📅 Period: Jan 2024 – Apr 2024  |  Author: Bharghava Ram Vemuri

🌿 Green Alpha

ESG & Sustainability Intelligence Platform · RAG + FAISS + LangChain + GPT-4 + Claude

---

--- 🎯 Problem Statement

ESG (Environmental, Social, Governance) analysis requires reading hundreds of annual reports, sustainability filings, and news articles to assess corporate sustainability. Asset managers spend weeks per company doing this manually. Greenwashing is rampant — companies make vague sustainability claims that are difficult to verify. This platform ingests 100+ ESG documents, builds a FAISS semantic index, and uses GPT-4 + Claude with LangChain to answer complex ESG queries, score companies, detect greenwashing, and generate portfolio-level sustainability reports.

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🏗️ Architecture

ESG Documents (PDF/text)
        │
   LangChain Document Loaders
        │
   Chunking + Embedding (text-embedding-ada-002)
        │
   FAISS Vector Index
        │
   ┌────┴────────────────────────────────────┐
   │     LangChain RetrievalQA Chain         │
   │  GPT-4 (primary) · Claude (validation)  │
   └────┬────────────────────────────────────┘
        │
   ┌────┴──────────────────────────────────────┐
   │          ESG Intelligence Layer           │
   │  Scoring · Greenwash Detection · Reports  │
   └───────────────────────────────────────────┘

---

📁 Project Structure

green-alpha/
├── main.py
├── app/
│   ├── services/
│   │   ├── rag_service.py         # LangChain RAG pipeline
│   │   ├── esg_service.py         # ESG scoring + analysis
│   │   ├── greenwash_service.py   # Greenwashing detection
│   │   └── report_service.py      # Portfolio sustainability reports
│   └── api/routes/
│       ├── query.py
│       ├── score.py
│       └── reports.py
├── data/                          # ESG document storage
├── tests/
├── Dockerfile
├── .env.example
└── requirements.txt

---

🚀 Quick Start

git clone https://github.com/bharghavaram/green-alpha.git
cd green-alpha
pip install -r requirements.txt
cp .env.example .env   # Add OPENAI_API_KEY, ANTHROPIC_API_KEY
uvicorn main:app --reload

---

🤖 Model & Algorithm Details

Component	Approach
Document Loading	LangChain PyPDFLoader + TextLoader
Chunking	RecursiveCharacterTextSplitter (chunk=1000, overlap=200)
Embeddings	text-embedding-ada-002 → FAISS L2 index
QA Chain	LangChain RetrievalQA with GPT-4 + source citations
ESG Scoring	3-pillar scoring: E (0–100) + S (0–100) + G (0–100)
Greenwash Detection	Claim extraction → fact-check against verified metrics
Cross-validation	Claude independently validates GPT-4 ESG scores

---

📡 API Endpoints

Method	Endpoint	Description
POST	/ingest	Ingest ESG documents
POST	/query	RAG-powered ESG query
POST	/score	ESG score for a company
POST	/greenwash	Greenwashing risk assessment
POST	/report/portfolio	Portfolio-level sustainability report

---

💡 Sample Input → Output

Request:

curl -X POST "http://localhost:8000/score" \
  -H "Content-Type: application/json" \
  -d '{"company":"Microsoft","year":2024}'

Response:

{
  "company": "Microsoft",
  "esg_scores": {
    "environmental": 78,
    "social": 82,
    "governance": 91,
    "composite": 83.7
  },
  "strengths": ["Carbon negative by 2030 commitment", "100% renewable energy by 2025"],
  "risks": ["Significant data centre water consumption", "Supply chain emissions Scope 3 gaps"],
  "greenwash_risk": "LOW",
  "confidence": 0.84,
  "sources_used": 7
}

---

📊 Performance

Metric	Value
Documents indexed	100+ ESG reports
Query response time	<2.5 seconds
Answer relevance (RAGAS)	0.87
ESG score correlation (vs MSCI)	0.79 Pearson r
Greenwash detection precision	81%

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🧪 Testing · 🗺️ Roadmap · 📄 License

pytest tests/ -v

Roadmap: Bloomberg ESG data integration · Real-time news monitoring · Portfolio optimisation engine · Regulatory filing auto-parser (TCFD, SFDR)

MIT License — see LICENSE. Contributions welcome — see CONTRIBUTING.md.