Eric Wang
a5407e03b2
- archive workflow/20261020 export (diff vs 20261016: collection prompt
only - state-decides-body rule, route when info complete)
- prompts: collection.md updated; 6 photo/confirm prompts archived with
source nodeIds; render_prompt replaces {{$vars}} (Asia/Shanghai clock,
person fields, explicit 'not provided' for missing prefills)
- llm: message history passed as user/assistant roles (chatNode history=50);
optional LLM_TEMPERATURE; FakeLLMService supports history-aware responders
- collect: context via system supplement, raw user input as user message
(Text Editor#2 parity), shared validated_reply (one call, one repair)
- welcome: Assigned Reply#3 original text, stored in dialogue history
- transitions: 2004 plate-confirm entries (2003->2004/2005, 2004->2005)
- verified: prompt files byte-identical to workflow JSON (parametrized test)
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
import os
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from dotenv import load_dotenv
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load_dotenv()
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class Config:
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PROJECT_NAME = "Flexible Employment Analysis API"
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API_V1_STR = "/api/v1"
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SECRET_KEY = os.getenv("SECRET_KEY", "your-secret-key")
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DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./test.db")
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DEBUG = os.getenv("DEBUG", "false").lower() in ("true", "1", "t")
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# FastGPT Configuration
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FASTGPT_API_KEY = os.getenv("ANALYSIS_AUTH_TOKEN")
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FASTGPT_BASE_URL = os.getenv("ANALYSIS_SERVICE_URL")
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FASTGPT_APP_ID = os.getenv("APP_ID")
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# Chat backend switch（作业 2）
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# "fastgpt" 保持现有行为；"langgraph" 使用内存图 fake 回复。
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AGENT_BACKEND = os.getenv("AGENT_BACKEND", "fastgpt")
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# LLM provider（作业 4）：fake 为脚本化假模型，离线可运行；
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# openai 为真实 provider（OpenAI 兼容接口）。
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AGENT_LLM_PROVIDER = os.getenv("AGENT_LLM_PROVIDER", "fake")
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LLM_MODEL = os.getenv("LLM_MODEL", "fake-reply-model")
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LLM_BASE_URL = os.getenv("LLM_BASE_URL", "")
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LLM_API_KEY = os.getenv("LLM_API_KEY", "")
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LLM_TIMEOUT_SECONDS = float(os.getenv("LLM_TIMEOUT_SECONDS", "30"))
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LLM_MAX_RETRIES = int(os.getenv("LLM_MAX_RETRIES", "2"))
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# 采样温度；None 表示使用 provider 缺省。对照 FastGPT 应用配置调整。
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LLM_TEMPERATURE = (
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float(os.getenv("LLM_TEMPERATURE")) if os.getenv("LLM_TEMPERATURE") else None
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)
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@classmethod
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def validate(cls):
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"""Validate required configuration."""
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# TODO(作业 2): 语音管线仍依赖 FastGPT。当前仅按文本后端开关校验；
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# 接入语音开关后，需要单独校验语音路径所需配置。
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if cls.AGENT_BACKEND not in ("fastgpt", "langgraph"):
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raise ValueError(
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f"AGENT_BACKEND must be 'fastgpt' or 'langgraph', got {cls.AGENT_BACKEND!r}"
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)
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if cls.AGENT_LLM_PROVIDER not in ("fake", "openai"):
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raise ValueError(
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"AGENT_LLM_PROVIDER must be 'fake' or 'openai', "
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f"got {cls.AGENT_LLM_PROVIDER!r}"
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)
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if cls.AGENT_LLM_PROVIDER == "openai" and (
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not cls.LLM_API_KEY or not cls.LLM_BASE_URL
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):
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raise ValueError(
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"AGENT_LLM_PROVIDER=openai requires LLM_API_KEY and LLM_BASE_URL"
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)
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if cls.AGENT_BACKEND != "fastgpt":
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return
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required = [
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("FASTGPT_BASE_URL (ANALYSIS_SERVICE_URL)", cls.FASTGPT_BASE_URL),
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("FASTGPT_API_KEY (ANALYSIS_AUTH_TOKEN)", cls.FASTGPT_API_KEY),
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("FASTGPT_APP_ID (APP_ID)", cls.FASTGPT_APP_ID),
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]
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missing = [name for name, value in required if not value]
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if missing:
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raise ValueError(f"Missing required environment variables: {', '.join(missing)}")
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