rfp_response_automation/README.md

# 🧠 Oracle GraphRAG for RFP Validation

**GraphRAG-based AI system for factual RFP requirement validation using Oracle 23ai, OCI Generative AI, and Vector Search**

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## 📌 Overview

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](https://docs.oracle.com/en/learn/oci-genai-pdf)

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


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## 🎯 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”*

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## 🧩 Core Capabilities

### ✅ RFP Requirement Parsing

Each question is parsed into a structured requirement:

```json
{
  "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

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### 🔎 Hybrid Retrieval Strategy

1. **Vector Search (FAISS)**
2. **Oracle Graph + Oracle Text**
3. **Graph-aware Re-ranking**

---

### 📊 Deterministic RFP Decision Output

```json
{
  "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

```json
{
  "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

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## 🛠️ 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](./files/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**.

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## 🧠 High-Level Architecture

1. **PDF Ingestion**
    - PDFs are read using OCR-aware loaders
    - Large documents are split into semantic chunks

2. **Semantic Chunking (LLM-driven)**
    - Headings, tables, metrics, and sections are normalized
    - Output is optimized for both vector search and fact extraction

![img.png](img.png)

3. **Vector Index (FAISS)**
    - Chunks are embedded using OCI Cohere multilingual embeddings
    - Enables semantic recall

4. **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

![img_1.png](img_1.png)

5. **RFP Requirement Parsing**
    - Each user question is converted into a structured requirement:
      ```json
      {
        "requirement_type": "NON_FUNCTIONAL",
        "subject": "authentication",
        "expected_value": "",
        "keywords": ["mfa", "ldap", "sso"]
      }
      ```

6. **Graph + Vector Fusion**
    - Graph terms reinforce document reranking
    - Ensures high-precision evidence retrieval

7. **Deterministic RFP Decision**
    - LLM outputs are constrained to:
        - `YES`
        - `NO`
        - `PARTIAL`
    - Always backed by quotes from source documents

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## 🗂️ 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

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## ✅ 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 |

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## ⚠️ 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

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## 📌 Intended Use Cases

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

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## 🧪 Demo Disclaimer

This code is:
- A **demo / reference implementation**
- Not production-hardened
- Intended for education, experimentation, and architecture discussions

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## 👤 Acknowledgments

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

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## 📎 References

[Analyze PDF Documents in Natural Language with OCI Generative AI](https://docs.oracle.com/en/learn/oci-genai-pdf)

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## ⚠️ Disclaimer

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