326 lines
12 KiB
Python
326 lines
12 KiB
Python
import json
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from collections import deque
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from typing import List
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from aiohttp import ClientSession
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from mautrix.types import MessageEvent
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from mautrix.util.config import BaseProxyConfig
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import maubot_llmplus.platforms
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from maubot_llmplus.platforms import Platform, ChatCompletion
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from maubot_llmplus.plugin import AbsExtraConfigPlugin
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class Deepseek(Platform):
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def __init__(self, config: BaseProxyConfig, http: ClientSession):
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super().__init__(config, http)
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async def create_chat_completion(self, plugin: AbsExtraConfigPlugin, evt: MessageEvent) -> ChatCompletion:
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full_context = []
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context = await maubot_llmplus.platforms.get_context(plugin, self, evt)
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full_context.extend(list(context))
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}"
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}
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data = {
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"model": self.model,
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"messages": full_context,
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}
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endpoint = f"{self.url}/chat/completions"
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async with self.http.post(
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endpoint, headers=headers, data=json.dumps(data)
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) as response:
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if response.status != 200:
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return ChatCompletion(
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result=False,
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message={},
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finish_reason=f"Error: {await response.text()}",
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model=None
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)
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response_json = await response.json()
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choice = response_json["choices"][0]
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return ChatCompletion(
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result=True,
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message=choice["message"],
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finish_reason=choice["finish_reason"],
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model=response_json.get("model", None)
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)
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async def list_models(self) -> List[str]:
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models = ["deepseek-chat", "deepseek-reasoner"]
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return [f"- {m}" for m in models]
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def get_type(self) -> str:
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return "deepseek"
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class OpenAi(Platform):
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max_tokens: int
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temperature: int
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def __init__(self, config: BaseProxyConfig, http: ClientSession) -> None:
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super().__init__(config, http)
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self.max_tokens = self.config['max_tokens']
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self.temperature = self.config['temperature']
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async def create_chat_completion(self, plugin: AbsExtraConfigPlugin, evt: MessageEvent) -> ChatCompletion:
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full_context = []
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context = await maubot_llmplus.platforms.get_context(plugin, self, evt)
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full_context.extend(list(context))
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}"
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}
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data = {
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"model": self.model,
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"messages": full_context,
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}
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if 'max_tokens' in self.config and self.max_tokens:
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# 如果是gpt5的，就用max_completion_tokens
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if 'gpt-5' in self.model:
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data["max_completion_tokens"] = self.max_tokens
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else:
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# 如果是gpt4之前的，就是用max_tokens
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data["max_tokens"] = self.max_tokens
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if 'temperature' in self.config and self.temperature:
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data["temperature"] = self.temperature
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endpoint = f"{self.url}/v1/chat/completions"
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async with self.http.post(
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endpoint, headers=headers, data=json.dumps(data)
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) as response:
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# plugin.log.debug(f"响应内容：{response.status}, {await response.json()}")
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if response.status != 200:
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return ChatCompletion(
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result=False,
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message={},
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finish_reason=f"Error: {await response.text()}",
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model=None
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)
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response_json = await response.json()
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choice = response_json["choices"][0]
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return ChatCompletion(
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result=True,
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message=choice["message"],
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finish_reason=choice["finish_reason"],
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model=choice.get("model", None)
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)
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async def list_models(self) -> List[str]:
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# 调用openai接口获取模型列表
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full_url = f"{self.url}/v1/models"
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headers = {'Authorization': f"Bearer {self.api_key}"}
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async with self.http.get(full_url, headers=headers) as response:
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if response.status != 200:
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return []
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response_data = await response.json()
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return [f"- {m['id']}" for m in response_data["data"]]
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def get_type(self) -> str:
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return "openai"
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class Anthropic(Platform):
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max_tokens: int
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def __init__(self, config: BaseProxyConfig, http: ClientSession) -> None:
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super().__init__(config, http)
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self.max_tokens = self.config['max_tokens']
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async def create_chat_completion(self, plugin: AbsExtraConfigPlugin, evt: MessageEvent) -> ChatCompletion:
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full_chat_context = []
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system_context = deque()
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chat_context = await maubot_llmplus.platforms.get_chat_context(system_context, plugin, self, evt)
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full_chat_context.extend(list(chat_context))
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endpoint = f"{self.url}/v1/messages"
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headers = {"x-api-key": self.api_key, "anthropic-version": "2023-06-01", "content-type": "application/json"}
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req_body = {"model": self.model, "max_tokens": self.max_tokens, "system": self.system_prompt,
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"messages": full_chat_context}
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async with self.http.post(endpoint, headers=headers, data=json.dumps(req_body)) as response:
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# plugin.log.debug(f"响应内容：{response.status}, {await response.json()}")
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if response.status != 200:
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return ChatCompletion(
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result=False,
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message={},
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finish_reason=f"Error: {await response.text()}",
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model=None
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)
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response_json = await response.json()
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text = "\n\n".join(c["text"] for c in response_json["content"])
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return ChatCompletion(
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result=True,
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message=dict(role="assistant", content=text),
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finish_reason=response_json['stop_reason'],
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model=response_json['model']
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)
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pass
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async def list_models(self) -> List[str]:
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# 调用openai接口获取模型列表
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full_url = f"{self.url}/v1/models"
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headers = {
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'anthropic-version': "2023-06-01",
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'X-Api-Key': f"{self.api_key}"
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}
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async with self.http.get(full_url, headers=headers) as response:
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if response.status != 200:
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return []
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response_data = await response.json()
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return [f"- {m['id']}" for m in response_data['data']]
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def get_type(self) -> str:
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return "anthropic"
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class Gemini(Platform):
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max_tokens: int
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temperature: float
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def __init__(self, config: BaseProxyConfig, http: ClientSession) -> None:
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super().__init__(config, http)
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self.max_tokens = self.config['max_tokens']
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self.temperature = self.config['temperature']
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async def create_chat_completion(self, plugin: AbsExtraConfigPlugin, evt: MessageEvent) -> ChatCompletion:
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context = await maubot_llmplus.platforms.get_context(plugin, self, evt)
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system_parts = []
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contents = []
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for msg in context:
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role = msg['role']
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content = msg['content']
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if role == 'system':
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system_parts.append({"text": content})
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elif role == 'assistant':
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contents.append({"role": "model", "parts": [{"text": content}]})
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else:
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contents.append({"role": "user", "parts": [{"text": content}]})
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request_body = {
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"contents": contents,
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"generationConfig": {}
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}
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if system_parts:
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request_body["system_instruction"] = {"parts": system_parts}
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if self.max_tokens:
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request_body["generationConfig"]["maxOutputTokens"] = self.max_tokens
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if self.temperature:
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request_body["generationConfig"]["temperature"] = self.temperature
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endpoint = f"{self.url}/v1beta/models/{self.model}:generateContent"
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headers = {
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"Content-Type": "application/json",
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"x-goog-api-key": self.api_key
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}
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async with self.http.post(endpoint, headers=headers, data=json.dumps(request_body)) as response:
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if response.status != 200:
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return ChatCompletion(
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result=False,
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message={},
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finish_reason=f"Error: {await response.text()}",
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model=None
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)
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response_json = await response.json()
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candidate = response_json["candidates"][0]
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text = "".join(part["text"] for part in candidate["content"]["parts"])
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return ChatCompletion(
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result=True,
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message={"role": "assistant", "content": text},
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finish_reason=candidate.get("finishReason", "STOP"),
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model=response_json.get("modelVersion", self.model)
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)
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async def list_models(self) -> List[str]:
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full_url = f"{self.url}/v1beta/models"
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headers = {"x-goog-api-key": self.api_key}
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async with self.http.get(full_url, headers=headers) as response:
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if response.status != 200:
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return []
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response_data = await response.json()
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return [
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f"- {m['name'].replace('models/', '')}"
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for m in response_data.get("models", [])
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if "generateContent" in m.get("supportedGenerationMethods", [])
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]
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def get_type(self) -> str:
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return "gemini"
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class XAi(Platform):
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max_tokens: int
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temperature: int
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def __init__(self, config: BaseProxyConfig, http: ClientSession) -> None:
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super().__init__(config, http)
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self.temperature = self.config['temperature']
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self.max_tokens = self.config['max_tokens']
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async def create_chat_completion(self, plugin: AbsExtraConfigPlugin, evt: MessageEvent) -> ChatCompletion:
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full_context = []
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context = await maubot_llmplus.platforms.get_context(plugin, self, evt)
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full_context.extend(list(context))
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}"
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}
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request_body = {
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"messages": full_context,
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"model": self.model,
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"stream": False
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}
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if 'max_tokens' in self.config and self.max_tokens:
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request_body["max_tokens"] = self.max_tokens
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if 'temperature' in self.config and self.temperature:
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request_body["temperature"] = self.temperature
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endpoint = f"{self.url}/v1/chat/completions"
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async with self.http.post(url=endpoint, data=json.dumps(request_body), headers=headers) as response:
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# plugin.log.debug(f"响应内容：{response.status}, {await response.json()}")
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if response.status != 200:
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return ChatCompletion(
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result=False,
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message={},
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finish_reason=f"Error: {await response.text()}",
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model=None
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)
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response_json = await response.json()
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choice = response_json["choices"][0]
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return ChatCompletion(
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result=True,
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message=choice["message"],
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finish_reason=choice["finish_reason"],
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model=response_json["model"]
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)
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pass
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async def list_models(self) -> List[str]:
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# 调用openai接口获取模型列表
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full_url = f"{self.url}/v1/models"
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headers = {'Content-Type': 'application/json', 'Authorization': f"Bearer {self.api_key}"}
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async with self.http.get(full_url, headers=headers) as response:
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if response.status != 200:
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return []
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response_data = await response.json()
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return [f"- {m['id']}" for m in response_data["data"]]
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pass
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def get_type(self) -> str:
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return "xai" |