Large Language Models (LLMs) and Knowledge Graphs

Integrate large language models (LLMs) with knowledge graphs using ArangoDB

Large language models (LLMs) and knowledge graphs are two prominent and contrasting concepts, each possessing unique characteristics and functionalities that significantly impact the methods we employ to extract valuable insights from constantly expanding and complex datasets.

LLMs, exemplified by OpenAI’s ChatGPT, represent a class of powerful language transformers. These models leverage advanced neural networks to exhibit a remarkable proficiency in understanding, generating, and participating in contextually-aware conversations.

On the other hand, knowledge graphs contain carefully structured data and are designed to capture intricate relationships among discrete and seemingly unrelated information. With knowledge graphs, you can explore contextual insights and execute structured queries that reveal hidden connections within complex datasets.

ArangoDB’s unique capabilities and flexible integration of knowledge graphs and LLMs provide a powerful and efficient solution for anyone seeking to extract valuable insights from diverse datasets.

Knowledge Graphs

A knowledge graph can be thought of as a dynamic and interconnected network of real-world entities and the intricate relationships that exist between them.

Key aspects of knowledge graphs:

  • Domain specific knowledge: You can tailor knowledge graphs to specific domains and industries.
  • Structured information: Makes it easy to query, analyze, and extract meaningful insights from your data.
  • Accessibility: You can build a Semantic Web knowledge graph or using custom data.

LLMs can help distill knowledge graphs from natural language by performing the following tasks:

  • Entity discovery
  • Relation extraction
  • Coreference resolution
  • End-to-end knowledge graph construction
  • (Text) Embeddings

ArangoDB Knowledge Graphs and LLMs

ArangoDB and LangChain

LangChain  is a framework for developing applications powered by language models.

LangChain enables applications that are:

  • Data-aware (connect a language model to other sources of data)
  • Agentic (allow a language model to interact with its environment)

The ArangoDB integration with LangChain provides you the ability to analyze data seamlessly via natural language, eliminating the need for query language design. By using LLM chat models such as OpenAI’s ChatGPT, you can “speak” to your data instead of querying it.

Get started with ArangoDB QA chain

The ArangoDB QA chain notebook  shows how to use LLMs to provide a natural language interface to an ArangoDB instance.

Run the notebook directly in Google Colab .

See also other machine learning interactive tutorials .

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