Analyzing graph data

Graph databases give us a powerful way to represent and query real-world relationships. There are a number of chains that make it easy to use LLMs to interact with various graph DBs.

📄️ Neo4j DB QA chain

This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.

📄️ FalkorDBQAChain

This notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.

📄️ HugeGraph QA Chain

This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.

📄️ KuzuQAChain

This notebook shows how to use LLMs to provide a natural language interface to Kùzu database.

📄️ Memgraph QA chain

This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.

📄️ NebulaGraphQAChain

This notebook shows how to use LLMs to provide a natural language interface to NebulaGraph database.

📄️ Graph QA

This notebook goes over how to do question answering over a graph data structure.

Graph databases are an excellent choice for applications based on network-like models. To standardize the syntax and semantics of such graphs, the W3C recommends Semantic Web Technologies, cp. Semantic Web. SPARQL serves as a query language analogously to SQL or Cypher for these graphs. This notebook demonstrates the application of LLMs as a natural language interface to a graph database by generating SPARQL.\

📄️ Neptune Open Cypher QA Chain

This QA chain queries Neptune graph database using openCypher and returns human readable response

📄️ Tree of Thought (ToT) example

The Tree of Thought (ToT) is a chain that allows you to query a Large Language Model (LLM) using the Tree of Thought technique. This is based on the paper "Large Language Model Guided Tree-of-Thought"