# llm_util

> **Warning**
>
> **Deprecated**: The `llm_util.schema()` procedure is now **deprecated**. Please
> use the `SHOW SCHEMA INFO;` query instead for [retrieving schema
> information](https://memgraph.com/docs/querying/schema). 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](https://www.langchain.com/) - a framework for
developing applications powered by language models.

| Trait               | Value            |
| ------------------- | ---------------- |
| **Module type**     | util             |
| **Implementation**  | Python           |
| **Parallelism**     | sequential       |

## Procedures

> **Info**
>
> You can execute this algorithm on [graph projections, subgraphs or portions of the graph](https://memgraph.com/docs/advanced-algorithms/run-algorithms#run-procedures-on-subgraph).

### 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:

- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](https://memgraph.com/docs/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
If subgraph is not specified, the algorithm is computed on the entire graph by default.

* `output_type: str (default='prompt_ready')` ➡ By default, the graph schema
  will include additional context and it will be prompt-ready. If set to 'raw',
  it will produce a simpler version that can be adjusted for the prompt. The
  parameter is case-insensitive. 

#### Output:

* `schema: mgp.Any` ➡ `str` containing prompt-ready graph schema description in
  a format suitable for large language models (LLMs), or `mgp.List` containing
  information on graph schema in raw format which can customized for LLMs. 

#### Usage:

To get the **prompt-ready graph schema**, use the following query:

```cypher
CALL llm_util.schema() YIELD schema RETURN schema;
```

To get the **raw graph schema**, use the following query:

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

## 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:

```cypher
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 <em>Graph
Schema</em> tab:

<div className={"imgSmaller"}>
![](https://memgraph.com/docs/pages/advanced-algorithms/available-algorithms/llm_util/memgraph-lab-schema.png)
</div>

### Prompt-ready graph schema 

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

```cypher
CALL llm_util.schema() YIELD schema RETURN schema;
```

or 

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

### Results
 

Below is the result of running the <code>schema</code> 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 <code>schema</code> procedure:

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

### Results
 

Below is the result of running the <code>schema</code> 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"
      }
   ]
}
```