bolade
123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
from datetime import datetime, timedelta
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from typing import Optional
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from db.db import get_db
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from db.models import InvestorInsightCache, InvestorTable, ProjectTable
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from fastapi import APIRouter, Depends, HTTPException
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from schemas.insight_schema import InsightResponse
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from services.compatibility_score import (
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calculate_project_investor_compatibility,
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generate_compatibility_explanation,
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)
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from services.insight import QueryProcessor
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from sqlalchemy.orm import Session
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router = APIRouter()
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@router.get(
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"/insights/{investor_id}", response_model=InsightResponse, tags=["Insights"]
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)
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async def get_insights(
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investor_id: int, project_id: Optional[int] = None, db: Session = Depends(get_db)
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):
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"""
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Get investor insights including investment pattern analysis, market position,
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and optionally compatibility score with a project.
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Args:
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investor_id: The ID of the investor to analyze
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project_id: Optional project ID to calculate compatibility score
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Returns:
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InsightResponse with investment_pattern_analysis, market_position,
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and compatibility_score (if project_id provided)
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"""
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# Get investor from database
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investor = db.query(InvestorTable).filter(InvestorTable.id == investor_id).first()
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if not investor:
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raise HTTPException(
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status_code=404, detail=f"Investor with id {investor_id} not found"
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)
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# Check if we have cached insights
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cached_insights = (
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db.query(InvestorInsightCache)
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.filter(InvestorInsightCache.investor_id == investor_id)
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.first()
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)
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# Determine if cache needs refresh (older than 1 month)
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needs_refresh = True
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if cached_insights:
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# Calculate if cache is older than 1 month
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cache_age = (
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datetime.now(cached_insights.last_refreshed.tzinfo)
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- cached_insights.last_refreshed
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)
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needs_refresh = cache_age > timedelta(days=30)
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# Fetch new insights if needed
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if needs_refresh:
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# Initialize the query processor for insights
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query_processor = QueryProcessor()
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# Get investment pattern analysis and market position using web search
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insights = await query_processor.get_investor_insights(
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investor_name=investor.name,
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investor_website=investor.website,
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investor_description=investor.description,
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investor_headquarters=investor.headquarters,
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investment_thesis=investor.investment_thesis,
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portfolio_highlights=investor.portfolio_highlights,
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)
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# Update or create cache entry
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if cached_insights:
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# Update existing cache
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cached_insights.investment_pattern_analysis = insights[
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"investment_pattern_analysis"
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]
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cached_insights.market_position = insights["market_position"]
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cached_insights.last_refreshed = datetime.now(
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cached_insights.last_refreshed.tzinfo
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)
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else:
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# Create new cache entry
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cached_insights = InvestorInsightCache(
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investor_id=investor_id,
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investment_pattern_analysis=insights["investment_pattern_analysis"],
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market_position=insights["market_position"],
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)
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db.add(cached_insights)
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db.commit()
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db.refresh(cached_insights)
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# Calculate compatibility score if project_id is provided
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compatibility_score = None
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if project_id:
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project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
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if not project:
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raise HTTPException(
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status_code=404, detail=f"Project with id {project_id} not found"
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)
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# Calculate the compatibility score
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score = calculate_project_investor_compatibility(
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project, investor, use_funds=True
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)
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# Generate detailed explanation
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compatibility_score = generate_compatibility_explanation(
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project, investor, score, use_funds=True
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)
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else:
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compatibility_score = "Select a project to see compatibility analysis"
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return InsightResponse(
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investment_pattern_analysis=cached_insights.investment_pattern_analysis,
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market_position=cached_insights.market_position,
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compatibility_score=compatibility_score,
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)
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