mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-02 03:55:55 +00:00
136 lines
5.0 KiB
Python
136 lines
5.0 KiB
Python
from __future__ import annotations
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import asyncio
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import os
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import typing
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from typing import Union, Mapping, Any, AsyncIterator
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import uuid
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import json
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import async_lru
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import ollama
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from .. import entities, errors, requester
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from ... import entities as llm_entities
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from ...tools import entities as tools_entities
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from ....core import app
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from ....utils import image
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REQUESTER_NAME: str = "ollama-chat"
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@requester.requester_class(REQUESTER_NAME)
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class OllamaChatCompletions(requester.LLMAPIRequester):
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"""Ollama平台 ChatCompletion API请求器"""
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client: ollama.AsyncClient
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request_cfg: dict
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def __init__(self, ap: app.Application):
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super().__init__(ap)
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self.ap = ap
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self.request_cfg = self.ap.provider_cfg.data['requester'][REQUESTER_NAME]
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async def initialize(self):
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os.environ['OLLAMA_HOST'] = self.request_cfg['base-url']
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self.client = ollama.AsyncClient(
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timeout=self.request_cfg['timeout']
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)
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async def _req(self,
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args: dict,
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) -> Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]:
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return await self.client.chat(
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**args
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)
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async def _closure(self, req_messages: list[dict], use_model: entities.LLMModelInfo,
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user_funcs: list[tools_entities.LLMFunction] = None) -> (
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llm_entities.Message):
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args: Any = self.request_cfg['args'].copy()
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args["model"] = use_model.name if use_model.model_name is None else use_model.model_name
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messages: list[dict] = req_messages.copy()
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for msg in messages:
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if 'content' in msg and isinstance(msg["content"], list):
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text_content: list = []
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image_urls: list = []
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for me in msg["content"]:
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if me["type"] == "text":
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text_content.append(me["text"])
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elif me["type"] == "image_url":
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image_url = await self.get_base64_str(me["image_url"]['url'])
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image_urls.append(image_url)
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msg["content"] = "\n".join(text_content)
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msg["images"] = [url.split(',')[1] for url in image_urls]
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if 'tool_calls' in msg: # LangBot 内部以 str 存储 tool_calls 的参数，这里需要转换为 dict
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for tool_call in msg['tool_calls']:
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tool_call['function']['arguments'] = json.loads(tool_call['function']['arguments'])
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args["messages"] = messages
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args["tools"] = []
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if user_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(user_funcs)
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if tools:
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args["tools"] = tools
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resp = await self._req(args)
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message: llm_entities.Message = await self._make_msg(resp)
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return message
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async def _make_msg(
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self,
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chat_completions: ollama.ChatResponse) -> llm_entities.Message:
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message: ollama.Message = chat_completions.message
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if message is None:
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raise ValueError("chat_completions must contain a 'message' field")
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ret_msg: llm_entities.Message = None
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if message.content is not None:
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ret_msg = llm_entities.Message(
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role="assistant",
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content=message.content
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)
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if message.tool_calls is not None and len(message.tool_calls) > 0:
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tool_calls: list[llm_entities.ToolCall] = []
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for tool_call in message.tool_calls:
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tool_calls.append(llm_entities.ToolCall(
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id=uuid.uuid4().hex,
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type="function",
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function=llm_entities.FunctionCall(
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name=tool_call.function.name,
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arguments=json.dumps(tool_call.function.arguments)
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)
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))
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ret_msg.tool_calls = tool_calls
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return ret_msg
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async def call(
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self,
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model: entities.LLMModelInfo,
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messages: typing.List[llm_entities.Message],
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funcs: typing.List[tools_entities.LLMFunction] = None,
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) -> llm_entities.Message:
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req_messages: list = []
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for m in messages:
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msg_dict: dict = m.dict(exclude_none=True)
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content: Any = msg_dict.get("content")
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if isinstance(content, list):
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if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
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msg_dict["content"] = "\n".join(part["text"] for part in content)
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req_messages.append(msg_dict)
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try:
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return await self._closure(req_messages, model, funcs)
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except asyncio.TimeoutError:
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raise errors.RequesterError('请求超时')
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@async_lru.alru_cache(maxsize=128)
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async def get_base64_str(
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self,
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original_url: str,
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) -> str:
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base64_image, image_format = await image.qq_image_url_to_base64(original_url)
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return f"data:image/{image_format};base64,{base64_image}"
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