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https://github.com/csunny/DB-GPT.git
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feat(model): Add new LLMClient and new build tools (#967)
This commit is contained in:
Fangyin Cheng
GitHub
282
dbgpt/model/utils/chatgpt_utils.py
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282
dbgpt/model/utils/chatgpt_utils.py
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from __future__ import annotations
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import os
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import logging
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from dataclasses import dataclass
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import importlib.metadata as metadata
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from typing import List, Dict, Any, Optional, TYPE_CHECKING, Union, AsyncIterator
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from dbgpt.core.interface.llm import ModelMetadata, LLMClient
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from dbgpt.core.interface.llm import ModelOutput, ModelRequest
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if TYPE_CHECKING:
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import httpx
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from httpx._types import ProxiesTypes
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from openai import AsyncAzureOpenAI
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from openai import AsyncOpenAI
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ClientType = Union[AsyncAzureOpenAI, AsyncOpenAI]
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logger = logging.getLogger(__name__)
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@dataclass
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class OpenAIParameters:
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"""A class to represent a LLM model."""
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api_type: str = "open_ai"
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api_base: Optional[str] = None
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api_key: Optional[str] = None
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api_version: Optional[str] = None
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full_url: Optional[str] = None
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proxies: Optional["ProxiesTypes"] = None
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def _initialize_openai_v1(init_params: OpenAIParameters):
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try:
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from openai import OpenAI
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except ImportError as exc:
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raise ValueError(
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"Could not import python package: openai "
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"Please install openai by command `pip install openai"
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) from exc
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if not metadata.version("openai") >= "1.0.0":
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raise ImportError("Please upgrade openai package to version 1.0.0 or above")
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api_type: Optional[str] = init_params.api_type
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api_base: Optional[str] = init_params.api_base
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api_key: Optional[str] = init_params.api_key
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api_version: Optional[str] = init_params.api_version
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full_url: Optional[str] = init_params.full_url
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api_type = api_type or os.getenv("OPENAI_API_TYPE", "open_ai")
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base_url = api_base or os.getenv(
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"OPENAI_API_BASE",
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os.getenv("AZURE_OPENAI_ENDPOINT") if api_type == "azure" else None,
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)
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api_key = api_key or os.getenv(
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"OPENAI_API_KEY",
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os.getenv("AZURE_OPENAI_KEY") if api_type == "azure" else None,
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)
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api_version = api_version or os.getenv("OPENAI_API_VERSION")
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if not base_url and full_url:
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base_url = full_url.split("/chat/completions")[0]
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if api_key is None:
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raise ValueError("api_key is required, please set OPENAI_API_KEY environment")
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if base_url is None:
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raise ValueError("base_url is required, please set OPENAI_BASE_URL environment")
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if base_url.endswith("/"):
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base_url = base_url[:-1]
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openai_params = {
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"api_key": api_key,
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"base_url": base_url,
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}
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return openai_params, api_type, api_version
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def _build_openai_client(init_params: OpenAIParameters):
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import httpx
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openai_params, api_type, api_version = _initialize_openai_v1(init_params)
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if api_type == "azure":
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from openai import AsyncAzureOpenAI
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return AsyncAzureOpenAI(
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api_key=openai_params["api_key"],
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api_version=api_version,
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azure_endpoint=openai_params["base_url"],
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http_client=httpx.AsyncClient(proxies=init_params.proxies),
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)
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else:
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from openai import AsyncOpenAI
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return AsyncOpenAI(
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**openai_params, http_client=httpx.AsyncClient(proxies=init_params.proxies)
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)
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class OpenAILLMClient(LLMClient):
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"""An implementation of LLMClient using OpenAI API.
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In order to have as few dependencies as possible, we directly use the http API.
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"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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api_type: Optional[str] = None,
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api_version: Optional[str] = None,
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model: Optional[str] = "gpt-3.5-turbo",
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proxies: Optional["ProxiesTypes"] = None,
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timeout: Optional[int] = 240,
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model_alias: Optional[str] = "chatgpt_proxyllm",
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context_length: Optional[int] = 8192,
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openai_client: Optional["ClientType"] = None,
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openai_kwargs: Optional[Dict[str, Any]] = None,
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):
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self._init_params = OpenAIParameters(
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api_type=api_type,
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api_base=api_base,
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api_key=api_key,
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api_version=api_version,
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proxies=proxies,
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)
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self._model = model
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self._proxies = proxies
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self._timeout = timeout
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self._model_alias = model_alias
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self._context_length = context_length
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self._client = openai_client
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self._openai_kwargs = openai_kwargs or {}
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@property
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def client(self) -> ClientType:
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if self._client is None:
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self._client = _build_openai_client(init_params=self._init_params)
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return self._client
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def _build_request(
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self, request: ModelRequest, stream: Optional[bool] = False
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) -> Dict[str, Any]:
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payload = {"model": request.model or self._model, "stream": stream}
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# Apply openai kwargs
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for k, v in self._openai_kwargs.items():
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payload[k] = v
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if request.temperature:
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payload["temperature"] = request.temperature
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if request.max_new_tokens:
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payload["max_tokens"] = request.max_new_tokens
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return payload
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async def generate(self, request: ModelRequest) -> ModelOutput:
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messages = request.to_openai_messages()
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payload = self._build_request(request)
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try:
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chat_completion = await self.client.chat.completions.create(
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messages=messages, **payload
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)
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text = chat_completion.choices[0].message.content
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usage = chat_completion.usage.dict()
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return ModelOutput(text=text, error_code=0, usage=usage)
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except Exception as e:
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return ModelOutput(
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text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}",
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error_code=1,
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)
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async def generate_stream(
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self, request: ModelRequest
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) -> AsyncIterator[ModelOutput]:
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messages = request.to_openai_messages()
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payload = self._build_request(request)
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try:
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chat_completion = await self.client.chat.completions.create(
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messages=messages, **payload
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)
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text = ""
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for r in chat_completion:
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if len(r.choices) == 0:
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continue
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if r.choices[0].delta.content is not None:
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content = r.choices[0].delta.content
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text += content
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yield ModelOutput(text=text, error_code=0)
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except Exception as e:
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yield ModelOutput(
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text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}",
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error_code=1,
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)
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async def models(self) -> List[ModelMetadata]:
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model_metadata = ModelMetadata(
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model=self._model_alias,
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context_length=await self.get_context_length(),
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)
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return [model_metadata]
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async def get_context_length(self) -> int:
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"""Get the context length of the model.
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Returns:
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int: The context length.
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# TODO: This is a temporary solution. We should have a better way to get the context length.
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eg. get real context length from the openai api.
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"""
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return self._context_length
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async def count_token(self, model: str, prompt: str) -> int:
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"""Count the number of tokens in a given prompt.
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TODO: Get the real number of tokens from the openai api or tiktoken package
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"""
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raise NotImplementedError()
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async def _to_openai_stream(
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model: str, output_iter: AsyncIterator[ModelOutput]
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) -> AsyncIterator[str]:
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"""Convert the output_iter to openai stream format.
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Args:
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model (str): The model name.
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output_iter (AsyncIterator[ModelOutput]): The output iterator.
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"""
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import json
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import shortuuid
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from fastchat.protocol.openai_api_protocol import (
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ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse,
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DeltaMessage,
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)
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id = f"chatcmpl-{shortuuid.random()}"
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(role="assistant"),
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finish_reason=None,
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)
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chunk = ChatCompletionStreamResponse(id=id, choices=[choice_data], model=model)
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yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
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previous_text = ""
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finish_stream_events = []
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async for model_output in output_iter:
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model_output: ModelOutput = model_output
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if model_output.error_code != 0:
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yield f"data: {json.dumps(model_output.to_dict(), ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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return
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decoded_unicode = model_output.text.replace("\ufffd", "")
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delta_text = decoded_unicode[len(previous_text) :]
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previous_text = (
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decoded_unicode
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if len(decoded_unicode) > len(previous_text)
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else previous_text
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)
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if len(delta_text) == 0:
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delta_text = None
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(content=delta_text),
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finish_reason=model_output.finish_reason,
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)
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chunk = ChatCompletionStreamResponse(id=id, choices=[choice_data], model=model)
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if delta_text is None:
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if model_output.finish_reason is not None:
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finish_stream_events.append(chunk)
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continue
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yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
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for finish_chunk in finish_stream_events:
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yield f"data: {finish_chunk.json(exclude_none=True, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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