api.md

Chatbot API Documentation

Overview

This document provides an overview of the Chatbot API endpoints, including how to use streaming chat completions.

Swagger UI

FastAPI automatically generates interactive API documentation (Swagger UI) for our endpoints. You can access this documentation by navigating to the /docs endpoint of our API when it's running.

For example, if you're running the API locally on port 8000, you can access the Swagger UI at:

http://localhost:8000/docs

Endpoints

POST /v1/chat/completions

Generate a chat completion based on the provided messages and model.

Request Body

{
  "model": "string",
  "messages": [
    {
      "role": "string",
      "content": "string"
    }
  ],
  "max_tokens": 0,
  "temperature": 0.7,
  "top_p": 1,
  "n": 1,
  "stream": false
}

• stream: Set to true to receive a streaming response.

Response

For non-streaming requests, the response will be a JSON object containing the generated completion.

For streaming requests, the response will be a series of server-sent events (SSE) containing chunks of the generated completion.

Streaming Chat Completions

To use streaming chat completions:

1. Set the stream parameter to true in your request.
2. Send a POST request to /v1/chat/completions.
3. Handle the server-sent events (SSE) in your client.

Example: Python Client for Streaming

import requests
import json

def stream_chat_completion(model, messages):
    url = "http://localhost:8000/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": "beareryour_api_key_here"
    }
    data = {
        "model": model,
        "messages": messages,
        "stream": True
    }

    with requests.post(url, json=data, headers=headers, stream=True) as response:
        for line in response.iter_lines():
            if line:
                decoded_line = line.decode('utf-8')
                if decoded_line.startswith("data: "):
                    chunk = json.loads(decoded_line[6:])
                    yield chunk
                elif decoded_line == "data: [DONE]":
                    break

# Usage
messages = [{"role": "user", "content": "Tell me a joke"}]
for chunk in stream_chat_completion("default", messages):
    print(chunk['choices'][0]['delta']['content'], end='', flush=True)

Example: JavaScript Client for Streaming

async function streamChatCompletion(model, messages) {
  const response = await fetch("http://localhost:8000/v1/chat/completions", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: "beareryour_api_key_here",
    },
    body: JSON.stringify({
      model,
      messages,
      stream: true,
    }),
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();

  while (true) {
    const { value, done } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    const lines = chunk.split("\n");

    for (const line of lines) {
      if (line.startsWith("data: ")) {
        if (line.includes("[DONE]")) break;
        const data = JSON.parse(line.slice(6));
        console.log(data.choices[0].delta.content);
      }
    }
  }
}

// Usage
const messages = [{ role: "user", content: "Tell me a joke" }];
streamChatCompletion("default", messages);

These examples demonstrate how to consume the streaming API from both Python and JavaScript clients. Adjust the URL and API key as needed for your specific deployment.

GraphQL

Example Query

query {
  todos {
    id
    title
    completed
    createdAt
  }
}

Example Mutation

mutation {
  createTodo(title: "Test Todo") {
    id
    title
    completed
    createdAt
  }
}

updating database with prisma

pip install prisma
prisma generate
prisma migrate dev --name init