bb16d3a5c9
访谈与阶段 - 新增 app/agents/stage_constants.py：集中 CHAT_STAGES、章节分类/顺序、阶段到默认 memoir 类别等，与 MemoirState 默认槽位顺序对齐；减少散落在 prompts 内的重复常量。 - 新增 app/agents/chat/prompt_context.py：以 ChatPromptContext 汇总 guided 系统提示所需字段（阶段、槽位、轮次、人设、记忆证据、回复长度模式、背景声线、职业等），统一走 get_guided_conversation_prompt。 - 大幅收敛 app/agents/chat/prompts_conversation.py；调整 prompts.py、stage_prompts.py、stage_detection.py；同步 interview_agent、profile_agent、helpers 与 state_schema，使对话侧构造提示的方式一致、可测。 回忆录流水线 - memoir/prompts.py 删除已迁至 stage_constants / 独立模板的大段常量与图片占位相关逻辑；classification / extraction / fidelity / narrative agents 与 orchest（全量历史仍可用于计数，注入模型时按轮次与字符上限截断）、image_prompt_fallback_disabled。 - dependencies 增加 get_llm_provider_fast（LRU 缓存，可与默认共用密钥与 base_url）。 任务与编排 - memoir_tasks：prepare_batches 注入 llm_fast；开启独立快档模型时打结构化日志。 - chapter_cover_tasks、story_image_tasks：与图片 prompt / JSON 工具路径或策略变更对齐（import 与行为一致）。 - story_pipeline_sync 等小处同步。 其它核心 - langchain_llm、text_normalize 随上述调用链微调。 开发者体验 - .cursor/settings.json：启用 redis-development、postman 插件。 测试 - 新增 test_image_prompt_policy：覆盖「禁止回退」等图片 prompt 策略。 - 更新 test_interview_prompts、test_interview_reply_length、test_experience_regressions、test_json_and_memory_utils，匹配新常量位置、json_utils 与对话/长度行为。
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
"""聊天 Agent 共享工具：历史获取、格式化、存储"""
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Any, List
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
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from app.core.redis import redis_service
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def _human_ai_rows(history: list[dict]) -> list[dict]:
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"""与 get_history_messages 一致：仅保留 human/ai，顺序与 Redis 列表一致。"""
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return [m for m in history if m.get("role") in ("human", "ai")]
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def _lc_messages_from_rows(rows: list[dict]) -> list[HumanMessage | AIMessage]:
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out: list[HumanMessage | AIMessage] = []
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for msg in rows:
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role = msg.get("role")
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if role == "human":
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out.append(HumanMessage(content=msg["content"]))
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elif role == "ai":
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out.append(AIMessage(content=msg["content"]))
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return out
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@dataclass(frozen=True)
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class HistoryWithWindow:
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"""单次 Redis 读取后的全量轮次计数 + 截断后注入 LLM 的消息列表。"""
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turn_total: int
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window: list[HumanMessage | AIMessage]
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async def get_history_with_window(
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conversation_id: str,
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*,
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max_pairs: int,
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max_chars: int,
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) -> HistoryWithWindow:
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"""一次读取 Redis：turn_total 由全量 human/ai 条数得到；仅对窗口切片构造 LangChain 消息。"""
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history = await redis_service.get_conversation_history(conversation_id)
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human_ai = _human_ai_rows(history)
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turn_total = len(human_ai) // 2
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window_raw = human_ai[-(max_pairs * 2) :] if max_pairs > 0 else human_ai[:]
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window = _lc_messages_from_rows(window_raw)
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total_chars = 0
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start = len(window)
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for i in range(len(window) - 1, -1, -1):
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msg = window[i]
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content = getattr(msg, "content", "") or ""
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total_chars += len(content)
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if total_chars > max_chars:
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start = i + 1
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break
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else:
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start = 0
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if start < len(window) and isinstance(window[start], AIMessage):
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start += 1
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trimmed = window[start:]
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return HistoryWithWindow(turn_total=turn_total, window=trimmed)
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async def get_history_messages(conversation_id: str) -> List[Any]:
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"""从 Redis 获取对话历史"""
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history = await redis_service.get_conversation_history(conversation_id)
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return _lc_messages_from_rows(_human_ai_rows(history))
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def format_history_string(messages: List[Any]) -> str:
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"""将 LangChain 消息列表格式化为调试日志用多段文本（含 System，不静默跳过）。"""
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history_parts: list[str] = []
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for msg in messages:
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if isinstance(msg, SystemMessage):
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history_parts.append(f"System: {msg.content}")
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elif isinstance(msg, HumanMessage):
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history_parts.append(f"Human: {msg.content}")
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elif isinstance(msg, AIMessage):
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history_parts.append(f"Assistant: {msg.content}")
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else:
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content = getattr(msg, "content", None)
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history_parts.append(f"{type(msg).__name__}: {content}")
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return "\n\n".join(history_parts)
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async def save_message(
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conversation_id: str,
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role: str,
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content: str,
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message_type: str = "text",
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voice_session_id: str | None = None,
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timestamp: datetime | str | int | None = None,
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audio_duration_seconds: int | None = None,
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) -> None:
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"""保存消息到 Redis"""
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await redis_service.add_message(
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conversation_id,
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role,
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content,
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message_type=message_type,
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voice_session_id=voice_session_id,
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timestamp=timestamp.isoformat()
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if isinstance(timestamp, datetime)
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else timestamp,
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audio_duration_seconds=audio_duration_seconds,
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)
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