Redis Vector

• Temp pgvector namespace

• Encrypted short-lived namespace in Pinecone

Characteristics

Feature	Permanent Knowledge Hub	Ephemeral Vector Index
Purpose	Policies, SOPs, RBI docs	One loan agreement
Retention	Years	Minutes / Hours / Days
PII Allowed	❌ No	✅ Yes
Reusable	✅ Yes	❌ No
Audited	✅ Fully	✅ Logged but auto-expired
Used For	Governance & reasoning	Contract explanation & Q&A

4️⃣

❓ “If loan agreement is generated, how does GenAI read it if it is NOT in Knowledge Hub?”

✅ Correct End-to-End Flow (Industry-Standard Design)

Step 1 — Loan Agreement Generated (Template Based)

This is NOT GenAI:

• It is rule-based

• It uses:

  • Loan amount

  • Tenure

  • Interest rate

  • Risk category

  • Regulatory clauses

✅ Output: loan_agreement.pdf

Step 2 — Event Fired

Event: LOAN_AGREEMENT_GENERATED
Payload:
{
  loanId,
  customerId,
  agreementDocURI,
  riskCategory
}

Step 3 — OCR + Text Extraction (Document AI)

GenAI does not read PDF directly.So first:

PDF → OCR → Clean Text

Now we have raw contract text.

Step 4 — Chunk, Embed & Temporary Indexing (Ephemeral Store)

Even for temporary storage, we still:

✅ Chunk✅ Embed✅ Index

Why?Because:

• LLMs cannot process full 20–40 page contracts in one go

• Chunking enables:

  • Clause-level retrieval

  • Risk-specific querying

  • Question-answering

So this becomes:

TempVectorIndex(loanId, customerSessionId, TTL=48 hrs)

Step 5 — GenAI Contract Explainer Uses:

1. Temp Vector Index → for this exact contract

2. Permanent Knowledge Hub → to interpret clauses using RBI & policy context

This is called:

✅ Dual RAG

• One ephemeral

• One permanent

Step 6 — Customer Asks:

“What is the penalty if I miss EMI?”

GenAI does:

1. Retrieve from temporary vector → Finds the actual penalty clause

2. Retrieve from knowledge hub → Finds RBI guideline & bank policy on penalties

3. Combines both → Generates explainable legal answer

✅ This is how GenAI explains a document that was never in Knowledge Hub permanently

Step 7 — TTL Expiry (Auto Deletion)

After 48–72 hours:

✅ Temp embeddings auto-deleted✅ No customer contract remains in vector DB✅ Only the original PDF remains in secure document vault (not vector DB)

This is RBI + GDPR + DPDP Act compliant.

5️⃣ Your Key Architectural Question:

❓ “Why not just scan & store text? Why chunk/embed/index even for temporary doc?”

Because:

Only Store Text	Chunk + Embed + Index
Keyword search only	Semantic search
No reasoning	Full clause reasoning
Cannot answer follow-up questions	Can answer multi-step questions
No risk scoring	Clause-wise risk extraction
No explainability	Full explainable AI
Not GenAI-ready	Fully GenAI-ready

So to enable GenAI, embeddings are always required — even for temporary memory.

6️⃣ Is Loan Agreement Generation GenAI?

✅ You correctly said:

“Loan agreement generation is template-based, not GenAI.”

That is absolutely correct.

Where GenAI is used instead:

Stage	GenAI Used?	Purpose
Agreement creation	❌ No	Rule engine + templates
Clause explanation	✅ Yes	Natural language explanation
Risk summarization	✅ Yes	What risks apply to this borrower
EMI explanation	✅ Yes	Personalized EMI understanding
Legal jargon simplification	✅ Yes	Customer-friendly language
Multi-language explanation	✅ Yes	Local language conversion

So in interview you must say:

“We do NOT use GenAI for contract creation — we use it for post-generation understanding, explanation, risk interpretation, and compliance-aware customer communication.”

That is a very strong architectural statement.

7️⃣

“In our AI-first digital lending platform, permanent knowledge like RBI policies, credit rules, and SOPs are stored in a governed vector Knowledge Hub. When a customer-specific loan agreement is generated using a template engine, we do not push that document into the permanent knowledge base. Instead, an event triggers an ephemeral LLMOps pipeline where the contract is OCR-processed, chunked, embedded, and indexed into a short-lived vector index with a strict TTL. During the review window, the GenAI Borrower Assistant uses a dual-RAG approach — retrieving actual clauses from the temporary vector index and interpreting them using the permanent policy knowledge hub. This allows us to explain penalties, risks, repayment terms, and legal clauses with full regulatory context. After the TTL expires, all embeddings are deleted to comply with RBI, DPDP, and data minimization laws.”

✅ Is knowledge hub permanent vector store?

Yes — pgvector / Pinecone / Weaviate.

✅ Is loan agreement also pushed there?

No — it goes to ephemeral vector index only.

✅ What is ephemeral vector index?

Short-term, session-based vector store with TTL.

✅ Why chunk & embed temporary docs?

Because GenAI cannot reason on raw text — embeddings are mandatory for semantic understanding.

✅ How GenAI reads agreement without Knowledge Hub?

Through temporary vector index + dual RAG.

1️⃣ What is your Knowledge Hub (Permanent Vector Store)?

Knowledge Hub = Permanent Vector Store (pgvector / Pinecone / Weaviate) that contains:

• RBI circulars

• Bank lending policies

• Credit risk rules

• AML / KYC SOPs

• Product terms & conditions

• Historical regulatory documents

Pipeline for Knowledge Hub:

Document Upload
→ OCR / Text Extraction
→ Chunking
→ Embedding
→ Indexing
→ Stored permanently in Vector Store
→ Accessed via RAG

✅ This data is:

• Long-living

• Versioned

• Audited

• Used by both Borrower Assistant + Underwriter Copilot

2️⃣ What is a Temporary / Ephemeral Vector Index?

This is NOT your main knowledge hub.

It is a short-lived, session-based vector store used for user-specific / transaction-specific documents like:

• Loan Agreement generated for Ram

• Sanction letter

• Offer letter

• Signed consent document

These documents:

• Are private to Ram

• Are valid only for short time

• Must not pollute enterprise knowledge hub

• Must be auto-deleted after TTL

So we create:

✅ Ephemeral Vector Index = Temporary, in-memory or short-lived vector store

Examples:

• Redis Vector

• pgvector table with TTL

• In-memory FAISS

• Weaviate TTL collections

3️⃣ Why Do We Still Need Chunking + Embedding + Indexing Even for Temporary Store?

“We can simply scan loan agreement and extract text, why chunk + embed?”

Because GenAI does NOT understand raw PDF text efficiently

If you only store plain text:

• LLM cannot do semantic search

• Cannot do clause-level Q&A

• Cannot retrieve specific risk clauses

• Cannot compare against policies

Therefore even temporary documents go through:

Loan Agreement PDF
→ OCR / Text Extraction
→ Chunking (Clause-wise)
→ Embedding
→ Stored in Ephemeral Vector Index
→ Used by RAG

✅ Without embeddings, you only have string search

✅ With embeddings, you have semantic intelligence

4️⃣ How GenAI Explains a Loan Agreement

“If loan agreement is not in knowledge hub, how does GenAI read it?”

Here is the correct production flow:

✅ Step 1: Loan Agreement Generated (Non-GenAI)

Loan agreement is generated using:

• Template Engine (Docx / HTML / PDF)

• Rule-based parameter substitution

Example:

Loan Amount = ₹12,00,000
Tenure = 60 months
Interest = 11.5%

At this stage:❌ No GenAI involved yet✅ It is just a deterministic document

✅ Step 2: Agreement Upload Triggers Event

Event Fired → loan.agreement.generated
Payload:
{
  loanId,
  customerId,
  documentPath,
  timestamp
}

✅ Step 3: LLMOps Pipeline is Triggered

This document goes to Temporary LLMOps Pipeline:

OCR
→ Text Extraction
→ Chunking (Clause-wise)
→ Embedding
→ Stored in Ephemeral Vector Index (TTL = 48 hours)

This is NOT permanent knowledge hubThis is session-specific knowledge memory

✅ Step 4: Borrower Assistant Uses RAG on Temporary + Permanent Stores

When Ram asks:

“Can you summarize my loan agreement?”

The Borrower Assistant does:

User Query
→ RAG Query
→ Search:
   1. Temporary Vector Index (Ram’s agreement)
   2. Permanent Knowledge Hub (Policy definitions)
→ Retrieved Chunks
→ LLM Generates:
   - Summary
   - Clause explanation
   - Risk highlights
   - EMI obligations

✅ This is how GenAI reads a document it never saw before

✅ Step 5: TTL Expiry (Auto Data Cleanup)

After:

• T+48 hours OR

• Agreement Signed OR

• Cooling-off period ends

Ephemeral Vector Index
→ Auto purge
→ RAM data removed
→ Regulatory compliance maintained

✅ This avoids:

• Data leakage

• Vector store pollution

• Compliance violations

5️⃣ Does RAG Retrieve From Temporary Store Also?

✅ YES — RAG can retrieve from multiple vector sources

Typical RAG routing:

Source	Purpose
Permanent Knowledge Hub	RBI rules, policies, risk definitions
Ephemeral Vector Index	Ram’s loan document, sanction letter
Feature Store (optional)	ML score explanations

So RAG works like:

Query → Router → 
[ Temporary Store + Permanent Store ] →
Merged Context → LLM → Response

6️⃣ Why Not Put Loan Agreement Directly in Knowledge Hub?

Because:

Risk	Why Not
Privacy	Loan agreement is PII-heavy
Data Explosion	Millions of agreements
Compliance	Must be auto-expired
Vector Pollution	Reduces search quality
Legal	User-specific docs must not be searchable by others

✅ Therefore: Ephemeral Index is mandatory in regulated BFSI systems

7️⃣ Final Clean Mental Model

“We maintain two vector layers. One is our permanent enterprise knowledge hub containing RBI regulations, product policies, credit risk rules, and SOPs. The second is a temporary, ephemeral vector index created only for customer-specific documents like loan agreements and sanction letters. When a loan agreement is generated through a template engine, an event triggers a short LLMOps pipeline where the document is OCR’d, chunked, embedded, and stored in a TTL-bound temporary vector index. The borrower assistant then uses RAG across both the permanent knowledge hub and this temporary vector store to generate summaries, explain clauses, highlight risks, and answer customer questions. After the cooling-off or signing window, this temporary vector data is automatically purged for compliance and privacy.”

This explanation alone shows true AI-first, BFSI-grade architecture maturity.

8️⃣

Question	Answer
Is loan agreement stored in knowledge hub?	❌ No — goes to ephemeral vector index
Is chunk/embed required for temporary docs?	✅ Yes — for semantic RAG
Can GenAI read without storing?	❌ No — must be embedded for retrieval
Does RAG query temporary store?	✅ Yes
What is ephemeral vector index?	✅ TTL-based, session-scoped vector store
Why not plain text?	❌ No semantic search, poor accuracy
What expires?	✅ Only embeddings, not original PDF