feat: Update query endpoint to return paginated investment responses with fund details

app/db/db.py

@@ -14,7 +14,7 @@ Base = declarative_base()
 # Get absolute path to the preprocessor database
 # APP_DIR = Path(__file__).parent.parent
 # PREPROCESSOR_DB = APP_DIR.parent / "preprocessor" / "version_two.db"
-DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./version_two.db")
+DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./investors.db")
 
 # Create engine
 engine = create_engine(DATABASE_URL, echo=False)

app/main.py

@@ -6,7 +6,7 @@ from dotenv import load_dotenv
 from fastapi import FastAPI, File, Form, UploadFile
 from pydantic import BaseModel
 from routers import companies, investors, projects
-from schemas.router_schemas import InvestorList
+from schemas.router_schemas import InvestmentResponse, PaginatedResponse
 from services.llm_parser import InvestorProcessor
 from services.querying import QueryProcessor
 
@@ -84,11 +84,16 @@ async def parse_csv(
         return results
 
 
-@app.post("/query", response_model=InvestorList, tags=["Querying"])
+@app.post(
+    "/query", response_model=PaginatedResponse[InvestmentResponse], tags=["Querying"]
+)
 async def query_investors(request: QueryRequest):
     """
     Query investors using natural language.
 
+    Returns fund-level matches (one row per fund) with investor details.
+    This ensures only relevant funds are included in the response.
+
     Supports queries like:
     - "Show me seed stage investors"
     - "Find fintech investors in Silicon Valley"

app/routers/companies.py

@@ -1,10 +1,10 @@
-from typing import List, Optional
+from typing import Optional
 
 from db.db import get_db
 from db.models import CompanyTable, InvestorTable
 from fastapi import APIRouter, Depends, HTTPException, Query
 from pydantic import BaseModel
-from schemas.router_schemas import CompanyData
+from schemas.router_schemas import CompanyData, PaginatedResponse
 from sqlalchemy.orm import Session, selectinload
 
 router = APIRouter(tags=["Company Routes"])
@@ -29,20 +29,34 @@ class CompanyUpdate(BaseModel):
     website: Optional[str] = None
 
 
-@router.get("/companies", response_model=List[CompanyData])
-def read_companies(db: Session = Depends(get_db)):
-    """Get all companies with their investor relationships"""
+@router.get("/companies", response_model=PaginatedResponse[CompanyData])
+def read_companies(
+    page: int = Query(1, ge=1, description="Page number (starts at 1)"),
+    page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
+    db: Session = Depends(get_db),
+):
+    """Get all companies with their investor relationships (paginated)"""
+    # Calculate offset
+    offset = (page - 1) * page_size
+
+    # Get total count
+    total_count = (
+        db.query(CompanyTable)
+        .filter(CompanyTable.name.isnot(None), CompanyTable.description.isnot(None))
+        .count()
+    )
+
+    # Get paginated results
     companies = (
         db.query(CompanyTable)
-        .filter(
-            CompanyTable.name.isnot(None),
-            CompanyTable.description.isnot(None)
-        )
+        .filter(CompanyTable.name.isnot(None), CompanyTable.description.isnot(None))
         .options(
             selectinload(CompanyTable.investors),
             selectinload(CompanyTable.members),
             selectinload(CompanyTable.sectors),
         )
+        .offset(offset)
+        .limit(page_size)
         .all()
     )
 
@@ -57,10 +71,19 @@ def read_companies(db: Session = Depends(get_db)):
         )
         company_data_list.append(company_data)
 
-    return company_data_list
+    # Calculate total pages
+    total_pages = (total_count + page_size - 1) // page_size
+
+    return PaginatedResponse(
+        items=company_data_list,
+        total=total_count,
+        page=page,
+        page_size=page_size,
+        total_pages=total_pages,
+    )
 
 
-@router.get("/companies/filter", response_model=List[CompanyData])
+@router.get("/companies/filter", response_model=PaginatedResponse[CompanyData])
 def filter_companies(
     industry: Optional[str] = Query(
         None, description="Filter by industry (partial match)"
@@ -76,9 +99,11 @@ def filter_companies(
     investor_name: Optional[str] = Query(
         None, description="Filter by investor name (partial match)"
     ),
+    page: int = Query(1, ge=1, description="Page number (starts at 1)"),
+    page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
     db: Session = Depends(get_db),
 ):
-    """Filter companies based on various criteria"""
+    """Filter companies based on various criteria (paginated)"""
 
     # Start with base query
     query = db.query(CompanyTable).options(
@@ -112,7 +137,12 @@ def filter_companies(
             InvestorTable.name.ilike(f"%{investor_name}%")
         )
 
-    companies = query.all()
+    # Get total count before pagination
+    total_count = query.count()
+
+    # Calculate offset and apply pagination
+    offset = (page - 1) * page_size
+    companies = query.offset(offset).limit(page_size).all()
 
     # Transform to CompanyData format
     company_data_list = []
@@ -125,7 +155,16 @@ def filter_companies(
         )
         company_data_list.append(company_data)
 
-    return company_data_list
+    # Calculate total pages
+    total_pages = (total_count + page_size - 1) // page_size
+
+    return PaginatedResponse(
+        items=company_data_list,
+        total=total_count,
+        page=page,
+        page_size=page_size,
+        total_pages=total_pages,
+    )
 
 
 @router.get("/companies/{company_id}", response_model=CompanyData)

app/routers/investors.py

@@ -1,13 +1,16 @@
-from typing import List, Optional
+from typing import Optional
 
 from db.db import get_db
-from db.models import InvestorTable, SectorTable
+from db.models import FundTable, InvestorTable, SectorTable
 from fastapi import APIRouter, Depends, HTTPException, Query
 from pydantic import BaseModel
 from schemas.router_schemas import (
+    CompanyMinimal,
+    InvestmentResponse,
     InvestmentStage,
     InvestorData,
-    InvestorFundData,
+    PaginatedResponse,
+    SectorMinimal,
 )
 from sqlalchemy.orm import Session, selectinload
 
@@ -18,32 +21,45 @@ router = APIRouter(tags=["Investor Routes"])
 class InvestorCreate(BaseModel):
     name: str
     description: Optional[str] = None
+    website: Optional[str] = None
+    headquarters: Optional[str] = None
     aum: int
     check_size_lower: int
     check_size_upper: int
     geographic_focus: str
-    stage_focus: InvestmentStage
     number_of_investments: int = 0
 
 
 class InvestorUpdate(BaseModel):
     name: Optional[str] = None
     description: Optional[str] = None
+    website: Optional[str] = None
+    headquarters: Optional[str] = None
     aum: Optional[int] = None
     check_size_lower: Optional[int] = None
     check_size_upper: Optional[int] = None
     geographic_focus: Optional[str] = None
-    stage_focus: Optional[InvestmentStage] = None
     number_of_investments: Optional[int] = None
 
 
-@router.get("/investors", response_model=List[InvestorFundData])
-def read_investors(db: Session = Depends(get_db)):
-    """Get all investors with their funds as separate entries
+@router.get("/investors", response_model=PaginatedResponse[InvestmentResponse])
+def read_investors(
+    page: int = Query(1, ge=1, description="Page number (starts at 1)"),
+    page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
+    db: Session = Depends(get_db),
+):
+    """Get all investors with their funds as separate entries (paginated)
 
     Each investor-fund combination is returned as a separate row.
     An investor with 3 funds will appear as 3 entries.
     """
+    # Calculate offset
+    offset = (page - 1) * page_size
+
+    # Get total count
+    total_count = db.query(InvestorTable).count()
+
+    # Get paginated results
     investors = (
         db.query(InvestorTable)
         .options(
@@ -52,80 +68,80 @@ def read_investors(db: Session = Depends(get_db)):
             selectinload(InvestorTable.sectors),
             selectinload(InvestorTable.funds),
         )
+        .offset(offset)
+        .limit(page_size)
         .all()
     )
 
-    # Transform to InvestorFundData format (one row per investor-fund combination)
-    investor_fund_list = []
+    # Transform to InvestmentResponse format (one row per investor-fund combination)
+    investment_responses = []
     for investor in investors:
+        # Get top 3 portfolio companies (id and name only)
+        portfolio_companies = [
+            CompanyMinimal(id=company.id, name=company.name)
+            for company in investor.portfolio_companies[:3]
+        ]
+
         # If investor has funds, create one entry per fund
         if investor.funds:
             for fund in investor.funds:
-                investor_fund_data = InvestorFundData(
-                    # Investor fields
-                    investor_id=investor.id,
-                    investor_name=investor.name,
-                    investor_description=investor.description,
-                    investor_website=investor.website,
-                    investor_headquarters=investor.headquarters,
+                # Get stage focus as comma-separated string
+                stage_focus = (
+                    ", ".join([stage.name for stage in fund.investment_stages])
+                    if fund.investment_stages
+                    else None
+                )
+
+                # Get top 3 sectors from fund (id and name only)
+                fund_sectors = [
+                    SectorMinimal(id=sector.id, name=sector.name)
+                    for sector in (fund.sectors[:3] if fund.sectors else [])
+                ]
+
+                investment_response = InvestmentResponse(
+                    id=investor.id,
+                    name=f"{investor.name} - {fund.fund_name}"
+                    if fund.fund_name
+                    else investor.name,
                     aum=investor.aum,
-                    aum_as_of_date=investor.aum_as_of_date,
-                    aum_source_url=investor.aum_source_url,
-                    investment_thesis=investor.investment_thesis,
-                    portfolio_highlights=investor.portfolio_highlights,
-                    number_of_investments=investor.number_of_investments,
-                    # Fund fields
-                    fund_id=fund.id,
-                    fund_name=fund.fund_name,
-                    fund_size=fund.fund_size,
-                    fund_size_source_url=fund.fund_size_source_url,
                     check_size_lower=fund.check_size_lower,
                     check_size_upper=fund.check_size_upper,
                     geographic_focus=fund.geographic_focus,
-                    fund_investment_stages=fund.investment_stages,  # Now a relationship
-                    fund_sectors=fund.sectors,  # Now a relationship
-                    # Related data (same for all funds of this investor)
-                    portfolio_companies=investor.portfolio_companies,
-                    team_members=investor.team_members,
-                    sectors=investor.sectors,
+                    stage_focus=stage_focus,
+                    portfolio_companies=portfolio_companies,
+                    sectors=fund_sectors,
+                    compatibility_score=1.0,
                 )
-                investor_fund_list.append(investor_fund_data)
+                investment_responses.append(investment_response)
         else:
             # If no funds, create one entry with null fund fields
-            investor_fund_data = InvestorFundData(
-                # Investor fields
-                investor_id=investor.id,
-                investor_name=investor.name,
-                investor_description=investor.description,
-                investor_website=investor.website,
-                investor_headquarters=investor.headquarters,
+            investment_response = InvestmentResponse(
+                id=investor.id,
+                name=investor.name,
                 aum=investor.aum,
-                aum_as_of_date=investor.aum_as_of_date,
-                aum_source_url=investor.aum_source_url,
-                investment_thesis=investor.investment_thesis,
-                portfolio_highlights=investor.portfolio_highlights,
-                number_of_investments=investor.number_of_investments,
-                # Fund fields (null)
-                fund_id=None,
-                fund_name=None,
-                fund_size=None,
-                fund_size_source_url=None,
                 check_size_lower=None,
                 check_size_upper=None,
                 geographic_focus=None,
-                fund_investment_stages=None,
-                fund_sectors=None,
-                # Related data
-                portfolio_companies=investor.portfolio_companies,
-                team_members=investor.team_members,
-                sectors=investor.sectors,
+                stage_focus=None,
+                portfolio_companies=portfolio_companies,
+                sectors=[],
+                compatibility_score=1.0,
             )
-            investor_fund_list.append(investor_fund_data)
+            investment_responses.append(investment_response)
 
-    return investor_fund_list
+    # Calculate total pages
+    total_pages = (total_count + page_size - 1) // page_size
+
+    return PaginatedResponse(
+        items=investment_responses,
+        total=total_count,
+        page=page,
+        page_size=page_size,
+        total_pages=total_pages,
+    )
 
 
-@router.get("/investors/filter", response_model=List[InvestorFundData])
+@router.get("/investors/filter", response_model=PaginatedResponse[InvestmentResponse])
 def filter_investors(
     stage: Optional[InvestmentStage] = Query(
         None, description="Filter by investment stage"
@@ -138,117 +154,109 @@ def filter_investors(
     sector: Optional[str] = Query(None, description="Sector name (partial match)"),
     min_aum: Optional[int] = Query(None, description="Minimum AUM"),
     max_aum: Optional[int] = Query(None, description="Maximum AUM"),
+    page: int = Query(1, ge=1, description="Page number (starts at 1)"),
+    page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
     db: Session = Depends(get_db),
 ):
-    """Filter investors based on various criteria
+    """Filter investors based on various criteria (paginated)
 
     Returns investor-fund combinations as separate rows.
-    An investor with 3 funds will appear as 3 entries.
+    Queries the funds table to find matching funds.
     """
 
-    # Start with base query
-    query = db.query(InvestorTable).options(
-        selectinload(InvestorTable.portfolio_companies),
-        selectinload(InvestorTable.team_members),
-        selectinload(InvestorTable.sectors),
-        selectinload(InvestorTable.funds),
+    # Start with base query on funds table
+    query = db.query(FundTable).options(
+        selectinload(FundTable.investor).selectinload(
+            InvestorTable.portfolio_companies
+        ),
+        selectinload(FundTable.investor).selectinload(InvestorTable.team_members),
+        selectinload(FundTable.investor).selectinload(InvestorTable.sectors),
+        selectinload(FundTable.investment_stages),
+        selectinload(FundTable.sectors),
     )
 
-    # Apply filters
-    if stage:
-        query = query.filter(InvestorTable.stage_focus == stage)
-
+    # Apply filters at fund level
     if min_check_size is not None:
-        query = query.filter(InvestorTable.check_size_lower >= min_check_size)
+        query = query.filter(FundTable.check_size_lower >= min_check_size)
 
     if max_check_size is not None:
-        query = query.filter(InvestorTable.check_size_upper <= max_check_size)
+        query = query.filter(FundTable.check_size_upper <= max_check_size)
 
     if geography:
-        query = query.filter(InvestorTable.geographic_focus.ilike(f"%{geography}%"))
+        query = query.filter(FundTable.geographic_focus.ilike(f"%{geography}%"))
 
+    # Apply filters at investor level (through relationship)
     if min_aum is not None:
-        query = query.filter(InvestorTable.aum >= min_aum)
+        query = query.join(FundTable.investor).filter(InvestorTable.aum >= min_aum)
 
     if max_aum is not None:
+        if min_aum is None:  # Only join if not already joined
+            query = query.join(FundTable.investor)
         query = query.filter(InvestorTable.aum <= max_aum)
 
-    # Filter by sector if provided
+    # Filter by sector if provided (at fund level)
     if sector:
-        query = query.join(InvestorTable.sectors).filter(
+        query = query.join(FundTable.sectors).filter(
             SectorTable.name.ilike(f"%{sector}%")
         )
 
-    investors = query.all()
+    # Get total count before pagination
+    total_count = query.count()
 
-    # Transform to InvestorFundData format (one row per investor-fund combination)
-    investor_fund_list = []
-    for investor in investors:
-        # If investor has funds, create one entry per fund
-        if investor.funds:
-            for fund in investor.funds:
-                investor_fund_data = InvestorFundData(
-                    # Investor fields
-                    investor_id=investor.id,
-                    investor_name=investor.name,
-                    investor_description=investor.description,
-                    investor_website=investor.website,
-                    investor_headquarters=investor.headquarters,
-                    aum=investor.aum,
-                    aum_as_of_date=investor.aum_as_of_date,
-                    aum_source_url=investor.aum_source_url,
-                    investment_thesis=investor.investment_thesis,
-                    portfolio_highlights=investor.portfolio_highlights,
-                    number_of_investments=investor.number_of_investments,
-                    # Fund fields
-                    fund_id=fund.id,
-                    fund_name=fund.fund_name,
-                    fund_size=fund.fund_size,
-                    fund_size_source_url=fund.fund_size_source_url,
-                    check_size_lower=fund.check_size_lower,
-                    check_size_upper=fund.check_size_upper,
-                    geographic_focus=fund.geographic_focus,
-                    fund_investment_stages=fund.investment_stages,  # Now a relationship
-                    fund_sectors=fund.sectors,  # Now a relationship
-                    # Related data
-                    portfolio_companies=investor.portfolio_companies,
-                    team_members=investor.team_members,
-                    sectors=investor.sectors,
-                )
-                investor_fund_list.append(investor_fund_data)
-        else:
-            # If no funds, create one entry with null fund fields
-            investor_fund_data = InvestorFundData(
-                # Investor fields
-                investor_id=investor.id,
-                investor_name=investor.name,
-                investor_description=investor.description,
-                investor_website=investor.website,
-                investor_headquarters=investor.headquarters,
-                aum=investor.aum,
-                aum_as_of_date=investor.aum_as_of_date,
-                aum_source_url=investor.aum_source_url,
-                investment_thesis=investor.investment_thesis,
-                portfolio_highlights=investor.portfolio_highlights,
-                number_of_investments=investor.number_of_investments,
-                # Fund fields (null)
-                fund_id=None,
-                fund_name=None,
-                fund_size=None,
-                fund_size_source_url=None,
-                check_size_lower=None,
-                check_size_upper=None,
-                geographic_focus=None,
-                fund_investment_stages=None,
-                fund_sectors=None,
-                # Related data
-                portfolio_companies=investor.portfolio_companies,
-                team_members=investor.team_members,
-                sectors=investor.sectors,
-            )
-            investor_fund_list.append(investor_fund_data)
+    # Calculate offset and apply pagination
+    offset = (page - 1) * page_size
+    funds = query.offset(offset).limit(page_size).all()
 
-    return investor_fund_list
+    # Transform to InvestmentResponse format (one row per fund)
+    investment_responses = []
+    for fund in funds:
+        investor = fund.investor
+
+        # Get top 3 portfolio companies (id and name only)
+        portfolio_companies = [
+            CompanyMinimal(id=company.id, name=company.name)
+            for company in investor.portfolio_companies[:3]
+        ]
+
+        # Get stage focus as comma-separated string
+        stage_focus = (
+            ", ".join([stage.name for stage in fund.investment_stages])
+            if fund.investment_stages
+            else None
+        )
+
+        # Get top 3 sectors from fund (id and name only)
+        fund_sectors = [
+            SectorMinimal(id=sector.id, name=sector.name)
+            for sector in (fund.sectors[:3] if fund.sectors else [])
+        ]
+
+        investment_response = InvestmentResponse(
+            id=investor.id,
+            name=f"{investor.name} - {fund.fund_name}"
+            if fund.fund_name
+            else investor.name,
+            aum=investor.aum,
+            check_size_lower=fund.check_size_lower,
+            check_size_upper=fund.check_size_upper,
+            geographic_focus=fund.geographic_focus,
+            stage_focus=stage_focus,
+            portfolio_companies=portfolio_companies,
+            sectors=fund_sectors,
+            compatibility_score=1.0,
+        )
+        investment_responses.append(investment_response)
+
+    # Calculate total pages
+    total_pages = (total_count + page_size - 1) // page_size
+
+    return PaginatedResponse(
+        items=investment_responses,
+        total=total_count,
+        page=page,
+        page_size=page_size,
+        total_pages=total_pages,
+    )
 
 
 @router.get("/investors/{investor_id}", response_model=InvestorData)
@@ -365,25 +373,32 @@ def delete_investor(investor_id: int, db: Session = Depends(get_db)):
     return {"message": "Investor deleted successfully"}
 
 
-@router.get("/investors/{investor_id}/similar", response_model=List[InvestorFundData])
+@router.get(
+    "/investors/{investor_id}/similar",
+    response_model=PaginatedResponse[InvestmentResponse],
+)
 def find_similar_investors(
     investor_id: int,
     limit: int = Query(10, description="Maximum number of similar investors to return"),
+    page: int = Query(1, ge=1, description="Page number (starts at 1)"),
+    page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
     db: Session = Depends(get_db),
 ):
-    """Find investors similar to a given investor based on characteristics
+    """Find investors similar to a given investor based on characteristics (paginated)
 
     Returns investor-fund combinations as separate rows.
+    Queries the funds table to find matching funds.
     """
 
-    # Get the target investor
+    # Get the target investor to get their funds for comparison
     target_investor = (
         db.query(InvestorTable)
         .options(
             selectinload(InvestorTable.portfolio_companies),
             selectinload(InvestorTable.team_members),
             selectinload(InvestorTable.sectors),
-            selectinload(InvestorTable.funds),
+            selectinload(InvestorTable.funds).selectinload(FundTable.investment_stages),
+            selectinload(InvestorTable.funds).selectinload(FundTable.sectors),
         )
         .filter(InvestorTable.id == investor_id)
         .first()
@@ -392,154 +407,149 @@ def find_similar_investors(
     if not target_investor:
         raise HTTPException(status_code=404, detail="Investor not found")
 
-    # Get target investor's sector IDs for comparison
-    target_sector_ids = {sector.id for sector in target_investor.sectors}
+    # Get target investor's sector IDs for comparison (from their funds)
+    target_sector_ids = set()
+    target_stage_ids = set()
+    target_check_ranges = []
+    target_geographies = []
 
-    # Query all other investors with their relationships
-    candidates = (
-        db.query(InvestorTable)
+    for fund in target_investor.funds:
+        if fund.sectors:
+            target_sector_ids.update({sector.id for sector in fund.sectors})
+        if fund.investment_stages:
+            target_stage_ids.update({stage.id for stage in fund.investment_stages})
+        if fund.check_size_lower and fund.check_size_upper:
+            target_check_ranges.append((fund.check_size_lower, fund.check_size_upper))
+        if fund.geographic_focus:
+            target_geographies.append(fund.geographic_focus.lower())
+
+    # Query all funds from other investors
+    candidate_funds = (
+        db.query(FundTable)
         .options(
-            selectinload(InvestorTable.portfolio_companies),
-            selectinload(InvestorTable.team_members),
-            selectinload(InvestorTable.sectors),
-            selectinload(InvestorTable.funds),
+            selectinload(FundTable.investor).selectinload(
+                InvestorTable.portfolio_companies
+            ),
+            selectinload(FundTable.investor).selectinload(InvestorTable.team_members),
+            selectinload(FundTable.investor).selectinload(InvestorTable.sectors),
+            selectinload(FundTable.investment_stages),
+            selectinload(FundTable.sectors),
         )
+        .join(FundTable.investor)
         .filter(InvestorTable.id != investor_id)
         .all()
     )
 
-    # Calculate similarity scores
-    scored_investors = []
-    for candidate in candidates:
+    # Calculate similarity scores for each fund
+    scored_funds = []
+    for fund in candidate_funds:
         score = 0
 
-        # Stage focus match (30 points)
-        if candidate.stage_focus == target_investor.stage_focus:
-            score += 30
-
         # Geographic focus match (20 points for exact, 10 for partial)
-        if candidate.geographic_focus and target_investor.geographic_focus:
-            if (
-                candidate.geographic_focus.lower()
-                == target_investor.geographic_focus.lower()
-            ):
-                score += 20
-            elif (
-                candidate.geographic_focus.lower()
-                in target_investor.geographic_focus.lower()
-                or target_investor.geographic_focus.lower()
-                in candidate.geographic_focus.lower()
-            ):
-                score += 10
+        if fund.geographic_focus and target_geographies:
+            fund_geo_lower = fund.geographic_focus.lower()
+            for target_geo in target_geographies:
+                if fund_geo_lower == target_geo:
+                    score += 20
+                    break
+                elif fund_geo_lower in target_geo or target_geo in fund_geo_lower:
+                    score += 10
+                    break
 
         # Check size overlap (20 points max)
-        if (
-            candidate.check_size_lower
-            and candidate.check_size_upper
-            and target_investor.check_size_lower
-            and target_investor.check_size_upper
-        ):
-            # Calculate overlap percentage
-            overlap_start = max(
-                candidate.check_size_lower, target_investor.check_size_lower
-            )
-            overlap_end = min(
-                candidate.check_size_upper, target_investor.check_size_upper
-            )
-            if overlap_end > overlap_start:
-                overlap = overlap_end - overlap_start
-                target_range = (
-                    target_investor.check_size_upper - target_investor.check_size_lower
-                )
-                overlap_ratio = overlap / target_range if target_range > 0 else 0
-                score += int(20 * overlap_ratio)
+        if fund.check_size_lower and fund.check_size_upper and target_check_ranges:
+            max_overlap_score = 0
+            for target_lower, target_upper in target_check_ranges:
+                overlap_start = max(fund.check_size_lower, target_lower)
+                overlap_end = min(fund.check_size_upper, target_upper)
+                if overlap_end > overlap_start:
+                    overlap = overlap_end - overlap_start
+                    target_range = target_upper - target_lower
+                    overlap_ratio = overlap / target_range if target_range > 0 else 0
+                    max_overlap_score = max(max_overlap_score, int(20 * overlap_ratio))
+            score += max_overlap_score
 
         # AUM similarity (15 points max)
-        if candidate.aum and target_investor.aum:
-            aum_diff = abs(candidate.aum - target_investor.aum)
-            max_aum = max(candidate.aum, target_investor.aum)
+        if fund.investor.aum and target_investor.aum:
+            aum_diff = abs(fund.investor.aum - target_investor.aum)
+            max_aum = max(fund.investor.aum, target_investor.aum)
             similarity_ratio = 1 - (aum_diff / max_aum) if max_aum > 0 else 0
             score += int(15 * similarity_ratio)
 
         # Sector overlap (30 points max)
-        candidate_sector_ids = {sector.id for sector in candidate.sectors}
-        if target_sector_ids and candidate_sector_ids:
-            common_sectors = target_sector_ids.intersection(candidate_sector_ids)
+        if fund.sectors and target_sector_ids:
+            fund_sector_ids = {sector.id for sector in fund.sectors}
+            common_sectors = target_sector_ids.intersection(fund_sector_ids)
             overlap_ratio = len(common_sectors) / len(target_sector_ids)
             score += int(30 * overlap_ratio)
 
-        if score > 0:  # Only include investors with some similarity
-            scored_investors.append((score, candidate))
+        # Investment stage match (15 points max)
+        if fund.investment_stages and target_stage_ids:
+            fund_stage_ids = {stage.id for stage in fund.investment_stages}
+            common_stages = target_stage_ids.intersection(fund_stage_ids)
+            overlap_ratio = len(common_stages) / len(target_stage_ids)
+            score += int(15 * overlap_ratio)
 
-    # Sort by score (descending) and take top N
-    scored_investors.sort(key=lambda x: x[0], reverse=True)
-    similar_investors = [inv for score, inv in scored_investors[:limit]]
+        if score > 0:  # Only include funds with some similarity
+            scored_funds.append((score, fund))
 
-    # Transform to InvestorFundData format (one row per investor-fund combination)
-    investor_fund_list = []
-    for investor in similar_investors:
-        # If investor has funds, create one entry per fund
-        if investor.funds:
-            for fund in investor.funds:
-                investor_fund_data = InvestorFundData(
-                    # Investor fields
-                    investor_id=investor.id,
-                    investor_name=investor.name,
-                    investor_description=investor.description,
-                    investor_website=investor.website,
-                    investor_headquarters=investor.headquarters,
-                    aum=investor.aum,
-                    aum_as_of_date=investor.aum_as_of_date,
-                    aum_source_url=investor.aum_source_url,
-                    investment_thesis=investor.investment_thesis,
-                    portfolio_highlights=investor.portfolio_highlights,
-                    number_of_investments=investor.number_of_investments,
-                    # Fund fields
-                    fund_id=fund.id,
-                    fund_name=fund.fund_name,
-                    fund_size=fund.fund_size,
-                    fund_size_source_url=fund.fund_size_source_url,
-                    check_size_lower=fund.check_size_lower,
-                    check_size_upper=fund.check_size_upper,
-                    geographic_focus=fund.geographic_focus,
-                    fund_investment_stages=fund.investment_stages,  # Now a relationship
-                    fund_sectors=fund.sectors,  # Now a relationship
-                    # Related data
-                    portfolio_companies=investor.portfolio_companies,
-                    team_members=investor.team_members,
-                    sectors=investor.sectors,
-                )
-                investor_fund_list.append(investor_fund_data)
-        else:
-            # If no funds, create one entry with null fund fields
-            investor_fund_data = InvestorFundData(
-                # Investor fields
-                investor_id=investor.id,
-                investor_name=investor.name,
-                investor_description=investor.description,
-                investor_website=investor.website,
-                investor_headquarters=investor.headquarters,
-                aum=investor.aum,
-                aum_as_of_date=investor.aum_as_of_date,
-                aum_source_url=investor.aum_source_url,
-                investment_thesis=investor.investment_thesis,
-                portfolio_highlights=investor.portfolio_highlights,
-                number_of_investments=investor.number_of_investments,
-                # Fund fields (null)
-                fund_id=None,
-                fund_name=None,
-                fund_size=None,
-                fund_size_source_url=None,
-                check_size_lower=None,
-                check_size_upper=None,
-                geographic_focus=None,
-                fund_investment_stages=None,
-                fund_sectors=None,
-                # Related data
-                portfolio_companies=investor.portfolio_companies,
-                team_members=investor.team_members,
-                sectors=investor.sectors,
-            )
-            investor_fund_list.append(investor_fund_data)
+    # Sort by score (descending) and take top N based on limit
+    scored_funds.sort(key=lambda x: x[0], reverse=True)
+    top_similar = scored_funds[:limit]
 
-    return investor_fund_list
+    # Apply pagination to the top similar funds
+    total_count = len(top_similar)
+    offset = (page - 1) * page_size
+    paginated_similar = top_similar[offset : offset + page_size]
+    similar_funds = [fund for score, fund in paginated_similar]
+
+    # Transform to InvestmentResponse format (one row per fund)
+    investment_responses = []
+    for fund in similar_funds:
+        investor = fund.investor
+
+        # Get top 3 portfolio companies (id and name only)
+        portfolio_companies = [
+            CompanyMinimal(id=company.id, name=company.name)
+            for company in investor.portfolio_companies[:3]
+        ]
+
+        # Get stage focus as comma-separated string
+        stage_focus = (
+            ", ".join([stage.name for stage in fund.investment_stages])
+            if fund.investment_stages
+            else None
+        )
+
+        # Get top 3 sectors from fund (id and name only)
+        fund_sectors = [
+            SectorMinimal(id=sector.id, name=sector.name)
+            for sector in (fund.sectors[:3] if fund.sectors else [])
+        ]
+
+        investment_response = InvestmentResponse(
+            id=investor.id,
+            name=f"{investor.name} - {fund.fund_name}"
+            if fund.fund_name
+            else investor.name,
+            aum=investor.aum,
+            check_size_lower=fund.check_size_lower,
+            check_size_upper=fund.check_size_upper,
+            geographic_focus=fund.geographic_focus,
+            stage_focus=stage_focus,
+            portfolio_companies=portfolio_companies,
+            sectors=fund_sectors,
+            compatibility_score=1.0,
+        )
+        investment_responses.append(investment_response)
+
+    # Calculate total pages
+    total_pages = (total_count + page_size - 1) // page_size
+
+    return PaginatedResponse(
+        items=investment_responses,
+        total=total_count,
+        page=page,
+        page_size=page_size,
+        total_pages=total_pages,
+    )

app/routers/projects.py

@@ -14,14 +14,26 @@ from schemas.project_schemas import (
     ProjectData,
     ProjectUpdate,
 )
+from schemas.router_schemas import PaginatedResponse
 from sqlalchemy.orm import Session, selectinload
 
 router = APIRouter(tags=["Project Routes"])
 
 
-@router.get("/projects", response_model=List[ProjectData])
-def read_projects(db: Session = Depends(get_db)):
-    """Get all projects with their related data"""
+@router.get("/projects", response_model=PaginatedResponse[ProjectData])
+def read_projects(
+    page: int = Query(1, ge=1, description="Page number (starts at 1)"),
+    page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
+    db: Session = Depends(get_db),
+):
+    """Get all projects with their related data (paginated)"""
+    # Calculate offset
+    offset = (page - 1) * page_size
+
+    # Get total count
+    total_count = db.query(ProjectTable).count()
+
+    # Get paginated results
     projects = (
         db.query(ProjectTable)
         .options(
@@ -29,6 +41,8 @@ def read_projects(db: Session = Depends(get_db)):
             selectinload(ProjectTable.investors),
             selectinload(ProjectTable.companies),
         )
+        .offset(offset)
+        .limit(page_size)
         .all()
     )
 
@@ -43,7 +57,16 @@ def read_projects(db: Session = Depends(get_db)):
         )
         project_data_list.append(project_data)
 
-    return project_data_list
+    # Calculate total pages
+    total_pages = (total_count + page_size - 1) // page_size
+
+    return PaginatedResponse(
+        items=project_data_list,
+        total=total_count,
+        page=page,
+        page_size=page_size,
+        total_pages=total_pages,
+    )
 
 
 @router.get("/projects/{project_id}", response_model=ProjectData)
@@ -151,7 +174,7 @@ def delete_project(project_id: int, db: Session = Depends(get_db)):
     return {"message": "Project deleted successfully"}
 
 
-@router.get("/projects/filter", response_model=List[ProjectData])
+@router.get("/projects/filter", response_model=PaginatedResponse[ProjectData])
 def filter_projects(
     stage: Optional[InvestmentStage] = Query(
         None, description="Filter by project stage"
@@ -166,9 +189,11 @@ def filter_projects(
     company_name: Optional[str] = Query(
         None, description="Company name (partial match)"
     ),
+    page: int = Query(1, ge=1, description="Page number (starts at 1)"),
+    page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
     db: Session = Depends(get_db),
 ):
-    """Filter projects based on various criteria"""
+    """Filter projects based on various criteria (paginated)"""
 
     # Start with base query
     query = db.query(ProjectTable).options(
@@ -205,7 +230,12 @@ def filter_projects(
             CompanyTable.name.ilike(f"%{company_name}%")
         )
 
-    projects = query.all()
+    # Get total count before pagination
+    total_count = query.count()
+
+    # Calculate offset and apply pagination
+    offset = (page - 1) * page_size
+    projects = query.offset(offset).limit(page_size).all()
 
     # Transform to ProjectData format
     project_data_list = []
@@ -218,7 +248,16 @@ def filter_projects(
         )
         project_data_list.append(project_data)
 
-    return project_data_list
+    # Calculate total pages
+    total_pages = (total_count + page_size - 1) // page_size
+
+    return PaginatedResponse(
+        items=project_data_list,
+        total=total_count,
+        page=page,
+        page_size=page_size,
+        total_pages=total_pages,
+    )
 
 
 # Association management routes

app/schemas/router_schemas.py

@@ -1,9 +1,12 @@
 from datetime import datetime
 from enum import Enum
-from typing import List, Optional
+from typing import Any, Generic, List, Optional, TypeVar
 
 from pydantic import BaseModel
 
+# Generic type for pagination
+T = TypeVar("T")
+
 
 class InvestmentStage(str, Enum):
     SEED = "SEED"
@@ -89,11 +92,20 @@ class InvestorSchema(BaseModel):
     id: int
     name: str
     description: Optional[str]
+    website: Optional[str] = None
+    headquarters: Optional[str] = None
     aum: int | None
+    aum_as_of_date: str | None = None
+    aum_source_url: str | None = None
     check_size_lower: int | None
     check_size_upper: int | None
     geographic_focus: str | None
-    stage_focus: InvestmentStage
+    investment_thesis: Any = (
+        None  # Flexible JSON field - can be list, dict, or list of dicts
+    )
+    portfolio_highlights: Any = (
+        None  # Flexible JSON field - can be list, dict, or list of dicts
+    )
     number_of_investments: int | None
     created_at: Optional[datetime] = None
     updated_at: Optional[datetime] = None
@@ -131,8 +143,8 @@ class InvestorFundData(BaseModel):
     aum: int | None
     aum_as_of_date: str | None
     aum_source_url: str | None
-    investment_thesis: List[str] | None
-    portfolio_highlights: List[str] | None
+    investment_thesis: Any = None  # Flexible JSON field
+    portfolio_highlights: Any = None  # Flexible JSON field
     number_of_investments: int | None
 
     # Fund fields
@@ -156,12 +168,29 @@ class InvestorFundData(BaseModel):
     class Config:
         from_attributes = True
 
+class InvestorMinimal(BaseModel):
+    """Minimal investor info with just id and name"""
+
+    id: int
+    name: str
+
+    class Config:
+        from_attributes = True
+
+class CompanySchemaMinimal(BaseModel):
+    id: int
+    name: str
+    industry: str | None
+    location: str | None
+    founded_year: Optional[int]
+    website: Optional[str]
+
+    class Config:
+        from_attributes = True
 
 class CompanyData(BaseModel):  # Renamed from CompaniesData for consistency
-    company: CompanySchema
-    sectors: List[SectorSchema]
-    members: List[CompanyMemberSchema]
-    investors: List[InvestorSchema]
+    company: CompanySchemaMinimal
+    investors: List[InvestorMinimal]
 
     class Config:
         from_attributes = True
@@ -175,3 +204,59 @@ class InvestorFundList(BaseModel):
     """List of investor-fund combinations"""
 
     investor_funds: List[InvestorFundData]
+
+
+class CompanyMinimal(BaseModel):
+    """Minimal company info with just id and name"""
+
+    id: int
+    name: str
+
+    class Config:
+        from_attributes = True
+
+
+class SectorMinimal(BaseModel):
+    """Minimal sector info with just id and name"""
+
+    id: int
+    name: str
+
+    class Config:
+        from_attributes = True
+
+
+class InvestmentResponse(BaseModel):
+    """Simplified investment response schema
+
+    One row per investor-fund combination with streamlined data
+    """
+
+    id: int  # Investor ID
+    name: (
+        str  # Combination of investor name and fund name (e.g., "Investor A - Fund A")
+    )
+    aum: int | None  # From investor
+    check_size_lower: int | None  # From fund
+    check_size_upper: int | None  # From fund
+    geographic_focus: str | None  # From fund
+    stage_focus: str | None  # Comma-separated stages from fund
+    portfolio_companies: List[CompanyMinimal]  # Top 3 companies from investor
+    sectors: List[SectorMinimal]  # Top 3 sectors from fund
+    compatibility_score: float  # 0 to 1 (default 1 for now)
+
+    class Config:
+        from_attributes = True
+
+
+class PaginatedResponse(BaseModel, Generic[T]):
+    """Generic paginated response schema"""
+
+    items: List[T]
+    total: int
+    page: int
+    page_size: int
+    total_pages: int
+
+    class Config:
+        from_attributes = True

app/services/llm_parser.py

@@ -1,3 +1,4 @@
+import asyncio
 import json
 import os
 import re
@@ -265,37 +266,20 @@ Return the lower and upper bounds in USD."""
     ) -> Optional[dict]:
         """
         Process company profile from CSV data.
-        Manually extracts fields without using LLM.
+        Only extracts founded_year and key_executives - rest is in base database.
         """
         profile = self.parse_json_profile(profile_json)
         if not profile:
             return None
 
         try:
-            # Extract basic info
+            # Only extract founded_year and key_executives
             company_data = {
                 "name": name.strip() if name else None,
-                "website": website.strip() if website else None,
-                "description": profile.get("companyDescription"),
-                "location": profile.get("geographicFocus"),
-                "industry": profile.get("sectorDescription"),
-                "founded_year": None,  # Not typically in the company JSON
+                "founded_year": None,
                 "key_executives": [],
-                "client_categories": profile.get("clientCategories", []),
-                "product_description": profile.get("productDescription"),
-                "linked_documents": profile.get("linkedDocuments", []),
-                "researcher_notes": profile.get("researcherNotes"),
-                "missing_important_fields": profile.get("missingImportantFields", []),
-                "sources": profile.get("sources", {}),
-                "investor_names": [],
             }
 
-            # Parse investor names from the Investor column
-            if investor_names and pd.notna(investor_names):
-                # Split by comma and clean
-                investors = [inv.strip() for inv in str(investor_names).split(",")]
-                company_data["investor_names"] = [inv for inv in investors if inv]
-
             # Process key executives/leadership
             key_executives = profile.get("keyExecutives", [])
             if not key_executives:
@@ -313,7 +297,7 @@ Return the lower and upper bounds in USD."""
                     )
 
             # Try to extract founding year from description
-            description = company_data.get("description", "")
+            description = profile.get("companyDescription", "")
             if description:
                 # Look for patterns like "founded in 2020", "Gegründet 2020", "founded 2020"
                 year_patterns = [
@@ -344,41 +328,28 @@ Return the lower and upper bounds in USD."""
     def _save_parsed_company_to_db(
         self, db: Session, company_data: dict
     ) -> Optional[CompanyTable]:
-        """Save manually parsed company data to database"""
+        """Save manually parsed company data to database - only updates founded_year and key_executives"""
         try:
-            # Check if company already exists
+            # Check if company already exists (should exist in base database)
             existing_company = (
                 db.query(CompanyTable).filter_by(name=company_data["name"]).first()
             )
 
             if existing_company:
-                # Update existing company
+                # Update only founded_year on existing company
                 company = existing_company
-                company.website = company_data.get("website") or company.website
-                company.location = company_data.get("location") or company.location
-                company.description = (
-                    company_data.get("description") or company.description
-                )
-                company.industry = company_data.get("industry") or company.industry
                 if company_data.get("founded_year"):
                     company.founded_year = company_data["founded_year"]
             else:
-                # Create new company
-                company = CompanyTable(
-                    name=company_data["name"],
-                    website=company_data.get("website"),
-                    location=company_data.get("location"),
-                    description=company_data.get("description"),
-                    industry=company_data.get("industry"),
-                    founded_year=company_data.get("founded_year"),
+                # Company should already be in base database, but if not found, skip
+                print(
+                    f"⚠️  Company '{company_data['name']}' not found in base database - skipping"
                 )
-                db.add(company)
-                db.flush()
+                return None
 
             # Add/update company members (key executives)
             # First, remove existing members if updating
-            if existing_company:
-                db.query(CompanyMember).filter_by(company_id=company.id).delete()
+            db.query(CompanyMember).filter_by(company_id=company.id).delete()
 
             for exec_data in company_data.get("key_executives", []):
                 member = CompanyMember(
@@ -391,19 +362,6 @@ Return the lower and upper bounds in USD."""
                 )
                 db.add(member)
 
-            # Link to investors if provided
-            for investor_name in company_data.get("investor_names", []):
-                # Find investor in database
-                investor = (
-                    db.query(InvestorTable)
-                    .filter_by(name=investor_name.strip())
-                    .first()
-                )
-                if investor:
-                    # Add company to investor's portfolio if not already there
-                    if company not in investor.portfolio_companies:
-                        investor.portfolio_companies.append(company)
-
             return company
 
         except Exception as e:
@@ -692,10 +650,55 @@ Return the lower and upper bounds in USD."""
             print(f"Error processing row {row_idx + 1}: {e}")
             return None
 
-    async def parse_investors(self, df: pd.DataFrame, save_to_db: bool = True):
+    async def _process_single_investor(
+        self, idx: int, row: pd.Series, total_rows: int
+    ) -> Optional[dict]:
+        """Process a single investor row"""
+        try:
+            name = row.get("Name", "").strip() if pd.notna(row.get("Name")) else None
+            website = (
+                row.get("Website", "").strip() if pd.notna(row.get("Website")) else None
+            )
+            profile_json = (
+                row.get("Final Investor Profile", "")
+                if pd.notna(row.get("Final Investor Profile"))
+                else None
+            )
+
+            if not name or not profile_json:
+                print(f"⚠️  Row {idx + 1}: Skipping - missing name or profile")
+                return None
+
+            print(f"📊 Processing {idx + 1}/{total_rows}: {name}")
+
+            # Process the investor profile
+            investor_data = await self.process_investor_profile(
+                name, website, profile_json
+            )
+
+            if investor_data:
+                print(f"   ✓ {name} parsed successfully")
+                return investor_data
+            else:
+                print(f"   ⚠️  {name} failed to process")
+                return None
+
+        except Exception as e:
+            print(f"❌ Error processing row {idx + 1}: {e}")
+            return None
+
+    async def parse_investors(
+        self, df: pd.DataFrame, save_to_db: bool = True, batch_size: int = 10
+    ):
         """
         Parse investors from DataFrame using manual JSON parsing and LLM for currency conversion.
+        Processes multiple investors concurrently for better performance.
         Expected CSV columns: Name, Website, Final Investor Profile, Final Profile sourcing
+
+        Args:
+            df: DataFrame with investor data
+            save_to_db: Whether to save to database
+            batch_size: Number of investors to process concurrently (default: 10)
         """
         results = []
         db = None
@@ -704,50 +707,31 @@ Return the lower and upper bounds in USD."""
 
         try:
             total_rows = len(df)
-            print(f"\n🚀 Starting to process {total_rows} investors...")
+            print(
+                f"\n🚀 Starting to process {total_rows} investors with batch size {batch_size}..."
+            )
 
-            for idx, row in df.iterrows():
-                try:
-                    name = (
-                        row.get("Name", "").strip()
-                        if pd.notna(row.get("Name"))
-                        else None
-                    )
-                    website = (
-                        row.get("Website", "").strip()
-                        if pd.notna(row.get("Website"))
-                        else None
-                    )
-                    profile_json = (
-                        row.get("Final Investor Profile", "")
-                        if pd.notna(row.get("Final Investor Profile"))
-                        else None
-                    )
+            # Process in batches
+            for batch_start in range(0, total_rows, batch_size):
+                batch_end = min(batch_start + batch_size, total_rows)
+                print(
+                    f"\n🔄 Processing batch {batch_start + 1}-{batch_end} of {total_rows}..."
+                )
 
-                    if not name or not profile_json:
-                        print(f"⚠️  Row {idx + 1}: Skipping - missing name or profile")
-                        continue
+                # Create tasks for concurrent processing
+                tasks = []
+                for idx in range(batch_start, batch_end):
+                    row = df.iloc[idx]
+                    task = self._process_single_investor(idx, row, total_rows)
+                    tasks.append(task)
 
-                    print(f"\n📊 Processing {idx + 1}/{total_rows}: {name}")
+                # Process batch concurrently
+                batch_results = await asyncio.gather(*tasks, return_exceptions=True)
 
-                    # Process the investor profile
-                    investor_data = await self.process_investor_profile(
-                        name, website, profile_json
-                    )
-
-                    if investor_data:
+                # Filter out None results and exceptions, then save to database
+                for investor_data in batch_results:
+                    if investor_data and not isinstance(investor_data, Exception):
                         results.append(investor_data)
-                        print("   ✓ Parsed successfully")
-                        print(f"   - HQ: {investor_data.get('headquarters')}")
-                        print(
-                            f"   - AUM: ${investor_data.get('aum'):,}"
-                            if investor_data.get("aum")
-                            else "   - AUM: Not Available"
-                        )
-                        print(f"   - Funds: {len(investor_data.get('funds', []))}")
-                        print(
-                            f"   - Team: {len(investor_data.get('team_members', []))}"
-                        )
 
                         # Save to database
                         if save_to_db and db:
@@ -756,33 +740,29 @@ Return the lower and upper bounds in USD."""
                                     db, investor_data
                                 )
                                 if saved_investor:
-                                    db.commit()
                                     print(
-                                        f"   ✅ Saved to database (ID: {saved_investor.id})"
+                                        f"   ✅ Saved {investor_data['name']} to database (ID: {saved_investor.id})"
                                     )
                                 else:
-                                    print("   ❌ Failed to save to database")
+                                    print(
+                                        f"   ❌ Failed to save {investor_data['name']} to database"
+                                    )
                             except Exception as e:
                                 db.rollback()
-                                print(f"   ❌ Database error: {e}")
-                    else:
-                        print("   ⚠️  Failed to process profile")
+                                print(
+                                    f"   ❌ Database error for {investor_data['name']}: {e}"
+                                )
+                    elif isinstance(investor_data, Exception):
+                        print(f"   ❌ Exception occurred: {investor_data}")
 
-                    # Commit every 10 investors to avoid memory issues
-                    if save_to_db and db and (idx + 1) % 10 == 0:
+                # Commit batch to database
+                if save_to_db and db:
+                    try:
                         db.commit()
-                        print(f"\n💾 Committed batch at row {idx + 1}")
-
-                except Exception as e:
-                    print(f"❌ Error processing row {idx + 1}: {e}")
-                    if db:
+                        print(f"💾 Committed batch {batch_start + 1}-{batch_end}")
+                    except Exception as e:
                         db.rollback()
-                    continue
-
-            # Final commit
-            if save_to_db and db:
-                db.commit()
-                print("\n✅ Final commit completed")
+                        print(f"❌ Failed to commit batch: {e}")
 
         except Exception as e:
             print(f"❌ Fatal error in parse_investors: {e}")
@@ -795,10 +775,60 @@ Return the lower and upper bounds in USD."""
         print(f"\n🎉 Completed! Processed {len(results)}/{total_rows} investors")
         return results
 
-    async def parse_companies(self, df: pd.DataFrame, save_to_db: bool = True):
+    async def _process_single_company(
+        self, idx: int, row: pd.Series, total_rows: int
+    ) -> Optional[dict]:
+        """Process a single company row"""
+        try:
+            name = row.get("Name", "").strip() if pd.notna(row.get("Name")) else None
+            website = (
+                row.get("Website", "").strip() if pd.notna(row.get("Website")) else None
+            )
+            investor_names = (
+                row.get("Investor", "").strip()
+                if pd.notna(row.get("Investor"))
+                else None
+            )
+            profile_json = (
+                row.get("Final Investor Profile", "")
+                if pd.notna(row.get("Final Investor Profile"))
+                else None
+            )
+
+            if not name or not profile_json:
+                print(f"⚠️  Row {idx + 1}: Skipping - missing name or profile")
+                return None
+
+            print(f"📊 Processing {idx + 1}/{total_rows}: {name}")
+
+            # Process the company profile
+            company_data = await self.process_company_profile(
+                name, website, profile_json, investor_names
+            )
+
+            if company_data:
+                print(f"   ✓ {name} parsed successfully")
+                return company_data
+            else:
+                print(f"   ⚠️  {name} failed to process")
+                return None
+
+        except Exception as e:
+            print(f"❌ Error processing row {idx + 1}: {e}")
+            return None
+
+    async def parse_companies(
+        self, df: pd.DataFrame, save_to_db: bool = True, batch_size: int = 10
+    ):
         """
         Parse companies from DataFrame using manual JSON parsing.
+        Processes multiple companies concurrently for better performance.
         Expected CSV columns: Name, Website, Investor, Final Investor Profile (actually company profile)
+
+        Args:
+            df: DataFrame with company data
+            save_to_db: Whether to save to database
+            batch_size: Number of companies to process concurrently (default: 10)
         """
         results = []
         db = None
@@ -807,58 +837,31 @@ Return the lower and upper bounds in USD."""
 
         try:
             total_rows = len(df)
-            print(f"\n🚀 Starting to process {total_rows} companies...")
+            print(
+                f"\n🚀 Starting to process {total_rows} companies with batch size {batch_size}..."
+            )
 
-            for idx, row in df.iterrows():
-                try:
-                    name = (
-                        row.get("Name", "").strip()
-                        if pd.notna(row.get("Name"))
-                        else None
-                    )
-                    website = (
-                        row.get("Website", "").strip()
-                        if pd.notna(row.get("Website"))
-                        else None
-                    )
-                    investor_names = (
-                        row.get("Investor", "").strip()
-                        if pd.notna(row.get("Investor"))
-                        else None
-                    )
-                    profile_json = (
-                        row.get("Final Investor Profile", "")
-                        if pd.notna(row.get("Final Investor Profile"))
-                        else None
-                    )
+            # Process in batches
+            for batch_start in range(0, total_rows, batch_size):
+                batch_end = min(batch_start + batch_size, total_rows)
+                print(
+                    f"\n🔄 Processing batch {batch_start + 1}-{batch_end} of {total_rows}..."
+                )
 
-                    if not name or not profile_json:
-                        print(f"⚠️  Row {idx + 1}: Skipping - missing name or profile")
-                        continue
+                # Create tasks for concurrent processing
+                tasks = []
+                for idx in range(batch_start, batch_end):
+                    row = df.iloc[idx]
+                    task = self._process_single_company(idx, row, total_rows)
+                    tasks.append(task)
 
-                    print(f"\n📊 Processing {idx + 1}/{total_rows}: {name}")
+                # Process batch concurrently
+                batch_results = await asyncio.gather(*tasks, return_exceptions=True)
 
-                    # Process the company profile
-                    company_data = await self.process_company_profile(
-                        name, website, profile_json, investor_names
-                    )
-
-                    if company_data:
+                # Filter out None results and exceptions, then save to database
+                for company_data in batch_results:
+                    if company_data and not isinstance(company_data, Exception):
                         results.append(company_data)
-                        print("   ✓ Parsed successfully")
-                        print(f"   - Location: {company_data.get('location')}")
-                        print(f"   - Industry: {company_data.get('industry')}")
-                        print(
-                            f"   - Founded: {company_data.get('founded_year')}"
-                            if company_data.get("founded_year")
-                            else "   - Founded: Unknown"
-                        )
-                        print(
-                            f"   - Executives: {len(company_data.get('key_executives', []))}"
-                        )
-                        print(
-                            f"   - Investors: {len(company_data.get('investor_names', []))}"
-                        )
 
                         # Save to database
                         if save_to_db and db:
@@ -867,33 +870,29 @@ Return the lower and upper bounds in USD."""
                                     db, company_data
                                 )
                                 if saved_company:
-                                    db.commit()
                                     print(
-                                        f"   ✅ Saved to database (ID: {saved_company.id})"
+                                        f"   ✅ Saved {company_data['name']} to database (ID: {saved_company.id})"
                                     )
                                 else:
-                                    print("   ❌ Failed to save to database")
+                                    print(
+                                        f"   ❌ Failed to save {company_data['name']} to database"
+                                    )
                             except Exception as e:
                                 db.rollback()
-                                print(f"   ❌ Database error: {e}")
-                    else:
-                        print("   ⚠️  Failed to process profile")
+                                print(
+                                    f"   ❌ Database error for {company_data['name']}: {e}"
+                                )
+                    elif isinstance(company_data, Exception):
+                        print(f"   ❌ Exception occurred: {company_data}")
 
-                    # Commit every 10 companies to avoid memory issues
-                    if save_to_db and db and (idx + 1) % 10 == 0:
+                # Commit batch to database
+                if save_to_db and db:
+                    try:
                         db.commit()
-                        print(f"\n💾 Committed batch at row {idx + 1}")
-
-                except Exception as e:
-                    print(f"❌ Error processing row {idx + 1}: {e}")
-                    if db:
+                        print(f"💾 Committed batch {batch_start + 1}-{batch_end}")
+                    except Exception as e:
                         db.rollback()
-                    continue
-
-            # Final commit
-            if save_to_db and db:
-                db.commit()
-                print("\n✅ Final commit completed")
+                        print(f"❌ Failed to commit batch: {e}")
 
         except Exception as e:
             print(f"❌ Fatal error in parse_companies: {e}")

app/services/querying.py

@@ -2,13 +2,18 @@ import os
 from typing import List
 
 from db.db import DATABASE_URL, get_db
-from db.models import InvestorTable
+from db.models import FundTable, InvestorTable
 from langchain import hub
 from langchain_community.agent_toolkits import SQLDatabaseToolkit
 from langchain_community.utilities import SQLDatabase
 from langchain_openai import ChatOpenAI
 from langgraph.prebuilt import create_react_agent
-from schemas.py_schemas import InvestorData, InvestorList
+from schemas.router_schemas import (
+    CompanyMinimal,
+    InvestmentResponse,
+    PaginatedResponse,
+    SectorMinimal,
+)
 from sqlalchemy.orm import selectinload
 
 # Connect to SQLite
@@ -21,16 +26,16 @@ class QueryProcessor:
         self.llm = ChatOpenAI(
             api_key=os.getenv("OPENROUTER_API_KEY"),
             base_url="https://openrouter.ai/api/v1",
-            model="openai/gpt-4o-mini",
+            model="x-ai/grok-4-fast",
             temperature=0,
         )
         self.toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
-        # Update system message to specifically request only investor IDs
+        # Update system message to specifically request only fund IDs
         system_message_updated = (
             prompt_template.format(dialect="SQLite", top_k=5)
-            + "\n\nIMPORTANT: You must ONLY return the investor IDs (id field) that match the user's criteria. "
+            + "\n\nIMPORTANT: You must ONLY return the fund IDs (id field from the funds table) that match the user's criteria. "
             + "Do NOT return any other information, explanations, or data. "
-            + "Your response should be ONLY a comma-separated list of numbers representing the investor IDs. "
+            + "Your response should be ONLY a comma-separated list of numbers representing the fund IDs. "
             + "Example format: 1, 5, 12, 23"
         )
         self.agent = create_react_agent(
@@ -39,9 +44,9 @@ class QueryProcessor:
             prompt=system_message_updated,
         )
 
-    def process_query(self, question: str) -> InvestorList:
-        """Process a query using the LLM and return investor data."""
-        # Let the LLM handle all database interactions and filtering to get IDs
+    def process_query(self, question: str) -> PaginatedResponse[InvestmentResponse]:
+        """Process a query using the LLM and return investment response data."""
+        # Let the LLM handle all database interactions and filtering to get fund IDs
         response = self.agent.invoke(
             {"messages": [("user", question)]},
         )
@@ -51,70 +56,122 @@ class QueryProcessor:
             response["messages"][-1].content if response.get("messages") else ""
         )
 
-        # Extract investor IDs from the AI response
-        investor_ids = self._extract_investor_ids_from_response(ai_response)
+        # Extract fund IDs from the AI response
+        fund_ids = self._extract_fund_ids_from_response(ai_response)
 
-        # Fetch full investor data using the IDs
-        return self._fetch_investors_by_ids(investor_ids)
+        # Fetch full fund data with investor relationships using the IDs
+        return self._fetch_funds_by_ids(fund_ids)
 
-    def _extract_investor_ids_from_response(self, ai_response: str) -> List[int]:
-        """Extract investor IDs from AI response."""
+    def _extract_fund_ids_from_response(self, ai_response: str) -> List[int]:
+        """Extract fund IDs from AI response."""
         import re
 
-        investor_ids = []
+        fund_ids = []
         try:
             # Try multiple patterns to extract IDs from the response
             # Pattern 1: Simple numbers (assuming they are IDs)
             numbers = re.findall(r"\b\d+\b", ai_response)
-            investor_ids = [int(num) for num in numbers]
+            fund_ids = [int(num) for num in numbers]
 
             # Pattern 2: If response contains explicit ID references
             id_matches = re.findall(r"\bid[:\s]*(\d+)", ai_response.lower())
             if id_matches:
-                investor_ids = [int(id_str) for id_str in id_matches]
+                fund_ids = [int(id_str) for id_str in id_matches]
 
         except Exception as e:
             print(f"Error extracting IDs from response: {e}")
             return []
 
-        return investor_ids
+        return fund_ids
 
-    def _fetch_investors_by_ids(self, investor_ids: List[int]) -> InvestorList:
-        """Fetch investors with all their relationships from the database using IDs."""
-        if not investor_ids:
-            return InvestorList(investors=[])
+    def _fetch_funds_by_ids(
+        self, fund_ids: List[int]
+    ) -> PaginatedResponse[InvestmentResponse]:
+        """Fetch funds with all their relationships from the database using fund IDs.
+        Constructs response similar to read_investors but starting from funds."""
+        if not fund_ids:
+            return PaginatedResponse(
+                items=[],
+                total=0,
+                page=1,
+                page_size=len(fund_ids) if fund_ids else 10,
+                total_pages=0,
+            )
 
         # Get database session
         db_session = next(get_db())
 
         try:
-            # Build query with all relationships loaded
-            query = (
-                db_session.query(InvestorTable)
+            # Query funds with all necessary relationships loaded
+            funds = (
+                db_session.query(FundTable)
                 .options(
-                    selectinload(InvestorTable.portfolio_companies),
-                    selectinload(InvestorTable.team_members),
-                    selectinload(InvestorTable.sectors),
-                    selectinload(InvestorTable.funds),
+                    selectinload(FundTable.investor).selectinload(
+                        InvestorTable.portfolio_companies
+                    ),
+                    selectinload(FundTable.investor).selectinload(
+                        InvestorTable.team_members
+                    ),
+                    selectinload(FundTable.investor).selectinload(
+                        InvestorTable.sectors
+                    ),
+                    selectinload(FundTable.investment_stages),
+                    selectinload(FundTable.sectors),
                 )
-                .filter(InvestorTable.id.in_(investor_ids))
+                .filter(FundTable.id.in_(fund_ids))
+                .all()
             )
 
-            investors = query.all()
+            # Transform to InvestmentResponse format (one row per fund)
+            investment_responses = []
+            for fund in funds:
+                investor = fund.investor
 
-            # Transform to InvestorData format
-            investor_data_list = []
-            for investor in investors:
-                investor_data = InvestorData(
-                    investor=investor,
-                    portfolio_companies=investor.portfolio_companies,
-                    team_members=investor.team_members,
-                    sectors=investor.sectors,
-                    funds=investor.funds,
+                # Get top 3 portfolio companies (id and name only)
+                portfolio_companies = [
+                    CompanyMinimal(id=company.id, name=company.name)
+                    for company in investor.portfolio_companies[:3]
+                ]
+
+                # Get stage focus as comma-separated string
+                stage_focus = (
+                    ", ".join([stage.name for stage in fund.investment_stages])
+                    if fund.investment_stages
+                    else None
                 )
-                investor_data_list.append(investor_data)
 
-            return InvestorList(investors=investor_data_list)
+                # Get top 3 sectors from fund (id and name only)
+                fund_sectors = [
+                    SectorMinimal(id=sector.id, name=sector.name)
+                    for sector in (fund.sectors[:3] if fund.sectors else [])
+                ]
+
+                investment_response = InvestmentResponse(
+                    id=investor.id,
+                    name=f"{investor.name} - {fund.fund_name}"
+                    if fund.fund_name
+                    else investor.name,
+                    aum=investor.aum,
+                    check_size_lower=fund.check_size_lower,
+                    check_size_upper=fund.check_size_upper,
+                    geographic_focus=fund.geographic_focus,
+                    stage_focus=stage_focus,
+                    portfolio_companies=portfolio_companies,
+                    sectors=fund_sectors,
+                    compatibility_score=1.0,
+                )
+                investment_responses.append(investment_response)
+
+            total_count = len(investment_responses)
+            total_pages = 1 if total_count > 0 else 0
+
+            return PaginatedResponse(
+                items=investment_responses,
+                total=total_count,
+                page=1,
+                page_size=total_count,
+                total_pages=total_pages,
+            )
 
         finally:
             db_session.close()