FAANG-Grade Project by Bhagwat Chate
Designed for C-suite utility, real-world auditability, and structured legal GenAI answers with zero hallucinations.
LexiGuard is a modular, production-ready GenAI system designed to analyze AI policy and legal documents (e.g., GDPR, HIPAA, EU AI Act, DPDP) and deliver:
- 📚 Contextual answers to compliance questions
- 🔍 Faithfulness, citation, and hallucination checks
- 🧾 Structured outputs conforming to legal formats
- 📌 Actionable next steps with risk classification
Built with AutoGen, LangChain, RAGAS, and Guardrails.ai, LexiGuard is engineered to meet enterprise-grade security, validation, and observability expectations.
Modern enterprises struggle with:
- Ambiguous compliance with AI regulations
- No structured answers from legal teams or LLMs
- Risk of hallucinated clauses or unverified citations
- Siloed policy documents across regions and teams
LexiGuard solves this with:
- RAG + Guardrails + Multi-Agent orchestration
- Verified citations and jurisdiction mapping
- Structured legal insights with “not legal advice” disclaimer
- Logs for every query with explainable eval metrics
| Feature | Description |
|---|---|
| 🧠 RAG with Legal Context | Semantic + metadata-based hybrid search on legal documents |
| ✅ Evaluation Layer | Faithfulness (RAGAS), Toxicity (TruLens), Hallucination & Risk detection |
| 🛡️ Guardrails + Pydantic | Schema enforcement with enums, disclaimers, and JSON output guarantees |
| 🔄 Multi-Agent AutoGen Orchestration | 13 modular agents with plug-and-play extensibility |
| 🌍 Jurisdiction Mapping | Compare laws like GDPR ↔ DPDP ↔ CCPA |
| 🧾 Version Comparison | Tracks policy updates and change logs |
| 📊 Logs + Scorecards | MongoDB/JSON logs with query trace, eval scores, risk tags |
| ☁️ Cloud Native & Secure | Dockerized, deployable on AWS EC2, S3, and MongoDB Atlas |
User → FastAPI → AutoGen Orchestrator
→ Retriever → RAG Context
→ AnswerGen → Eval Layer (RAGAS, TruLens, Citation)
→ Guardrails Layer (Schema, Enums, Disclaimers)
→ Actionability + Logging
→ Structured, Verified Response
➡️ See: /docs/architecture.png for detailed visual.
See locked folder structure in /docs/folder_structure.md.
| Layer | Stack Used |
|---|---|
| Agent Runtime | AutoGen FunctionCallingAgent, GroupChat, custom orchestrator |
| RAG | LangChain loaders, chunkers, OpenAI Ada-002 + Cohere Legal embeddings |
| Vector Store | Qdrant with metadata filtering (jurisdiction, clause type, severity) |
| LLMs | OpenAI GPT-4o + Cohere Command R+ (fallback) |
| Evaluation | RAGAS, TruLens, OpenAI Moderation API |
| Validation | Pydantic + Guardrails.ai enforced schemas |
| API | FastAPI with modular routes |
| Infra | Docker, Redis, MongoDB/JSON Logs, AWS EC2/S3 |
# 1. Clone the repo
git clone https://github.com/bhagwat-chate/lexiguard.git && cd lexiguard
# 2. Setup virtualenv
python -m venv venv && source venv/bin/activate
# 3. Install dependencies
pip install -r requirements.txt
# 4. Load documents into vector store
python scripts/load_documents.py
# 5. Run the FastAPI server
uvicorn api.main:app --reloadOptional: docker-compose up for full stack (Redis + MongoDB + API)
| Prompt | Output Description |
|---|---|
| “Is sharing personal data with 3rd parties allowed under DPDP?” | Cited clause, risk tag = “Medium”, follow-up = “Need DPO review” |
| “How does GDPR differ from DPDP on consent?” | Cross-jurisdiction expander returns clause diff summary |
| “What changed in our privacy policy since last version?” | Version comparator highlights added/removed sections |
- ✅ 100% JSON-structured outputs (schema-conforming)
- ✅ 90%+ RAGAS faithfulness score
- ✅ <1% hallucination and toxicity rate
- ✅ Deployment-ready API on AWS
- ✅ Works across GDPR, HIPAA, DPDP documents
HLD.md– High-level architectureLLD.md– Class and component-level breakdown/docs/agent_docs/– Each agent’s responsibility, input/output/docs/prompts.md– Standardized prompt strategies/docs/examples/– Sample queries and JSON responses
MIT License — built for educational, enterprise, and interview showcase use.
Bhagwat Chate
AI/ML Lead | GenAI Architect
📫 LinkedIn • GitHub