LLM Function schemas

Nous provides an automated way to create LLM function schemas, avoiding the duplicate work of creating a function schema separate from the code implementation.

The ts-morph package is used to parse the source code at build-time/runtime to generate the schemas.

Defining functions

The following is an example of a class which exposes class methods as callable functions to the LLM agents.

import { func, funcClass } from '#functionSchema/functions';

@funcClass(__filename)
export class Jira {
    instance: AxiosInstance;

    constructor() {
        // Initialise axios instance...
    }

    /**
     * Gets the description of a JIRA issue
     * @param {string} issueId the issue id (e.g XYZ-123)
     * @returns {Promise<string>} the issue description
     */
    @func()
    async getJiraDescription(issueId: string): Promise<string> {
        const response = await this.instance.get(`issue/${issueId}`);
        return response.data.fields.description;
    }
}

The @funcClass(__filename) annotation must be on the class so ts-morph can find the source file, generate the function schema and register it.

The @func() annotation must be on each class method to be exposed as a LLM callable function.

If the schema files don't exist at runtime then they will automatically be generated. To improve startup time the schema files are cached under the folder .nous/functions and only re-built if the source file modified date is newer. Also, the schema files can be generated at build time with the npm run functionSchemas script.

Function calling agents can transform the object implementing the FunctionSchema interface into the format required, e.g. the custom XML format, or native function calling types for OpenAI, Anthropic, Gemini etc.

The @func annotation also adds OpenTelemetry tracing to the function call.

Function arguments

Function arguments must simple types or serializable to JSON.

For array type arguments the runtime will convert a string that is a JSON array to an array object, otherwise it will split it by the newline character.

Agent functions

Currently, functions can only be made available as a group of all the functions defined in a class.

import { GitLab } from '#functions/gitlab'
import { Jira } from '#functions/jira'

await runAgent({
    agentName: 'ABC-123 discovery',
    llms: GPT(),
    functions: [GitLab, Jira],
    initialPrompt: "What project in GitLab has the code to complete Jira ABC-123?",
})

Each iteration of the autonomous agent control loop updates the system prompt with the available functions, so its possibly to dynamically change which functions are available in a long-running agent.

Application registration

To ensure the all functions have been registered when the application is running, add the function class to the array in functionRegistry.ts. This is required by the web interface for the function selection list to be complete and for the npm run functionsSchemas command to pre-build all the schema.

See the Tools/Integrations page for information on the provided function callable integrations.