Conversational API

Create a conversational API for your application

Create a chat API endpoint that handles streaming responses from the C1 model.

1. Install required dependencies

   Install the necessary packages for your backend API:

   npm

   shellscript

   npm install openai @crayonai/stream
   
2. Create the message store

   First, create a simple in-memory message store to manage conversation history. This in-memory store just stores the list of messages for a given threadId including messages that are not sent to the client like the tool call messages.

   app/api/chat/messageStore.ts

   TypeScript

   import OpenAI from "openai";
   
   export type DBMessage = OpenAI.Chat.ChatCompletionMessageParam & {
     id?: string;
   };
   
   const messagesStore: {
     [threadId: string]: DBMessage[];
   } = {};
   
   export const getMessageStore = (threadId: string) => {
     if (!messagesStore[threadId]) {
       messagesStore[threadId] = [];
     }
     const messageList = messagesStore[threadId];
     return {
       addMessage: (message: DBMessage) => {
         messageList.push(message);
       },
       getOpenAICompatibleMessageList: () => {
         return messageList.map((m) => {
           const message = { ...m };
           delete message.id;
           return message;
         });
       },
     };
   };
   
3. Create the chat endpoint

   Create the main API endpoint that handles incoming chat requests with streaming:

   app/api/chat/route.ts

   TypeScript

   import { NextRequest, NextResponse } from "next/server";
   import OpenAI from "openai";
   import { transformStream } from "@crayonai/stream";
   import { DBMessage, getMessageStore } from "./messageStore";
   
   export async function POST(req: NextRequest) {
     const { prompt, threadId, responseId } = (await req.json()) as {
       prompt: DBMessage;
       threadId: string;
       responseId: string;
     };
   
     // Initialize the OpenAI client
     const client = new OpenAI({
       baseURL: "https://api.thesys.dev/v1/embed/",
       apiKey: process.env.THESYS_API_KEY,
     });
   
     // Get message store and add user message
     const messageStore = getMessageStore(threadId);
     messageStore.addMessage(prompt);
   
     // Create streaming chat completion
     const llmStream = await client.chat.completions.create({
       model: "c1/anthropic/claude-sonnet-4/v-20251230",
       messages: messageStore.getOpenAICompatibleMessageList(),
       stream: true,
     });
   
     // Transform the response stream
     const responseStream = transformStream(
       llmStream,
       (chunk) => {
         return chunk.choices[0].delta.content;
       },
       {
         onEnd: ({ accumulated }) => {
           const message = accumulated.filter((message) => message).join("");
           messageStore.addMessage({
             role: "assistant",
             content: message,
             id: responseId,
           });
         },
       }
     ) as ReadableStream;
   
     // Return the streaming response
     return new NextResponse(responseStream, {
       headers: {
         "Content-Type": "text/event-stream",
         "Cache-Control": "no-cache, no-transform",
         Connection: "keep-alive",
       },
     });
   }
   
4. Set your API key

   Make sure to set your Thesys API key as an environment variable:

   export THESYS_API_KEY=<your-api-key>
   

   Next.js (.env.local)

   shellscript

   THESYS_API_KEY=<your-api-key>
   

Your API endpoint is now ready to handle streaming chat conversations with the C1 model!