d3f5c82d98
- 新增统一日志工具，支持日志文件路径和级别配置 - 记录 CLI/chat、Agent、LLM、action、MCP、LangGraph、checkpoint 等关键流程 - 对日志中的 token、secret、api_key、Authorization 等敏感信息做脱敏 - chat 新增 llm test 命令，用于验证当前 LLM client 是否正常加载 - 同步 README、打包文档和 run.sh 帮助说明 - 补充日志脱敏和 llm test 相关测试
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
"""供 CLI 和外部嵌入使用的 LLM client 工厂。"""
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from __future__ import annotations
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import os
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import logging
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from pam_deploy_graph.logging_utils import json_for_log
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from .base import LlmClient
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from .openai_compatible import OpenAICompatibleLlmClient, load_prompt_text
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from .rule_based import RuleBasedLlmClient
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logger = logging.getLogger(__name__)
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def build_llm_client(
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*,
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base_url: str | None = None,
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api_key: str | None = None,
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model: str | None = None,
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action_analysis_prompt_path: str | None = None,
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) -> LlmClient:
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"""根据显式参数或环境变量构造 LLM client。"""
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actual_base_url = base_url if base_url is not None else os.getenv("PAM_LLM_BASE_URL", "")
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actual_api_key = api_key if api_key is not None else os.getenv("PAM_LLM_API_KEY", "")
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actual_model = model if model is not None else os.getenv("PAM_LLM_MODEL", "")
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actual_action_prompt_path = (
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action_analysis_prompt_path
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if action_analysis_prompt_path is not None
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else os.getenv("PAM_LLM_ACTION_ANALYSIS_PROMPT_FILE", "")
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)
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logger.info(
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"构建 LLM client base_url=%s model=%s has_api_key=%s action_prompt_path=%s explicit=%s",
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actual_base_url,
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actual_model,
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bool(actual_api_key),
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actual_action_prompt_path,
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json_for_log(
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{
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"base_url": base_url,
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"api_key": api_key,
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"model": model,
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"action_analysis_prompt_path": action_analysis_prompt_path,
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}
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),
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)
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if not actual_base_url and not actual_api_key and not actual_model:
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logger.info("未配置真实 LLM，使用 RuleBasedLlmClient fallback")
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return RuleBasedLlmClient()
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missing = []
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if not actual_base_url:
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missing.append("base_url")
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if not actual_model:
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missing.append("model")
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if missing:
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logger.info("LLM 配置不完整 missing=%s", missing)
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raise ValueError(f"LLM 配置不完整，缺少: {', '.join(missing)}")
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client = OpenAICompatibleLlmClient(
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base_url=actual_base_url,
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api_key=actual_api_key,
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model=actual_model,
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action_analysis_prompt=load_prompt_text(actual_action_prompt_path),
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)
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logger.info("真实 LLM client 构建完成 client=%s model=%s has_api_key=%s", type(client).__name__, actual_model, bool(actual_api_key))
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return client
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