Build a Knowledge Graph-Based Agent with Llama 3.1, Nvidia Nim, and LangChain

The article discusses the development of a knowledge graph-based agent utilizing Llama 3.1, NVIDIA NIM, and LangChain to enhance retrieval-augmented generation (RAG) applications. It emphasizes the advantages of using structured data from knowledge graphs over unstructured text for generating more accurate responses. The author outlines a method for implementing a retriever that accesses structured information from the FDA Adverse Event Reporting System (FAERS) to answer queries about drug side effects. The process involves dynamic query generation, where a logic layer creates database queries based on user input, ensuring consistency and control. The article details the setup of a Neo4j graph database to store adverse event data and the integration of Llama 3.1, which supports function-calling capabilities. A specific function, `get_side_effects`, is designed to retrieve common side effects of drugs based on user-defined parameters such as drug name, age, and manufacturer. The function constructs a Cypher query to extract relevant data from the knowledge graph. The article concludes by illustrating how to configure an LLM agent that utilizes this tool to respond to inquiries about drug side effects, showcasing the potential of combining LLMs with knowledge graphs for improved information retrieval.

- Knowledge graphs can enhance the accuracy of retrieval-augmented generation applications.

- Dynamic query generation allows for controlled and consistent database queries based on user input.

- Llama 3.1's function-calling capabilities facilitate advanced LLM use cases.

- The `get_side_effects` function retrieves drug side effects using specified search criteria.

- Integration of Neo4j with Llama 3.1 enables effective querying of structured data.