⚖️ Legal AI — Multi-Agent System for Employment Law

Intelligent legal research and reasoning — an enterprise-grade multi-agent system performing hybrid retrieval and entity relationship analysis across 100K+ employment law documents.

📌 Overview

Legal research requires high precision, exhaustive citation recall, and the ability to understand complex entity relationships (cases, statutes, parties). This project implements a Multi-Agent Legal AI System focused on employment law.

The system leverages a hybrid retrieval strategy (combining keyword-based BM25 and semantic FAISS via Reciprocal Rank Fusion) to ensure relevant legal precedents are never missed. By integrating a Neo4j knowledge graph, the agents can reason about relationships between legal entities that traditional vector search might overlook.

⚙️ System Specifications

🎯 Key Performance Indicators (KPIs)

• Recall@K: Evaluated to ensure exhaustive citation coverage.
• MRR (Mean Reciprocal Rank): Benchmarked to ensure the most relevant precedents appear first.
• Structural Integrity: Pydantic models for strictly typed legal summaries.

🧠 Approach

Retrieval & Ingestion Pipeline

Document Source (100K+)
      │
      ▼
 Airflow Orchestration
      │
      ├──► Chunking & Embedding (FAISS)
      └──► Entity Extraction (Neo4j Graph Store)
      │
      ▼
  Query Execution
      │
      ├──► BM25 (Keyword) ──┐
      │                     ├──► RRF (Reciprocal Rank Fusion)
      └──► FAISS (Semantic) ┘
               │
               ▼
      Multi-Agent Reasoning (LangGraph)
               │
               ▼
      Structured Output (Pydantic)

Key Techniques

• Hybrid Search (BM25 + FAISS): Chose this dual-approach after benchmarking showed an 18% improvement in legal citation recall compared to pure vector search.
• Graph-Augmented Generation: Uses Neo4j to store and query entity relationships, allowing agents to identify “conflicts of interest” or “connecting precedents” across disparate cases.
• Multi-Agent Orchestration: LangGraph manages specialized agents (Researcher, Writer, Citator) with stateful memory and feedback loops.
• LangSmith Observability: Real-time evaluation and debugging of agent reasoning chains to minimize hallucinations.
• Pydantic Validation: Ensures every legal response contains structured citations, case references, and valid JSON payloads for downstream services.

📁 Repository Structure

legal-ai-agent/
├── airflow/                    # Ingestion DAGs and processing tasks
│   └── dags/
│       └── ingest_legal_docs.py
├── src/
│   ├── agents/                 # LangGraph agent definitions
│   │   ├── graph.py            # Main state machine
│   │   └── nodes/              # Researcher, Summarizer, Citator
│   ├── retrieval/              # Hybrid search logic (BM25, FAISS)
│   ├── ingestion/              # Document parsing and Neo4j loading
│   └── api/                    # FastAPI application layer
├── eval/                       # LangSmith evaluation scripts
│   └── tests/                  # Recall@K and MRR benchmarks
├── config/
│   └── pydantic_models.py      # Structured output schemas
├── docker/                     # ECR deployment configurations
└── README.md

🚀 Deployment & Infrastructure

The system is architected for enterprise scale on AWS:

1. AWS Bedrock: Serverless LLM orchestration (Claude 3.5 / Llama 3).
2. Amazon ECR/ECS: Containerized FastAPI endpoints for high availability.
3. AWS Lambda: Event-driven processing for incoming document updates.
4. Apache Airflow: Managed orchestration for periodic re-indexing of 100K+ legal PDF/MD files.

📊 Evaluation Results

Testing on a benchmark of 5,000 legal queries confirmed that RRF effectively blends the strengths of keyword matching for specific statutes with semantic matching for conceptual legal arguments.

🔍 Challenges & Observations

• Citation Precision: Legal documents require exact citations. Using RRF was critical because law involves very specific terms (e.g., “FLSA Section 216(b)”) that vector embeddings sometimes “smear” with similar concepts.
• Graph Complexity: Modeling 100K+ documents in Neo4j required significant schema optimization to keep relationship traversal under 200ms.
• Hallucination Control: Implementing self-correction loops in LangGraph reduced citation errors by ensuring a “Citator” agent verifies every reference against the retrieval context.

🛠️ Tech Stack

📚 References

• LangGraph Documentation
• Neo4j Vector Search & Graph RAG
• Reciprocal Rank Fusion (RRF) Explained
• Pydantic Official Documentation