isekai-toolkit/service/openai_api.py

from __future__ import annotations
import json
from typing import TypedDict

import aiohttp
import config
import numpy as np
from aiohttp_sse_client2 import client as sse_client

from service.tiktoken import TikTokenService

class ChatCompleteMessageLog(TypedDict):
    role: str
    content: str

class ChatCompleteResponse(TypedDict):
    message: str
    prompt_tokens: int
    message_tokens: int
    total_tokens: int
    finish_reason: str

class OpenAIApi:
    @staticmethod
    def create():
        return OpenAIApi(config.OPENAI_API or "https://api.openai.com", config.OPENAI_TOKEN)

    def __init__(self, api_url: str, token: str):
        self.api_url = api_url
        self.token = token

    async def get_embeddings(self, doc_list: list):
        token_usage = 0
        async with aiohttp.ClientSession() as session:
            text_list = [doc["text"] for doc in doc_list]
            params = {
                "model": "text-embedding-ada-002",
                "input": text_list,
            }
            async with session.post(self.api_url + "/v1/embeddings",
                                    headers={"Authorization": f"Bearer {self.token}"},
                                    json=params,
                                    timeout=30,
                                    proxy=config.REQUEST_PROXY) as resp:

                data = await resp.json()

                for one_data in data["data"]:
                    embedding = one_data["embedding"]
                    index = one_data["index"]
                    
                    if index < len(doc_list):
                        if embedding is not None:
                            embedding = np.array(embedding)
                        doc_list[index]["embedding"] = embedding

                token_usage = int(data["usage"]["total_tokens"])

        return (doc_list, token_usage)
    
    async def make_message_list(self, question: str, system_prompt: str, conversation: list[ChatCompleteMessageLog] = []) -> list[ChatCompleteMessageLog]:
        summaryContent = None

        messageList: list[ChatCompleteMessageLog] = []
        for message in conversation:
            if message["role"] == "summary":
                summaryContent = message["content"]
            elif message["role"] == "user" or message["role"] == "assistant":
                messageList.append(message)

        if summaryContent:
            system_prompt += "\n\n" + summaryContent

        messageList.insert(0, ChatCompleteMessageLog(role="assistant", content=system_prompt))
        messageList.append(ChatCompleteMessageLog(role="user", content=question))

        return messageList
    
    async def chat_complete(self, question: str, system_prompt: str, conversation: list[ChatCompleteMessageLog] = [], user = None):
        messageList = await self.make_message_list(question, system_prompt, conversation)

        params = {
            "model": "gpt-3.5-turbo",
            "messages": messageList,
            "user": user,
        }
        params = {k: v for k, v in params.items() if v is not None}

        async with aiohttp.ClientSession() as session:
            async with session.post(self.api_url + "/v1/chat/completions",
                                    headers={"Authorization": f"Bearer {self.token}"},
                                    json=params,
                                    timeout=30,
                                    proxy=config.REQUEST_PROXY) as resp:
                
                data = await resp.json()

                if "choices" in data and len(data["choices"]) > 0:
                    choice = data["choices"][0]

                    message = choice["message"]["content"]
                    finish_reason = choice["finish_reason"]

                    prompt_tokens = int(data["usage"]["prompt_tokens"])
                    message_tokens = int(data["usage"]["completion_tokens"])
                    total_tokens = int(data["usage"]["total_tokens"])

                    return ChatCompleteResponse(message=message,
                                                prompt_tokens=prompt_tokens,
                                                message_tokens=message_tokens,
                                                total_tokens=total_tokens,
                                                finish_reason=finish_reason)
                
        return None

    async def chat_complete_stream(self, question: str, system_prompt: str, conversation: list[ChatCompleteMessageLog] = [], on_message = None, user = None):
        tiktoken = await TikTokenService.create()

        messageList = await self.make_message_list(question, system_prompt, conversation)

        prompt_tokens = 0
        for message in messageList:
            prompt_tokens += await tiktoken.get_tokens(message["content"])

        params = {
            "model": "gpt-3.5-turbo",
            "messages": messageList,
            "stream": True,
            "user": user,
        }
        params = {k: v for k, v in params.items() if v is not None}

        res_message: list[str] = []
        finish_reason = None

        async with sse_client.EventSource(
            self.api_url + "/v1/chat/completions",
            option={
                "method": "POST"
            },
            headers={"Authorization": f"Bearer {self.token}"},
            json=params,
            proxy=config.REQUEST_PROXY
        ) as session:
            async for event in session:
                """
                {"id":"something","object":"chat.completion.chunk","created":1681261845,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{"role":"assistant"},"index":0,"finish_reason":null}]}
                {"id":"something","object":"chat.completion.chunk","created":1681261845,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{"content":"Thank"},"index":0,"finish_reason":null}]}
                {"id":"something","object":"chat.completion.chunk","created":1681261845,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{},"index":0,"finish_reason":"stop"}]}
                [DONE]
                """
                content_started = False

                if event.data == "[DONE]":
                    break
                elif event.data[0] == "{" and event.data[-1] == "}":
                    data = json.loads(event.data)
                    if "choices" in data and len(data["choices"]) > 0:
                        choice = data["choices"][0]

                        if choice["finish_reason"] is not None:
                            finish_reason = choice["finish_reason"]

                        delta_content = choice["delta"]
                        if "content" in delta_content:
                            delta_message: str = delta_content["content"]

                            # Skip empty lines before content
                            if not content_started:
                                if delta_message.replace("\n", "") == "":
                                    continue
                                else:
                                    content_started = True

                            res_message.append(delta_message)

                            if config.DEBUG:
                                print(delta_message, end="", flush=True)

                            if on_message is not None:
                                await on_message(delta_message)

        res_message_str = "".join(res_message)
        message_tokens = await tiktoken.get_tokens(res_message_str)
        total_tokens = prompt_tokens + message_tokens

        return ChatCompleteResponse(message=res_message_str,
                                    prompt_tokens=prompt_tokens,
                                    message_tokens=message_tokens,
                                    total_tokens=total_tokens,
                                    finish_reason=finish_reason)