Refactor investor-related schemas and models; update database configuration and enhance investor processing logic

app/api/investors.py

@@ -1,6 +1,6 @@
-from fastapi.routing import apirouter
+from fastapi import APIRouter
 
-router = apirouter()
+router = APIRouter()
 
 @router.get("/investors")
 def read_investors():

app/db/db.py

@@ -9,7 +9,7 @@ from sqlalchemy.orm import Session, sessionmaker
 Base = declarative_base()
 
 # Database configuration
-DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///investors.db")
+DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///investors_2.db")
 
 # Create engine
 engine = create_engine(DATABASE_URL, echo=False)

app/db/models.py

@@ -33,6 +33,8 @@ investor_sector_association = Table(
     Column("investor_id", Integer, ForeignKey("investors.id")),
     Column("sector_id", Integer, ForeignKey("sectors.id")),
 )
+
+
 class InvestorTable(Base):
     __tablename__ = "investors"
 
@@ -42,7 +44,7 @@ class InvestorTable(Base):
     aum = Column(Integer, nullable=False)  # Assets Under Management
     check_size_lower = Column(Integer, nullable=False)  # Lower bound
     check_size_upper = Column(Integer, nullable=False)  # Upper bound
-    geography = Column(String, nullable=False)
+    geographic_focus = Column(String, nullable=False)
     stage_focus = Column(Enum(InvestmentStage), nullable=False)
     number_of_investments = Column(Integer, default=0)
     created_at = Column(DateTime, default=datetime.datetime.now(datetime.UTC))
@@ -58,6 +60,12 @@ class InvestorTable(Base):
         secondary=investor_company_association,
         back_populates="investors",
     )
+    team_members = relationship("InvestorTeamMember", back_populates="investor")
+    sectors = relationship(
+        "SectorTable",
+        secondary=investor_sector_association,
+        back_populates="investors",
+    )
 
 
 class CompanyTable(Base):
@@ -88,16 +96,22 @@ class SectorTable(Base):
     __tablename__ = "sectors"
 
     id = Column(Integer, primary_key=True, index=True)
-    name = Column(String, unique=True, nullable=False)
+    name = Column(String, nullable=False)
-
 
+    # Add relationship back to investors
+    investors = relationship(
+        "InvestorTable",
+        secondary=investor_sector_association,
+        back_populates="sectors",
+    )
 
 
 class InvestorTeamMember(Base):
+    __tablename__ = "investor_team"
     id = Column(Integer, primary_key=True, index=True)
     name = Column(String, nullable=False)
     role = Column(String, nullable=False)
-    email = Column(String, unique=True, nullable=False)
+    email = Column(String, nullable=False)
 
     investor_id = Column(Integer, ForeignKey("investors.id"))
     investor = relationship("InvestorTable", back_populates="team_members")

app/main.py

@@ -1,14 +1,15 @@
 import io
-from api import investors
+
 import pandas as pd
-from db.db import db_dependency
+from api import investors
+from db.db import db_dependency, init_database
 from fastapi import FastAPI, File, UploadFile
 from services.openrouter import InvestorProcessor
 from services.querying import QueryProcessor
 
 app = FastAPI()
 app.include_router(investors.router)
-# init_database()
+init_database()
 
 
 @app.get("/")

app/py_schemas.py

@@ -0,0 +1,77 @@
+from pydantic import BaseModel
+from datetime import datetime
+from typing import List, Optional
+from enum import Enum
+
+
+class InvestmentStage(str, Enum):
+    SEED = "seed"
+    SERIES_A = "series_a"
+    SERIES_B = "series_b"
+    SERIES_C = "series_c"
+    GROWTH = "growth"
+    LATE_STAGE = "late_stage"
+
+
+class SectorSchema(BaseModel):
+    id: int
+    name: str
+
+    class Config:
+        from_attributes = True
+
+
+class CompanySchema(BaseModel):
+    id: int
+    name: str
+    industry: str
+    location: str
+    founded_year: Optional[int]
+    website: Optional[str]
+    created_at: Optional[datetime]
+    updated_at: Optional[datetime]
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorTeamMemberSchema(BaseModel):
+    id: int
+    name: str
+    role: str
+    email: str
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorSchema(BaseModel):
+    id: int
+    name: str
+    description: Optional[str]
+    aum: int
+    check_size_lower: int
+    check_size_upper: int
+    geographic_focus: str
+    stage_focus: InvestmentStage
+    number_of_investments: int
+    created_at: Optional[datetime]
+    updated_at: Optional[datetime]
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorData(BaseModel):
+    """Comprehensive investor data schema for LLM processing"""
+    investor: InvestorSchema
+    portfolio_companies: List[CompanySchema] = []
+    team_members: List[InvestorTeamMemberSchema] = []
+    sectors: List[SectorSchema] = []
+
+    class Config:
+        from_attributes = True
+        
+
+class InvestorList(BaseModel):
+    investors: List[InvestorData]

app/services/openrouter.py

@@ -3,15 +3,18 @@ from typing import List, Optional
 
 import chromadb
 import pandas as pd
-from db.models import InvestorTable
+from db.models import CompanyTable, InvestorTable, InvestorTeamMember, SectorTable
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI
-from pydantic_schemas import Investor, InvestorList
+from py_schemas import InvestorData
+from pydantic import BaseModel
 from settings import settings
 
-# Add these imports for your databases
+
-# from sqlalchemy.ext.asyncio import AsyncSession
+class InvestorList(BaseModel):
-# from your_vector_db import VectorDBClient
+    """Schema for LLM structured output"""
+
+    investor_list: List[InvestorData]
 
 
 class InvestorProcessor:
@@ -25,22 +28,27 @@ class InvestorProcessor:
 Given the following CSV data rows:
 {question}
 
-For each row, extract and structure the following fields:
+For each row, extract and structure the following fields for the investor:
 - name: The investor's full name
+- description: Description of the investor
 - aum: Assets under management (as integer, use 0 if not available)
-- check_size: Investment check size (as string)
+- check_size_lower: Lower bound of investment check size (as integer)
-- sector_focus: Sector focus (as string)
+- check_size_upper: Upper bound of investment check size (as integer)
-- stage_focus: Investment stage focus (as string)
+- geographic_focus: Geographic region focus
-- region: Geographic region (as string)
+- stage_focus: Investment stage focus (must be one of: seed, series_a, series_b, series_c, growth, late_stage)
-- investment_thesis: Investment thesis (as string)
+- number_of_investments: Number of investments made (default 0)
-- investor_description: Description of the investor (as string)
+
+Also extract related data:
+- portfolio_companies: List of companies they've invested in
+- team_members: List of team members with name, role, email
+- sectors: List of sectors they focus on
 
 Important: 
-- If a field is not available in the data, use appropriate default values (empty string for text fields, 0 for numbers)
+- If a field is not available, use appropriate defaults
-- Ensure all text fields are properly escaped and contain no control characters
+- stage_focus must be one of the valid enum values
 - Return clean, valid JSON only
 
-Return the data as a structured list of investors."""
+Return the data as a structured list of comprehensive investor data."""
 
         self.prompt = PromptTemplate(
             template=self.template, input_variables=["question"]
@@ -49,7 +57,7 @@ Return the data as a structured list of investors."""
         self.llm = ChatOpenAI(
             api_key=settings.OPENROUTER_API_KEY,
             base_url="https://openrouter.ai/api/v1",
-            model="openai/gpt-oss-120b:fre",
+            model="google/gemini-2.5-flash-lite",
             temperature=0,
         )
 
@@ -65,7 +73,9 @@ Return the data as a structured list of investors."""
             },
         )
 
-    async def _process_batch(self, batch: pd.DataFrame, batch_idx: int) -> List:
+    async def _process_batch(
+        self, batch: pd.DataFrame, batch_idx: int
+    ) -> List[InvestorData]:
         """Process a single batch of data"""
         # Convert batch to string representation - clean the data
         batch_str = ""
@@ -102,25 +112,101 @@ Return the data as a structured list of investors."""
             print(f"Error processing batch {batch_idx + 1}: {e}")
             return []
 
-    async def _save_to_sql(self, investors: List[Investor]) -> None:
+    async def _save_to_sql(self, investor_data_list: List[InvestorData]) -> None:
-        """Save investors to SQL database"""
+        """Save investors and related data to SQL database"""
         if not self.sql_session:
             return
 
-        # Implement SQL saving logic here
+        try:
-        for investor in investors:
+            for investor_data in investor_data_list:
+                # Save investor
                 db_investor = InvestorTable(
-                name=investor.name,
+                    name=investor_data.investor.name,
-                aum=investor.aum,
+                    description=investor_data.investor.description,
-                check_size=investor.check_size,
+                    aum=investor_data.investor.aum,
-                sector_focus=investor.sector_focus,
+                    check_size_lower=investor_data.investor.check_size_lower,
-                stage_focus=investor.stage_focus,
+                    check_size_upper=investor_data.investor.check_size_upper,
-                region=investor.region,
+                    geographic_focus=investor_data.investor.geographic_focus,
+                    stage_focus=investor_data.investor.stage_focus,
+                    number_of_investments=investor_data.investor.number_of_investments,
                 )
                 self.sql_session.add(db_investor)
-        self.sql_session.commit()
+                self.sql_session.flush()  # Get the ID
 
-    async def _save_to_vector_db(self, investors: List[Investor]) -> None:
+                # Save sectors and create associations
+                for sector_data in investor_data.sectors:
+                    # Check if sector exists, create if not
+                    existing_sector = (
+                        self.sql_session.query(SectorTable)
+                        .filter(SectorTable.name == sector_data.name)
+                        .first()
+                    )
+
+                    if not existing_sector:
+                        db_sector = SectorTable(name=sector_data.name)
+                        self.sql_session.add(db_sector)
+                        self.sql_session.flush()
+                        # Add sector to investor's sectors
+                        db_investor.sectors.append(db_sector)
+                    else:
+                        # Add existing sector to investor if not already there
+                        if existing_sector not in db_investor.sectors:
+                            db_investor.sectors.append(existing_sector)
+
+                # Save companies and create portfolio associations
+                for company_data in investor_data.portfolio_companies:
+                    # Check if company exists, create if not
+                    existing_company = (
+                        self.sql_session.query(CompanyTable)
+                        .filter(CompanyTable.name == company_data.name)
+                        .first()
+                    )
+
+                    if not existing_company:
+                        db_company = CompanyTable(
+                            name=company_data.name,
+                            industry=company_data.industry,
+                            location=company_data.location,
+                            founded_year=company_data.founded_year,
+                            website=company_data.website,
+                        )
+                        self.sql_session.add(db_company)
+                        self.sql_session.flush()
+
+                        # Add to investor's portfolio
+                        db_investor.portfolio_companies.append(db_company)
+                    else:
+                        # Add existing company to portfolio if not already there
+                        if existing_company not in db_investor.portfolio_companies:
+                            db_investor.portfolio_companies.append(existing_company)
+
+                # Save team members
+                for team_member_data in investor_data.team_members:
+                    # Check if team member exists
+                    existing_member = (
+                        self.sql_session.query(InvestorTeamMember)
+                        .filter(InvestorTeamMember.email == team_member_data.email)
+                        .first()
+                    )
+
+                    if not existing_member:
+                        db_team_member = InvestorTeamMember(
+                            name=team_member_data.name,
+                            role=team_member_data.role,
+                            email=team_member_data.email,
+                            investor_id=db_investor.id,
+                        )
+                        self.sql_session.add(db_team_member)
+
+            self.sql_session.commit()
+            print(f"Successfully saved {len(investor_data_list)} investors to database")
+
+        except Exception as e:
+            self.sql_session.rollback()
+            print(f"Error saving to SQL database: {e}")
+            raise
+
+    async def _save_to_vector_db(self, investor_data_list: List[InvestorData]) -> None:
         """Save investors to vector database"""
         if not self.vector_db_client:
             return
@@ -129,19 +215,47 @@ Return the data as a structured list of investors."""
         metadatas = []
         ids = []
 
-        for i, investor in enumerate(investors):
+        for i, investor_data in enumerate(investor_data_list):
-            doc_text = f"{investor.investor_description}\nInvestment Thesis: {investor.investment_thesis}"
+            investor = investor_data.investor
+            sectors = ", ".join([s.name for s in investor_data.sectors])
+            companies = ", ".join([c.name for c in investor_data.portfolio_companies])
+
+            doc_text = f"""
+            Investor: {investor.name}
+            Description: {investor.description or "N/A"}
+            AUM: ${investor.aum:,}
+            Check Size: ${investor.check_size_lower:,} - ${investor.check_size_upper:,}
+            Geographic Focus: {investor.geographic_focus}
+            Stage Focus: {investor.stage_focus.value}
+            Sectors: {sectors}
+            Portfolio Companies: {companies}
+            """.strip()
+
             documents.append(doc_text)
-            metadatas.append({"name": investor.name})
+            metadatas.append(
-            ids.append(f"investor_{i}_{investor.name.replace(' ', '_')}")
+                {
+                    "name": investor.name,
+                    "stage_focus": investor.stage_focus.value,
+                    "geographic_focus": investor.geographic_focus,
+                    "aum": investor.aum,
+                }
+            )
+            ids.append(
+                f"investor_{i}_{investor.name.replace(' ', '_').replace('/', '_')}"
+            )
 
         if documents:
-            # Use add method with proper parameters
+            try:
                 self.collection.add(documents=documents, metadatas=metadatas, ids=ids)
+                print(
+                    f"Successfully saved {len(documents)} investors to vector database"
+                )
+            except Exception as e:
+                print(f"Error saving to vector database: {e}")
 
     async def process_csv(
         self, df: pd.DataFrame, batch_size: int = 10, max_concurrent: int = 10
-    ) -> List:
+    ) -> List[InvestorData]:
         """Process CSV data in parallel batches and save to databases"""
         results = []
 
@@ -172,6 +286,7 @@ Return the data as a structured list of investors."""
 
         # Save to databases
         if results:
+            print(f"Successfully processed {len(results)} investors")
             await self._save_to_sql(results)
             await self._save_to_vector_db(results)
 

app/services/querying.py

@@ -6,7 +6,7 @@ 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 pydantic_schemas import Investor, InvestorList
+from py_schemas import InvestorList
 from settings import settings
 
 # Connect to SQLite
@@ -29,7 +29,7 @@ class QueryProcessor:
             api_key=settings.OPENROUTER_API_KEY,
             base_url="https://openrouter.ai/api/v1",
             model="google/gemini-2.5-flash-lite",
-            temperature=0,
+            temperature=0.3,
         )
         self.toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
         self.agent = create_react_agent(