OpenAI Function calling tools



---

OpenAI Function calling tools

OpenAI Function calling tools is a repository that offers a set of tools to help you easy to build a function calling model with OpenAI API.

More information about function calling

Sample: https://chatFn.io

🪓 Tools

The repo provides the following tools you can use out of the box:

- 🗺️ ShowPoisOnMap: A tool that can show points of interest on a map.
- 🌐 ReverseGeocode: A tool that can convert coordinates into a human-readable address.
- ⏰ Clock: A clock that can tell you the time.
- 🧮 Calculator: A simple calculator that can do basic arithmetic. Input should be a math expression.
- 🔍 GoogleCustomSearch: A wrapper around the Google Custom Search API. Useful for when you need to answer questions about current events. Input should be a search query.
- 🔍 BingCustomSearch: A wrapper around the Bing Custom Search API. Useful for when you need to answer questions about current events. Input should be a search query.
- 🔍 SerperCustomSearch: A wrapper around the SerpAPI. Useful for when you need to answer questions about current events. Input should be a search query.
- 🏞️ SerperImagesSearch: Use SerpAPI to search images. Input should be a search query.
- 📁 fs: WriteFileTool abd ReadFileTool access to the file system. Input should be a file path and text written to the file.
- 🪩 webbrowser: A web browser that can open a website. Input should be a URL.
- 🚧 sql: Input to this tool is a detailed and correct SQL query, output is a result from the database.
- 🚧 JavaScriptInterpreter: A JavaScript interpreter. Input should be a JavaScript program string.

> You can use { Tool } factory function to create a tool instance. See /tools for more examples.

📦 Quick Install

``bash
npm install openai-function-calling-tools
`

📖 Usage

$3

use JavaScriptInterpreter to calculate 0.1 + 0.2

`js
import { Configuration, OpenAIApi } from "openai";
import { createCalculator } from "openai-function-calling-tools"

const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);

const QUESTION = "What is 100*2?";

const messages = [
{
role: "user",
content: QUESTION,
},
];

✨ STEP 1: new the tools you want to use

const [calculator, calculatorSchema] = createCalculator();

✨ STEP 2: add the tools to the functions object

const functions = {
calculator,
};

const getCompletion = async (messages) => {
const response = await openai.createChatCompletion({
model: "gpt-3.5-turbo-0613",
messages,
# ✨ STEP 3: add the tools to the schema
functions: [calculatorSchema],
temperature: 0,
});

return response;
};

console.log("Question: " + QUESTION);
let response = await getCompletion(messages);

if (response.data.choices[0].finish_reason === "function_call") {
const fnName = response.data.choices[0].message.function_call.name;
const args = response.data.choices[0].message.function_call.arguments;

console.log("Function call: " + fnName);
console.log("Arguments: " + args);

# ✨ STEP 4: call the function
const fn = functions[fnName];
const result = fn(JSON.parse(args));

console.log("Calling Function Result: " + result);

messages.push({
role: "assistant",
content: null,
function_call: {
name: fnName,
arguments: args,
},
});

messages.push({
role: "function",
name: fnName,
content: JSON.stringify({ result: result }),
});

// call the completion again
response = await getCompletion(messages);

console.log(response.data.choices[0].message.content);
}
`

$3

> 📝 Note: You need to apply for a Google Custom Search API key and a Google Custom Search Engine ID to use this tool.

#### The following is a sequence diagram of the example
`mermaid
sequenceDiagram
participant U as User
participant M as Main Function
participant O as OpenAI API
participant F as Functions Object
participant GC as Google Custom Search

U->>M: Execute main function
M->>M: Initialize configuration and API
M->>M: Define QUESTION variable
M->>M: Create Google Custom Search tool
M->>F: Add tool to functions object
loop Chat Completion Loop
M->>O: Request chat completion
O-->>M: Return response
alt If finish reason is "stop"
M->>U: Display answer and exit loop
else If finish reason is "function_call"
M->>M: Parse function call name and arguments
M->>F: Invoke corresponding function
F->>GC: Perform Google Custom Search
GC-->>F: Return search results
F->>M: Receive function result
M->>M: Add result to message queue
M->>M: Output function call details
else Other cases
M->>M: Continue loop
end
end
`
#### Code

`js
const { Configuration, OpenAIApi } = require("openai");
const { createGoogleCustomSearch } = require("openai-function-calling-tools");

const main = async () => {
const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);

const QUESTION = "How many tesla model 3 sale in 2022?"

const messages = [
{
role: "user",
content: QUESTION,
},
];

// ✨ STEP 1: new the tools you want to use
const [googleCustomSearch, googleCustomSearchSchema] =
createGoogleCustomSearch({
apiKey: process.env.GOOGLE_API_KEY,
googleCSEId: process.env.GOOGLE_CSE_ID,
});


// ✨ STEP 2: add the tools to the functions object
const functions = {
googleCustomSearch,
};

const getCompletion = async (messages) => {
const response = await openai.createChatCompletion({
model: "gpt-3.5-turbo-0613",
messages,
// ✨ STEP 3: add the tools schema to the functions parameter
functions: [googleCustomSearchSchema],
temperature: 0,
});

return response;
};
let response;

console.log("Question: " + QUESTION);

while (true) {
response = await getCompletion(messages);

if (response.data.choices[0].finish_reason === "stop") {
console.log(response.data.choices[0].message.content);
break;
} else if (response.data.choices[0].finish_reason === "function_call") {
const fnName = response.data.choices[0].message.function_call.name;
const args = response.data.choices[0].message.function_call.arguments;

const fn = functions[fnName];
const result = await fn(JSON.parse(args));

console.log(Function call: ${fnName}, Arguments: ${args});
console.log(Calling Function ${fnName} Result: + result);

messages.push({
role: "assistant",
content: "",
function_call: {
name: fnName,
arguments: args,
},
});

messages.push({
role: "function",
name: fnName,
content: JSON.stringify({ result: result }),
});
}
}
};

main();
`




$3

Example to extract schema from a function call

Tree structure:

`js
import { Configuration, OpenAIApi } from "openai";

const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);

const getCompletion = async (messages) => {
const response = await openai.createChatCompletion({
model: "gpt-3.5-turbo-0613",
messages: [
{
role: "user",
content: root
├── folder1
│ ├── file1.txt
│ └── file2.txt
└── folder2
├── file3.txt
└── subfolder1
└── file4.txt
},
],
functions: [
{
"name": "buildTree",
"description": "build a tree structure",
"parameters": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The name of the node"
},
"children": {
"type": "array",
"description": "The tree nodes",
"items": {
"$ref": "#"
}
},
"type": {
"type": "string",
"description": "The type of the node",
"enum": [
"file",
"folder"
]
}
},
"required": [
"name",
"children",
"type"
]
}
}
],
temperature: 0,
});

return response;
};

let response = await getCompletion();

if (response.data.choices[0].finish_reason === "function_call") {
const args = response.data.choices[0].message.function_call.arguments;
// 🌟 output the Tree structure data
console.log(args);
}
``

💻 Supported Environments

- Node.js v16 or higher
- Cloudflare Workers
- Vercel / Next.js (Backend, Serverless and Edge functions 🔥)
- Supabase Edge Functions
- 🚧 Browser

🛡️ Safe for Production


🌟 Inspiration

- LangChainAI