mirror of
https://github.com/primedigitaltech/market-assistant.git
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9fd32a3f84
新增 pipeline/openai_gateway：credentials、text_chat、ingredients_op、ingredients_defaults 等；AI_crawler 仅加载 .env 与重导出。CrawlerOpenAiCompatible 与京东详情/流水线改走 import pipeline.openai_gateway，不再依赖 sys.path 注入爬虫目录。llm/providers/shared 转重导 gateway。详情脚本补充 backend 入 path。 Made-with: Cursor
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
"""
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OpenAI 兼容 `chat/completions` 纯文本（system + user），与多模态配料识别共用 `OPENAI_*` / `LLM_*` 环境配置。
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"""
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from __future__ import annotations
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import os
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from typing import Any
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import requests
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from .chat_content import normalize_message_content
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from .credentials import _resolve_credentials, resolve_text_model_name
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from .estimate import estimate_chat_input_tokens
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from .timeouts import chat_completion_read_timeout as _chat_completion_timeout
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def strip_outer_markdown_fence(text: str) -> str:
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"""若模型用 ``` / ```markdown 包裹全文，去掉最外层围栏。"""
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t = (text or "").strip()
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if not t.startswith("```"):
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return t
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lines = t.split("\n")
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if lines and lines[0].strip().startswith("```"):
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lines = lines[1:]
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while lines and lines[-1].strip() == "```":
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lines = lines[:-1]
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return "\n".join(lines).strip()
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def chat_completion_text(
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*,
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system_prompt: str,
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user_prompt: str,
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api_key: str | None = None,
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base_url: str | None = None,
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model: str | None = None,
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temperature: float = 0.2,
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max_tokens: int = 8192,
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timeout: int | tuple[float, float] | None = None,
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extra_json: dict[str, Any] | None = None,
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) -> str:
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if timeout is None:
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timeout = _chat_completion_timeout()
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k, b, _ = _resolve_credentials(api_key, base_url, None)
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m = resolve_text_model_name(model)
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body: dict[str, Any] = {
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"model": m,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": temperature,
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"max_tokens": max_tokens,
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}
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if extra_json:
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body.update(extra_json)
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ctx_raw = (
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os.environ.get("LLM_CONTEXT_WINDOW")
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or os.environ.get("OPENAI_CONTEXT_WINDOW")
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or "32768"
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).strip()
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try:
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context_window = max(4096, int(ctx_raw))
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except ValueError:
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context_window = 32768
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buf = 256
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input_est = estimate_chat_input_tokens(system_prompt, user_prompt)
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if input_est >= context_window - buf - 256:
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raise ValueError(
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f"提示词过长（估算输入约 {input_est} tokens，上下文上限 {context_window}），"
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"请缩小报告/摘要输入或换更大上下文的模型；也可设置环境变量 LLM_CONTEXT_WINDOW。"
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)
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avail = context_window - input_est - buf
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want = int(body.get("max_tokens") or max_tokens)
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body["max_tokens"] = max(256, min(want, max(avail, 256)))
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r = requests.post(
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f"{b}/chat/completions",
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headers={
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"Authorization": f"Bearer {k}",
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"Content-Type": "application/json",
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},
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json=body,
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timeout=timeout,
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)
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try:
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r.raise_for_status()
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except requests.HTTPError as e:
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snippet = ""
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if e.response is not None:
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snippet = (e.response.text or "")[:1200].replace("\r\n", "\n").replace("\n", " ")
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if snippet:
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raise requests.HTTPError(
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f"{e!s} | body: {snippet}",
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response=e.response,
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request=e.request,
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) from e
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raise
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data = r.json()
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msg = (data.get("choices") or [{}])[0].get("message") or {}
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return normalize_message_content(msg.get("content"))
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