浅念
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
import asyncio
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import json
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import logging
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from collections.abc import AsyncGenerator
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from zhipuai import ZhipuAI
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from app.providers.base import AIProvider
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logger = logging.getLogger(__name__)
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DEFAULT_MODEL = "glm-4-flash"
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class GLMProvider(AIProvider):
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def __init__(self, api_key: str, model: str = "glm-4-flash", base_url: str = ""):
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self.client = ZhipuAI(api_key=api_key, base_url=base_url or None)
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self.model = model
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async def chat(self, messages: list[dict], system_prompt: str) -> str:
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full_messages = [{"role": "system", "content": system_prompt}] + messages
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return await self._call(full_messages, temperature=0.3)
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async def stream_chat(
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self, messages: list[dict], system_prompt: str
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) -> AsyncGenerator[str, None]:
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full_messages = [{"role": "system", "content": system_prompt}] + messages
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def _sync_stream():
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resp = self.client.chat.completions.create(
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model=self.model,
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messages=full_messages,
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stream=True,
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temperature=0.3,
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)
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for chunk in resp:
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delta = chunk.choices[0].delta
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if delta and delta.content:
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yield delta.content
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try:
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loop = asyncio.get_event_loop()
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gen = _sync_stream()
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def _next_token(g):
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try:
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return next(g)
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except StopIteration:
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return None
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while True:
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token = await loop.run_in_executor(None, _next_token, gen)
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if token is None:
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break
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yield token
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except Exception:
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logger.exception("GLM streaming call failed")
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raise
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async def chat_with_extraction(
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self, messages: list[dict], system_prompt: str
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) -> tuple[str, dict]:
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full_messages = [{"role": "system", "content": system_prompt}] + messages
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if not messages:
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full_messages.append({"role": "user", "content": "请根据以上要求输出结果。"})
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raw = await self._call(full_messages, temperature=0.1)
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extracted = self._parse_json(raw)
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return raw, extracted
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async def _call(self, messages: list[dict], temperature: float = 0.3) -> str:
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def _sync_call():
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resp = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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temperature=temperature,
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)
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return resp.choices[0].message.content
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try:
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return await asyncio.to_thread(_sync_call)
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except Exception:
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logger.exception("GLM API call failed")
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raise
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@staticmethod
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def _parse_json(text: str) -> dict:
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try:
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start = text.index("{")
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end = text.rindex("}") + 1
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return json.loads(text[start:end])
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except (ValueError, json.JSONDecodeError):
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logger.warning("Failed to parse JSON from model output")
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return {}
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