666 lines
22 KiB
Python
666 lines
22 KiB
Python
from __future__ import annotations
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import asyncio
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import csv
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import io
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import logging
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import uuid
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from datetime import datetime, timedelta, timezone
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from app.services.scoring.scoring_service import ScoringResultV2
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from sqlalchemy import func, select, and_, cast, Integer
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.orm import selectinload
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from app.database import AsyncSessionLocal
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from app.models.citation_record import CitationRecord
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from app.models.query import Query
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from app.models.query_task import QueryTask
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from app.services.ai_engine.platform_bridge import execute_single_platform as _execute_single_platform_bridge
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logger = logging.getLogger(__name__)
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async def _verify_query_ownership(
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db: AsyncSession,
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query_id: uuid.UUID,
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user_id: uuid.UUID,
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) -> Query | None:
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stmt = select(Query).where(Query.id == query_id, Query.user_id == user_id)
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result = await db.execute(stmt)
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return result.scalar_one_or_none()
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async def get_citations(
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db: AsyncSession,
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user_id: uuid.UUID,
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query_id: uuid.UUID | None = None,
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platform: str | None = None,
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start_date: datetime | None = None,
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end_date: datetime | None = None,
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skip: int = 0,
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limit: int = 20,
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) -> tuple[list[CitationRecord], int]:
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# Build base filter: citations belonging to the user's queries
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conditions = [Query.user_id == user_id]
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if query_id is not None:
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conditions.append(CitationRecord.query_id == query_id)
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# Also verify query ownership explicitly when query_id is provided
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query = await _verify_query_ownership(db, query_id, user_id)
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if query is None:
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return [], 0
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if platform is not None:
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conditions.append(CitationRecord.platform == platform)
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if start_date is not None:
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conditions.append(CitationRecord.queried_at >= start_date)
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if end_date is not None:
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conditions.append(CitationRecord.queried_at <= end_date)
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stmt = (
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select(CitationRecord)
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.join(Query, CitationRecord.query_id == Query.id)
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.where(and_(*conditions))
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.order_by(CitationRecord.queried_at.desc())
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.offset(skip)
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.limit(limit)
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)
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result = await db.execute(stmt)
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items = result.scalars().all()
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count_stmt = (
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select(func.count())
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.select_from(CitationRecord)
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.join(Query, CitationRecord.query_id == Query.id)
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.where(and_(*conditions))
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)
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count_result = await db.execute(count_stmt)
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total = count_result.scalar_one()
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return list(items), total
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async def get_citation_stats(
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db: AsyncSession,
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user_id: uuid.UUID,
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query_id: uuid.UUID | None = None,
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brand_id: uuid.UUID | None = None,
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) -> dict:
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"""
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Get citation statistics.
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Args:
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db: Database session
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user_id: User ID
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query_id: Optional query ID to filter by specific query
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brand_id: Optional brand ID to filter by specific brand
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Returns:
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dict with citation statistics
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"""
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# Build base conditions
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base_conditions = [Query.user_id == user_id]
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# If brand_id is provided, filter by brand name matching
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if brand_id is not None:
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from app.models.brand import Brand
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brand_stmt = select(Brand.name).where(
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Brand.id == brand_id,
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Brand.user_id == user_id,
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)
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brand_result = await db.execute(brand_stmt)
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brand_name = brand_result.scalar_one_or_none()
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if brand_name is None:
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return {
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"total_queries": 0,
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"total_citations": 0,
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"citation_rate": 0.0,
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"avg_position": None,
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"by_platform": {},
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"trend": [],
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}
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base_conditions.append(Query.target_brand == brand_name)
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# If query_id is provided, verify ownership and filter
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if query_id is not None:
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query = await _verify_query_ownership(db, query_id, user_id)
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if query is None:
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return {
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"total_queries": 0,
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"total_citations": 0,
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"citation_rate": 0.0,
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"avg_position": None,
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"by_platform": {},
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"trend": [],
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}
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base_conditions.append(CitationRecord.query_id == query_id)
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base_where = and_(*base_conditions)
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# Total queries and citations
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total_queries_stmt = (
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select(func.count())
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.select_from(CitationRecord)
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.join(Query, CitationRecord.query_id == Query.id)
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.where(base_where)
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)
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total_queries_result = await db.execute(total_queries_stmt)
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total_queries = total_queries_result.scalar_one()
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total_citations_stmt = (
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select(func.count())
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.select_from(CitationRecord)
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.join(Query, CitationRecord.query_id == Query.id)
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.where(base_where, CitationRecord.cited.is_(True))
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)
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total_citations_result = await db.execute(total_citations_stmt)
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total_citations = total_citations_result.scalar_one()
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citation_rate = total_citations / total_queries if total_queries > 0 else 0.0
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# Average position (only for cited records with a position)
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avg_pos_stmt = (
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select(func.avg(CitationRecord.citation_position))
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.join(Query, CitationRecord.query_id == Query.id)
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.where(
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base_where,
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CitationRecord.cited.is_(True),
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CitationRecord.citation_position.isnot(None),
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)
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)
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avg_pos_result = await db.execute(avg_pos_stmt)
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avg_position = avg_pos_result.scalar_one()
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avg_position = round(avg_position, 1) if avg_position is not None else None
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# By platform stats
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platform_stmt = (
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select(
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CitationRecord.platform,
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func.count().label("queries"),
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func.sum(cast(CitationRecord.cited, Integer)).label("citations"),
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func.avg(CitationRecord.citation_position).label("avg_position"),
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)
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.join(Query, CitationRecord.query_id == Query.id)
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.where(base_where)
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.group_by(CitationRecord.platform)
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)
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platform_result = await db.execute(platform_stmt)
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by_platform = {}
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for row in platform_result.all():
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platform_name = row.platform
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queries = row.queries
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citations = row.citations or 0
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rate = citations / queries if queries > 0 else 0.0
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plat_avg_pos = row.avg_position
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plat_avg_pos = round(plat_avg_pos, 1) if plat_avg_pos is not None else None
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by_platform[platform_name] = {
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"queries": queries,
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"citations": citations,
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"rate": round(rate, 2),
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"avg_position": plat_avg_pos,
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}
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# Trend: past 30 days grouped by week
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# Use naive datetime to avoid mixing with naive datetimes from database
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now = datetime.utcnow()
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thirty_days_ago = now - timedelta(days=30)
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# Cross-database week grouping expression
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dialect = db.bind.dialect.name if db.bind else "postgresql"
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if dialect == "postgresql":
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week_expr = func.date_trunc("week", CitationRecord.queried_at)
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else:
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# SQLite compatible week grouping (YYYY-WW format)
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week_expr = func.strftime("%Y-%W", CitationRecord.queried_at)
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trend_stmt = (
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select(
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week_expr.label("week_start"),
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func.sum(cast(CitationRecord.cited, Integer)).label("citations"),
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)
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.join(Query, CitationRecord.query_id == Query.id)
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.where(
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base_where,
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CitationRecord.queried_at >= thirty_days_ago,
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)
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.group_by(week_expr)
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.order_by(week_expr)
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)
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trend_result = await db.execute(trend_stmt)
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trend = []
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for row in trend_result.all():
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week_start = row.week_start
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if isinstance(week_start, datetime):
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date_str = week_start.date().isoformat()
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else:
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date_str = str(week_start)
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trend.append({
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"date": date_str,
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"citations": int(row.citations or 0),
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})
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return {
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"total_queries": total_queries,
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"total_citations": total_citations,
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"citation_rate": round(citation_rate, 2),
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"avg_position": avg_position,
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"by_platform": by_platform,
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"trend": trend,
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}
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async def trigger_query_now(
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db: AsyncSession,
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user_id: uuid.UUID,
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query_id: uuid.UUID,
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) -> QueryTask:
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query = await _verify_query_ownership(db, query_id, user_id)
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if query is None:
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raise ValueError("Query not found")
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if query.status != "active":
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raise ValueError("Query is not active")
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platforms = query.platforms or []
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if not platforms:
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raise ValueError("No platforms configured for this query")
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first_task = None
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for platform in platforms:
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task = QueryTask(
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query_id=query_id,
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platform=platform,
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status="pending",
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)
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db.add(task)
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if first_task is None:
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first_task = task
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await db.commit()
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if first_task is not None:
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await db.refresh(first_task)
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# 新增：立即在后台执行查询任务
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asyncio.create_task(
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_execute_query_tasks(
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query_id=query_id,
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platforms=platforms,
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keyword=query.keyword,
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target_brand=query.target_brand,
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brand_aliases=query.brand_aliases or [],
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user_id=user_id,
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)
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)
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return first_task
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async def _execute_query_tasks(
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query_id: uuid.UUID,
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platforms: list,
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keyword: str,
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target_brand: str,
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brand_aliases: list,
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user_id: uuid.UUID | None = None,
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):
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"""后台执行查询任务 — 通过 Agent 框架执行，失败时回退到直接引擎"""
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from app.agent_framework.agents.citation_detector import CitationDetectorAgent
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agent = CitationDetectorAgent()
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try:
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async with AsyncSessionLocal() as db:
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# 验证 query 归属该用户
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if user_id is not None:
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query = await _verify_query_ownership(db, query_id, user_id)
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if query is None:
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logger.error(f"查询 {query_id} 不属于用户 {user_id}，跳过执行")
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return
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stmt = select(QueryTask).where(
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QueryTask.query_id == query_id,
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QueryTask.status == "pending",
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QueryTask.platform.in_(platforms),
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)
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result = await db.execute(stmt)
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tasks = result.scalars().all()
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for task in tasks:
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try:
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task.status = "running"
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task.started_at = datetime.utcnow()
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task.error_message = None
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await db.commit()
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citation_result = await _execute_single_platform_via_agent(
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agent=agent,
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keyword=keyword,
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platform=task.platform,
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target_brand=target_brand,
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brand_aliases=brand_aliases or [],
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)
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if citation_result:
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record = CitationRecord.from_citation_result(
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query_id=query_id,
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platform=task.platform,
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result=citation_result,
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)
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db.add(record)
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task.status = "success"
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task.completed_at = datetime.utcnow()
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await db.commit()
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except Exception as e:
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await db.rollback()
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task.status = "failed"
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task.error_message = str(e)
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task.completed_at = datetime.utcnow()
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await db.commit()
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logger.error(f"查询任务执行失败: {task.id}, 错误: {e}")
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except Exception as e:
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logger.error(f"查询引擎执行失败: {e}")
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finally:
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await agent.close()
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async def _execute_single_platform_via_agent(
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agent,
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keyword: str,
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platform: str,
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target_brand: str,
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brand_aliases: list,
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) -> dict:
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try:
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return await agent.execute_single_platform_compat(
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keyword=keyword,
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platform=platform,
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target_brand=target_brand,
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brand_aliases=brand_aliases,
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)
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except Exception as agent_err:
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logger.warning(
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f"Agent 框架执行单平台检测失败 ({platform}): {agent_err}，"
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"回退到直接引擎"
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)
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return await _execute_single_platform_bridge(
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keyword=keyword,
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platform=platform,
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target_brand=target_brand,
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brand_aliases=brand_aliases,
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)
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PLATFORM_NAMES = {
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"wenxin": "文心一言",
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"kimi": "Kimi",
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"tongyi": "通义千问",
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"doubao": "豆包",
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"qingyan": "智谱清言",
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"tiangong": "天工AI",
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"xinghuo": "讯飞星火",
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"baidu_ai": "百度AI搜索",
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"yuanbao": "腾讯元宝",
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}
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async def export_citations_pdf(
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db: AsyncSession,
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user_id: uuid.UUID,
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query_id: uuid.UUID | None = None,
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v2_result: ScoringResultV2 | None = None,
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) -> bytes:
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"""生成PDF格式报告"""
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import os
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from fpdf import FPDF
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# 验证查询所有权（如果提供了 query_id）
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if query_id is not None:
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query = await _verify_query_ownership(db, query_id, user_id)
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if query is None:
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raise ValueError("Query not found")
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# 构建查询条件
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conditions = [Query.user_id == user_id]
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if query_id is not None:
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conditions.append(CitationRecord.query_id == query_id)
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# 查询数据，使用 selectinload 加载 query 关系
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stmt = (
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select(CitationRecord)
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.options(selectinload(CitationRecord.query))
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.join(Query, CitationRecord.query_id == Query.id)
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.where(and_(*conditions))
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.order_by(CitationRecord.queried_at.desc())
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)
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result = await db.execute(stmt)
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records = result.scalars().all()
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pdf = FPDF()
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pdf.add_page()
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# 加载中文字体
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font_paths = [
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"/System/Library/Fonts/PingFang.ttc",
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"/System/Library/Fonts/STHeiti Light.ttc",
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"/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc",
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"/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
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]
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font_loaded = False
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for fp in font_paths:
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if os.path.exists(fp):
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pdf.add_font("Chinese", "", fp, uni=True)
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pdf.set_font("Chinese", size=12)
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font_loaded = True
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break
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if not font_loaded:
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pdf.set_font("Helvetica", size=12)
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# 封面
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pdf.set_font_size(24)
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pdf.cell(0, 40, "GEO 品牌曝光度分析报告", new_x="LMARGIN", new_y="NEXT", align="C")
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pdf.set_font_size(12)
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pdf.cell(0, 10, f"生成日期: {datetime.now().strftime('%Y-%m-%d %H:%M')}", new_x="LMARGIN", new_y="NEXT", align="C")
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pdf.ln(20)
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# 汇总统计
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total = len(records)
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cited_count = sum(1 for r in records if r.cited)
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rate = f"{cited_count / total * 100:.1f}%" if total > 0 else "0%"
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total_position = 0
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position_count = 0
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for r in records:
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if r.citation_position is not None:
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total_position += r.citation_position
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position_count += 1
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avg_pos = f"{total_position / position_count:.1f}" if position_count > 0 else "-"
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pdf.set_font_size(16)
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pdf.cell(0, 12, "一、汇总统计", new_x="LMARGIN", new_y="NEXT")
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pdf.set_font_size(11)
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pdf.cell(0, 8, f"总查询次数: {total}", new_x="LMARGIN", new_y="NEXT")
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pdf.cell(0, 8, f"引用次数: {cited_count}", new_x="LMARGIN", new_y="NEXT")
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pdf.cell(0, 8, f"引用率: {rate}", new_x="LMARGIN", new_y="NEXT")
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pdf.cell(0, 8, f"平均引用位置: {avg_pos}", new_x="LMARGIN", new_y="NEXT")
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pdf.ln(10)
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# 平台分布
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pdf.set_font_size(16)
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pdf.cell(0, 12, "二、平台分布", new_x="LMARGIN", new_y="NEXT")
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pdf.set_font_size(11)
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platform_stats = {}
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for r in records:
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if r.platform not in platform_stats:
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platform_stats[r.platform] = {"total": 0, "cited": 0}
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platform_stats[r.platform]["total"] += 1
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if r.cited:
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platform_stats[r.platform]["cited"] += 1
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for platform, stats in platform_stats.items():
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name = PLATFORM_NAMES.get(platform, platform)
|
||
p_rate = f"{stats['cited'] / stats['total'] * 100:.1f}%" if stats['total'] > 0 else "0%"
|
||
pdf.cell(0, 8, f" {name}: 查询{stats['total']}次, 引用{stats['cited']}次, 引用率{p_rate}", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.ln(10)
|
||
|
||
# 详细数据表格
|
||
pdf.set_font_size(16)
|
||
pdf.cell(0, 12, "三、详细数据", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.set_font_size(9)
|
||
|
||
col_widths = [30, 25, 20, 20, 15, 80]
|
||
headers = ["查询关键词", "平台", "是否引用", "置信度", "位置", "引用文本"]
|
||
for i, h in enumerate(headers):
|
||
pdf.cell(col_widths[i], 8, h, border=1, align="C")
|
||
pdf.ln()
|
||
|
||
for r in records:
|
||
keyword = r.query.keyword if r.query else ""
|
||
platform_name = PLATFORM_NAMES.get(r.platform, r.platform)
|
||
cited_str = "是" if r.cited else "否"
|
||
conf = f"{r.confidence:.2f}" if r.confidence is not None else "-"
|
||
pos = str(r.citation_position) if r.citation_position is not None else "-"
|
||
text = (r.citation_text or "")[:40]
|
||
|
||
row_data = [keyword[:15], platform_name, cited_str, conf, pos, text]
|
||
for i, d in enumerate(row_data):
|
||
pdf.cell(col_widths[i], 7, d, border=1)
|
||
pdf.ln()
|
||
|
||
if v2_result is not None:
|
||
pdf.add_page()
|
||
pdf.set_font_size(16)
|
||
pdf.cell(0, 12, "四、V2 品牌可见性评分", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.set_font_size(11)
|
||
pdf.cell(0, 8, f"综合评分: {v2_result.overall_score:.2f}/100", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.cell(0, 8, f"健康等级: {v2_result.health_level}", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.ln(5)
|
||
pdf.cell(0, 8, f"提及率: {v2_result.mention_rate.score:.2f}/{v2_result.mention_rate.max_score:.0f} ({v2_result.mention_rate.percentage:.1f}%)", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.cell(0, 8, f"推荐排名: {v2_result.recommendation_rank.score:.2f}/{v2_result.recommendation_rank.max_score:.0f} ({v2_result.recommendation_rank.percentage:.1f}%)", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.cell(0, 8, f"情感倾向: {v2_result.sentiment_score.score:.2f}/{v2_result.sentiment_score.max_score:.0f} ({v2_result.sentiment_score.percentage:.1f}%)", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.cell(0, 8, f"引用质量: {v2_result.citation_quality.score:.2f}/{v2_result.citation_quality.max_score:.0f} ({v2_result.citation_quality.percentage:.1f}%)", new_x="LMARGIN", new_y="NEXT")
|
||
pdf.cell(0, 8, f"竞品对比: {v2_result.competitive_position.score:.2f}/{v2_result.competitive_position.max_score:.0f} ({v2_result.competitive_position.percentage:.1f}%)", new_x="LMARGIN", new_y="NEXT")
|
||
|
||
return pdf.output()
|
||
|
||
|
||
async def export_citations_csv(
|
||
db: AsyncSession,
|
||
user_id: uuid.UUID,
|
||
query_id: uuid.UUID,
|
||
v2_result: ScoringResultV2 | None = None,
|
||
) -> str:
|
||
query = await _verify_query_ownership(db, query_id, user_id)
|
||
if query is None:
|
||
raise ValueError("Query not found")
|
||
|
||
stmt = (
|
||
select(CitationRecord)
|
||
.where(CitationRecord.query_id == query_id)
|
||
.order_by(CitationRecord.queried_at.desc())
|
||
)
|
||
result = await db.execute(stmt)
|
||
records = result.scalars().all()
|
||
|
||
output = io.StringIO()
|
||
writer = csv.writer(output)
|
||
headers = [
|
||
"查询关键词",
|
||
"目标品牌",
|
||
"查询日期",
|
||
"查询平台",
|
||
"是否引用",
|
||
"引用位置",
|
||
"引用文本",
|
||
"匹配置信度",
|
||
"匹配类型",
|
||
"竞争品牌",
|
||
]
|
||
if v2_result is not None:
|
||
headers.extend([
|
||
"overall_score",
|
||
"health_level",
|
||
"mention_rate",
|
||
"recommendation_rank",
|
||
"sentiment_score",
|
||
"citation_quality",
|
||
"competitive_position",
|
||
])
|
||
writer.writerow(headers)
|
||
|
||
total_queries = len(records)
|
||
total_citations = 0
|
||
total_position = 0
|
||
position_count = 0
|
||
|
||
for record in records:
|
||
if record.cited:
|
||
total_citations += 1
|
||
if record.citation_position is not None:
|
||
total_position += record.citation_position
|
||
position_count += 1
|
||
|
||
date_str = ""
|
||
if record.queried_at:
|
||
date_str = record.queried_at.strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
platform_name = PLATFORM_NAMES.get(record.platform, record.platform)
|
||
|
||
match_type_display = ""
|
||
if record.match_type == "exact":
|
||
match_type_display = "精确匹配"
|
||
elif record.match_type == "alias":
|
||
match_type_display = "别名匹配"
|
||
elif record.match_type == "fuzzy":
|
||
match_type_display = "模糊匹配"
|
||
|
||
confidence_str = ""
|
||
if record.confidence is not None:
|
||
confidence_str = f"{record.confidence:.2f}"
|
||
|
||
row = [
|
||
query.keyword,
|
||
query.target_brand,
|
||
date_str,
|
||
platform_name,
|
||
"是" if record.cited else "否",
|
||
record.citation_position if record.citation_position is not None else "",
|
||
record.citation_text or "",
|
||
confidence_str,
|
||
match_type_display,
|
||
", ".join(record.competitor_brands) if record.competitor_brands else "",
|
||
]
|
||
if v2_result is not None:
|
||
row.extend([
|
||
round(v2_result.overall_score, 2),
|
||
v2_result.health_level,
|
||
round(v2_result.mention_rate.score, 2),
|
||
round(v2_result.recommendation_rank.score, 2),
|
||
round(v2_result.sentiment_score.score, 2),
|
||
round(v2_result.citation_quality.score, 2),
|
||
round(v2_result.competitive_position.score, 2),
|
||
])
|
||
writer.writerow(row)
|
||
|
||
# 汇总统计
|
||
writer.writerow([])
|
||
writer.writerow(["汇总统计"])
|
||
writer.writerow(["总查询次数", total_queries])
|
||
writer.writerow(["引用次数", total_citations])
|
||
citation_rate = (total_citations / total_queries * 100) if total_queries > 0 else 0.0
|
||
writer.writerow(["引用率", f"{citation_rate:.1f}%"])
|
||
avg_position = (total_position / position_count) if position_count > 0 else 0.0
|
||
writer.writerow(["平均引用位置", f"{avg_position:.1f}"])
|
||
writer.writerow(["报告生成时间", datetime.now().strftime("%Y-%m-%d %H:%M:%S")])
|
||
|
||
return output.getvalue()
|