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llm_util

Deprecated: The llm_util.schema() procedure is now deprecated. Please use the SHOW SCHEMA INFO; query instead for retrieving schema information. Make sure the --schema-info-enabled=true flag is set for this query to work.

A module that contains procedures describing graphs in a format best suited for large language models (LLMs). The module was originally built for schema generation in LangChain - a framework for developing applications powered by language models.

Source code

Trait	Value
Module type	util
Implementation	Python
Parallelism	sequential

Trait

Value

Module type

util

Implementation

Python

Parallelism

sequential

Procedures

schema()

The schema() procedure generates the graph database schema in a prompt-ready or raw format. The prompt-ready format is optimized to describe the database schema in words best recognized by large language models (LLMs). The raw format offers all the necessary information about the graph schema in a format that can be customized for later use with LLMs.

Input:

Output:

Usage:

Example

Below are examples on how to get a prompt ready graph schema, and a raw graph schema.

Create a graph

Create a graph by running the following Cypher query:

CREATE (n:Person {name: "Kate", age: 27})-[:IS_FRIENDS_WITH {since: "2023-06-21"}]->(m:Person:Student {name: "James", age: 30, year: "second"})-[:STUDIES_AT]->(:University {name: "University of Zagreb"}) 
CREATE (p:Person:Student {name: "Anthony", age: 25})-[:STUDIES_AT]->(:University {name: "University of Vienna"}) 
WITH n, m 
CREATE (n)-[:LIVES_IN]->(:City {name: "Zagreb"})<-[:LIVES_IN]-(m);

Database schema

The schema of the created graph can be seen in Memgraph Lab, under the Graph Schema tab:

[image]

Prompt-ready graph schema

Once the graph is created, run the following code to call the schema procedure:

CALL llm_util.schema() YIELD schema RETURN schema;

CALL llm_util.schema('prompt_ready') YIELD schema RETURN schema;

Results

Below is the result of running the schema procedure:

Node properties are the following:
Node name: 'Person', Node properties: [{'property': 'name', 'type': 'str'}, {'property': 'age', 'type': 'int'}, {'property': 'year', 'type': 'str'}]
Node name: 'Student', Node properties: [{'property': 'name', 'type': 'str'}, {'property': 'age', 'type': 'int'}, {'property': 'year', 'type': 'str'}]
Node name: 'University', Node properties: [{'property': 'name', 'type': 'str'}]
Node name: 'City', Node properties: [{'property': 'name', 'type': 'str'}]

Relationship properties are the following:
Relationship Name: 'IS_FRIENDS_WITH', Relationship Properties: [{'property': 'since', 'type': 'str'}]

The relationships are the following:
['(:Person)-[:IS_FRIENDS_WITH]->(:Person)']
['(:Person)-[:IS_FRIENDS_WITH]->(:Student)']
['(:Person)-[:LIVES_IN]->(:City)']
['(:Person)-[:STUDIES_AT]->(:University)']
['(:Student)-[:STUDIES_AT]->(:University)']
['(:Student)-[:LIVES_IN]->(:City)']

Raw graph schema

Once the graph is created, run the following code to call the schema procedure:

CALL llm_util.schema('raw') YIELD schema RETURN schema;

Results

Below is the result of running the schema procedure:

{
   "node_props": {
      "City": [
         {
            "property": "name",
            "type": "str"
         }
      ],
      "Person": [
         {
            "property": "name",
            "type": "str"
         },
         {
            "property": "age",
            "type": "int"
         },
         {
            "property": "year",
            "type": "str"
         }
      ],
      "Student": [
         {
            "property": "name",
            "type": "str"
         },
         {
            "property": "age",
            "type": "int"
         },
         {
            "property": "year",
            "type": "str"
         }
      ],
      "University": [
         {
            "property": "name",
            "type": "str"
         }
      ]
   },
   "rel_props": {
      "IS_FRIENDS_WITH": [
         {
            "property": "since",
            "type": "str"
         }
      ]
   },
   "relationships": [
      {
         "end": "Person",
         "start": "Person",
         "type": "IS_FRIENDS_WITH"
      },
      {
         "end": "Student",
         "start": "Person",
         "type": "IS_FRIENDS_WITH"
      },
      {
         "end": "City",
         "start": "Person",
         "type": "LIVES_IN"
      },
      {
         "end": "University",
         "start": "Person",
         "type": "STUDIES_AT"
      },
      {
         "end": "University",
         "start": "Student",
         "type": "STUDIES_AT"
      },
      {
         "end": "City",
         "start": "Student",
         "type": "LIVES_IN"
      }
   ]
}