Title: OpenAI Assistant in a proxy server · Issue #53 · OvidijusParsiunas/deep-chat
URL Source: https://github.com/OvidijusParsiunas/deep-chat/issues/53
Markdown Content: OpenAI Assistant in a proxy server #53
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Information how to use/implement the component
Current Behavior:
I am utilising your deep-chat-nextjs server as a proxy for OpenAI and the request
DeepChatDesired Behavior:
I would like to request support for passing assistant details in the request prop.
Additional Information
If an update is not feasible, it would be greatly appreciated if you could provide guidance on how to achieve the requested functionality manually using custom headers and what it would look like on the nextjs example server.
Hi. When connecting Deep Chat to your own backend (like a NextJs function), the specific code for calling another service such as OpenAI Assistants API should be written in the backend and not in Deep Chat.
We try to keep the backend examples as simple as possible so that developers can tailor them to their use-cases, hence we would prefer not to expand our examples for this reason.
In regards to your specific problem, connecting to OpenAI Assistants API is a quite complex task as it requires the use of the Assistants, Threads, Messages and Runs APIs.
If you want to do this manually in your own backend, you can use the code that I will paste below.
Code for the index.tsx file:
import {RequestDetails} from 'deep-chat/dist/types/interceptors';
import {Response} from 'deep-chat/dist/types/response';
import styles from '../styles/Index.module.css';
import dynamic from 'next/dynamic';
import React from 'react';
export default function IndexPage() {
// Need to import the component dynamically as it uses the 'window' property.
// If you have found a better way of adding the component in next, please create a new issue ticket so we can update the example!
const DeepChat = dynamic(() => import('deep-chat-react').then((mod) => mod.DeepChat), {
ssr: false,
});
// quick way to store state and not re-render the chat
const threadId = React.useRef<string | null>('');
return (
<>
<main className={styles.main}>
<h1 className={styles.serverTitle}>Server for OpenAI</h1>
<a href="https://openai.com/blog/openai-api" target="_blank" rel="noreferrer">
<img
className={styles.serverTitleIcon}
src="https://raw.githubusercontent.com/OvidijusParsiunas/deep-chat/HEAD/website/static/img/openAILogo.png"
style=width
alt={'Title icon'}
/>
</a>
<h3>Make sure to set the OPENAI_API_KEY environment variable in your server</h3>
<div className={styles.components}>
<div className={styles.diagonalLine} style=background></div>
{/* by setting maxMessages requestBodyLimits to 0 or lower - each request will send full chat history:
https://deepchat.dev/docs/connect/#requestBodyLimits */}
<DeepChat
style=borderRadius
introMessage=text
request=url
requestBodyLimits=maxMessages
errorMessages=displayServiceErrorMessages
requestInterceptor={(details: RequestDetails) => {
if (details.body instanceof FormData) {
if (threadId.current) details.body.append('thread_id', JSON.stringify(threadId.current));
} else if (threadId.current) {
details.body.thread_id = threadId.current;
}
return details;
}}
responseInterceptor={(response: Response & {thread_id: string}) => {
threadId.current = response.thread_id;
return response;
}}
/>
</div>
</main>
</>
);
}
Code for the chat.ts file. Make sure to set your API_KEY and ASSISTANT_ID variables:
import {DeepChatOpenAITextRequestBody} from '../../../types/deepChatTextRequestBody';
import {MessageContent} from 'deep-chat/dist/types/messages';
import errorHandler from '../../../utils/errorHandler';
import {NextRequest, NextResponse} from 'next/server';
import {
OpenAIAssistantMessagesResult,
OpenAIAssistantInitReqResult,
OpenAIRunResult,
} from 'deep-chat/dist/types/openAIResult';
export const config = {
runtime: 'edge',
};
const API_KEY = '';
const ASSISTANT_ID = '';
async function handler(req: NextRequest) {
// Text messages are stored inside request body using the Deep Chat JSON format:
// https://deepchat.dev/docs/connect
const textRequestBody = (await req.json()) as {messages: MessageContent[]; thread_id?: string};
console.log(textRequestBody);
let thread_id: string = textRequestBody.thread_id;
let run_id: string;
if (thread_id) {
const messageBody = createMessageBody(textRequestBody);
// Create a new thread and automatically run it
// https://platform.openai.com/docs/api-reference/messages/createMessage
await fetch(`https://api.openai.com/v1/threads/${thread_id}/messages`, {
headers: createHeaders(),
method: 'POST',
body: JSON.stringify(messageBody),
});
// https://platform.openai.com/docs/api-reference/runs/createRun
const createAndRunResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/runs`, {
headers: createHeaders(),
method: 'POST',
body: JSON.stringify({assistant_id: ASSISTANT_ID}),
});
const createAndRunResult = (await createAndRunResponse.json()) as OpenAIAssistantInitReqResult;
run_id = createAndRunResult.id;
} else {
const threadBody = createThreadBody(textRequestBody);
// Create a new thread and automatically run it
// https://platform.openai.com/docs/api-reference/runs/createThreadAndRun
const createMessageAndRunResponse = await fetch('https://api.openai.com/v1/threads/runs', {
headers: createHeaders(),
method: 'POST',
body: JSON.stringify(threadBody),
});
const createMessageAndRunResult = (await createMessageAndRunResponse.json()) as OpenAIAssistantInitReqResult;
thread_id = createMessageAndRunResult.thread_id;
run_id = createMessageAndRunResult.id;
}
// Get the result
const resultText = await pollForResult(thread_id, run_id);
// Sends response back to Deep Chat using the Response format:
// https://deepchat.dev/docs/connect/#Response
return NextResponse.json({text: resultText, thread_id: thread_id});
}
export function createThreadBody(body: DeepChatOpenAITextRequestBody) {
// Text messages are stored inside request body using the Deep Chat JSON format:
// https://deepchat.dev/docs/connect
return {
assistant_id: ASSISTANT_ID,
thread: {
messages: [
{
role: 'user',
content: body.messages[body.messages.length - 1].text,
},
],
},
};
}
export function createMessageBody(body: DeepChatOpenAITextRequestBody) {
// Text messages are stored inside request body using the Deep Chat JSON format:
// https://deepchat.dev/docs/connect
return {
role: 'user',
content: body.messages[body.messages.length - 1].text,
};
}
export function createHeaders() {
return {
'Content-Type': 'application/json',
Authorization: `Bearer ${API_KEY}`,
'OpenAI-Beta': 'assistants=v1',
};
}
async function pollForResult(thread_id: string, run_id: string) {
// Get the run status
// https://platform.openai.com/docs/api-reference/runs/listRuns
const runStatusResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/runs/${run_id}`, {
headers: createHeaders(),
method: 'GET',
});
const {status} = (await runStatusResponse.json()) as OpenAIRunResult;
if (status === 'queued' || status === 'in_progress') {
return await pollForResult(thread_id, run_id);
} else if (status === 'completed') {
// https://platform.openai.com/docs/api-reference/messages/listMessages
const messagesResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/messages`, {
headers: createHeaders(),
method: 'GET',
});
const messagesResult = (await messagesResponse.json()) as OpenAIAssistantMessagesResult;
return messagesResult.data[0].content[0].text.value;
}
throw runStatusResponse.status;
}
export default errorHandler(handler);
Important things to note are that we first create a thread_id and then re-use it for future conversations.
These examples should work right out of the box and you can tailor them to your use-case. If you have any questions specific to the OpenAI API, I recommend to instead check their documentation or use the Developer Forum.
Let me know if you have any specific questions to the above examples. Thanks!
Perfect 💯 appreciate your time with this 🙏
May you assist finally with the same implementation using streaming, see chat-stream.ts.
The chat-stream.ts file is designed to call a Server Sent Events endpoint to stream the response back to the UI. OpenAI Assistants currently do not support streaming as noted in the Limitations section of this document - “Support for streaming output (including Messages and Run Steps).”.
However, instead you can simply keep the same code as you have in the chat.ts file and simply use the stream property to simulate the stream.
All you will need to do is add the following property to Deep Chat in index.ts:
stream=simulation.
I require guidance on integrating the new function calling methods using Assistant API into the above Next.js server example. Could you provide a simple example for this? Additionally, I am interested in offering financial support, but I noticed that GitHub doesn’t accept PayPal for such transactions. Is there an alternative method to sponsor your work?
Don’t worry about financial support.
GitHub recently disabled direct sponsorships via PayPal in favour of Stripe, but if you really want to support you can use this email oparsiunas@googlemail.com for my PayPal. To note, I am motivated to work on this component by knowing that people are using it and every new GitHub Star keeps me going 🌟 . I am also just finishing up my career break and am looking for a job, so financially I will be ok. Thanks again for your kind thoughts!
When it comes to integrating functions/tools for Assistants API, majority of the setup really needs to be done when setting up your assistant. I recommend using the Assistant Playground to first add your function:
(This Assistant is now removed as I used it for a demo)
After clicking the Add button, you will see a template for an example function setup code. This is where things get a little complicated as they require the understanding on what functions/tools are and how they are used. If you are new to them I recommend reading this document or watching this video which really helped me (to note that video is using the old API, but it helps understanding the concept of OpenAI functions).
When adding your function, you can just use the template, and for my case I have used a get_weather function which contains the following template:
{
"name": "get_weather",
"description": "Determine weather in my location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"c",
"f"
]
}
},
"required": [
"location"
]
}
}
Given the function above, you can change the pollForResult function and add a new functions to the chat.ts example from above:
async function pollForResult(thread_id: string, run_id: string) {
// Get the run status
// https://platform.openai.com/docs/api-reference/runs/listRuns
const runStatusResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/runs/${run_id}`, {
headers: createHeaders(),
method: 'GET',
});
const {status, required_action} = (await runStatusResponse.json()) as OpenAIRunResult;
if (status === 'queued' || status === 'in_progress') {
return await pollForResult(thread_id, run_id);
}
if (status === 'completed') {
// https://platform.openai.com/docs/api-reference/messages/listMessages
const messagesResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/messages`, {
headers: createHeaders(),
method: 'GET',
});
const messagesResult = (await messagesResponse.json()) as OpenAIAssistantMessagesResult;
return messagesResult.data[0].content[0].text.value;
}
const toolCalls = required_action?.submit_tool_outputs?.tool_calls;
if (status === 'requires_action' && toolCalls) {
console.log('hello');
return await handleTools(toolCalls, thread_id, run_id);
}
throw runStatusResponse.status;
}
async function handleTools(toolCalls: ToolCalls, thread_id: string, run_id: string) {
const functions = toolCalls.map((call) => {
return {name: call.function.name, arguments: call.function.arguments};
});
const handlerResponse = functions.map((functionDetails) => getCurrentWeather(functionDetails.arguments));
const tool_outputs = handlerResponse.map((resp, index) => {
return {tool_call_id: toolCalls[index].id, output: resp};
});
// https://platform.openai.com/docs/api-reference/runs/submitToolOutputs
await fetch(`https://api.openai.com/v1/threads/${thread_id}/runs/${run_id}/submit_tool_outputs`, {
headers: createHeaders(),
method: 'POST',
body: JSON.stringify({tool_outputs}),
});
await new Promise((resolve) => setTimeout(resolve, 1000)); // wait for OpenAI to read the output
return await pollForResult(thread_id, run_id);
}
function getCurrentWeather(location: string) {
location = location.toLowerCase();
if (location.includes('tokyo')) {
return 'Good';
}
if (location.includes('san francisco')) {
return 'Mild';
}
return 'Very Hot';
}
In this example the getCurrentWeather function is what handles the actual output.
Once you have all the code that is setup, this should work as follows:
To note, the Assistants tools API does not always work and sometimes it is unable to read the function response, but the code in this example is correct
Hopefully this helps you!
This has helped me greatly thank you.
thanks, this works fine with text messages, what about files upload ?
Hi @AhmeedBen.
To upload files, the first thing you will need to do is enable the mixedFiles (or any other that suits your use-case better) property on the Deep Chat component.
When you send files from Deep Chat, you must remember that the request format is encapsulated inside FormData as referenced in the Request message section.
Here is the new chat.ts code:
import {DeepChatOpenAITextRequestBody} from '../../../types/deepChatTextRequestBody';
import errorHandler from '../../../utils/errorHandler';
import {NextRequest, NextResponse} from 'next/server';
import {
OpenAIAssistantMessagesResult,
OpenAIAssistantInitReqResult,
OpenAIRunResult,
} from 'deep-chat/dist/types/openAIResult';
export const config = {
runtime: 'edge',
};
const API_KEY = '';
const ASSISTANT_ID = '';
async function handler(req: NextRequest) {
const formData = await req.formData();
// aggregate data
const files: File[] = [];
const textRequestBody: DeepChatOpenAITextRequestBody = {messages: []};
let thread_id: string | undefined;
formData.forEach((data) => {
if (typeof data === 'object') {
files.push(data);
} else {
const parsedData = JSON.parse(data);
if (typeof parsedData === 'string') {
thread_id = parsedData;
} else {
textRequestBody.messages.push(parsedData);
}
}
});
// store files
const file_ids = files ? await storeFiles(files) : undefined;
let run_id: string;
if (thread_id) {
const messageBody = createMessageBody(textRequestBody, file_ids);
// Create a new message
// https://platform.openai.com/docs/api-reference/messages/createMessage
await fetch(`https://api.openai.com/v1/threads/${thread_id}/messages`, {
headers: createHeaders(),
method: 'POST',
body: JSON.stringify(messageBody),
});
// https://platform.openai.com/docs/api-reference/runs/createRun
const createAndRunResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/runs`, {
headers: createHeaders(),
method: 'POST',
body: JSON.stringify({assistant_id: ASSISTANT_ID}),
});
const createAndRunResult = (await createAndRunResponse.json()) as OpenAIAssistantInitReqResult;
run_id = createAndRunResult.id;
} else {
const threadBody = createThreadBody(textRequestBody, file_ids);
// Create a new thread and automatically run it
// https://platform.openai.com/docs/api-reference/runs/createThreadAndRun
const createMessageAndRunResponse = await fetch('https://api.openai.com/v1/threads/runs', {
headers: createHeaders(),
method: 'POST',
body: JSON.stringify(threadBody),
});
const createMessageAndRunResult = (await createMessageAndRunResponse.json()) as OpenAIAssistantInitReqResult;
thread_id = createMessageAndRunResult.thread_id;
run_id = createMessageAndRunResult.id;
}
// Get the result
const resultText = await pollForResult(thread_id, run_id);
// Sends response back to Deep Chat using the Response format:
// https://deepchat.dev/docs/connect/#Response
return NextResponse.json({text: resultText, thread_id: thread_id});
}
async function storeFiles(files: File[]) {
const headers = createHeaders();
delete headers['Content-Type']; // don't need when sending files
const requests = files.map(async (file) => {
const formData = new FormData();
formData.append('purpose', 'assistants');
formData.append('file', file);
return new Promise<{id: string}>(async (resolve) => {
const response = await fetch('https://api.openai.com/v1/files', {headers, method: 'POST', body: formData});
const content = await response.json();
resolve(content);
});
});
try {
return (await Promise.all(requests)).map((result) => result.id);
} catch (err) {
console.log('error');
console.log(err);
}
}
export function createThreadBody(body: DeepChatOpenAITextRequestBody, file_ids: string[]) {
// Text messages are stored inside request body using the Deep Chat JSON format:
// https://deepchat.dev/docs/connect
return {
assistant_id: ASSISTANT_ID,
thread: {
messages: [
{
role: 'user',
content: body.messages[body.messages.length - 1].text,
file_ids,
},
],
},
};
}
export function createMessageBody(body: DeepChatOpenAITextRequestBody, file_ids: string[]) {
// Text messages are stored inside request body using the Deep Chat JSON format:
// https://deepchat.dev/docs/connect
return {
role: 'user',
content: body.messages[body.messages.length - 1].text,
file_ids,
};
}
export function createHeaders() {
return {
'Content-Type': 'application/json',
Authorization: `Bearer ${API_KEY}`,
'OpenAI-Beta': 'assistants=v1',
};
}
async function pollForResult(thread_id: string, run_id: string) {
// Get the run status
// https://platform.openai.com/docs/api-reference/runs/listRuns
const runStatusResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/runs/${run_id}`, {
headers: createHeaders(),
method: 'GET',
});
const {status} = (await runStatusResponse.json()) as OpenAIRunResult;
if (status === 'queued' || status === 'in_progress') {
return await pollForResult(thread_id, run_id);
} else if (status === 'completed') {
// https://platform.openai.com/docs/api-reference/messages/listMessages
const messagesResponse = await fetch(`https://api.openai.com/v1/threads/${thread_id}/messages`, {
headers: createHeaders(),
method: 'GET',
});
const messagesResult = (await messagesResponse.json()) as OpenAIAssistantMessagesResult;
return messagesResult.data[0].content[0].text.value;
}
throw runStatusResponse.status;
}
export default errorHandler(handler);
I tried the code but still get the error:
API Error: [SyntaxError: Unexpected token ‘o’, “[object File]” is not valid JSON]
text messages works fine
It could be that your assistant is not configured to work with files, does uploading files work for you in OpenAI Playground?
yes the assistant works with files in the playground.
I just tested the code I commented above and it works fine for me.
It is hard say what is causing your issue, but if you have copied the code exactly as described then it is definitely something else. I would recommend to make sure that the API_KEY and the ASSISTANT_ID have been set correctly.
Could you perhaps share the prompt you are using, what kind of files you are uploading and what kind of response you expect to get?
Other than this, the amount of support I can provide is limited, however if you try to debug your app and find the line that is causing the error that would be helpful.
Thanks.
the execution stops here:
formData.forEach((data) => {
if (data instanceof File) {
files.push(data);
} else {
textRequestBody.messages.push(JSON.parse(data) as MessageContent);
}
});
the first condition is not fulfilled, despite of uploading file
That is very strange, for some reason the data is not recognised as a file.
Is your index.tsx file different than the one in this comment. Of course with the addition of mixedFiles.
I changed the condition to typeof data === ‘object’ , and it works fine,
I don’t know this will affect the other types of uploads.
Happy to hear it works for you, I have also updated my example to reflect this. Thank you!
Thank you very much,
Another thing, a new thread is created every time we add a new file.
I tried to use
const textRequestBody = (await req.json()) as {messages: []; thread_id?: string};
but it doesn’t work.
I have updated the chat.ts and index.tsx examples to help handle the same thread_id (session) from FormData.