✨ Build 🛠 With Next Gen LLM Function Calling ✨
Iudex is an agent accessible via API that provides more accurate, secure, and scalable LLM function calling.
- Scales to support 1000s of functions per query, not 10s
- Supports arbitrarily complex queries and automatically handles edgecases
- Ensures accuracy and interpretability using automated task orchestration
- Iudex never has to ingest your code or data
Sign Up at iudex.ai or shoot a message at support@iudex.ai to get an API key.
npm install iudex
# or
yarn add iudex
# or
pnpm add iudexBasic example
import dotenv from 'dotenv';
dotenv.config();
import { Iudex } from 'iudex';
/* 1. Instantiate client */
const iudex = new Iudex({ apiKey: process.env.IUDEX_API_KEY });
/* 2. Upload function json schemas */
await iudex.uploadFunctions([
{
name: 'getCurrentWeather',
description: 'Gets the current weather',
parameters: {
type: 'object',
properties: {
location: {
type: 'string',
description: 'The city and state, e.g. San Francisco, CA',
},
unit: { type: 'string', enum: ['celsius', 'fahrenheit'] },
},
required: ['location'],
},
},
]);
// Example function defined in code
function getCurrentWeather({ location, unit }: { location: string; unit: string }) {
if (location.toLowerCase().includes('tokyo')) {
return { location: 'Tokyo', temperature: '10', unit: 'celsius' };
} else if (location.toLowerCase().includes('san francisco')) {
return { location: 'San Francisco', temperature: '72', unit: 'fahrenheit' };
} else if (location.toLowerCase().includes('paris')) {
return { location: 'Paris', temperature: '22', unit: 'fahrenheit' };
} else {
return { location, temperature: 'unknown' };
}
}
/* 3. Create a way to reference functions using strings */
iudex.linkFunctions((fnName: string) => {
return {
getCurrentWeather,
}[fnName];
});
/* 4. Send a message to Iudex */
const message = 'What is the weather in tokyo?';
const iudexReply = await iudex.sendMessage(message);
/* 5. See result */
console.log(`Result for "${message}": ${iudexReply}`);Example where using Iudex replaces OpenAI
Iudex replaces instances of the OpenAI client where function calling is used.
fnMap just needs to be defined to link all functions you want the function calling
api to be able to call. For Iudex all parameters except messages is ignored.
Functions only need to be uploaded once.
import dotenv from 'dotenv';
dotenv.config();
import OpenAI from 'openai';
import { Iudex } from 'iudex';
import _ from 'lodash';
/* 1. Instantiate client */
const iudex = new Iudex({ apiKey: process.env.IUDEX_API_KEY });
/* 2. Upload function json schemas */
await iudex.uploadFunctions([
{
name: 'getCurrentWeather',
description: 'Gets the current weather',
parameters: {
type: 'object',
properties: {
location: {
type: 'string',
description: 'The city and state, e.g. San Francisco, CA',
},
unit: { type: 'string', enum: ['celsius', 'fahrenheit'] },
},
required: ['location'],
},
},
]);
// Example function defined in code
function getCurrentWeather({ location, unit }: { location: string; unit: string }) {
if (location.toLowerCase().includes('tokyo')) {
return { location: 'Tokyo', temperature: '10', unit: 'celsius' };
} else if (location.toLowerCase().includes('san francisco')) {
return { location: 'San Francisco', temperature: '72', unit: 'fahrenheit' };
} else if (location.toLowerCase().includes('paris')) {
return { location: 'Paris', temperature: '22', unit: 'fahrenheit' };
} else {
return { location, temperature: 'unknown' };
}
}
/* 3. Create a way to reference functions using strings */
const fnMap: Record<string, (...args: any[]) => any> = {
getCurrentWeather,
};
/* 4. Track message history */
const messages = createMessages();
/* 5. Initial message goes here */
messages.push({ role: 'user', content: `what is the weather in San Francisco?` });
/* 6. Wait for AI response for which function to call */
messages.push(await iudex.chat.completions.create({
model: 'gpt-4-turbo-preview',
messages: messages.value,
tools: [],
tool_choice: 'auto',
}).then(res => res.choices[0].message));
/* 7. Keep calling functions until the initial message request is resolved */
let toolMessage = _.last(messages.value);
// Loop while the latest message contains the AI's request for a function to be called
while (toolMessage && messageHasToolCall(toolMessage)) {
// Extract tool_call_id, function name, and function arguments
const { function: fnCall, id: tool_call_id } = toolMessage.tool_calls[0];
const { name: fnName, arguments: fnArgs } = fnCall;
// Call the function
const fnReturn = await fnMap[fnName](JSON.parse(fnArgs));
// Push function return to message history
messages.push({
role: 'tool',
tool_call_id,
content: JSON.stringify(fnReturn),
});
// Push to ai response message history
messages.push(await iudex.chat.completions.create({
model: 'gpt-4-turbo-preview',
messages: messages.value,
}).then(res => res.choices[0].message));
// Update toolMessage
toolMessage = _.last(messages.value);
}
/* 8. Print final result */
console.log('Result: ', toolMessage);
//// Helpers
// Helper message object that also outputs to console
class Messages {
messagesHist: OpenAI.ChatCompletionMessageParam[] = [];
push = (...items: OpenAI.ChatCompletionMessageParam[]) => {
console.log('new message:', items);
this.messagesHist.push(...items);
};
get value() {
return this.messagesHist;
}
}
function createMessages() {
return new Messages();
}
// Helper to check if a message has a tool call
type OpenAIToolCallMessage = OpenAI.ChatCompletionAssistantMessageParam
& { tool_calls: OpenAI.ChatCompletionMessageToolCall[] };
function messageHasToolCall(
message: OpenAI.ChatCompletionMessageParam,
): message is OpenAIToolCallMessage {
return !!(message as OpenAI.ChatCompletionAssistantMessageParam).tool_calls;
}