Integrating TigerGraph and Large Language Models for Generative AI

Large Language Models (LLMs) have proven their usefulness in general-purpose information retrieval, but businesses often struggle with how to ask questions about their specific data using these models. Since LLMs are neural networks with fixed-size inputs, they can only process a limited amount of data at once, known as context length or tokens. To overcome this limitation and enable LLMs to reason with sensitive data that cannot be incorporated into training datasets, highly scalable, deduplicated, and relationship-rich external data sources like graph databases are needed. TigerGraph is one such solution that allows for the execution of scalable graph algorithms and can integrate with LangChain, a Python library that integrates LLMs with various data sources. By building LangChain tools to interact with TigerGraph, developers can reduce hallucinations in model responses and enable them to answer questions whose answers were not present in their training datasets. This integration opens the door for business analysts to be more productive and have richer information at their fingertips.