This project implements an AI-driven RFP validation engine designed to answer formal RFP requirements using explicit, verifiable evidence extracted from technical documentation.

Instead of responding to open-ended conceptual questions, the system evaluates whether a requirement is met, returning YES / NO / PARTIAL, along with exact textual evidence and full traceability.

The solution combines:

Retrieval-Augmented Generation (RAG) over PDFs
GraphRAG for structured factual relationships
Oracle 23ai Property Graph + Oracle Text
OCI Generative AI (LLMs & Embeddings)
FAISS vector search
Flask REST API

This project is based on the article: Analyze PDF Documents in Natural Language with OCI Generative AI

See the details about this material to setup/configure your development environment, Oracle Autonomous Database AI and other components.

🎯 Why RFP-Centric (and not Concept Q&A)

While typical knowledge base projects focus on extracting information about concepts, step-by-step instructions, and numerous answers to questions asked about a particular subject, an RFP requires a very special approach.

Note: Traditional RAG systems are optimized for conceptual explanations. RFPs require objective validation, not interpretation.

This project shifts the AI role from:

❌ “Explain how the product works”
to
✅ “Prove whether this requirement is met, partially met, or not met”

🧩 Core Capabilities

✅ RFP Requirement Parsing

Each question is parsed into a structured requirement:

{
  "requirement_type": "COMPLIANCE | FUNCTIONAL | NON_FUNCTIONAL",
  "subject": "authentication",
  "expected_value": "MFA",
  "decision_type": "YES_NO | YES_NO_PARTIAL",
  "keywords": ["authentication", "mfa", "identity"]
}

🧠 Knowledge Graph (GraphRAG)

Facts are extracted only when explicitly stated in documentation and stored as graph triples:

REQUIREMENT -[HAS_METRIC]-> messages per hour
REQUIREMENT -[HAS_VALUE]-> < 1 hour
REQUIREMENT -[SUPPORTED_BY]-> Document section

This ensures:

No hallucination
No inferred assumptions
Full auditability

🔎 Hybrid Retrieval Strategy

Vector Search (FAISS)
Oracle Graph + Oracle Text
Graph-aware Re-ranking

📊 Deterministic RFP Decision Output

{
  "answer": "YES | NO | PARTIAL",
  "justification": "Short factual explanation",
  "evidence": [
    {
      "quote": "Exact text from the document",
      "source": "Document or section"
    }
  ]
}

🏗️ Architecture

PDFs
 └─► Semantic Chunking
     └─► FAISS Vector Index
         └─► RAG Retrieval
             └─► GraphRAG (Oracle 23ai)
                 └─► Evidence-based LLM Decision
                     └─► REST API Response

🚀 REST API

Health Check

GET /health

RFP Validation

POST /chat

{
  "question": "Does the platform support MFA and integration with corporate identity providers?"
}

🧪 Example Use Cases

Enterprise RFP / RFQ validation
Pre-sales technical due diligence
Compliance checks
SaaS capability assessment
Audit-ready AI answers

🛠️ Technology Stack

Oracle Autonomous Database 23ai
OCI Generative AI
LangChain / LangGraph
FAISS
Flask
Python

🔐 Design Principles

Evidence-first
Deterministic outputs
No hallucination tolerance
Explainability

GraphRAG for RFP Validation – Code Walkthrough

Status: Demo / Reference Implementation
Derived from: Official Oracle Generative AI & GraphRAG learning material
https://docs.oracle.com/en/learn/oci-genai-pdf

🎯 Purpose of This Code

This code implements a GraphRAG-based pipeline focused on RFP (Request for Proposal) validation, not generic Q&A.

Download the code graphrag_rerank.py

The main goal is to:

Extract explicit, verifiable facts from large PDF contracts and datasheets
Store those facts as structured graph relationships
Answer RFP questions using YES / NO / PARTIAL decisions
Always provide document-backed evidence, never hallucinations

This represents a strategic shift from concept-based LLM answers to compliance-grade validation.

🧠 High-Level Architecture

PDF Ingestion
- PDFs are read using OCR-aware loaders
- Large documents are split into semantic chunks
Semantic Chunking (LLM-driven)
- Headings, tables, metrics, and sections are normalized
- Output is optimized for both vector search and fact extraction
Vector Index (FAISS)
- Chunks are embedded using OCI Cohere multilingual embeddings
- Enables semantic recall
Knowledge Graph (Oracle 23ai)
- Explicit facts are extracted as triples:
  - REQUIREMENT -[HAS_METRIC]-> RTO
  - REQUIREMENT -[HAS_VALUE]-> 1 hour
- Stored in Oracle Property Graph tables

RFP Requirement Parsing

Each user question is converted into a structured requirement:

{
  "requirement_type": "NON_FUNCTIONAL",
  "subject": "authentication",
  "expected_value": "",
  "keywords": ["mfa", "ldap", "sso"]
}

Graph + Vector Fusion
- Graph terms reinforce document reranking
- Ensures high-precision evidence retrieval
Deterministic RFP Decision
- LLM outputs are constrained to:
  - YES
  - NO
  - PARTIAL
- Always backed by quotes from source documents

🗂️ Key Code Sections Explained

Oracle Autonomous & Graph Setup

Creates entity and relation tables if not present
Builds an Oracle PROPERTY GRAPH
Uses Oracle Text indexes for semantic filtering

`create_knowledge_graph()`

Uses LLM to extract ONLY explicit facts
No inference, no assumptions
Inserts entities and relations safely using MERGE

`parse_rfp_requirement()`

Converts free-text questions into structured RFP requirements
Enforces strict JSON output using <json> tags
Includes safe fallback logic

`query_knowledge_graph()`

Uses Oracle Text (CONTAINS) with sanitized queries
Filters graph facts by RFP keywords
Returns only relevant evidence

Graph-aware Re-ranking

Combines:
- Vector similarity
- Graph-derived terms
Improves precision on contractual questions

Final RFP Decision Chain

Implemented with LangChain RunnableMap
Clean separation of:
- Requirement parsing
- Context retrieval
- Decision generation

✅ Why This Is NOT a Generic RAG

Traditional RAG	This GraphRAG
Answers concepts	Validates requirements
May hallucinate	Evidence-only
Free-form text	Deterministic YES/NO
No structure	Knowledge graph
Chatbot	RFP analyst

⚠️ Important Design Principles

Evidence-first: If not explicitly stated → NO
No inference: LLM is forbidden to assume
Auditability: Every answer is traceable
Enterprise-grade: Designed for legal, procurement, compliance

📌 Intended Use Cases

RFP response automation
Vendor compliance validation
Contractual due diligence
Pre-sales technical qualification
Regulatory checks

🧪 Demo Disclaimer

This code is:

A demo / reference implementation
Not production-hardened
Intended for education, experimentation, and architecture discussions

👤 Acknowledgments

Author - Cristiano Hoshikawa (Oracle LAD A-Team Solution Engineer)

📎 References

Analyze PDF Documents in Natural Language with OCI Generative AI

⚠️ Disclaimer

This is a demo / reference architecture.
Final answers depend strictly on indexed documentation.

README.md Unescape Escape

🧠 Oracle GraphRAG for RFP Validation

📌 Overview