refactor(server): 将记忆可视化工具和统计功能整合进主服务
将原先独立的记忆可视化工具（Memory Visualizer）和LLM使用统计逻辑深度整合到项目主服务中。 主要变更包括： - **移除独立的可视化工具**: 删除了 `tools/memory_visualizer` 目录下的所有独立服务器、脚本和文档，清理了项目结构。 - **API路由整合**: 在主 FastAPI 应用中注册了记忆可视化工具的路由，使其成为核心功能的一部分，可通过 `/visualizer` 访问。 - **统计逻辑重构**: 将LLM使用统计的计算逻辑从API路由层 `statistic_router.py` 中剥离，迁移到 `src/chat/utils/statistic.py` 中，实现了逻辑的解耦和复用。API路由现在直接调用重构后的统计任务。 - **依赖清理与添加**: 添加了 `jinja2` 作为模板渲染的依赖，并清除了与独立可视化工具相关的旧依赖。 此次重构简化了项目的维护和部署，将原本分散的功能统一管理，提升了代码的内聚性和可维护性。
This commit is contained in:
minecraft1024a
committed by
Windpicker-owo
Windpicker-owo
parent
dad6a3fc6f
commit
5702dd8a9f
@@ -4,10 +4,10 @@ from typing import Any, Literal
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from fastapi import APIRouter, HTTPException, Query
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from src.common.database.compatibility import db_get
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from src.common.database.core.models import LLMUsage
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from src.chat.utils.statistic import (
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StatisticOutputTask,
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)
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from src.common.logger import get_logger
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from src.config.config import model_config
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logger = get_logger("LLM统计API")
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@@ -37,108 +37,6 @@ COST_BY_USER = "costs_by_user"
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COST_BY_MODEL = "costs_by_model"
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COST_BY_MODULE = "costs_by_module"
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async def _collect_stats_in_period(start_time: datetime, end_time: datetime) -> dict[str, Any]:
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"""在指定时间段内收集LLM使用统计信息"""
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records = await db_get(
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model_class=LLMUsage,
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filters={"timestamp": {"$gte": start_time, "$lt": end_time}},
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)
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if not records:
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return {}
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# 创建一个从 model_identifier 到 name 的映射
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model_identifier_to_name_map = {model.model_identifier: model.name for model in model_config.models}
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stats: dict[str, Any] = {
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TOTAL_REQ_CNT: 0,
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TOTAL_COST: 0.0,
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REQ_CNT_BY_TYPE: defaultdict(int),
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REQ_CNT_BY_USER: defaultdict(int),
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REQ_CNT_BY_MODEL: defaultdict(int),
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REQ_CNT_BY_MODULE: defaultdict(int),
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IN_TOK_BY_TYPE: defaultdict(int),
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IN_TOK_BY_USER: defaultdict(int),
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IN_TOK_BY_MODEL: defaultdict(int),
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IN_TOK_BY_MODULE: defaultdict(int),
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OUT_TOK_BY_TYPE: defaultdict(int),
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OUT_TOK_BY_USER: defaultdict(int),
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OUT_TOK_BY_MODEL: defaultdict(int),
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OUT_TOK_BY_MODULE: defaultdict(int),
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TOTAL_TOK_BY_TYPE: defaultdict(int),
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TOTAL_TOK_BY_USER: defaultdict(int),
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TOTAL_TOK_BY_MODEL: defaultdict(int),
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TOTAL_TOK_BY_MODULE: defaultdict(int),
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COST_BY_TYPE: defaultdict(float),
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COST_BY_USER: defaultdict(float),
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COST_BY_MODEL: defaultdict(float),
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COST_BY_MODULE: defaultdict(float),
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}
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for record in records:
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if not isinstance(record, dict):
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continue
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stats[TOTAL_REQ_CNT] += 1
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request_type = record.get("request_type") or "unknown"
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user_id = record.get("user_id") or "unknown"
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# 从数据库获取的是真实模型名 (model_identifier)
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real_model_name = record.get("model_name") or "unknown"
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module_name = request_type.split(".")[0] if "." in request_type else request_type
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# 尝试通过真实模型名找到配置文件中的模型名
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config_model_name = model_identifier_to_name_map.get(real_model_name, real_model_name)
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prompt_tokens = record.get("prompt_tokens") or 0
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completion_tokens = record.get("completion_tokens") or 0
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total_tokens = prompt_tokens + completion_tokens
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cost = 0.0
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try:
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# 使用配置文件中的模型名来获取模型信息
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model_info = model_config.get_model_info(config_model_name)
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if model_info:
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input_cost = (prompt_tokens / 1000000) * model_info.price_in
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output_cost = (completion_tokens / 1000000) * model_info.price_out
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cost = round(input_cost + output_cost, 6)
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except KeyError as e:
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logger.info(str(e))
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logger.warning(f"模型 '{config_model_name}' (真实名称: '{real_model_name}') 在配置中未找到，成本计算将使用默认值 0.0")
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stats[TOTAL_COST] += cost
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# 按类型统计
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stats[REQ_CNT_BY_TYPE][request_type] += 1
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stats[IN_TOK_BY_TYPE][request_type] += prompt_tokens
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stats[OUT_TOK_BY_TYPE][request_type] += completion_tokens
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stats[TOTAL_TOK_BY_TYPE][request_type] += total_tokens
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stats[COST_BY_TYPE][request_type] += cost
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# 按用户统计
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stats[REQ_CNT_BY_USER][user_id] += 1
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stats[IN_TOK_BY_USER][user_id] += prompt_tokens
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stats[OUT_TOK_BY_USER][user_id] += completion_tokens
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stats[TOTAL_TOK_BY_USER][user_id] += total_tokens
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stats[COST_BY_USER][user_id] += cost
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# 按模型统计 (使用配置文件中的名称)
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stats[REQ_CNT_BY_MODEL][config_model_name] += 1
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stats[IN_TOK_BY_MODEL][config_model_name] += prompt_tokens
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stats[OUT_TOK_BY_MODEL][config_model_name] += completion_tokens
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stats[TOTAL_TOK_BY_MODEL][config_model_name] += total_tokens
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stats[COST_BY_MODEL][config_model_name] += cost
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# 按模块统计
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stats[REQ_CNT_BY_MODULE][module_name] += 1
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stats[IN_TOK_BY_MODULE][module_name] += prompt_tokens
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stats[OUT_TOK_BY_MODULE][module_name] += completion_tokens
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stats[TOTAL_TOK_BY_MODULE][module_name] += total_tokens
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stats[COST_BY_MODULE][module_name] += cost
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return stats
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@router.get("/llm/stats")
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async def get_llm_stats(
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period_type: Literal[
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@@ -179,7 +77,8 @@ async def get_llm_stats(
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if start_time is None:
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raise HTTPException(status_code=400, detail="无法确定查询的起始时间")
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period_stats = await _collect_stats_in_period(start_time, end_time)
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stats_data = await StatisticOutputTask._collect_model_request_for_period([("custom", start_time)])
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period_stats = stats_data.get("custom", {})
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if not period_stats:
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return {"period": {"start": start_time.isoformat(), "end": end_time.isoformat()}, "data": {}}
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