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README.md
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# 🧠 Oracle GraphRAG for RFP Validation
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**GraphRAG-based AI system for factual RFP requirement validation using Oracle 23ai, OCI Generative AI, and Vector Search**
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---
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## 📌 Overview
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This project implements an **AI-driven RFP validation engine** designed to answer *formal RFP requirements* using **explicit, verifiable evidence** extracted from technical documentation.
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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.
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The solution combines:
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- Retrieval-Augmented Generation (RAG) over PDFs
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- GraphRAG for structured factual relationships
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- Oracle 23ai Property Graph + Oracle Text
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- OCI Generative AI (LLMs & Embeddings)
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- FAISS vector search
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- Flask REST API
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This project is based on the article: [Analyze PDF Documents in Natural Language with OCI Generative AI](https://docs.oracle.com/en/learn/oci-genai-pdf)
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See the details about this material to setup/configure your development environment, Oracle Autonomous Database AI and other components.
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---
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## 🎯 Why RFP-Centric (and not Concept Q&A)
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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.
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>**Note:** Traditional RAG systems are optimized for *conceptual explanations*. RFPs require **objective validation**, not interpretation.
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This project shifts the AI role from:
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❌ *“Explain how the product works”*
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to
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✅ *“Prove whether this requirement is met, partially met, or not met”*
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---
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## 🧩 Core Capabilities
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### ✅ RFP Requirement Parsing
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Each question is parsed into a structured requirement:
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```json
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{
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"requirement_type": "COMPLIANCE | FUNCTIONAL | NON_FUNCTIONAL",
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"subject": "authentication",
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"expected_value": "MFA",
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"decision_type": "YES_NO | YES_NO_PARTIAL",
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"keywords": ["authentication", "mfa", "identity"]
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}
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```
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---
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### 🧠 Knowledge Graph (GraphRAG)
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Facts are extracted **only when explicitly stated** in documentation and stored as graph triples:
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```
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REQUIREMENT -[HAS_METRIC]-> messages per hour
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REQUIREMENT -[HAS_VALUE]-> < 1 hour
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REQUIREMENT -[SUPPORTED_BY]-> Document section
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```
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This ensures:
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- No hallucination
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- No inferred assumptions
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- Full auditability
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---
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### 🔎 Hybrid Retrieval Strategy
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1. **Vector Search (FAISS)**
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2. **Oracle Graph + Oracle Text**
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3. **Graph-aware Re-ranking**
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---
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### 📊 Deterministic RFP Decision Output
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```json
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{
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"answer": "YES | NO | PARTIAL",
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"justification": "Short factual explanation",
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"evidence": [
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{
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"quote": "Exact text from the document",
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"source": "Document or section"
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}
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]
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}
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```
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---
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## 🏗️ Architecture
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```
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PDFs
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└─► Semantic Chunking
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└─► FAISS Vector Index
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└─► RAG Retrieval
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└─► GraphRAG (Oracle 23ai)
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└─► Evidence-based LLM Decision
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└─► REST API Response
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```
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---
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## 🚀 REST API
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### Health Check
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GET /health
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### RFP Validation
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POST /chat
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```json
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{
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"question": "Does the platform support MFA and integration with corporate identity providers?"
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}
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```
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---
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## 🧪 Example Use Cases
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- Enterprise RFP / RFQ validation
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- Pre-sales technical due diligence
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- Compliance checks
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- SaaS capability assessment
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- Audit-ready AI answers
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---
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## 🛠️ Technology Stack
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- Oracle Autonomous Database 23ai
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- OCI Generative AI
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- LangChain / LangGraph
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- FAISS
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- Flask
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- Python
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---
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## 🔐 Design Principles
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- Evidence-first
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- Deterministic outputs
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- No hallucination tolerance
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- Explainability
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---
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# GraphRAG for RFP Validation – Code Walkthrough
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> **Status:** Demo / Reference Implementation
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> **Derived from:** Official Oracle Generative AI & GraphRAG learning material
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> https://docs.oracle.com/en/learn/oci-genai-pdf
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---
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## 🎯 Purpose of This Code
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This code implements a **GraphRAG-based pipeline focused on RFP (Request for Proposal) validation**, not generic Q&A.
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>**Download** the code [graphrag_rerank.py](./files/graphrag_rerank.py)
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The main goal is to:
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- Extract **explicit, verifiable facts** from large PDF contracts and datasheets
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- Store those facts as **structured graph relationships**
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- Answer RFP questions using **YES / NO / PARTIAL** decisions
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- Always provide **document-backed evidence**, never hallucinations
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This represents a **strategic shift** from concept-based LLM answers to **compliance-grade validation**.
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---
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## 🧠 High-Level Architecture
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1. **PDF Ingestion**
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- PDFs are read using OCR-aware loaders
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- Large documents are split into semantic chunks
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2. **Semantic Chunking (LLM-driven)**
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- Headings, tables, metrics, and sections are normalized
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- Output is optimized for both vector search and fact extraction
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3. **Vector Index (FAISS)**
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- Chunks are embedded using OCI Cohere multilingual embeddings
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- Enables semantic recall
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4. **Knowledge Graph (Oracle 23ai)**
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- Explicit facts are extracted as triples:
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- `REQUIREMENT -[HAS_METRIC]-> RTO`
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- `REQUIREMENT -[HAS_VALUE]-> 1 hour`
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- Stored in Oracle Property Graph tables
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5. **RFP Requirement Parsing**
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- Each user question is converted into a structured requirement:
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```json
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{
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"requirement_type": "NON_FUNCTIONAL",
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"subject": "authentication",
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"expected_value": "",
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"keywords": ["mfa", "ldap", "sso"]
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}
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```
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6. **Graph + Vector Fusion**
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- Graph terms reinforce document reranking
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- Ensures high-precision evidence retrieval
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7. **Deterministic RFP Decision**
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- LLM outputs are constrained to:
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- `YES`
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- `NO`
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- `PARTIAL`
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- Always backed by quotes from source documents
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---
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## 🗂️ Key Code Sections Explained
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### Oracle Autonomous & Graph Setup
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- Creates entity and relation tables if not present
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- Builds an Oracle **PROPERTY GRAPH**
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- Uses Oracle Text indexes for semantic filtering
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### `create_knowledge_graph()`
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- Uses LLM to extract **ONLY explicit facts**
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- No inference, no assumptions
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- Inserts entities and relations safely using MERGE
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### `parse_rfp_requirement()`
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- Converts free-text questions into structured RFP requirements
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- Enforces strict JSON output using `<json>` tags
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- Includes safe fallback logic
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### `query_knowledge_graph()`
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- Uses Oracle Text (`CONTAINS`) with sanitized queries
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- Filters graph facts by RFP keywords
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- Returns only relevant evidence
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### Graph-aware Re-ranking
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- Combines:
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- Vector similarity
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- Graph-derived terms
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- Improves precision on contractual questions
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### Final RFP Decision Chain
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- Implemented with LangChain `RunnableMap`
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- Clean separation of:
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- Requirement parsing
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- Context retrieval
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- Decision generation
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---
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## ✅ Why This Is NOT a Generic RAG
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| Traditional RAG | This GraphRAG |
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|----------------|---------------|
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| Answers concepts | Validates requirements |
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| May hallucinate | Evidence-only |
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| Free-form text | Deterministic YES/NO |
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| No structure | Knowledge graph |
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| Chatbot | RFP analyst |
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---
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## ⚠️ Important Design Principles
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- **Evidence-first**: If not explicitly stated → NO
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- **No inference**: LLM is forbidden to assume
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- **Auditability**: Every answer is traceable
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- **Enterprise-grade**: Designed for legal, procurement, compliance
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---
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## 📌 Intended Use Cases
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- RFP response automation
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- Vendor compliance validation
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- Contractual due diligence
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- Pre-sales technical qualification
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- Regulatory checks
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---
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## 🧪 Demo Disclaimer
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This code is:
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- A **demo / reference implementation**
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- Not production-hardened
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- Intended for education, experimentation, and architecture discussions
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---
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## 👤 Acknowledgments
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- **Author** - Cristiano Hoshikawa (Oracle LAD A-Team Solution Engineer)
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---
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## 📎 References
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[Analyze PDF Documents in Natural Language with OCI Generative AI](https://docs.oracle.com/en/learn/oci-genai-pdf)
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---
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## ⚠️ Disclaimer
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This is a demo / reference architecture.
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Final answers depend strictly on indexed documentation.
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