173 lines
5.4 KiB
Python
173 lines
5.4 KiB
Python
"""
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与 `get_llm_provider().langchain_llm` 配合使用的 LangChain Runnable 约定。
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langchain-openai 要求用顶层 `response_format` 绑定 JSON 模式,禁止对 `.bind()` 传入
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`model_kwargs={"response_format": ...}`(会错误传入底层 `completions.create`)。
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"""
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from __future__ import annotations
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import hashlib
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import time
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from typing import Any
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from app.core.agent_logging import (
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agent_summary_enabled,
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agent_verbose_enabled,
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log_agent_payload,
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)
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from app.core.logging import get_logger
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logger = get_logger(__name__)
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# OpenAI / DeepSeek:使用 response_format=json_object 时,prompt 须含子串「json」
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# (见 DeepSeek JSON Output 指南)。
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_JSON_OBJECT_PROMPT_SUFFIX = (
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"\n\n【JSON】只输出一个合法 JSON 对象,不要其它说明文字或 markdown。"
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)
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def ensure_json_object_prompt_has_json_keyword(prompt: str) -> str:
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"""
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若整段 prompt 中未出现 ``json``(大小写不敏感),追加一行合规提示。
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供所有 ``response_format: json_object`` 调用在发请求前统一处理。
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"""
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p = prompt or ""
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if "json" in p.casefold():
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return p
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return f"{p.rstrip()}{_JSON_OBJECT_PROMPT_SUFFIX}"
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def bind_json_object_mode(llm: Any, *, max_tokens: int) -> Any:
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"""返回绑定 `response_format=json_object` 与 `max_tokens` 的 Runnable(通常为 ChatOpenAI)。"""
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return llm.bind(
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response_format={"type": "json_object"},
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max_tokens=max_tokens,
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)
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def _prompt_sha12(prompt: str) -> str:
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return hashlib.sha256((prompt or "").encode("utf-8")).hexdigest()[:12]
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def invoke_json_object(
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llm: Any,
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prompt: str,
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*,
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max_tokens: int,
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agent: str | None = None,
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retry_empty: bool = True,
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) -> str:
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"""
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同步调用 JSON object 模式;空 content 时可选重试一次(缓解 DeepSeek 偶发空输出)。
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仅依赖 bind_json_object_mode,不引用 features。
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"""
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prompt_for_api = ensure_json_object_prompt_has_json_keyword(prompt)
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bound = bind_json_object_mode(llm, max_tokens=max_tokens)
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tag = agent or "json_object"
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sha = _prompt_sha12(prompt_for_api)
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attempts = 2 if retry_empty else 1
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t0 = time.perf_counter()
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last_content = ""
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for attempt in range(attempts):
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response = bound.invoke(prompt_for_api)
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content = (getattr(response, "content", None) or "").strip()
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last_content = content
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if content:
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if attempt > 0:
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logger.info(
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"json_object 空内容重试成功 agent={} prompt_sha12={}",
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tag,
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sha,
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)
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_log_json_object_done(
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tag, sha, prompt_for_api, content, attempt + 1, t0, success=True
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)
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return content
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if attempt == 0 and retry_empty:
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logger.warning(
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"json_object 返回空 content,将重试 agent={} attempt={} prompt_sha12={}",
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tag,
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attempt,
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sha,
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)
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logger.warning("json_object 仍为空 agent={} prompt_sha12={}", tag, sha)
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_log_json_object_done(
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tag, sha, prompt_for_api, last_content, attempts, t0, success=False
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)
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return ""
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async def ainvoke_json_object(
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llm: Any,
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prompt: str,
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*,
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max_tokens: int,
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agent: str | None = None,
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retry_empty: bool = True,
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) -> str:
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"""异步版 `invoke_json_object`。"""
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prompt_for_api = ensure_json_object_prompt_has_json_keyword(prompt)
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bound = bind_json_object_mode(llm, max_tokens=max_tokens)
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tag = agent or "json_object"
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sha = _prompt_sha12(prompt_for_api)
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attempts = 2 if retry_empty else 1
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t0 = time.perf_counter()
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last_content = ""
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for attempt in range(attempts):
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response = await bound.ainvoke(prompt_for_api)
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content = (getattr(response, "content", None) or "").strip()
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last_content = content
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if content:
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if attempt > 0:
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logger.info(
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"json_object 空内容重试成功 agent={} prompt_sha12={}",
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tag,
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sha,
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)
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_log_json_object_done(
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tag, sha, prompt_for_api, content, attempt + 1, t0, success=True
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)
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return content
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if attempt == 0 and retry_empty:
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logger.warning(
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"json_object 返回空 content,将重试 agent={} attempt={} prompt_sha12={}",
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tag,
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attempt,
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sha,
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)
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logger.warning("json_object 仍为空 agent={} prompt_sha12={}", tag, sha)
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_log_json_object_done(
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tag, sha, prompt_for_api, last_content, attempts, t0, success=False
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)
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return ""
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def _log_json_object_done(
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tag: str,
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sha: str,
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prompt: str,
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content: str,
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attempts_used: int,
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t0: float,
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*,
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success: bool,
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) -> None:
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ms = (time.perf_counter() - t0) * 1000
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if agent_summary_enabled():
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prompt_chars = len(prompt or "")
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logger.info(
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"llm_json_object agent={} prompt_sha12={} duration_ms={:.2f} "
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"prompt_char_count={} response_len={} attempts={} success={}",
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tag,
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sha,
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ms,
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prompt_chars,
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len(content or ""),
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attempts_used,
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success,
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)
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if agent_verbose_enabled():
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log_agent_payload(logger, f"{tag}.prompt", prompt)
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log_agent_payload(logger, f"{tag}.response", content)
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