A naukri.com initiative
Towards Data Science
2w
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Agentic GraphRAG for Commercial Contracts
- Legal contracts play a vital role in defining business relationships and obligations.
- Understanding and analyzing legal contracts can be complex and time-consuming.
- The implementation of Agentic Graphrag can streamline the process of working with legal contracts.
- Structuring legal contracts into a knowledge graph in Neo4j facilitates easy querying and analysis.
- Agentic Graphrag enables precise and context-aware retrieval, overcoming the limitations of naive retrieval methods.
- A knowledge graph for legal contracts contains structured and unstructured information.
- The graph represents entities like companies, agreements, and clauses along with their relationships.
- Structured extraction using LLMs helps identify key information in contracts.
- The structured data can be stored in a knowledge graph for refined searches and precise retrieval.
- Entity resolution in legal contracts involves resolving variations in referencing entities.
- Agentic GraphRAG involves using LLMs as central reasoning engines supplemented with tools and memory.
- Tools like Cypher queries and semantic search improve retrieval of key contract information.
- A benchmark dataset aids in evaluating the performance of the implemented system.
- Results show that models like Gemini 1.5 Pro, Gemini 2.0 Flash, and GPT-4o perform well for most tool calls.
- The application of LLMs in legal contract analysis shows promise for navigating complex domains effectively.
- A web application powered by LangGraph and FastAPI provides a user-friendly interface for interacting with legal contract data.
- The project demonstrates the potential for LLMs to act as powerful agents in legal contract analysis when paired with the right tools.
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