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e60e8170f9
MA_LLM_TEXT_PROVIDER=deepseek|deep_seek；.env.example 增加 ②-D；单测与 Kimi 同形。 Made-with: Cursor
141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
"""
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DeepSeek 官方 OpenAI 兼容 `chat/completions`（`https://api.deepseek.com`），**仅用于纯文本**（`call_llm` / 报告 / 策略）。
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与 `OPENAI_*` / 配料多模 分离，独立 `DEEPSEEK_*` 凭据。
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启用：`MA_LLM_TEXT_PROVIDER=deepseek`（或 `deep_seek`）。
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环境变量：见 `.env.example`（`DEEPSEEK_API_KEY`、基址、模型、上下文字数预检 等）。
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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 pipeline.openai_gateway.chat_content import normalize_message_content
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from pipeline.openai_gateway.estimate import (
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estimate_chat_input_tokens as estimate_crawler_style_input_tokens,
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)
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_DEFAULT_BASE = "https://api.deepseek.com/v1"
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_DEFAULT_MODEL = "deepseek-chat"
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# 常见 64k 级；若用长上下文/官方调整上限可改 DEEPSEEK_CONTEXT_WINDOW
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_DEFAULT_CTX = 64_000
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_BUF = 256
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_WANT_MAX = 8192
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def _read_timeout() -> tuple[float, float]:
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read = 600
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raw = (
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os.environ.get("DEEPSEEK_TIMEOUT")
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or os.environ.get("LLM_CHAT_TIMEOUT")
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or os.environ.get("OPENAI_TIMEOUT")
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or ""
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).strip()
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if raw:
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try:
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read = max(60, int(raw))
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except ValueError:
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pass
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conn = 30.0
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raw_c = (os.environ.get("LLM_CHAT_CONNECT_TIMEOUT") or "").strip()
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if raw_c:
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try:
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conn = max(5.0, float(raw_c))
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except ValueError:
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pass
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return (conn, float(read))
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def _resolve_deepseek_credentials() -> tuple[str, str, str]:
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key = (os.environ.get("DEEPSEEK_API_KEY") or "").strip()
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if not key:
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raise ValueError(
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"使用 deepseek 文本适配器需设置 DEEPSEEK_API_KEY，"
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"与配料/视觉所用 OPENAI_API_KEY 分开配置。"
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)
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base = (os.environ.get("DEEPSEEK_BASE_URL") or _DEFAULT_BASE).strip().rstrip("/")
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model = (
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(os.environ.get("DEEPSEEK_TEXT_MODEL") or os.environ.get("DEEPSEEK_MODEL") or _DEFAULT_MODEL)
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).strip()
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return key, base, model
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def _context_window() -> int:
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raw = (os.environ.get("DEEPSEEK_CONTEXT_WINDOW") or str(_DEFAULT_CTX)).strip()
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try:
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return max(4096, int(raw))
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except ValueError:
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return _DEFAULT_CTX
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def _default_temperature() -> float:
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return 0.2
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class DeepSeekTextLlm:
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"""DeepSeek `chat/completions`；与 `KimiMoonshotTextLlm` 同形（max_tokens 预检）。"""
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def complete_text(
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self,
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system_prompt: str,
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user_prompt: str,
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*,
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temperature: float | None = None,
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) -> str:
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api_key, base, model = _resolve_deepseek_credentials()
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body: dict[str, Any] = {
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"model": model,
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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": _default_temperature() if temperature is None else float(temperature),
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"max_tokens": _WANT_MAX,
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}
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est = estimate_crawler_style_input_tokens(system_prompt, user_prompt)
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context_window = _context_window()
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if 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 {est} tokens，DEEPSEEK_CONTEXT_WINDOW={context_window}），"
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"请缩小输入或调大 DEEPSEEK_TEXT_MODEL / DEEPSEEK_CONTEXT_WINDOW。"
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)
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avail = context_window - est - _BUF
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want = int(body.get("max_tokens") or _WANT_MAX)
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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"{base}/chat/completions",
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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},
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json=body,
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timeout=_read_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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def estimate_input_tokens(self, system_prompt: str, user_prompt: str) -> int:
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return estimate_crawler_style_input_tokens(system_prompt, user_prompt)
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def context_window_tokens(self) -> int:
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return _context_window()
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