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

- Added main ingestion logic in main.py to process CSV files for investors and companies.
- Implemented data cleaning functions for names, strings, integers, and websites.
- Established relationships between investors, companies, and sectors using SQLAlchemy ORM.
- Created models for investors, companies, sectors, and their relationships in models.py.
- Set up logging for error tracking during data processing.
- Initialized database and created necessary tables.

.gitignore

@@ -8,8 +8,9 @@
 
 /chroma_db
 
-/*__pycache__*/
+*__pycache__
+
+
+*.cypython
 
-/*.db
 
-/*.cypython-*

README.md

@@ -1,577 +0,0 @@
-# LLM-Powered Investor & Company Management API
-
-A comprehensive FastAPI-based system for managing investor and company data with LLM-powered CSV parsing, semantic search, and advanced filtering capabilities.
-
-## Features
-
--   **FastAPI REST API**: Modern, auto-documented API with OpenAPI/Swagger support
--   **CSV Data Processing**: Parse complex investor data from CSV files using LLM assistance
--   **Dual Database Storage**: Structured data in SQL database and semantic search via ChromaDB
--   **Natural Language Queries**: AI-powered query processing for complex investor searches
--   **Advanced Filtering**: Filter investors and companies by multiple criteria
--   **Relationship Management**: Many-to-many relationships between investors, companies, and sectors
--   **Auto-Generated Documentation**: Interactive API docs at `/docs`
-
-## Architecture
-
-### Components
-
-1. **FastAPI Application (`app/main.py`)**: Main API server with route configuration
-2. **Database Models (`app/db/models.py`)**: SQLAlchemy models for investors, companies, sectors
-3. **Pydantic Schemas (`app/py_schemas.py`)**: Request/response validation and serialization
-4. **API Routes**:
-    - `app/api/investors.py`: Investor CRUD operations and filtering
-    - `app/api/companies.py`: Company CRUD operations and filtering
-5. **Services**:
-    - `app/services/openrouter.py`: LLM-powered CSV processing
-    - `app/services/querying.py`: Natural language query processing
-6. **Database (`app/db/`)**: Database connection, models, and schemas
-
-### Data Flow
-
-```
-CSV Upload → LLM Processing → Data Extraction → SQL Storage → Vector Storage → API Endpoints
-                                    ↓
-Natural Language Query → AI Analysis → Database Filtering → Structured Response
-```
-
-## Installation
-
-### Prerequisites
-
--   Python 3.12+
--   FastAPI and dependencies
-
-### Setup
-
-1. Clone the repository and navigate to the project directory:
-
-```bash
-cd /path/to/anton_wireframe
-```
-
-2. Install dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-3. Configure environment variables:
-
-```bash
-cp .env.example .env
-# Edit .env and add your OpenRouter API key for LLM features
-```
-
-4. Initialize the database:
-
-```bash
-cd app
-python -c "from db.db import init_database; init_database()"
-```
-
-5. Start the API server:
-
-```bash
-cd app
-uvicorn main:app --reload --host localhost --port 8000
-```
-
-The API will be available at:
-
--   **API Base**: http://localhost:8000
--   **Interactive Docs**: http://localhost:8000/docs
--   **ReDoc**: http://localhost:8000/redoc
-
-## Database Schema
-
-### SQL Database (SQLite)
-
-#### Investors Table
-
--   **Basic Info**: name, description, geographic_focus
--   **Investment Data**: aum, check_size_lower, check_size_upper
--   **Stage Focus**: investment stage (SEED, SERIES_A, etc.)
--   **Relationships**: Many-to-many with companies and sectors
--   **Team**: One-to-many with team members
--   **Metadata**: created_at, updated_at timestamps
-
-#### Companies Table
-
--   **Basic Info**: name, industry, location
--   **Details**: founded_year, website
--   **Relationships**: Many-to-many with investors
--   **Metadata**: created_at, updated_at timestamps
-
-#### Association Tables
-
--   **investor_companies**: Links investors to their portfolio companies
--   **investor_sectors**: Links investors to their focus sectors
--   **investor_team**: Team member details for each investor
-
-#### Supporting Tables
-
--   **sectors**: Investment focus areas (fintech, healthcare, etc.)
-
-### Vector Database (ChromaDB)
-
-Stores embeddings for semantic search of:
-
--   Investor descriptions
--   Investment thesis focus areas
--   Combined investor profiles
-
-## API Usage
-
-### Interactive Documentation
-
-Visit http://localhost:8000/docs for the auto-generated Swagger UI where you can:
-
--   Explore all endpoints
--   Test API calls directly
--   View request/response schemas
--   See example requests
-
-### Core Endpoints
-
-#### Investor Management
-
-```bash
-# Get all investors with relationships
-GET /investors
-
-# Filter investors by criteria
-GET /investors/filter?stage=GROWTH&geography=US&sector=fintech&min_check_size=1000000
-
-# Get specific investor
-GET /investors/{investor_id}
-
-# Create new investor
-POST /investors
-{
-  "name": "Example VC",
-  "description": "Early stage fintech investor",
-  "aum": 50000000,
-  "check_size_lower": 100000,
-  "check_size_upper": 2000000,
-  "geographic_focus": "US",
-  "stage_focus": "SEED",
-  "number_of_investments": 25
-}
-
-# Update investor
-PUT /investors/{investor_id}
-
-# Delete investor
-DELETE /investors/{investor_id}
-```
-
-#### Company Management
-
-```bash
-# Get all companies with investor relationships
-GET /companies
-
-# Filter companies by criteria
-GET /companies/filter?industry=fintech&location=San Francisco&founded_after=2015
-
-# Get specific company
-GET /companies/{company_id}
-
-# Create new company
-POST /companies
-{
-  "name": "Example Startup",
-  "industry": "fintech",
-  "location": "San Francisco",
-  "founded_year": 2020,
-  "website": "https://example.com"
-}
-
-# Update company
-PUT /companies/{company_id}
-
-# Delete company
-DELETE /companies/{company_id}
-```
-
-#### CSV Processing
-
-```bash
-# Upload and process CSV file
-POST /parse-csv
-Content-Type: multipart/form-data
-File: investors.csv
-```
-
-#### Natural Language Queries
-
-```bash
-# Query investors using natural language
-POST /query
-{
-  "question": "Show me growth stage fintech investors in Silicon Valley with check sizes over $1 million"
-}
-```
-
-### Advanced Filtering Examples
-
-#### Investor Filters
-
-```bash
-# Early stage investors in Europe
-GET /investors/filter?stage=SEED&geography=Europe
-
-# High AUM growth investors
-GET /investors/filter?stage=GROWTH&min_aum=100000000
-
-# Healthcare investors with large checks
-GET /investors/filter?sector=healthcare&min_check_size=5000000
-
-# Specific geographic focus
-GET /investors/filter?geography=Silicon Valley
-```
-
-#### Company Filters
-
-```bash
-# Recent fintech companies
-GET /companies/filter?industry=fintech&founded_after=2020
-
-# Companies with websites
-GET /companies/filter?has_website=true
-
-# Companies backed by specific investor
-GET /companies/filter?investor_name=Sequoia
-
-# Location-based filtering
-GET /companies/filter?location=New York
-```
-
-### Response Format
-
-All endpoints return structured JSON with full relationship data:
-
-```json
-{
-    "investor": {
-        "id": 1,
-        "name": "Example VC",
-        "description": "Early stage investor",
-        "aum": 50000000,
-        "check_size_lower": 100000,
-        "check_size_upper": 2000000,
-        "geographic_focus": "US",
-        "stage_focus": "SEED",
-        "number_of_investments": 25
-    },
-    "portfolio_companies": [
-        {
-            "id": 1,
-            "name": "StartupCo",
-            "industry": "fintech",
-            "location": "San Francisco"
-        }
-    ],
-    "team_members": [
-        {
-            "id": 1,
-            "name": "John Partner",
-            "role": "Managing Partner",
-            "email": "john@examplevc.com"
-        }
-    ],
-    "sectors": [
-        {
-            "id": 1,
-            "name": "fintech"
-        }
-    ]
-}
-```
-
-## Data Processing Pipeline
-
-### 1. CSV Parsing
-
--   Reads CSV with pandas
--   Handles nested JSON fields in columns
--   Validates data with Pydantic models
-
-### 2. JSON Field Processing
-
--   Direct parsing for well-formed JSON
--   LLM-assisted cleaning for malformed JSON (when enabled)
--   Graceful fallback to empty objects
-
-### 3. Data Extraction
-
-Extracts key fields:
-
--   Company name and website
--   Investor description
--   Investment thesis/focus areas
--   Headquarters location
--   Assets Under Management (AUM)
--   Fund information
-
-### 4. LLM Enhancement (Optional)
-
-When `--use-llm` is enabled:
-
--   Standardizes investor descriptions
--   Normalizes investment focus areas
--   Cleans headquarters location format
--   Repairs malformed JSON data
-
-### 5. Dual Storage
-
--   **SQL Database**: Structured, queryable data
--   **Vector Database**: Semantic search capabilities
-
-## Configuration
-
-### Environment Variables (.env)
-
-```bash
-# OpenRouter API Configuration (required for LLM features)
-OPENROUTER_API_KEY=your_openrouter_api_key_here
-
-# Database Configuration (optional, defaults to SQLite)
-DATABASE_URL=sqlite:///investors.db
-
-# FastAPI Configuration
-API_HOST=localhost
-API_PORT=8000
-```
-
-### LLM Configuration
-
--   **Provider**: OpenRouter (supports multiple models)
--   **Default Model**: google/gemini-2.5-flash-lite
--   **Temperature**: 0.3 for enhancement, 0 for structured data
--   **Fallback**: Graceful degradation when API unavailable
-
-## Natural Language Query Processing
-
-The system supports intelligent natural language queries that automatically extract filters and search criteria:
-
-### Query Examples
-
-```bash
-# Stage-based queries
-"Show me seed stage investors"
-"Find growth stage VCs"
-
-# Geographic queries
-"Investors in Silicon Valley"
-"European venture capital firms"
-
-# Sector-specific queries
-"Fintech investors"
-"Healthcare and biotech VCs"
-
-# Size-based queries
-"Investors with $5M+ check sizes"
-"High AUM growth investors"
-
-# Combined queries
-"Growth stage fintech investors in the US with check sizes over $1 million"
-"European healthcare investors focusing on early stage"
-```
-
-### Query Processing Features
-
--   **Automatic Filter Extraction**: Detects investment stages, geographies, sectors, and check sizes
--   **Semantic Understanding**: Uses AI to interpret complex queries
--   **Database Integration**: Combines AI analysis with efficient SQL filtering
--   **Complete Relationships**: Returns full investor data with portfolio companies, team members, and sectors
-
-### Query Response
-
-The `/query` endpoint returns a structured `InvestorList` with complete relationship data, making it easy to get comprehensive information about matching investors.
-
-## Error Handling
-
-### API Error Responses
-
-The API provides clear HTTP status codes and error messages:
-
-```json
-// 404 Not Found
-{
-  "detail": "Investor not found"
-}
-
-// 422 Validation Error
-{
-  "detail": [
-    {
-      "loc": ["body", "stage_focus"],
-      "msg": "value is not a valid enumeration member",
-      "type": "type_error.enum"
-    }
-  ]
-}
-```
-
-### Robust Processing
-
--   **Data Validation**: Pydantic models ensure data integrity
--   **Relationship Management**: Automatic handling of foreign key constraints
--   **LLM Fallbacks**: Graceful degradation when AI services unavailable
--   **Transaction Safety**: Database rollbacks on errors
--   **Comprehensive Logging**: Detailed error tracking and debugging
-
-### Common Issues and Solutions
-
-1. **Invalid Enum Values**
-
-    - Solution: Use uppercase enum values (SEED, GROWTH, etc.)
-    - Check: Investment stages must match defined enum
-
-2. **Missing OpenRouter API Key**
-
-    - Solution: Set OPENROUTER_API_KEY in environment
-    - Fallback: CSV processing continues without LLM enhancement
-
-3. **Database Connection Issues**
-
-    - Solution: Verify DATABASE_URL configuration
-    - Default: Uses SQLite (no external dependencies)
-
-4. **Relationship Errors**
-    - Solution: Ensure proper foreign key relationships
-    - Check: Use existing sector/company IDs or create new ones
-
-## Performance
-
-### Benchmarks (Approximate)
-
--   **API Response Time**: <200ms for standard queries
--   **Database Queries**: <50ms for filtered searches with relationships
--   **CSV Processing**: ~5-15 seconds per row (depends on LLM API latency)
--   **Natural Language Queries**: ~2-5 seconds (AI processing + database query)
--   **Vector Search**: <100ms for semantic similarity queries
-
-### Optimization Features
-
-1. **Eager Loading**: Efficient relationship loading with `selectinload()`
-2. **Query Optimization**: Smart filtering to reduce database load
-3. **Caching**: Database connection pooling and session management
-4. **Pagination**: Built-in limits to prevent overwhelming responses
-5. **Async Processing**: FastAPI async capabilities for better performance
-
-### Production Recommendations
-
-1. **Database**: Consider PostgreSQL for production workloads
-2. **Caching**: Add Redis for frequently accessed data
-3. **Load Balancing**: Deploy multiple API instances behind a load balancer
-4. **Monitoring**: Implement logging and metrics collection
-5. **Rate Limiting**: Add API rate limiting for public endpoints
-
-## File Structure
-
-```
-anton_wireframe/
-├── app/
-│   ├── main.py                    # FastAPI application and main endpoints
-│   ├── py_schemas.py              # Pydantic models for validation
-│   ├── settings.py                # Configuration management
-│   ├── api/
-│   │   ├── __init__.py
-│   │   ├── investors.py           # Investor CRUD and filtering endpoints
-│   │   └── companies.py           # Company CRUD and filtering endpoints
-│   ├── db/
-│   │   ├── __init__.py
-│   │   ├── db.py                  # Database connection and session management
-│   │   ├── models.py              # SQLAlchemy database models
-│   │   └── new_schema.py          # Additional schema definitions
-│   └── services/
-│       ├── __init__.py
-│       ├── openrouter.py          # LLM-powered CSV processing
-│       ├── querying.py            # Natural language query processing
-│       └── langgraph_agent.py     # AI agent configuration
-├── chroma_db/                     # Vector database directory
-├── requirements.txt               # Python dependencies
-├── README.md                      # This documentation
-└── .env                          # Environment configuration
-```
-
-## Example Usage Scenarios
-
-### 1. Upload and Process Investor Data
-
-```bash
-# Upload CSV file via API
-curl -X POST "http://localhost:8000/parse-csv" \
-  -H "Content-Type: multipart/form-data" \
-  -F "file=@investors.csv"
-```
-
-### 2. Find Specific Investors
-
-```bash
-# Natural language search
-curl -X POST "http://localhost:8000/query" \
-  -H "Content-Type: application/json" \
-  -d '{"question": "Show me growth stage fintech investors in Silicon Valley with check sizes over $2 million"}'
-
-# Structured filtering
-curl "http://localhost:8000/investors/filter?stage=GROWTH&sector=fintech&geography=Silicon%20Valley&min_check_size=2000000"
-```
-
-### 3. Company Research
-
-```bash
-# Find companies in specific sector
-curl "http://localhost:8000/companies/filter?industry=fintech&founded_after=2020"
-
-# Find companies backed by specific investor
-curl "http://localhost:8000/companies/filter?investor_name=Sequoia"
-```
-
-### 4. Investment Analysis
-
-```bash
-# Get investor with full portfolio
-curl "http://localhost:8000/investors/1"
-
-# Find all companies in a specific location
-curl "http://localhost:8000/companies/filter?location=San%20Francisco"
-```
-
-## Development
-
-### Running in Development Mode
-
-```bash
-cd app
-uvicorn main:app --reload --host localhost --port 8000
-```
-
-### Testing the API
-
-1. **Interactive Testing**: Visit http://localhost:8000/docs
-2. **Manual Testing**: Use curl or Postman with the examples above
-3. **Database Inspection**: Use SQLite browser to inspect `investors_2.db`
-
-### Adding New Features
-
-1. **New Endpoints**: Add routes to `api/investors.py` or `api/companies.py`
-2. **New Models**: Update `db/models.py` and `py_schemas.py`
-3. **New Filters**: Extend filtering logic in route handlers
-4. **New LLM Features**: Modify `services/openrouter.py` or `services/querying.py`
-
-## License
-
-This project is part of the MKD Anton Wireframe system.
-
-## Support
-
-For issues and questions:
-
-1. Check logs for detailed error messages
-2. Verify environment configuration
-3. Test with limited datasets first
-4. Review CSV data format requirements

app/api/investors.py

@@ -1,233 +0,0 @@
-from typing import List, Optional
-
-from db.db import get_db
-from db.models import InvestorTable, SectorTable
-from fastapi import APIRouter, Depends, HTTPException, Query
-from py_schemas import InvestmentStage, InvestorData
-from pydantic import BaseModel
-from sqlalchemy.orm import Session, selectinload
-
-router = APIRouter(tags=["Investor Routes"])
-
-
-# Request schemas for creating/updating
-class InvestorCreate(BaseModel):
-    name: str
-    description: 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: str = None
-    description: str = None
-    aum: int = None
-    check_size_lower: int = None
-    check_size_upper: int = None
-    geographic_focus: str = None
-    stage_focus: InvestmentStage = None
-    number_of_investments: int = None
-
-
-@router.get("/investors", response_model=List[InvestorData])
-def read_investors(db: Session = Depends(get_db)):
-    """Get all investors with their related data"""
-    investors = (
-        db.query(InvestorTable)
-        .options(
-            selectinload(InvestorTable.portfolio_companies),
-            selectinload(InvestorTable.team_members),
-            selectinload(InvestorTable.sectors),
-        )
-        .all()
-    )
-
-    # Transform InvestorTable objects to InvestorData format
-    investor_data_list = []
-    for investor in investors:
-        investor_data = InvestorData(
-            investor=investor,  # This maps to InvestorSchema
-            portfolio_companies=investor.portfolio_companies,
-            team_members=investor.team_members,
-            sectors=investor.sectors,
-        )
-        investor_data_list.append(investor_data)
-
-    return investor_data_list
-
-
-@router.get("/investors/filter", response_model=List[InvestorData])
-def filter_investors(
-    stage: Optional[InvestmentStage] = Query(
-        None, description="Filter by investment stage"
-    ),
-    min_check_size: Optional[int] = Query(None, description="Minimum check size"),
-    max_check_size: Optional[int] = Query(None, description="Maximum check size"),
-    geography: Optional[str] = Query(
-        None, description="Geographic focus (partial match)"
-    ),
-    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"),
-    db: Session = Depends(get_db),
-):
-    """Filter investors based on various criteria"""
-
-    # Start with base query
-    query = db.query(InvestorTable).options(
-        selectinload(InvestorTable.portfolio_companies),
-        selectinload(InvestorTable.team_members),
-        selectinload(InvestorTable.sectors),
-    )
-
-    # Apply filters
-    if stage:
-        query = query.filter(InvestorTable.stage_focus == stage)
-
-    if min_check_size is not None:
-        query = query.filter(InvestorTable.check_size_lower >= min_check_size)
-
-    if max_check_size is not None:
-        query = query.filter(InvestorTable.check_size_upper <= max_check_size)
-
-    if geography:
-        query = query.filter(InvestorTable.geographic_focus.ilike(f"%{geography}%"))
-
-    if min_aum is not None:
-        query = query.filter(InvestorTable.aum >= min_aum)
-
-    if max_aum is not None:
-        query = query.filter(InvestorTable.aum <= max_aum)
-
-    # Filter by sector if provided
-    if sector:
-        query = query.join(InvestorTable.sectors).filter(
-            SectorTable.name.ilike(f"%{sector}%")
-        )
-
-    investors = query.all()
-
-    # 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,
-        )
-        investor_data_list.append(investor_data)
-
-    return investor_data_list
-
-
-@router.get("/investors/{investor_id}", response_model=InvestorData)
-def read_investor(investor_id: int, db: Session = Depends(get_db)):
-    """Get a specific investor by ID"""
-    investor = (
-        db.query(InvestorTable)
-        .options(
-            selectinload(InvestorTable.portfolio_companies),
-            selectinload(InvestorTable.team_members),
-            selectinload(InvestorTable.sectors),
-        )
-        .filter(InvestorTable.id == investor_id)
-        .first()
-    )
-
-    if not investor:
-        raise HTTPException(status_code=404, detail="Investor not found")
-
-    # Transform to InvestorData format
-    return InvestorData(
-        investor=investor,
-        portfolio_companies=investor.portfolio_companies,
-        team_members=investor.team_members,
-        sectors=investor.sectors,
-    )
-
-
-@router.post("/investors", response_model=InvestorData)
-def create_investor(investor: InvestorCreate, db: Session = Depends(get_db)):
-    """Create a new investor"""
-    db_investor = InvestorTable(**investor.dict())
-    db.add(db_investor)
-    db.commit()
-    db.refresh(db_investor)
-
-    # Reload with relationships
-    investor_with_relations = (
-        db.query(InvestorTable)
-        .options(
-            selectinload(InvestorTable.portfolio_companies),
-            selectinload(InvestorTable.team_members),
-            selectinload(InvestorTable.sectors),
-        )
-        .filter(InvestorTable.id == db_investor.id)
-        .first()
-    )
-
-    # Transform to InvestorData format
-    return InvestorData(
-        investor=investor_with_relations,
-        portfolio_companies=investor_with_relations.portfolio_companies,
-        team_members=investor_with_relations.team_members,
-        sectors=investor_with_relations.sectors,
-    )
-
-
-@router.put("/investors/{investor_id}", response_model=InvestorData)
-def update_investor(
-    investor_id: int, investor: InvestorUpdate, db: Session = Depends(get_db)
-):
-    """Update an existing investor"""
-    db_investor = (
-        db.query(InvestorTable).filter(InvestorTable.id == investor_id).first()
-    )
-    if not db_investor:
-        raise HTTPException(status_code=404, detail="Investor not found")
-
-    update_data = investor.dict(exclude_unset=True)
-    for field, value in update_data.items():
-        setattr(db_investor, field, value)
-
-    db.commit()
-    db.refresh(db_investor)
-
-    # Reload with relationships
-    investor_with_relations = (
-        db.query(InvestorTable)
-        .options(
-            selectinload(InvestorTable.portfolio_companies),
-            selectinload(InvestorTable.team_members),
-            selectinload(InvestorTable.sectors),
-        )
-        .filter(InvestorTable.id == investor_id)
-        .first()
-    )
-
-    # Transform to InvestorData format
-    return InvestorData(
-        investor=investor_with_relations,
-        portfolio_companies=investor_with_relations.portfolio_companies,
-        team_members=investor_with_relations.team_members,
-        sectors=investor_with_relations.sectors,
-    )
-
-
-@router.delete("/investors/{investor_id}")
-def delete_investor(investor_id: int, db: Session = Depends(get_db)):
-    """Delete an investor"""
-    db_investor = (
-        db.query(InvestorTable).filter(InvestorTable.id == investor_id).first()
-    )
-    if not db_investor:
-        raise HTTPException(status_code=404, detail="Investor not found")
-
-    db.delete(db_investor)
-    db.commit()
-    return {"message": "Investor deleted successfully"}

app/command.py

@@ -1,46 +0,0 @@
-from sqlalchemy.orm import Session
-from db.models import InvestorTable
-from db.db import get_db
-
-def update_stage_focus_values():
-    """Update existing stage_focus values from lowercase to uppercase"""
-    db = next(get_db())
-    
-    try:
-        # Mapping of old lowercase values to new uppercase values
-        stage_mappings = {
-            'seed': 'SEED',
-            'series_a': 'SERIES_A', 
-            'series_b': 'SERIES_B',
-            'series_c': 'SERIES_C',
-            'growth': 'GROWTH',
-            'late_stage': 'LATE_STAGE'
-        }
-        
-        updated_count = 0
-        
-        for old_value, new_value in stage_mappings.items():
-            # Update records with the old value
-            result = db.query(InvestorTable).filter(
-                InvestorTable.stage_focus == old_value
-            ).update(
-                {InvestorTable.stage_focus: new_value},
-                synchronize_session=False
-            )
-            
-            updated_count += result
-            print(f"Updated {result} records from '{old_value}' to '{new_value}'")
-        
-        db.commit()
-        print(f"Successfully updated {updated_count} total records")
-        
-    except Exception as e:
-        db.rollback()
-        print(f"Error updating stage_focus values: {e}")
-        raise
-    finally:
-        db.close()
-
-# Run the update
-if __name__ == "__main__":
-    update_stage_focus_values()

app/db/db.py

@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 from typing import Annotated
 
 from fastapi import Depends
@@ -9,7 +10,11 @@ from sqlalchemy.orm import Session, sessionmaker
 Base = declarative_base()
 
 # Database configuration
-DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///investors.db")
+# Use the preprocessor's database for consistency
+# 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:///./investors.db")
 
 # Create engine
 engine = create_engine(DATABASE_URL, echo=False)
@@ -32,9 +37,13 @@ db_dependency = Annotated[Session, Depends(get_db)]
 def init_database():
     """Initialize the database by creating all tables"""
     Base.metadata.create_all(bind=engine)
-    print("Database initialized successfully!")
 
 
 def get_session_sync() -> Session:
     """Get a database session for synchronous operations"""
     return SessionLocal()
+
+
+def get_db_session():
+    """Get a database session for direct use."""
+    return SessionLocal()

app/db/models.py

@@ -1,13 +1,20 @@
-import datetime
 import enum
 
-from sqlalchemy import Column, DateTime, ForeignKey, Integer, String, Table, Text
+from sqlalchemy import Column, DateTime, ForeignKey, Integer, String, Table, Text, func
-from sqlalchemy.orm import relationship
+from sqlalchemy.orm import declarative_mixin, relationship
-from sqlalchemy.types import Enum
+from sqlalchemy.types import JSON, Enum
 
 from db.db import Base
 
 
+@declarative_mixin
+class TimestampMixin:
+    created_at = Column(
+        DateTime(timezone=True), server_default=func.now(), nullable=False
+    )
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+
 class InvestmentStage(enum.Enum):
     SEED = "SEED"
     SERIES_A = "SERIES_A"
@@ -16,6 +23,7 @@ class InvestmentStage(enum.Enum):
     GROWTH = "GROWTH"
     LATE_STAGE = "LATE_STAGE"
 
+
 # Association table for many-to-many relationship between investors and companies
 investor_company_association = Table(
     "investor_companies",
@@ -34,23 +42,97 @@ investor_sector_association = Table(
 )
 
 
-class InvestorTable(Base):
+company_sector_association = Table(
+    "company_sector",
+    Base.metadata,
+    Column("company_id", Integer, ForeignKey("companies.id")),
+    Column("sector_id", Integer, ForeignKey("sectors.id")),
+)
+
+project_sector_association = Table(
+    "project_sector",
+    Base.metadata,
+    Column("project_id", Integer, ForeignKey("projects.id")),
+    Column("sector_id", Integer, ForeignKey("sectors.id")),
+)
+
+project_investor_association = Table(
+    "project_investors",
+    Base.metadata,
+    Column("project_id", Integer, ForeignKey("projects.id")),
+    Column("investor_id", Integer, ForeignKey("investors.id")),
+)
+
+project_company_association = Table(
+    "project_companies",
+    Base.metadata,
+    Column("project_id", Integer, ForeignKey("projects.id")),
+    Column("company_id", Integer, ForeignKey("companies.id")),
+)
+
+# Association table for fund-stage many-to-many
+fund_investment_stages_association = Table(
+    "fund_investment_stages",
+    Base.metadata,
+    Column("fund_id", Integer, ForeignKey("funds.id")),
+    Column("stage_id", Integer, ForeignKey("investment_stages.id")),
+)
+
+# Association table for fund-sector many-to-many
+fund_sectors_association = Table(
+    "fund_sectors",
+    Base.metadata,
+    Column("fund_id", Integer, ForeignKey("funds.id")),
+    Column("sector_id", Integer, ForeignKey("sectors.id")),
+)
+
+
+class InvestorTable(Base, TimestampMixin):
     __tablename__ = "investors"
 
     id = Column(Integer, primary_key=True, index=True)
     name = Column(String, nullable=False)
     description = Column(Text, nullable=True)
-    aum = Column(Integer, nullable=False)  # Assets Under Management
+
-    check_size_lower = Column(Integer, nullable=False)  # Lower bound
+    # Basic investor info
-    check_size_upper = Column(Integer, nullable=False)  # Upper bound
+    website = Column(String, nullable=True)
-    geographic_focus = Column(String, nullable=False)
+    headquarters = Column(String, nullable=True)
-    stage_focus = Column(Enum(InvestmentStage), nullable=False)
+
-    number_of_investments = Column(Integer, default=0)
+    # AUM fields
-    created_at = Column(DateTime, default=datetime.datetime.now(datetime.UTC))
+    aum = Column(Integer, nullable=True)  # Store as integer for numerical filtering
-    updated_at = Column(
+    aum_as_of_date = Column(String, nullable=True)
-        DateTime,
+    aum_source_url = Column(String, nullable=True)
-        default=datetime.datetime.now(datetime.UTC),
+
-        onupdate=datetime.datetime.now(datetime.UTC),
+    # Check size (deprecated in favor of fund-level data, but keeping for backward compatibility)
+    check_size_lower = Column(Integer, nullable=True)
+    check_size_upper = Column(Integer, nullable=True)
+
+    # Geographic focus (deprecated in favor of fund-level, but keeping for backward compatibility)
+    geographic_focus = Column(String, nullable=True)
+
+    # Investment thesis and portfolio
+    investment_thesis = Column(JSON, nullable=True)  # Array of thesis statements
+    portfolio_highlights = Column(
+        JSON, nullable=True
+    )  # Array of portfolio company names
+    linked_documents = Column(JSON, nullable=True)  # Array of document URLs
+
+    # Research metadata
+    researcher_notes = Column(Text, nullable=True)
+    missing_important_fields = Column(
+        JSON, nullable=True
+    )  # Array of missing field names
+    sources = Column(JSON, nullable=True)  # JSON object with source URLs
+
+    # Portfolio info
+    number_of_investments = Column(Integer, default=0, nullable=True)
+
+    # Relationships
+    team_members = relationship(
+        "InvestorMember", back_populates="investor", cascade="all, delete-orphan"
+    )
+    funds = relationship(
+        "FundTable", back_populates="investor", cascade="all, delete-orphan"
     )
 
     # Relationship to portfolio companies
@@ -59,30 +141,84 @@ 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",
     )
 
+    projects = relationship(
+        "ProjectTable",
+        secondary=project_investor_association,
+        back_populates="investors",
+    )
 
-class CompanyTable(Base):
+
+class InvestorMember(Base, TimestampMixin):
+    __tablename__ = "investor_members"
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False)
+    role = Column(String, nullable=True)
+    title = Column(String, nullable=True)  # Alternative to role
+    email = Column(String, nullable=True)
+    source_url = Column(String, nullable=True)  # URL where member info was found
+
+    investor_id = Column(Integer, ForeignKey("investors.id"))
+    investor = relationship("InvestorTable", back_populates="team_members")
+
+
+class FundTable(Base, TimestampMixin):
+    __tablename__ = "funds"
+
+    id = Column(Integer, primary_key=True, index=True)
+    investor_id = Column(Integer, ForeignKey("investors.id"), nullable=False)
+
+    # Fund details
+    fund_name = Column(String, nullable=True)
+    fund_size = Column(
+        Integer, nullable=True
+    )  # Store as integer for numerical filtering
+    fund_size_source_url = Column(String, nullable=True)
+
+    # Check size range (parsed from estimated_investment_size by LLM)
+    check_size_lower = Column(Integer, nullable=True)
+    check_size_upper = Column(Integer, nullable=True)
+
+    source_url = Column(String, nullable=True)
+    source_provider = Column(String, nullable=True)  # e.g., "Perplexity"
+
+    # Geographic focus as simple string
+    geographic_focus = Column(String, nullable=True)
+
+    # Relationships
+    investor = relationship("InvestorTable", back_populates="funds")
+    investment_stages = relationship(
+        "InvestmentStageTable",
+        secondary=fund_investment_stages_association,
+        back_populates="funds",
+    )
+    sectors = relationship(
+        "SectorTable",
+        secondary=fund_sectors_association,
+        back_populates="funds",
+    )
+
+
+class CompanyTable(Base, TimestampMixin):
     __tablename__ = "companies"
 
     id = Column(Integer, primary_key=True, index=True)
     name = Column(String, nullable=False)
-    industry = Column(String, nullable=False)
+    industry = Column(String, nullable=True)
-    location = Column(String, nullable=False)
+    location = Column(String, nullable=True)
+    description = Column(String, nullable=True)
     founded_year = Column(Integer, nullable=True)
     website = Column(String, nullable=True)
-    created_at = Column(DateTime, default=datetime.datetime.now(datetime.UTC))
-    updated_at = Column(
-        DateTime,
-        default=datetime.datetime.now(datetime.UTC),
-        onupdate=datetime.datetime.now(datetime.UTC),
-    )
 
+    members = relationship(
+        "CompanyMember", back_populates="company", cascade="all, delete-orphan"
+    )
     # Relationship back to investors
     investors = relationship(
         "InvestorTable",
@@ -90,27 +226,88 @@ class CompanyTable(Base):
         back_populates="portfolio_companies",
     )
 
+    sectors = relationship(
+        "SectorTable", secondary=company_sector_association, back_populates="companies"
+    )
 
-class SectorTable(Base):
+    projects = relationship(
+        "ProjectTable",
+        secondary=project_company_association,
+        back_populates="companies",
+    )
+
+
+class CompanyMember(Base, TimestampMixin):
+    __tablename__ = "company_members"
+    id = Column(Integer, primary_key=True)
+    name = Column(String)
+    linkedin = Column(String, nullable=True)
+    role = Column(String, nullable=True)
+    company_id = Column(Integer, ForeignKey("companies.id"), nullable=False)
+
+    company = relationship("CompanyTable", back_populates="members")
+
+
+class InvestmentStageTable(Base, TimestampMixin):
+    __tablename__ = "investment_stages"
+
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False, unique=True)
+
+    # Relationships
+    funds = relationship(
+        "FundTable",
+        secondary=fund_investment_stages_association,
+        back_populates="investment_stages",
+    )
+
+
+class SectorTable(Base, TimestampMixin):
     __tablename__ = "sectors"
 
     id = Column(Integer, primary_key=True, index=True)
     name = Column(String, nullable=False)
 
-    # Add relationship back to investors
+    # Relationships
     investors = relationship(
         "InvestorTable",
         secondary=investor_sector_association,
         back_populates="sectors",
     )
+    companies = relationship(
+        "CompanyTable", secondary=company_sector_association, back_populates="sectors"
+    )
+    projects = relationship(
+        "ProjectTable", secondary=project_sector_association, back_populates="sector"
+    )
+    funds = relationship(
+        "FundTable",
+        secondary=fund_sectors_association,
+        back_populates="sectors",
+    )
 
 
-class InvestorTeamMember(Base):
+class ProjectTable(Base, TimestampMixin):
-    __tablename__ = "investor_team"
+    __tablename__ = "projects"
+
     id = Column(Integer, primary_key=True, index=True)
     name = Column(String, nullable=False)
-    role = Column(String, nullable=False)
+    valuation = Column(Integer, nullable=True)
-    email = Column(String, nullable=False)
 
-    investor_id = Column(Integer, ForeignKey("investors.id"))
+    stage = Column(Enum(InvestmentStage), nullable=True)
-    investor = relationship("InvestorTable", back_populates="team_members")
+    location = Column(String, nullable=True)
+    description = Column(Text, nullable=True)
+    start_date = Column(DateTime, nullable=True)
+    end_date = Column(DateTime, nullable=True)
+
+    sector = relationship(
+        "SectorTable", secondary=project_sector_association, back_populates="projects"
+    )
+    investors = relationship(
+        "InvestorTable",
+        secondary=project_investor_association,
+        back_populates="projects",
+    )
+    companies = relationship(
+        "CompanyTable", secondary=project_company_association, back_populates="projects"
+    )

app/db/new_schema.py

@@ -1,115 +0,0 @@
-import json
-from typing import List, Optional
-
-from pydantic import BaseModel
-from sqlalchemy import JSON, Column, DateTime, Integer, String, Text
-from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy.sql import func
-
-Base = declarative_base()
-
-
-class Investor(Base):
-    __tablename__ = "investors"
-
-    id = Column(Integer, primary_key=True, autoincrement=True)
-    name = Column(String(500), nullable=False)
-    website = Column(String(1000))
-
-    # Core investment information
-    investor_description = Column(Text)
-    investment_thesis_focus = Column(JSON)  # List of focus areas
-    headquarters = Column(String(1000))
-
-    # AUM information
-    aum_amount = Column(String(200))
-    aum_as_of_date = Column(String(100))
-    aum_source_url = Column(String(1000))
-
-    # Fund information
-    funds_info = Column(JSON)  # Complex fund data
-
-    # Raw data columns for reference
-    crunchbase_urls = Column(Text)
-    crunchbase_extract = Column(Text)
-    linkedin_profile = Column(Text)
-    source_truth_profile = Column(Text)
-
-    # Metadata
-    created_at = Column(DateTime(timezone=True), server_default=func.now())
-    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
-
-    def __repr__(self):
-        return f"<Investor(name='{self.name}', website='{self.website}')>"
-
-
-# Pydantic models for data validation and parsing
-class AUMInfo(BaseModel):
-    aumAmount: Optional[str] = None
-    asOfDate: Optional[str] = None
-    sourceUrl: Optional[str] = None
-
-
-class FundInfo(BaseModel):
-    fundName: Optional[str] = None
-    fundSize: Optional[str] = None
-    vintage: Optional[str] = None
-    status: Optional[str] = None
-    description: Optional[str] = None
-
-
-class InvestorProfile(BaseModel):
-    websiteURL: Optional[str] = None
-    investorDescription: Optional[str] = None
-    investmentThesisFocus: Optional[List[str]] = None
-    headquarters: Optional[str] = None
-    overallAssetsUnderManagement: Optional[AUMInfo] = None
-    funds: Optional[List[FundInfo]] = None
-
-
-class CSVRow(BaseModel):
-    name: str
-    website: Optional[str] = None
-    investment_firm_profile: Optional[str] = None
-    crunchbase_linkedin_urls: Optional[str] = None
-    crunchbase_firm_extract: Optional[str] = None
-    linkedin_investment_profile: Optional[str] = None
-    source_of_truth_profile: Optional[str] = None
-
-    def get_combined_description(self) -> str:
-        """Combine all description fields for vector embedding"""
-        descriptions = []
-
-        if self.investment_firm_profile:
-            try:
-                profile_data = json.loads(self.investment_firm_profile)
-                if isinstance(profile_data, dict):
-                    desc = profile_data.get("investorDescription", "")
-                    if desc:
-                        descriptions.append(desc)
-            except (json.JSONDecodeError, TypeError):
-                pass
-
-        if self.crunchbase_firm_extract:
-            descriptions.append(self.crunchbase_firm_extract)
-
-        if self.linkedin_investment_profile:
-            descriptions.append(self.linkedin_investment_profile)
-
-        if self.source_of_truth_profile:
-            descriptions.append(self.source_of_truth_profile)
-
-        return " ".join(descriptions)
-
-    def get_investment_focus(self) -> List[str]:
-        """Extract investment thesis focus"""
-        if self.investment_firm_profile:
-            try:
-                profile_data = json.loads(self.investment_firm_profile)
-                if isinstance(profile_data, dict):
-                    focus = profile_data.get("investmentThesisFocus", [])
-                    if isinstance(focus, list):
-                        return focus
-            except (json.JSONDecodeError, TypeError):
-                pass
-        return []

app/main.py

@@ -1,17 +1,27 @@
 import io
 
 import pandas as pd
-from api import companies, investors
+from db.db import Base, db_dependency, engine
-from db.db import db_dependency, init_database
+from dotenv import load_dotenv
-from fastapi import FastAPI, File, UploadFile
+from fastapi import FastAPI, File, Form, UploadFile
-from py_schemas import InvestorList
 from pydantic import BaseModel
-from services.openrouter_v2 import InvestorProcessor
+from routers import companies, investors, projects
+from schemas.router_schemas import InvestmentResponse, PaginatedResponse
+from services.llm_parser import InvestorProcessor
 from services.querying import QueryProcessor
 
-app = FastAPI()
+load_dotenv()
+
+
+def init_database():
+    """Initialize the database by creating all tables"""
+    Base.metadata.create_all(bind=engine)
+
+
 init_database()
 
+app = FastAPI()
+
 
 # Request models
 class QueryRequest(BaseModel):
@@ -20,7 +30,7 @@ class QueryRequest(BaseModel):
     class Config:
         json_schema_extra = {
             "example": {
-                "question": "Show me growth stage fintech investors in the US with check sizes over $1 million"
+                "question": "Find me deep tech investors that do deals in Europe under 5 million."
             }
         }
 
@@ -31,38 +41,75 @@ def health():
 
 
 @app.post("/parse-csv", tags=["CSV Upload"], response_model=list[dict])
-async def parse_csv(db: db_dependency, file: UploadFile = File(...)):
+async def parse_csv(
+    db: db_dependency, file: UploadFile = File(...), is_investor: int = Form(...)
+):
+    """
+    Parse and import CSV data into the database.
+
+    **For investors:**
+    - Expected columns: Name, Website, Final Investor Profile, Final Profile sourcing
+    - Manually parses JSON profiles for efficiency
+    - Uses LLM only for currency conversion to USD
+    - Handles AUM, fund sizes, and check sizes as integers
+
+    **For companies:**
+    - Expected columns: Name, Website, Investor, Final Investor Profile (company profile)
+    - 100% manual JSON parsing - no LLM needed
+    - Extracts company details, executives, investors, and client categories
+    - Automatically links companies to investors in database
+
+    **Benefits:**
+    - Fast processing (5-10s per record)
+    - Low cost (minimal or no LLM usage)
+    - Accurate data extraction
+    - Automatic database persistence
+    """
     # Read uploaded CSV with pandas
     content = await file.read()
     df = pd.read_csv(io.StringIO(content.decode("utf-8")))
 
     # Process the dataframe
-    processor = InvestorProcessor(sql_session=db)
+    processor = InvestorProcessor()
-    results = await processor.process_csv(df)
 
-    # Convert Pydantic objects to dictionaries
+    if is_investor == 1:
-    return [r.model_dump() for r in results]
+        # Manual parser with LLM currency conversion
+        results = await processor.parse_investors(df, save_to_db=True)
+        # Results are already dicts from the new parser
+        return results
+    else:
+        # Manual parser for companies (no LLM needed)
+        results = await processor.parse_companies(df, save_to_db=True)
+        # Results are already dicts from the new parser
+        return results
 
 
-@app.post("/query", response_model=InvestorList, tags=["Querying"])
+@app.post(
-async def query_investors(db: db_dependency, request: QueryRequest):
+    "/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"
     - "Growth stage investors with $5M+ check sizes"
     - "Healthcare investors in Europe"
     """
-    processor = QueryProcessor(sql_session=db)
+    processor = QueryProcessor()
     results = processor.process_query(request.question)
     return results
 
 
 app.include_router(investors.router)
 app.include_router(companies.router)
+app.include_router(projects.router)
+
 if __name__ == "__main__":
     import uvicorn
 
-    uvicorn.run(app="main:app", host="localhost", port=8000, reload=True)
+    uvicorn.run(app="main:app", host="0.0.0.0", port=8585, reload=True)

app/py_schemas.py

@@ -1,79 +0,0 @@
-from datetime import datetime
-from enum import Enum
-from typing import List, Optional
-
-from pydantic import BaseModel
-
-
-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/pydantic_schemas.py

@@ -1,38 +0,0 @@
-from typing import List
-
-from pydantic import BaseModel
-
-
-class Investor(BaseModel):
-    name: str
-    aum: int
-    check_size: str
-    sector_focus: str
-    stage_focus: str
-    region: str
-    investment_thesis: str
-    investor_description: str
-
-
-class InvestorList(BaseModel):
-    investor_list: List[Investor]
-
-
-class QueryResponse(BaseModel):
-    name: str
-    aum: int
-    check_size: str
-    sector_focus: str
-    stage_focus: str
-    region: str
-    investment_thesis: str
-    investor_description: str
-    reason: str
-
-
-class QueryRequest(BaseModel):
-    question: str
-
-
-class QueryResponseList(BaseModel):
-    responses: List[QueryResponse]

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 py_schemas import CompanySchema
 from pydantic import BaseModel
+from schemas.router_schemas import CompanyData, PaginatedResponse
 from sqlalchemy.orm import Session, selectinload
 
 router = APIRouter(tags=["Company Routes"])
@@ -15,6 +15,7 @@ class CompanyCreate(BaseModel):
     name: str
     industry: str
     location: str
+    description: Optional[str] = None
     founded_year: Optional[int] = None
     website: Optional[str] = None
 
@@ -23,52 +24,66 @@ class CompanyUpdate(BaseModel):
     name: Optional[str] = None
     industry: Optional[str] = None
     location: Optional[str] = None
+    description: Optional[str] = None
     founded_year: Optional[int] = None
     website: Optional[str] = None
 
 
-# Response schema with relationships
+@router.get("/companies", response_model=PaginatedResponse[CompanyData])
-class CompanyData(BaseModel):
+def read_companies(
-    """Comprehensive company data schema"""
+    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
 
-    company: CompanySchema
+    # Get total count
-    investors: List["InvestorBasic"] = []
+    total_count = (
+        db.query(CompanyTable)
+        .filter(CompanyTable.name.isnot(None), CompanyTable.description.isnot(None))
+        .count()
+    )
 
-    class Config:
+    # Get paginated results
-        from_attributes = True
-
-
-class InvestorBasic(BaseModel):
-    """Basic investor info for company responses"""
-
-    id: int
-    name: str
-    geographic_focus: str
-    stage_focus: str
-    check_size_lower: int
-    check_size_upper: int
-
-    class Config:
-        from_attributes = True
-
-
-@router.get("/companies", response_model=List[CompanyData])
-def read_companies(db: Session = Depends(get_db)):
-    """Get all companies with their investor relationships"""
     companies = (
-        db.query(CompanyTable).options(selectinload(CompanyTable.investors)).all()
+        db.query(CompanyTable)
+        .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()
     )
 
     # Transform CompanyTable objects to CompanyData format
     company_data_list = []
     for company in companies:
-        company_data = CompanyData(company=company, investors=company.investors)
+        company_data = CompanyData(
+            company=company,
+            investors=company.investors,
+            members=company.members,
+            sectors=company.sectors,
+        )
         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)"
@@ -84,12 +99,18 @@ 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(selectinload(CompanyTable.investors))
+    query = db.query(CompanyTable).options(
+        selectinload(CompanyTable.investors),
+        selectinload(CompanyTable.members),
+        selectinload(CompanyTable.sectors),
+    )
 
     # Apply filters
     if industry:
@@ -116,15 +137,34 @@ 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 = []
     for company in companies:
-        company_data = CompanyData(company=company, investors=company.investors)
+        company_data = CompanyData(
+            company=company,
+            investors=company.investors,
+            members=company.members,
+            sectors=company.sectors,
+        )
         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)
@@ -132,7 +172,11 @@ def read_company(company_id: int, db: Session = Depends(get_db)):
     """Get a specific company by ID with its investors"""
     company = (
         db.query(CompanyTable)
-        .options(selectinload(CompanyTable.investors))
+        .options(
+            selectinload(CompanyTable.investors),
+            selectinload(CompanyTable.members),
+            selectinload(CompanyTable.sectors),
+        )
         .filter(CompanyTable.id == company_id)
         .first()
     )
@@ -141,7 +185,12 @@ def read_company(company_id: int, db: Session = Depends(get_db)):
         raise HTTPException(status_code=404, detail="Company not found")
 
     # Transform to CompanyData format
-    return CompanyData(company=company, investors=company.investors)
+    return CompanyData(
+        company=company,
+        investors=company.investors,
+        members=company.members,
+        sectors=company.sectors,
+    )
 
 
 @router.post("/companies", response_model=CompanyData)
@@ -155,14 +204,21 @@ def create_company(company: CompanyCreate, db: Session = Depends(get_db)):
     # Reload with relationships
     company_with_relations = (
         db.query(CompanyTable)
-        .options(selectinload(CompanyTable.investors))
+        .options(
+            selectinload(CompanyTable.investors),
+            selectinload(CompanyTable.members),
+            selectinload(CompanyTable.sectors),
+        )
         .filter(CompanyTable.id == db_company.id)
         .first()
     )
 
     # Transform to CompanyData format
     return CompanyData(
-        company=company_with_relations, investors=company_with_relations.investors
+        company=company_with_relations,
+        investors=company_with_relations.investors,
+        members=company_with_relations.members,
+        sectors=company_with_relations.sectors,
     )
 
 
@@ -185,14 +241,21 @@ def update_company(
     # Reload with relationships
     company_with_relations = (
         db.query(CompanyTable)
-        .options(selectinload(CompanyTable.investors))
+        .options(
+            selectinload(CompanyTable.investors),
+            selectinload(CompanyTable.members),
+            selectinload(CompanyTable.sectors),
+        )
         .filter(CompanyTable.id == company_id)
         .first()
     )
 
     # Transform to CompanyData format
     return CompanyData(
-        company=company_with_relations, investors=company_with_relations.investors
+        company=company_with_relations,
+        investors=company_with_relations.investors,
+        members=company_with_relations.members,
+        sectors=company_with_relations.sectors,
     )
 
 

app/routers/investors.py

@@ -0,0 +1,555 @@
+from typing import Optional
+
+from db.db import get_db
+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,
+    PaginatedResponse,
+    SectorMinimal,
+)
+from sqlalchemy.orm import Session, selectinload
+
+router = APIRouter(tags=["Investor Routes"])
+
+
+# Request schemas for creating/updating
+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
+    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
+    number_of_investments: Optional[int] = None
+
+
+@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(
+            selectinload(InvestorTable.portfolio_companies),
+            selectinload(InvestorTable.team_members),
+            selectinload(InvestorTable.sectors),
+            selectinload(InvestorTable.funds),
+        )
+        .offset(offset)
+        .limit(page_size)
+        .all()
+    )
+
+    # 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:
+                # 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)
+        else:
+            # If no funds, create one entry with null fund fields
+            investment_response = InvestmentResponse(
+                id=investor.id,
+                name=investor.name,
+                aum=investor.aum,
+                check_size_lower=None,
+                check_size_upper=None,
+                geographic_focus=None,
+                stage_focus=None,
+                portfolio_companies=portfolio_companies,
+                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/filter", response_model=PaginatedResponse[InvestmentResponse])
+def filter_investors(
+    stage: Optional[InvestmentStage] = Query(
+        None, description="Filter by investment stage"
+    ),
+    min_check_size: Optional[int] = Query(None, description="Minimum check size"),
+    max_check_size: Optional[int] = Query(None, description="Maximum check size"),
+    geography: Optional[str] = Query(
+        None, description="Geographic focus (partial match)"
+    ),
+    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 (paginated)
+
+    Returns investor-fund combinations as separate rows.
+    Queries the funds table to find matching 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 at fund level
+    if min_check_size is not None:
+        query = query.filter(FundTable.check_size_lower >= min_check_size)
+
+    if max_check_size is not None:
+        query = query.filter(FundTable.check_size_upper <= max_check_size)
+
+    if 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.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 (at fund level)
+    if sector:
+        query = query.join(FundTable.sectors).filter(
+            SectorTable.name.ilike(f"%{sector}%")
+        )
+
+    # Get total count before pagination
+    total_count = query.count()
+
+    # Calculate offset and apply pagination
+    offset = (page - 1) * page_size
+    funds = query.offset(offset).limit(page_size).all()
+
+    # 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)
+def read_investor(investor_id: int, db: Session = Depends(get_db)):
+    """Get a specific investor by ID with all their funds"""
+    investor = (
+        db.query(InvestorTable)
+        .options(
+            selectinload(InvestorTable.portfolio_companies),
+            selectinload(InvestorTable.team_members),
+            selectinload(InvestorTable.sectors),
+            selectinload(InvestorTable.funds),
+        )
+        .filter(InvestorTable.id == investor_id)
+        .first()
+    )
+
+    if not investor:
+        raise HTTPException(status_code=404, detail="Investor not found")
+
+    # Transform to InvestorData format (includes funds array)
+    return InvestorData(
+        investor=investor,
+        portfolio_companies=investor.portfolio_companies,
+        team_members=investor.team_members,
+        sectors=investor.sectors,
+        funds=investor.funds,
+    )
+
+
+@router.post("/investors", response_model=InvestorData)
+def create_investor(investor: InvestorCreate, db: Session = Depends(get_db)):
+    """Create a new investor"""
+    db_investor = InvestorTable(**investor.dict())
+    db.add(db_investor)
+    db.commit()
+    db.refresh(db_investor)
+
+    # Reload with relationships
+    investor_with_relations = (
+        db.query(InvestorTable)
+        .options(
+            selectinload(InvestorTable.portfolio_companies),
+            selectinload(InvestorTable.team_members),
+            selectinload(InvestorTable.sectors),
+            selectinload(InvestorTable.funds),
+        )
+        .filter(InvestorTable.id == db_investor.id)
+        .first()
+    )
+
+    # Transform to InvestorData format
+    return InvestorData(
+        investor=investor_with_relations,
+        portfolio_companies=investor_with_relations.portfolio_companies,
+        team_members=investor_with_relations.team_members,
+        sectors=investor_with_relations.sectors,
+        funds=investor_with_relations.funds,
+    )
+
+
+@router.put("/investors/{investor_id}", response_model=InvestorData)
+def update_investor(
+    investor_id: int, investor: InvestorUpdate, db: Session = Depends(get_db)
+):
+    """Update an existing investor"""
+    db_investor = (
+        db.query(InvestorTable).filter(InvestorTable.id == investor_id).first()
+    )
+    if not db_investor:
+        raise HTTPException(status_code=404, detail="Investor not found")
+
+    update_data = investor.dict(exclude_unset=True)
+    for field, value in update_data.items():
+        setattr(db_investor, field, value)
+
+    db.commit()
+    db.refresh(db_investor)
+
+    # Reload with relationships
+    investor_with_relations = (
+        db.query(InvestorTable)
+        .options(
+            selectinload(InvestorTable.portfolio_companies),
+            selectinload(InvestorTable.team_members),
+            selectinload(InvestorTable.sectors),
+            selectinload(InvestorTable.funds),
+        )
+        .filter(InvestorTable.id == investor_id)
+        .first()
+    )
+
+    # Transform to InvestorData format
+    return InvestorData(
+        investor=investor_with_relations,
+        portfolio_companies=investor_with_relations.portfolio_companies,
+        team_members=investor_with_relations.team_members,
+        sectors=investor_with_relations.sectors,
+        funds=investor_with_relations.funds,
+    )
+
+
+@router.delete("/investors/{investor_id}")
+def delete_investor(investor_id: int, db: Session = Depends(get_db)):
+    """Delete an investor"""
+    db_investor = (
+        db.query(InvestorTable).filter(InvestorTable.id == investor_id).first()
+    )
+    if not db_investor:
+        raise HTTPException(status_code=404, detail="Investor not found")
+
+    db.delete(db_investor)
+    db.commit()
+    return {"message": "Investor deleted successfully"}
+
+
+@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 (paginated)
+
+    Returns investor-fund combinations as separate rows.
+    Queries the funds table to find matching funds.
+    """
+
+    # 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(FundTable.investment_stages),
+            selectinload(InvestorTable.funds).selectinload(FundTable.sectors),
+        )
+        .filter(InvestorTable.id == investor_id)
+        .first()
+    )
+
+    if not target_investor:
+        raise HTTPException(status_code=404, detail="Investor not found")
+
+    # Get target investor's sector IDs for comparison (from their funds)
+    target_sector_ids = set()
+    target_stage_ids = set()
+    target_check_ranges = []
+    target_geographies = []
+
+    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(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 for each fund
+    scored_funds = []
+    for fund in candidate_funds:
+        score = 0
+
+        # Geographic focus match (20 points for exact, 10 for partial)
+        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 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 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)
+        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)
+
+        # 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)
+
+        if score > 0:  # Only include funds with some similarity
+            scored_funds.append((score, fund))
+
+    # 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]
+
+    # 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

@@ -0,0 +1,486 @@
+from typing import List, Optional
+
+from db.db import get_db
+from db.models import (
+    CompanyTable,
+    InvestorTable,
+    ProjectTable,
+    SectorTable,
+)
+from fastapi import APIRouter, Depends, HTTPException, Query
+from schemas.project_schemas import (
+    InvestmentStage,
+    ProjectCreate,
+    ProjectData,
+    ProjectUpdate,
+)
+from schemas.router_schemas import PaginatedResponse
+from sqlalchemy.orm import Session, selectinload
+
+router = APIRouter(tags=["Project Routes"])
+
+
+@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(
+            selectinload(ProjectTable.sector),
+            selectinload(ProjectTable.investors),
+            selectinload(ProjectTable.companies),
+        )
+        .offset(offset)
+        .limit(page_size)
+        .all()
+    )
+
+    # Transform ProjectTable objects to ProjectData format
+    project_data_list = []
+    for project in projects:
+        project_data = ProjectData(
+            project=project,
+            sector=project.sector,
+            investors=project.investors,
+            companies=project.companies,
+        )
+        project_data_list.append(project_data)
+
+    # 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)
+def read_project(project_id: int, db: Session = Depends(get_db)):
+    """Get a specific project by ID"""
+    project = (
+        db.query(ProjectTable)
+        .options(
+            selectinload(ProjectTable.sector),
+            selectinload(ProjectTable.investors),
+            selectinload(ProjectTable.companies),
+        )
+        .filter(ProjectTable.id == project_id)
+        .first()
+    )
+
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    return ProjectData(
+        project=project,
+        sector=project.sector,
+        investors=project.investors,
+        companies=project.companies,
+    )
+
+
+@router.post("/projects", response_model=ProjectData)
+def create_project(project: ProjectCreate, db: Session = Depends(get_db)):
+    """Create a new project"""
+    db_project = ProjectTable(**project.dict())
+    db.add(db_project)
+    db.commit()
+    db.refresh(db_project)
+
+    # Reload with relationships
+    db_project = (
+        db.query(ProjectTable)
+        .options(
+            selectinload(ProjectTable.sector),
+            selectinload(ProjectTable.investors),
+            selectinload(ProjectTable.companies),
+        )
+        .filter(ProjectTable.id == db_project.id)
+        .first()
+    )
+
+    return ProjectData(
+        project=db_project,
+        sector=db_project.sector,
+        investors=db_project.investors,
+        companies=db_project.companies,
+    )
+
+
+@router.put("/projects/{project_id}", response_model=ProjectData)
+def update_project(
+    project_id: int, project: ProjectUpdate, db: Session = Depends(get_db)
+):
+    """Update an existing project"""
+    db_project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+
+    if not db_project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Update only provided fields
+    update_data = project.dict(exclude_unset=True)
+    for key, value in update_data.items():
+        setattr(db_project, key, value)
+
+    db.commit()
+    db.refresh(db_project)
+
+    # Reload with relationships
+    db_project = (
+        db.query(ProjectTable)
+        .options(
+            selectinload(ProjectTable.sector),
+            selectinload(ProjectTable.investors),
+            selectinload(ProjectTable.companies),
+        )
+        .filter(ProjectTable.id == project_id)
+        .first()
+    )
+
+    return ProjectData(
+        project=db_project,
+        sector=db_project.sector,
+        investors=db_project.investors,
+        companies=db_project.companies,
+    )
+
+
+@router.delete("/projects/{project_id}")
+def delete_project(project_id: int, db: Session = Depends(get_db)):
+    """Delete a project"""
+    db_project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+
+    if not db_project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    db.delete(db_project)
+    db.commit()
+
+    return {"message": "Project deleted successfully"}
+
+
+@router.get("/projects/filter", response_model=PaginatedResponse[ProjectData])
+def filter_projects(
+    stage: Optional[InvestmentStage] = Query(
+        None, description="Filter by project stage"
+    ),
+    min_valuation: Optional[int] = Query(None, description="Minimum valuation"),
+    max_valuation: Optional[int] = Query(None, description="Maximum valuation"),
+    location: Optional[str] = Query(None, description="Location (partial match)"),
+    sector: Optional[str] = Query(None, description="Sector name (partial match)"),
+    investor_name: Optional[str] = Query(
+        None, description="Investor name (partial match)"
+    ),
+    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 (paginated)"""
+
+    # Start with base query
+    query = db.query(ProjectTable).options(
+        selectinload(ProjectTable.sector),
+        selectinload(ProjectTable.investors),
+        selectinload(ProjectTable.companies),
+    )
+
+    # Apply filters
+    if stage:
+        query = query.filter(ProjectTable.stage == stage)
+
+    if min_valuation is not None:
+        query = query.filter(ProjectTable.valuation >= min_valuation)
+
+    if max_valuation is not None:
+        query = query.filter(ProjectTable.valuation <= max_valuation)
+
+    if location:
+        query = query.filter(ProjectTable.location.ilike(f"%{location}%"))
+
+    if sector:
+        query = query.join(ProjectTable.sector).filter(
+            SectorTable.name.ilike(f"%{sector}%")
+        )
+
+    if investor_name:
+        query = query.join(ProjectTable.investors).filter(
+            InvestorTable.name.ilike(f"%{investor_name}%")
+        )
+
+    if company_name:
+        query = query.join(ProjectTable.companies).filter(
+            CompanyTable.name.ilike(f"%{company_name}%")
+        )
+
+    # 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 = []
+    for project in projects:
+        project_data = ProjectData(
+            project=project,
+            sector=project.sector,
+            investors=project.investors,
+            companies=project.companies,
+        )
+        project_data_list.append(project_data)
+
+    # 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
+@router.post("/projects/{project_id}/investors/{investor_id}")
+def add_investor_to_project(
+    project_id: int, investor_id: int, db: Session = Depends(get_db)
+):
+    """Add an investor to a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Check if investor exists
+    investor = db.query(InvestorTable).filter(InvestorTable.id == investor_id).first()
+    if not investor:
+        raise HTTPException(status_code=404, detail="Investor not found")
+
+    # Check if association already exists
+    if investor in project.investors:
+        raise HTTPException(
+            status_code=400, detail="Investor already associated with project"
+        )
+
+    # Add association
+    project.investors.append(investor)
+    db.commit()
+
+    return {"message": "Investor added to project successfully"}
+
+
+@router.delete("/projects/{project_id}/investors/{investor_id}")
+def remove_investor_from_project(
+    project_id: int, investor_id: int, db: Session = Depends(get_db)
+):
+    """Remove an investor from a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Check if investor exists
+    investor = db.query(InvestorTable).filter(InvestorTable.id == investor_id).first()
+    if not investor:
+        raise HTTPException(status_code=404, detail="Investor not found")
+
+    # Check if association exists
+    if investor not in project.investors:
+        raise HTTPException(
+            status_code=400, detail="Investor not associated with project"
+        )
+
+    # Remove association
+    project.investors.remove(investor)
+    db.commit()
+
+    return {"message": "Investor removed from project successfully"}
+
+
+@router.post("/projects/{project_id}/companies/{company_id}")
+def add_company_to_project(
+    project_id: int, company_id: int, db: Session = Depends(get_db)
+):
+    """Add a company to a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Check if company exists
+    company = db.query(CompanyTable).filter(CompanyTable.id == company_id).first()
+    if not company:
+        raise HTTPException(status_code=404, detail="Company not found")
+
+    # Check if association already exists
+    if company in project.companies:
+        raise HTTPException(
+            status_code=400, detail="Company already associated with project"
+        )
+
+    # Add association
+    project.companies.append(company)
+    db.commit()
+
+    return {"message": "Company added to project successfully"}
+
+
+@router.delete("/projects/{project_id}/companies/{company_id}")
+def remove_company_from_project(
+    project_id: int, company_id: int, db: Session = Depends(get_db)
+):
+    """Remove a company from a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Check if company exists
+    company = db.query(CompanyTable).filter(CompanyTable.id == company_id).first()
+    if not company:
+        raise HTTPException(status_code=404, detail="Company not found")
+
+    # Check if association exists
+    if company not in project.companies:
+        raise HTTPException(
+            status_code=400, detail="Company not associated with project"
+        )
+
+    # Remove association
+    project.companies.remove(company)
+    db.commit()
+
+    return {"message": "Company removed from project successfully"}
+
+
+@router.post("/projects/{project_id}/sectors/{sector_id}")
+def add_sector_to_project(
+    project_id: int, sector_id: int, db: Session = Depends(get_db)
+):
+    """Add a sector to a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Check if sector exists
+    sector = db.query(SectorTable).filter(SectorTable.id == sector_id).first()
+    if not sector:
+        raise HTTPException(status_code=404, detail="Sector not found")
+
+    # Check if association already exists
+    if sector in project.sector:
+        raise HTTPException(
+            status_code=400, detail="Sector already associated with project"
+        )
+
+    # Add association
+    project.sector.append(sector)
+    db.commit()
+
+    return {"message": "Sector added to project successfully"}
+
+
+@router.delete("/projects/{project_id}/sectors/{sector_id}")
+def remove_sector_from_project(
+    project_id: int, sector_id: int, db: Session = Depends(get_db)
+):
+    """Remove a sector from a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Check if sector exists
+    sector = db.query(SectorTable).filter(SectorTable.id == sector_id).first()
+    if not sector:
+        raise HTTPException(status_code=404, detail="Sector not found")
+
+    # Check if association exists
+    if sector not in project.sector:
+        raise HTTPException(
+            status_code=400, detail="Sector not associated with project"
+        )
+
+    # Remove association
+    project.sector.remove(sector)
+    db.commit()
+
+    return {"message": "Sector removed from project successfully"}
+
+
+# Bulk association management
+@router.post("/projects/{project_id}/investors")
+def add_multiple_investors_to_project(
+    project_id: int, investor_ids: List[int], db: Session = Depends(get_db)
+):
+    """Add multiple investors to a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Get all investors
+    investors = db.query(InvestorTable).filter(InvestorTable.id.in_(investor_ids)).all()
+
+    if len(investors) != len(investor_ids):
+        raise HTTPException(status_code=404, detail="One or more investors not found")
+
+    # Add associations (only if not already associated)
+    added_count = 0
+    for investor in investors:
+        if investor not in project.investors:
+            project.investors.append(investor)
+            added_count += 1
+
+    db.commit()
+
+    return {"message": f"Added {added_count} investors to project successfully"}
+
+
+@router.post("/projects/{project_id}/companies")
+def add_multiple_companies_to_project(
+    project_id: int, company_ids: List[int], db: Session = Depends(get_db)
+):
+    """Add multiple companies to a project"""
+    # Check if project exists
+    project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Get all companies
+    companies = db.query(CompanyTable).filter(CompanyTable.id.in_(company_ids)).all()
+
+    if len(companies) != len(company_ids):
+        raise HTTPException(status_code=404, detail="One or more companies not found")
+
+    # Add associations (only if not already associated)
+    added_count = 0
+    for company in companies:
+        if company not in project.companies:
+            project.companies.append(company)
+            added_count += 1
+
+    db.commit()
+
+    return {"message": f"Added {added_count} companies to project successfully"}

app/schemas/project_schemas.py

@@ -0,0 +1,117 @@
+from datetime import datetime
+from enum import Enum
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+
+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 InvestorSchema(BaseModel):
+    id: int
+    name: str
+    description: Optional[str]
+    aum: int | None
+    check_size_lower: int | None
+    check_size_upper: int | None
+    geographic_focus: str | None
+    stage_focus: InvestmentStage
+    number_of_investments: int | None
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class CompanySchema(BaseModel):
+    id: int
+    name: str
+    industry: str | None
+    location: str | None
+    description: Optional[str]
+    founded_year: Optional[int]
+    website: Optional[str]
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class ProjectSchema(BaseModel):
+    id: int
+    name: str
+    valuation: int | None
+    stage: InvestmentStage | None
+    location: str | None
+    description: Optional[str]
+    start_date: Optional[datetime]
+    end_date: Optional[datetime]
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class ProjectCreate(BaseModel):
+    name: str
+    valuation: Optional[int] = None
+    stage: Optional[InvestmentStage] = None
+    location: Optional[str] = None
+    description: Optional[str] = None
+    start_date: Optional[datetime] = None
+    end_date: Optional[datetime] = None
+
+
+class ProjectUpdate(BaseModel):
+    name: Optional[str] = None
+    valuation: Optional[int] = None
+    stage: Optional[InvestmentStage] = None
+    location: Optional[str] = None
+    description: Optional[str] = None
+    start_date: Optional[datetime] = None
+    end_date: Optional[datetime] = None
+
+
+class ProjectData(BaseModel):
+    """Comprehensive project data schema"""
+
+    project: ProjectSchema
+    sector: List[SectorSchema]
+    investors: List[InvestorSchema]
+    companies: List[CompanySchema]
+
+    class Config:
+        from_attributes = True
+
+
+class ProjectInvestorAssociation(BaseModel):
+    project_id: int
+    investor_id: int
+
+
+class ProjectCompanyAssociation(BaseModel):
+    project_id: int
+    company_id: int
+
+
+class ProjectSectorAssociation(BaseModel):
+    project_id: int
+    sector_id: int

app/schemas/py_schemas.py

@@ -0,0 +1,352 @@
+from enum import Enum
+from typing import List, Optional
+
+from pydantic import BaseModel, Field, field_validator
+
+
+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):
+    """
+    Expert parser: Only extract sector information if clearly identifiable.
+    Leave name empty if uncertain about the sector classification.
+    """
+
+    id: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Sector ID, must be 0 or greater. Use 0 if uncertain.",
+    )
+    name: Optional[str] = Field(
+        default=None,
+        description="Sector name. Leave empty string if not clearly identifiable from the data.",
+    )
+
+    @field_validator("name", mode="before")
+    @classmethod
+    def empty_string_to_none(cls, v):
+        """Convert empty strings to None"""
+        if v == "" or (isinstance(v, str) and v.strip() == ""):
+            return None
+        return v
+
+    @field_validator("id", mode="before")
+    @classmethod
+    def zero_to_none(cls, v):
+        """Convert 0 to None for optional id field"""
+        if v == 0:
+            return None
+        return v
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorMemberSchema(BaseModel):
+    """
+    Expert parser: Only extract team member information if clearly identifiable.
+    Leave fields empty if uncertain about the member details.
+    """
+
+    id: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Member ID, must be 0 or greater. Use 0 if uncertain.",
+    )
+    name: Optional[str] = Field(
+        default=None,
+        description="Team member name. Leave empty string if not clearly identifiable.",
+    )
+    role: Optional[str] = Field(
+        default=None,
+        description="Team member role/title. Leave empty string if not clearly identifiable.",
+    )
+    email: Optional[str] = Field(
+        default=None,
+        description="Team member email. Leave empty string if not clearly identifiable or not provided.",
+    )
+    investor_id: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Investor ID, must be 0 or greater. Use 0 if uncertain.",
+    )
+
+    @field_validator("name", "role", "email", mode="before")
+    @classmethod
+    def empty_string_to_none(cls, v):
+        """Convert empty strings to None"""
+        if v == "" or (isinstance(v, str) and v.strip() == ""):
+            return None
+        return v
+
+    @field_validator("id", "investor_id", mode="before")
+    @classmethod
+    def zero_to_none(cls, v):
+        """Convert 0 to None for optional integer fields"""
+        if v == 0:
+            return None
+        return v
+
+    class Config:
+        from_attributes = True
+
+
+class CompanyMemberSchema(BaseModel):
+    """
+    Expert parser: Only extract company member information if clearly identifiable.
+    Leave fields empty if uncertain about the member details.
+    """
+
+    id: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Member ID, must be 0 or greater. Use 0 if uncertain.",
+    )
+    name: Optional[str] = Field(
+        default=None,
+        description="Company member name. Leave empty if not clearly identifiable.",
+    )
+    linkedin: Optional[str] = Field(
+        default=None,
+        description="LinkedIn profile URL. Leave empty if not provided or uncertain.",
+    )
+    role: Optional[str] = Field(
+        default=None,
+        description="Company member role/title. Leave empty if not clearly identifiable.",
+    )
+    company_id: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Company ID, must be 0 or greater. Use 0 if uncertain.",
+    )
+
+    @field_validator("name", "linkedin", "role", mode="before")
+    @classmethod
+    def empty_string_to_none(cls, v):
+        """Convert empty strings to None"""
+        if v == "" or (isinstance(v, str) and v.strip() == ""):
+            return None
+        return v
+
+    @field_validator("id", "company_id", mode="before")
+    @classmethod
+    def zero_to_none(cls, v):
+        """Convert 0 to None for optional integer fields"""
+        if v == 0:
+            return None
+        return v
+
+    class Config:
+        from_attributes = True
+
+
+class CompanySchema(BaseModel):
+    """
+    Expert parser: Only extract company information if clearly identifiable.
+    Leave optional fields empty if uncertain. Integer values must be 0 or greater.
+    """
+
+    id: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Company ID, must be 0 or greater. Use 0 if uncertain.",
+    )
+    name: Optional[str] = Field(
+        default=None,
+        description="Company name. Leave empty string if not clearly identifiable.",
+    )
+    industry: Optional[str] = Field(
+        default=None,
+        description="Company industry/sector. Leave empty string if not clearly identifiable.",
+    )
+    location: Optional[str] = Field(
+        default=None,
+        description="Company location/address. Leave empty string if not clearly identifiable.",
+    )
+    description: Optional[str] = Field(
+        default=None,
+        description="Company description. Leave empty if not clearly available or uncertain.",
+    )
+    founded_year: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Year company was founded, must be 0 or greater. Leave None if not clearly identifiable or uncertain.",
+    )
+    website: Optional[str] = Field(
+        default=None,
+        description="Company website URL. Leave empty if not provided or uncertain.",
+    )
+
+    @field_validator(
+        "name", "industry", "location", "description", "website", mode="before"
+    )
+    @classmethod
+    def empty_string_to_none(cls, v):
+        """Convert empty strings to None"""
+        if v == "" or (isinstance(v, str) and v.strip() == ""):
+            return None
+        return v
+
+    @field_validator("id", "founded_year", mode="before")
+    @classmethod
+    def zero_to_none(cls, v):
+        """Convert 0 to None for founded_year"""
+        if v == 0:
+            return None
+        return v
+
+    @field_validator("founded_year", mode="before")
+    @classmethod
+    def validate_founded_year(cls, v):
+        """Expert parser: Only accept clearly identifiable founding years"""
+        if v is None or v == "Not Available" or v == "" or v == "Unknown":
+            return None
+        if isinstance(v, str):
+            try:
+                year = int(v)
+                return year if year >= 0 else None
+            except ValueError:
+                return None
+        return v if isinstance(v, int) and v >= 0 else None
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorSchema(BaseModel):
+    """
+    Expert parser: Only extract investor information if clearly identifiable.
+    Leave optional fields empty if uncertain. All numeric values must be 0 or greater.
+    """
+
+    id: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Investor ID, must be 0 or greater. Use 0 if uncertain.",
+    )
+    name: Optional[str] = Field(
+        default=None,
+        description="Investor name. Do not return any special characters, Just the name as a string.",
+    )
+    description: Optional[str] = Field(
+        default=None,
+        description="Investor description. Leave empty if not clearly available or uncertain.",
+    )
+    aum: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Assets Under Management in USD, must be 0 or greater. Use 0 if not clearly identifiable or uncertain.",
+    )
+    check_size_lower: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Lower bound of typical investment check size in USD, must be 0 or greater. Use 0 if not clearly identifiable.",
+    )
+    check_size_upper: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Upper bound of typical investment check size in USD, must be 0 or greater. Use 0 if not clearly identifiable.",
+    )
+    geographic_focus: Optional[str] = Field(
+        default=None,
+        description="Geographic investment focus. Do not return any special characters, Just locations separated by commas. Leave empty if not clearly identifiable.",
+    )
+    number_of_investments: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="Total number of investments made, must be 0 or greater. Use 0 if not clearly identifiable.",
+    )
+
+    @field_validator("name", "description", "geographic_focus", mode="before")
+    @classmethod
+    def empty_string_to_none(cls, v):
+        """Convert empty strings to None"""
+        if v == "" or (isinstance(v, str) and v.strip() == ""):
+            return None
+        return v
+
+    @field_validator(
+        "id",
+        "aum",
+        "check_size_lower",
+        "check_size_upper",
+        "number_of_investments",
+        mode="before",
+    )
+    @classmethod
+    def zero_to_none(cls, v):
+        """Convert 0 to None for optional integer fields"""
+        if v == 0:
+            return None
+        return v
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorData(BaseModel):
+    """
+    Expert parser: Comprehensive investor data schema for LLM processing.
+    Only populate fields with clearly identifiable information. Leave lists empty if uncertain.
+    """
+
+    investor: InvestorSchema = Field(
+        description="Core investor information. Only populate with clearly identifiable data."
+    )
+    portfolio_companies: List[CompanySchema] = Field(
+        default=[],
+        description="List of portfolio companies. Leave empty if not clearly identifiable.",
+    )
+    team_members: List[InvestorMemberSchema] = Field(
+        default=[],
+        description="List of team members. Leave empty if not clearly identifiable.",
+    )
+    sectors: List[SectorSchema] = Field(
+        default=[],
+        description="List of investment sectors. Leave empty if not clearly identifiable.",
+    )
+
+    class Config:
+        from_attributes = True
+
+
+class CompanyData(BaseModel):
+    """
+    Expert parser: Comprehensive company data schema for LLM processing.
+    Only populate fields with clearly identifiable information. Leave lists empty if uncertain.
+    """
+
+    company: CompanySchema = Field(
+        description="Core company information. Only populate with clearly identifiable data."
+    )
+    sectors: List[SectorSchema] = Field(
+        default=[],
+        description="List of company sectors. Leave empty if not clearly identifiable.",
+    )
+    members: List[CompanyMemberSchema] = Field(
+        default=[],
+        description="List of company members. Leave empty if not clearly identifiable.",
+    )
+    investors: List[InvestorSchema] = Field(
+        default=[],
+        description="List of investors. Leave empty if not clearly identifiable.",
+    )
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorList(BaseModel):
+    """Expert parser: List of investors with clearly identifiable information only."""
+
+    investors: List[InvestorData] = Field(
+        default=[],
+        description="List of investors. Leave empty if no clearly identifiable investors.",
+    )

app/schemas/router_schemas.py

@@ -0,0 +1,262 @@
+from datetime import datetime
+from enum import Enum
+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"
+    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 InvestmentStageSchema(BaseModel):
+    id: int
+    name: str
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorMemberSchema(BaseModel):
+    id: int
+    name: str
+    role: str | None
+    email: str | None
+
+    class Config:
+        from_attributes = True
+
+
+class FundSchema(BaseModel):
+    id: int
+    fund_name: str | None
+    fund_size: int | None  # Changed to int for numerical filtering
+    fund_size_source_url: str | None
+    check_size_lower: int | None  # NEW: Lower bound of check size range
+    check_size_upper: int | None  # NEW: Upper bound of check size range
+    source_url: str | None
+    source_provider: str | None
+    geographic_focus: str | None  # Changed from List[str] to string
+    investment_stages: List[InvestmentStageSchema] | None  # Changed to relationship
+    sectors: List[SectorSchema] | None  # Changed to relationship
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class CompanyMemberSchema(BaseModel):
+    id: int
+    name: Optional[str]
+    linkedin: Optional[str]
+    role: Optional[str]
+    company_id: int
+
+    class Config:
+        from_attributes = True
+
+
+class CompanySchema(BaseModel):
+    id: int
+    name: str
+    industry: str | None
+    location: str | None
+    description: Optional[str]
+    founded_year: Optional[int]
+    website: Optional[str]
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+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
+    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
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorData(BaseModel):
+    """Comprehensive investor data schema - used for individual investor requests"""
+
+    investor: InvestorSchema
+    portfolio_companies: List[CompanySchema]
+    team_members: List[InvestorMemberSchema]
+    sectors: List[SectorSchema]
+    funds: List[FundSchema]
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorFundData(BaseModel):
+    """Investor-Fund combined data - used for list/filter requests
+
+    Each row represents one investor-fund combination.
+    An investor with 3 funds will appear as 3 separate entries.
+    """
+
+    # Investor fields
+    investor_id: int
+    investor_name: str
+    investor_description: Optional[str]
+    investor_website: Optional[str]
+    investor_headquarters: Optional[str]
+    aum: int | None
+    aum_as_of_date: str | None
+    aum_source_url: str | None
+    investment_thesis: Any = None  # Flexible JSON field
+    portfolio_highlights: Any = None  # Flexible JSON field
+    number_of_investments: int | None
+
+    # Fund fields
+    fund_id: int | None
+    fund_name: str | None
+    fund_size: int | None  # Changed to int for numerical filtering
+    fund_size_source_url: str | None
+    check_size_lower: int | None  # NEW: Lower bound of check size range
+    check_size_upper: int | None  # NEW: Upper bound of check size range
+    geographic_focus: str | None  # Changed from List[str] to string
+    fund_investment_stages: (
+        List[InvestmentStageSchema] | None
+    )  # Changed to relationship
+    fund_sectors: List[SectorSchema] | None  # Changed to relationship
+
+    # Related data
+    portfolio_companies: List[CompanySchema]
+    team_members: List[InvestorMemberSchema]
+    sectors: List[SectorSchema]
+
+    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: CompanySchemaMinimal
+    investors: List[InvestorMinimal]
+
+    class Config:
+        from_attributes = True
+
+
+class InvestorList(BaseModel):
+    investors: List[InvestorData]
+
+
+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/openrouter.py

@@ -1,293 +0,0 @@
-import asyncio
-from typing import List, Optional
-
-import chromadb
-import pandas as pd
-from db.models import CompanyTable, InvestorTable, InvestorTeamMember, SectorTable
-from langchain_core.prompts import PromptTemplate
-from langchain_openai import ChatOpenAI
-from py_schemas import InvestorData
-from pydantic import BaseModel
-from settings import settings
-
-
-class InvestorList(BaseModel):
-    """Schema for LLM structured output"""
-
-    investor_list: List[InvestorData]
-
-
-class InvestorProcessor:
-    def __init__(
-        self,
-        sql_session: Optional[object] = None,
-        vector_db_client: Optional[object] = None,
-    ):
-        self.template = """You are an expert data extraction assistant. Extract investor information from the provided CSV data and return it as a list of structured records.
-
-Given the following CSV data rows:
-{question}
-
-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_lower: Lower bound of investment check size (as integer)
-- check_size_upper: Upper bound of investment check size (as integer)
-- geographic_focus: Geographic region focus
-- stage_focus: Investment stage focus (must be one of: seed, series_a, series_b, series_c, growth, late_stage)
-- number_of_investments: Number of investments made (default 0)
-
-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, use appropriate defaults
-- stage_focus must be one of the valid enum values
-- Return clean, valid JSON only
-
-Return the data as a structured list of comprehensive investor data."""
-
-        self.prompt = PromptTemplate(
-            template=self.template, input_variables=["question"]
-        )
-
-        self.llm = ChatOpenAI(
-            api_key=settings.OPENROUTER_API_KEY,
-            base_url="https://openrouter.ai/api/v1",
-            model="google/gemini-2.5-flash-lite",
-            temperature=0,
-        )
-
-        self.structured_llm = self.llm.with_structured_output(InvestorList)
-        self.sql_session = sql_session
-        self.vector_db_client = vector_db_client
-
-        self.vector_db_client = chromadb.PersistentClient(path="./chroma_db")
-        self.collection = self.vector_db_client.get_or_create_collection(
-            name="investor_descriptions",
-            metadata={
-                "description": "Investor descriptions and investment thesis focus"
-            },
-        )
-
-    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 = ""
-        for idx, row in batch.iterrows():
-            # Clean values to remove control characters
-            cleaned_row = {}
-            for key, value in row.items():
-                if pd.notna(value):
-                    # Convert to string and clean control characters
-                    clean_value = (
-                        str(value)
-                        .replace("\n", " ")
-                        .replace("\r", " ")
-                        .replace("\t", " ")
-                    )
-                    # Remove other control characters
-                    clean_value = "".join(
-                        char
-                        for char in clean_value
-                        if ord(char) >= 32 or char in ["\n", "\r", "\t"]
-                    )
-                    cleaned_row[key] = clean_value
-
-            row_str = ", ".join(
-                [f"{key}: {value}" for key, value in cleaned_row.items()]
-            )
-            batch_str += f"Row {idx + 1}: {row_str}\n"
-
-        try:
-            print(f"Processing batch {batch_idx + 1}...")
-            batch_results = await self.structured_llm.ainvoke(batch_str)
-            return batch_results.investor_list
-        except Exception as e:
-            print(f"Error processing batch {batch_idx + 1}: {e}")
-            return []
-
-    async def _save_to_sql(self, investor_data_list: List[InvestorData]) -> None:
-        """Save investors and related data to SQL database"""
-        if not self.sql_session:
-            return
-
-        try:
-            for investor_data in investor_data_list:
-                # Save investor
-                db_investor = InvestorTable(
-                    name=investor_data.investor.name,
-                    description=investor_data.investor.description,
-                    aum=investor_data.investor.aum,
-                    check_size_lower=investor_data.investor.check_size_lower,
-                    check_size_upper=investor_data.investor.check_size_upper,
-                    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.flush()  # Get the ID
-
-                # 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
-
-        documents = []
-        metadatas = []
-        ids = []
-
-        for i, investor_data in enumerate(investor_data_list):
-            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,
-                    "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:
-            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[InvestorData]:
-        """Process CSV data in parallel batches and save to databases"""
-        results = []
-
-        # Create batches
-        batches = []
-        for i in range(0, len(df), batch_size):
-            batch = df.iloc[i : i + batch_size]
-            batches.append((batch, i // batch_size))
-
-        # Process batches with concurrency control
-        semaphore = asyncio.Semaphore(max_concurrent)
-
-        async def process_with_semaphore(batch_data):
-            batch, batch_idx = batch_data
-            async with semaphore:
-                return await self._process_batch(batch, batch_idx)
-
-        # Execute all batches concurrently
-        batch_results = await asyncio.gather(
-            *[process_with_semaphore(batch_data) for batch_data in batches],
-            return_exceptions=True,
-        )
-
-        # Collect results, filtering out exceptions
-        for batch_result in batch_results:
-            if not isinstance(batch_result, Exception):
-                results.extend(batch_result)
-
-        # 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)
-
-        return results

app/services/openrouter_v2.py

@@ -1,290 +0,0 @@
-import asyncio
-from typing import List, Optional
-
-import chromadb
-import pandas as pd
-from db.models import CompanyTable, InvestorTable, InvestorTeamMember, SectorTable
-from langchain_core.prompts import PromptTemplate
-from langchain_openai import ChatOpenAI
-from py_schemas import InvestorData
-from pydantic import BaseModel
-from settings import settings
-
-
-class InvestorOutput(BaseModel):
-    """Schema for LLM structured output"""
-
-    investor_data: InvestorData
-
-
-class InvestorProcessor:
-    def __init__(
-        self,
-        sql_session: Optional[object] = None,
-        vector_db_client: Optional[object] = None,
-    ):
-        self.template = """You are an expert data extraction assistant. Extract investor information from the provided CSV data and return it as a structured record.
-
-Given the following CSV data row:
-{question}
-
-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_lower: Lower bound of investment check size (as integer)
-- check_size_upper: Upper bound of investment check size (as integer)
-- geographic_focus: Geographic region focus
-- stage_focus: Investment stage focus (must be one of: seed, series_a, series_b, series_c, growth, late_stage)
-- number_of_investments: Number of investments made (default 0)
-
-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, use appropriate defaults
-- stage_focus must be one of the valid enum values
-- Return clean, valid JSON only
-
-Return the data as a single comprehensive investor data record."""
-
-        self.prompt = PromptTemplate(
-            template=self.template, input_variables=["question"]
-        )
-
-        self.llm = ChatOpenAI(
-            api_key=settings.OPENROUTER_API_KEY,
-            base_url="https://openrouter.ai/api/v1",
-            model="google/gemini-2.5-flash-lite",
-            temperature=0,
-        )
-
-        self.structured_llm = self.llm.with_structured_output(InvestorOutput)
-        self.sql_session = sql_session
-        self.vector_db_client = vector_db_client
-
-        self.vector_db_client = chromadb.PersistentClient(path="./chroma_db")
-        self.collection = self.vector_db_client.get_or_create_collection(
-            name="investor_descriptions",
-            metadata={
-                "description": "Investor descriptions and investment thesis focus"
-            },
-        )
-
-    async def _process_row(
-        self, row: pd.Series, row_idx: int
-    ) -> Optional[InvestorData]:
-        """Process a single row of data"""
-        # Clean values to remove control characters
-        cleaned_row = {}
-        for key, value in row.items():
-            if pd.notna(value):
-                # Convert to string and clean control characters
-                clean_value = (
-                    str(value)
-                    .replace("\n", " ")
-                    .replace("\r", " ")
-                    .replace("\t", " ")
-                )
-                # Remove other control characters
-                clean_value = "".join(
-                    char
-                    for char in clean_value
-                    if ord(char) >= 32 or char in ["\n", "\r", "\t"]
-                )
-                cleaned_row[key] = clean_value
-
-        row_str = ", ".join(
-            [f"{key}: {value}" for key, value in cleaned_row.items()]
-        )
-
-        try:
-            print(f"Processing row {row_idx + 1}...")
-            result = await self.structured_llm.ainvoke(row_str)
-            if result.investor_data:
-                return result.investor_data
-            return None
-        except Exception as e:
-            print(f"Error processing row {row_idx + 1}: {e}")
-            return None
-
-    async def _save_to_sql(self, investor_data_list: List[InvestorData]) -> None:
-        """Save investors and related data to SQL database"""
-        if not self.sql_session:
-            return
-
-        try:
-            for investor_data in investor_data_list:
-                # Save investor
-                db_investor = InvestorTable(
-                    name=investor_data.investor.name,
-                    description=investor_data.investor.description,
-                    aum=investor_data.investor.aum,
-                    check_size_lower=investor_data.investor.check_size_lower,
-                    check_size_upper=investor_data.investor.check_size_upper,
-                    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.flush()  # Get the ID
-
-                # 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
-
-        documents = []
-        metadatas = []
-        ids = []
-
-        for i, investor_data in enumerate(investor_data_list):
-            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,
-                    "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:
-            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, max_concurrent: int = 10
-    ) -> List[InvestorData]:
-        """Process CSV data one row at a time and save to databases"""
-        results = []
-
-        # Create semaphore for concurrency control
-        semaphore = asyncio.Semaphore(max_concurrent)
-
-        async def process_row_with_semaphore(row_data):
-            row, row_idx = row_data
-            async with semaphore:
-                return await self._process_row(row, row_idx)
-
-        # Create row tasks
-        row_tasks = []
-        for idx, row in df.iterrows():
-            row_tasks.append((row, idx))
-
-        # Execute all rows concurrently
-        row_results = await asyncio.gather(
-            *[process_row_with_semaphore(row_data) for row_data in row_tasks],
-            return_exceptions=True,
-        )
-
-        # Collect results, filtering out exceptions and None values
-        for row_result in row_results:
-            if not isinstance(row_result, Exception) and row_result is not None:
-                results.append(row_result)
-
-        # 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)
-
-        return results

app/services/querying.py

@@ -1,88 +1,52 @@
-from typing import List, Optional
+import os
+from typing import List
 
-import chromadb
+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 py_schemas import InvestorData, InvestorList
+from schemas.router_schemas import (
-from settings import settings
+    CompanyMinimal,
+    InvestmentResponse,
+    PaginatedResponse,
+    SectorMinimal,
+)
 from sqlalchemy.orm import selectinload
 
 # Connect to SQLite
-
 prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
-db = SQLDatabase.from_uri("sqlite:///investors.db")
+db = SQLDatabase.from_uri(DATABASE_URL)
-system_message = (
-    prompt_template.format(dialect="SQLite", top_k=5)
-    + "\n Get answers from the Sql database and the vector database"
-)
 
 
 class QueryProcessor:
-    def __init__(
+    def __init__(self):
-        self,
-        sql_session: Optional[object] = None,
-        vector_db_client: Optional[object] = None,
-    ):
-        self.sql_session = sql_session
         self.llm = ChatOpenAI(
-            api_key=settings.OPENROUTER_API_KEY,
+            api_key=os.getenv("OPENROUTER_API_KEY"),
             base_url="https://openrouter.ai/api/v1",
-            model="google/gemini-2.5-flash-lite",
+            model="x-ai/grok-4-fast",
-            temperature=0.3,
+            temperature=0,
         )
         self.toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
+        # 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 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 fund IDs. "
+            + "Example format: 1, 5, 12, 23"
+        )
         self.agent = create_react_agent(
             model=self.llm,
-            tools=self.toolkit.get_tools() + [self.query_vector_database],
+            tools=self.toolkit.get_tools(),
-            prompt=system_message,
+            prompt=system_message_updated,
-        )
-        self.vector_db_client = vector_db_client
-
-        self.vector_db_client = chromadb.PersistentClient(path="./chroma_db")
-        self.collection = self.vector_db_client.get_or_create_collection(
-            name="investor_descriptions",
-            metadata={
-                "description": "Investor descriptions and investment thesis focus"
-            },
         )
 
-    def query_sql_database(self, query: str) -> Optional[InvestorList]:
+    def process_query(self, question: str) -> PaginatedResponse[InvestmentResponse]:
-        """Query the SQL database for investor information."""
+        """Process a query using the LLM and return investment response data."""
-        if not self.sql_session:
+        # Let the LLM handle all database interactions and filtering to get fund IDs
-            return None
-
-        # Implement SQL querying logic here
-        result = self.sql_session.execute(query)
-        investors = result.scalars().all()
-        return InvestorList(investors=investors)
-
-    def query_vector_database(self, query: str) -> Optional[InvestorList]:
-        """Query the vector database for investor information."""
-        if not self.vector_db_client:
-            return None
-        print("VECTOR STORE WAS CALLED")
-
-        # Query the collection directly, not passing collection as parameter
-        results = self.collection.query(
-            query_texts=[query],  # ChromaDB expects a list of query texts
-            n_results=3,  # Specify how many results you want
-        )
-        print(results)
-
-        # ChromaDB returns results in a different structure
-        # results will have 'documents', 'metadatas', 'ids', 'distances'
-        return results
-
-    def process_query(self, question: str) -> InvestorList:
-        """Process a query using the LLM and return structured investor data."""
-        # Extract filters from the query first
-        filters = self._extract_filters_from_query(question)
-
-        # Get AI response for additional context
         response = self.agent.invoke(
             {"messages": [("user", question)]},
         )
@@ -92,189 +56,122 @@ class QueryProcessor:
             response["messages"][-1].content if response.get("messages") else ""
         )
 
-        # Try to extract investor IDs or names from the AI response
+        # Extract fund IDs from the AI response
-        investor_ids = self._extract_investor_info_from_response(ai_response)
+        fund_ids = self._extract_fund_ids_from_response(ai_response)
 
-        # Fetch filtered investor data with relationships from database
+        # Fetch full fund data with investor relationships using the IDs
-        return self._fetch_investors_with_relationships(investor_ids, filters)
+        return self._fetch_funds_by_ids(fund_ids)
 
-    def _extract_investor_info_from_response(self, ai_response: str) -> List[int]:
+    def _extract_fund_ids_from_response(self, ai_response: str) -> List[int]:
-        """Extract investor IDs from AI response. This is a simple implementation."""
+        """Extract fund IDs from AI response."""
-        # This is a basic implementation - you might want to make it more sophisticated
-        # based on how your AI formats responses
-        investor_ids = []
-
-        # If the AI can't provide structured data, fall back to getting all investors
-        # that match basic criteria
-        try:
-            # Try to extract numbers that might be IDs
-            import re
-
-            ids = re.findall(r"\bid:\s*(\d+)", ai_response.lower())
-            investor_ids = [int(id_str) for id_str in ids]
-        except Exception:
-            pass
-
-        return investor_ids if investor_ids else []
-
-    def _extract_filters_from_query(self, question: str) -> dict:
-        """Extract filter criteria from natural language query."""
-        question_lower = question.lower()
-        filters = {}
-
-        # Extract stage filters
-        if any(
-            stage in question_lower
-            for stage in [
-                "seed",
-                "series a",
-                "series b",
-                "series c",
-                "growth",
-                "late stage",
-            ]
-        ):
-            if "seed" in question_lower:
-                filters["stage"] = "SEED"
-            elif "series a" in question_lower:
-                filters["stage"] = "SERIES_A"
-            elif "series b" in question_lower:
-                filters["stage"] = "SERIES_B"
-            elif "series c" in question_lower:
-                filters["stage"] = "SERIES_C"
-            elif "growth" in question_lower:
-                filters["stage"] = "GROWTH"
-            elif "late stage" in question_lower:
-                filters["stage"] = "LATE_STAGE"
-
-        # Extract geographic filters
-        if any(
-            geo in question_lower
-            for geo in [
-                "us",
-                "usa",
-                "united states",
-                "europe",
-                "asia",
-                "silicon valley",
-                "bay area",
-            ]
-        ):
-            if (
-                "us" in question_lower
-                or "usa" in question_lower
-                or "united states" in question_lower
-            ):
-                filters["geography"] = "US"
-            elif "europe" in question_lower:
-                filters["geography"] = "Europe"
-            elif "asia" in question_lower:
-                filters["geography"] = "Asia"
-            elif "silicon valley" in question_lower or "bay area" in question_lower:
-                filters["geography"] = "Silicon Valley"
-
-        # Extract sector filters
-        sectors = [
-            "fintech",
-            "healthcare",
-            "saas",
-            "ai",
-            "biotech",
-            "consumer",
-            "enterprise",
-            "crypto",
-            "blockchain",
-        ]
-        for sector in sectors:
-            if sector in question_lower:
-                filters["sector"] = sector
-                break
-
-        # Extract check size filters (simple patterns)
         import re
 
-        amounts = re.findall(
+        fund_ids = []
-            r"\$?(\d+(?:,\d{3})*(?:\.\d+)?)\s*(?:million|m|k|thousand)", question_lower
+        try:
-        )
+            # Try multiple patterns to extract IDs from the response
-        if amounts:
+            # Pattern 1: Simple numbers (assuming they are IDs)
-            amount = amounts[0].replace(",", "")
+            numbers = re.findall(r"\b\d+\b", ai_response)
-            if "million" in question_lower or "m" in question_lower:
+            fund_ids = [int(num) for num in numbers]
-                filters["min_check_size"] = int(float(amount) * 1000000)
-            elif "thousand" in question_lower or "k" in question_lower:
-                filters["min_check_size"] = int(float(amount) * 1000)
 
-        return filters
+            # Pattern 2: If response contains explicit ID references
+            id_matches = re.findall(r"\bid[:\s]*(\d+)", ai_response.lower())
+            if id_matches:
+                fund_ids = [int(id_str) for id_str in id_matches]
 
-    def _fetch_investors_with_relationships(
+        except Exception as e:
-        self, investor_ids: List[int] = None, filters: dict = None
+            print(f"Error extracting IDs from response: {e}")
-    ) -> InvestorList:
+            return []
-        """Fetch investors with all their relationships from the database."""
-        if not self.sql_session:
-            return InvestorList(investors=[])
 
-        # Import here to avoid circular imports
+        return fund_ids
-        from db.models import SectorTable
 
-        # Build query with all relationships loaded
+    def _fetch_funds_by_ids(
-        query = self.sql_session.query(InvestorTable).options(
+        self, fund_ids: List[int]
-            selectinload(InvestorTable.portfolio_companies),
+    ) -> PaginatedResponse[InvestmentResponse]:
-            selectinload(InvestorTable.team_members),
+        """Fetch funds with all their relationships from the database using fund IDs.
-            selectinload(InvestorTable.sectors),
+        Constructs response similar to read_investors but starting from funds."""
-        )
+        if not fund_ids:
-
+            return PaginatedResponse(
-        # Apply filters if provided
+                items=[],
-        if filters:
+                total=0,
-            if "stage" in filters:
+                page=1,
-                from db.models import InvestmentStage
+                page_size=len(fund_ids) if fund_ids else 10,
-
+                total_pages=0,
-                stage_enum = getattr(InvestmentStage, filters["stage"])
-                query = query.filter(InvestorTable.stage_focus == stage_enum)
-
-            if "geography" in filters:
-                query = query.filter(
-                    InvestorTable.geographic_focus.ilike(f"%{filters['geography']}%")
-                )
-
-            if "min_check_size" in filters:
-                query = query.filter(
-                    InvestorTable.check_size_lower >= filters["min_check_size"]
-                )
-
-            if "max_check_size" in filters:
-                query = query.filter(
-                    InvestorTable.check_size_upper <= filters["max_check_size"]
-                )
-
-            if "min_aum" in filters:
-                query = query.filter(InvestorTable.aum >= filters["min_aum"])
-
-            if "max_aum" in filters:
-                query = query.filter(InvestorTable.aum <= filters["max_aum"])
-
-            if "sector" in filters:
-                query = query.join(InvestorTable.sectors).filter(
-                    SectorTable.name.ilike(f"%{filters['sector']}%")
-                )
-
-        # Filter by IDs if provided
-        if investor_ids:
-            query = query.filter(InvestorTable.id.in_(investor_ids))
-        else:
-            # If no specific IDs and no filters, limit to prevent overwhelming response
-            if not filters:
-                query = query.limit(10)
-
-        investors = query.all()
-
-        # 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,
             )
-            investor_data_list.append(investor_data)
 
-        return InvestorList(investors=investor_data_list)
+        # Get database session
+        db_session = next(get_db())
+
+        try:
+            # Query funds with all necessary relationships loaded
+            funds = (
+                db_session.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),
+                )
+                .filter(FundTable.id.in_(fund_ids))
+                .all()
+            )
+
+            # 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)
+
+            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()

app/settings.py

@@ -1,11 +0,0 @@
-from pydantic_settings import BaseSettings
-
-
-class Settings(BaseSettings):
-    OPENROUTER_API_KEY: str
-
-    class Config:
-        env_file = ".env"
-
-
-settings = Settings()

base_db_generator/main.py

@@ -0,0 +1,315 @@
+import logging
+import re
+import unicodedata
+
+import pandas as pd
+from models import CompanyTable, InvestorTable, SectorTable, engine, init_database
+from sqlalchemy.orm import sessionmaker
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Import the schema
+init_database()
+
+
+# ===================== Ingesting Original Data =====================#
+def parse_investor_names(investor_names_str):
+    """Parse comma-separated investor names and return a list"""
+    if pd.isna(investor_names_str) or investor_names_str == "":
+        return []
+
+    # Split by comma and clean whitespace
+    # investors = [name.strip() for name in str(investor_names_str).split(",")]
+    investors = [
+        clean_name(name.strip()) for name in str(investor_names_str).split(",")
+    ]
+    return [investor for investor in investors if investor]
+
+
+def parse_industries(industries_str):
+    """Parse comma-separated industries and return a list"""
+    if pd.isna(industries_str) or industries_str == "":
+        return []
+
+    # Split by comma and clean whitespace
+    industries = [industry.strip() for industry in str(industries_str).split(",")]
+    return [industry for industry in industries if industry]
+
+
+def clean_special_characters(text):
+    """Clean special characters from text, converting to ASCII equivalents"""
+    if not text:
+        return text
+
+    # First remove ellipses and other problematic patterns
+    text = str(text).replace("...", "").replace("..", "")
+
+    # Normalize unicode characters to their closest ASCII equivalents
+    normalized = unicodedata.normalize("NFKD", text)
+
+    # Remove accents and convert to ASCII
+    ascii_text = normalized.encode("ascii", "ignore").decode("ascii")
+
+    # Remove any remaining non-alphanumeric characters except spaces, hyphens, and periods
+    cleaned = re.sub(r"[^a-zA-Z0-9\s\-\.]", "", ascii_text)
+
+    # Clean up multiple spaces
+    cleaned = re.sub(r"\s+", " ", cleaned).strip()
+
+    return cleaned
+
+
+def clean_string(value):
+    """Clean string values, converting empty/null/nan/0 to None and removing special characters"""
+    if (
+        pd.isna(value)
+        or value == ""
+        or str(value).lower() in ["nan", "null", "none", "0", "0.0"]
+    ):
+        return None
+
+    # First clean special characters
+    cleaned = clean_special_characters(str(value).strip())
+
+    # Check if result is just "0" after cleaning
+    if cleaned in ["0", "0.0", "null", "nan", "none"]:
+        return None
+
+    return cleaned if cleaned else None
+
+
+def clean_name(value):
+    """Clean names (companies, investors) with special character handling"""
+    if (
+        pd.isna(value)
+        or value == ""
+        or str(value).lower() in ["nan", "null", "none", "0", "0.0"]
+    ):
+        return None
+
+    # Clean special characters but be more permissive for names
+    text = str(value).strip()
+    # First remove ellipses and other problematic patterns
+    # text = text.replace("...", "").replace("..", "")
+
+    # Normalize unicode characters
+    normalized = unicodedata.normalize("NFKD", text)
+
+    # Convert to ASCII but keep more characters for business names
+    ascii_text = normalized.encode("ascii", "ignore").decode("ascii")
+
+    # Allow alphanumeric, spaces, hyphens, periods, parentheses, and ampersands
+    cleaned = re.sub(r"[^a-zA-Z0-9\s\-\.\(\)&]", "", ascii_text)
+
+    # Clean up multiple spaces
+    cleaned = re.sub(r"\s+", " ", cleaned).strip()
+
+    # Remove any trailing or leading periods
+    cleaned = cleaned.strip(".")
+
+    cleaned = cleaned.replace("..", "").replace("...", "")
+    # Check if result is just "0" after cleaning
+    if cleaned in ["0", "0.0", "null", "nan", "none"]:
+        return None
+
+    return cleaned if cleaned else None
+
+
+def clean_integer(value):
+    """Clean integer values, converting empty/null/nan/0 to None"""
+    if pd.isna(value) or str(value).lower() in ["nan", "null", "none", "", "0", "0.0"]:
+        return None
+    try:
+        cleaned_val = int(float(value))
+        return cleaned_val if cleaned_val > 0 else None
+    except (ValueError, TypeError):
+        return None
+
+
+def parse_website(website_str: str):
+    try:
+        _, end = website_str.split(":")
+
+        if end == "0":
+            return None
+        return "https:" + end
+    except Exception:
+        return None
+
+
+def ingest_data():
+    # Create database engine and session
+    Session = sessionmaker(bind=engine)
+    session = Session()
+
+    # Load CSV files
+    print("Loading CSV files...")
+    companies_df = pd.read_csv("companies.csv")
+    investors_df = pd.read_csv("investors.csv")
+
+    print(f"📊 Companies CSV: {len(companies_df)} rows")
+    print(f"📊 Investors CSV: {len(investors_df)} rows")
+
+    # Step 1: Ingest Investors
+    print("\n🔄 Step 1: Ingesting Investors...")
+    investors_processed = 0
+
+    for index, row in investors_df.iterrows():
+        try:
+            investor_name = clean_name(row.get("Filtered investor names", ""))
+
+            if investor_name:
+                # Check if investor already exists
+                existing_investor = (
+                    session.query(InvestorTable).filter_by(name=investor_name).first()
+                )
+                if not existing_investor:
+                    investor = InvestorTable(
+                        name=investor_name,
+                        description=clean_string(row.get("Business model", "")),
+                        headquarters=clean_string(row.get("HQ", "")),
+                        website=parse_website(str(row.get("Website", "")).strip()),
+                        number_of_investments=clean_integer(
+                            row.get("Number of investments")
+                        ),
+                    )
+                    session.add(investor)
+                    investors_processed += 1
+
+                    if investors_processed % 1000 == 0:
+                        session.commit()
+                        print(f"  Committed {investors_processed} investors")
+
+        except Exception as e:
+            logger.error(f"Error processing investor {index}: {e}")
+            continue
+
+    session.commit()
+    print(f"✅ Investors completed: {investors_processed} processed")
+
+    # Step 2: Ingest Companies and Rounds
+    print("\n🔄 Step 2: Ingesting Companies and Sectors...")
+    companies_processed = 0
+    sectors_created = set()
+
+    for index, row in companies_df.iterrows():
+        try:
+            # Process company
+            company_name = clean_name(row.get("Organization Name", ""))
+            if not company_name:
+                continue
+
+            # Check if company already exists
+            existing_company = (
+                session.query(CompanyTable).filter_by(name=company_name).first()
+            )
+            if existing_company:
+                company = existing_company
+            else:
+                # Create company
+                company = CompanyTable(
+                    name=company_name,
+                    description=clean_string(row.get("Organization Description", "")),
+                    location=clean_string(row.get("Organization Location", "")),
+                    industry=clean_string(row.get("Organization Industries", "")),
+                    website=clean_string(row.get("Organization Website", "")),
+                )
+                session.add(company)
+                session.flush()  # Get the company ID
+                companies_processed += 1
+
+            # Process investor relationships
+            investor_names_str = row.get("Investor Names", "")
+            if pd.notna(investor_names_str) and investor_names_str:
+                investor_names = parse_investor_names(investor_names_str)
+
+                for investor_name in investor_names:
+                    # Find investor in database
+                    investor = (
+                        session.query(InvestorTable)
+                        .filter_by(name=investor_name.strip())
+                        .first()
+                    )
+
+                    if investor:
+                        # Add investor-company relationship
+                        if company not in investor.portfolio_companies:
+                            investor.portfolio_companies.append(company)
+                    else:
+                        print("This company has an investor not in DB:", investor_name)
+
+            # Process sectors/industries
+            industries_str = row.get("Organization Industries", "")
+            if pd.notna(industries_str) and industries_str:
+                industries = parse_industries(industries_str)
+
+                for industry_name in industries:
+                    industry_name = industry_name.strip()
+                    if industry_name:
+                        # Check if sector exists
+                        sector = (
+                            session.query(SectorTable)
+                            .filter_by(name=industry_name)
+                            .first()
+                        )
+                        if not sector:
+                            sector = SectorTable(name=industry_name)
+                            session.add(sector)
+                            session.flush()
+                            sectors_created.add(industry_name)
+
+                        # Add company-sector relationship
+                        if sector not in company.sectors:
+                            company.sectors.append(sector)
+
+            # Commit every 100 companies
+            if companies_processed % 100 == 0 and companies_processed > 0:
+                session.commit()
+                print(f"  Processed {companies_processed} companies...")
+
+        except Exception as e:
+            logger.error(f"Error processing company {index}: {e}")
+            session.rollback()
+            continue
+
+    # Step 3: Link investors to sectors based on portfolio companies
+    print("\n🔄 Step 3: Linking Investors to Sectors...")
+    investors_linked_to_sectors = 0
+    all_investors = session.query(InvestorTable).all()
+    for investor in all_investors:
+        sectors = set()
+        for company in investor.portfolio_companies:
+            for sector in company.sectors:
+                sectors.add(sector)
+        # Add sectors to investor if not already present
+        for sector in sectors:
+            if sector not in investor.sectors:
+                investor.sectors.append(sector)
+        if sectors:
+            investors_linked_to_sectors += 1
+    session.commit()
+    print(f"✅ Linked {investors_linked_to_sectors} investors to sectors")
+
+    # Final commit
+    session.commit()
+
+    # Final counts
+    final_investors = session.query(InvestorTable).count()
+    final_companies = session.query(CompanyTable).count()
+    final_sectors = session.query(SectorTable).count()
+
+    print("\n🎉 Ingestion Complete!")
+    print(f"   Investors: {final_investors}")
+    print(f"   Companies: {final_companies}")
+    print(f"   Sectors: {final_sectors}")
+
+    session.close()
+
+
+if __name__ == "__main__":
+    ingest_data()
+    # print(clean_name("A... Energi"))
+    # print(clean_name("B.. Tech"))
+    # print(clean_name("A... Energi"))

base_db_generator/models.py

@@ -0,0 +1,381 @@
+import enum
+from typing import Annotated
+
+from fastapi import Depends
+from sqlalchemy import (
+    Column,
+    DateTime,
+    ForeignKey,
+    Integer,
+    String,
+    Table,
+    Text,
+    create_engine,
+    func,
+)
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import Session, declarative_mixin, relationship, sessionmaker
+from sqlalchemy.types import JSON, Enum
+
+Base = declarative_base()
+
+# Database configuration
+# DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./investors.db")
+
+# Create engine
+engine = create_engine("sqlite:///./investors.db", echo=False)
+
+# Create session factory
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+db_dependency = Annotated[Session, Depends(get_db)]
+
+
+def init_database():
+    """Initialize the database by creating all tables"""
+    Base.metadata.create_all(bind=engine)
+
+
+def get_session_sync() -> Session:
+    """Get a database session for synchronous operations"""
+    return SessionLocal()
+
+
+def get_db_session():
+    """Get a database session for direct use."""
+    return SessionLocal()
+
+
+@declarative_mixin
+class TimestampMixin:
+    created_at = Column(
+        DateTime(timezone=True), server_default=func.now(), nullable=False
+    )
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+
+class InvestmentStage(enum.Enum):
+    SEED = "SEED"
+    SERIES_A = "SERIES_A"
+    SERIES_B = "SERIES_B"
+    SERIES_C = "SERIES_C"
+    GROWTH = "GROWTH"
+    LATE_STAGE = "LATE_STAGE"
+
+
+# Association table for many-to-many relationship between investors and companies
+investor_company_association = Table(
+    "investor_companies",
+    Base.metadata,
+    Column("investor_id", Integer, ForeignKey("investors.id")),
+    Column("company_id", Integer, ForeignKey("companies.id")),
+)
+
+
+# Association table for investor-sector many-to-many
+investor_sector_association = Table(
+    "investor_sectors",
+    Base.metadata,
+    Column("investor_id", Integer, ForeignKey("investors.id")),
+    Column("sector_id", Integer, ForeignKey("sectors.id")),
+)
+
+
+company_sector_association = Table(
+    "company_sector",
+    Base.metadata,
+    Column("company_id", Integer, ForeignKey("companies.id")),
+    Column("sector_id", Integer, ForeignKey("sectors.id")),
+)
+
+project_sector_association = Table(
+    "project_sector",
+    Base.metadata,
+    Column("project_id", Integer, ForeignKey("projects.id")),
+    Column("sector_id", Integer, ForeignKey("sectors.id")),
+)
+
+project_investor_association = Table(
+    "project_investors",
+    Base.metadata,
+    Column("project_id", Integer, ForeignKey("projects.id")),
+    Column("investor_id", Integer, ForeignKey("investors.id")),
+)
+
+project_company_association = Table(
+    "project_companies",
+    Base.metadata,
+    Column("project_id", Integer, ForeignKey("projects.id")),
+    Column("company_id", Integer, ForeignKey("companies.id")),
+)
+
+# Association table for investor-stage many-to-many
+investor_stage_association = Table(
+    "investor_stages",
+    Base.metadata,
+    Column("investor_id", Integer, ForeignKey("investors.id")),
+    Column("stage_id", Integer, ForeignKey("investment_stages.id")),
+)
+
+# Association table for fund-stage many-to-many
+fund_investment_stages_association = Table(
+    "fund_investment_stages",
+    Base.metadata,
+    Column("fund_id", Integer, ForeignKey("funds.id")),
+    Column("stage_id", Integer, ForeignKey("investment_stages.id")),
+)
+
+# Association table for fund-sector many-to-many
+fund_sectors_association = Table(
+    "fund_sectors",
+    Base.metadata,
+    Column("fund_id", Integer, ForeignKey("funds.id")),
+    Column("sector_id", Integer, ForeignKey("sectors.id")),
+)
+
+
+class InvestorTable(Base, TimestampMixin):
+    __tablename__ = "investors"
+
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False)
+    description = Column(Text, nullable=True)
+
+    # Basic investor info
+    website = Column(String, nullable=True)
+    headquarters = Column(String, nullable=True)
+
+    # AUM fields
+    aum = Column(Integer, nullable=True)  # Store as integer for numerical filtering
+    aum_as_of_date = Column(String, nullable=True)
+    aum_source_url = Column(String, nullable=True)
+
+    # Check size (deprecated in favor of fund-level data, but keeping for backward compatibility)
+    check_size_lower = Column(Integer, nullable=True)
+    check_size_upper = Column(Integer, nullable=True)
+
+    # Geographic focus (deprecated in favor of fund-level, but keeping for backward compatibility)
+    geographic_focus = Column(String, nullable=True)
+
+    # Investment thesis and portfolio
+    investment_thesis = Column(JSON, nullable=True)  # Array of thesis statements
+    portfolio_highlights = Column(
+        JSON, nullable=True
+    )  # Array of portfolio company names
+    linked_documents = Column(JSON, nullable=True)  # Array of document URLs
+
+    # Research metadata
+    researcher_notes = Column(Text, nullable=True)
+    missing_important_fields = Column(
+        JSON, nullable=True
+    )  # Array of missing field names
+    sources = Column(JSON, nullable=True)  # JSON object with source URLs
+
+    # Portfolio info
+    number_of_investments = Column(Integer, nullable=True)
+
+    # Relationships
+    team_members = relationship(
+        "InvestorMember", back_populates="investor", cascade="all, delete-orphan"
+    )
+    funds = relationship(
+        "FundTable", back_populates="investor", cascade="all, delete-orphan"
+    )
+
+    # Many-to-many relationship with investment stages
+    investment_stages = relationship(
+        "InvestmentStageTable",
+        secondary=investor_stage_association,
+        back_populates="investors",
+    )
+
+    # Relationship to portfolio companies
+    portfolio_companies = relationship(
+        "CompanyTable",
+        secondary=investor_company_association,
+        back_populates="investors",
+    )
+
+    sectors = relationship(
+        "SectorTable",
+        secondary=investor_sector_association,
+        back_populates="investors",
+    )
+
+    projects = relationship(
+        "ProjectTable",
+        secondary=project_investor_association,
+        back_populates="investors",
+    )
+
+
+class InvestorMember(Base, TimestampMixin):
+    __tablename__ = "investor_members"
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False)
+    role = Column(String, nullable=True)
+    title = Column(String, nullable=True)  # Alternative to role
+    email = Column(String, nullable=True)
+    source_url = Column(String, nullable=True)  # URL where member info was found
+
+    investor_id = Column(Integer, ForeignKey("investors.id"))
+    investor = relationship("InvestorTable", back_populates="team_members")
+
+
+class FundTable(Base, TimestampMixin):
+    __tablename__ = "funds"
+
+    id = Column(Integer, primary_key=True, index=True)
+    investor_id = Column(Integer, ForeignKey("investors.id"), nullable=False)
+
+    # Fund details
+    fund_name = Column(String, nullable=True)
+    fund_size = Column(
+        Integer, nullable=True
+    )  # Store as integer for numerical filtering
+    fund_size_source_url = Column(String, nullable=True)
+
+    # Check size range (parsed from estimated_investment_size by LLM)
+    check_size_lower = Column(Integer, nullable=True)
+    check_size_upper = Column(Integer, nullable=True)
+
+    source_url = Column(String, nullable=True)
+    source_provider = Column(String, nullable=True)  # e.g., "Perplexity"
+
+    # Geographic focus as simple string
+    geographic_focus = Column(String, nullable=True)
+
+    # Relationships
+    investor = relationship("InvestorTable", back_populates="funds")
+    investment_stages = relationship(
+        "InvestmentStageTable",
+        secondary=fund_investment_stages_association,
+        back_populates="funds",
+    )
+    sectors = relationship(
+        "SectorTable",
+        secondary=fund_sectors_association,
+        back_populates="funds",
+    )
+
+
+class InvestmentStageTable(Base, TimestampMixin):
+    __tablename__ = "investment_stages"
+
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False, unique=True)
+
+    # Relationships
+    investors = relationship(
+        "InvestorTable",
+        secondary=investor_stage_association,
+        back_populates="investment_stages",
+    )
+    funds = relationship(
+        "FundTable",
+        secondary=fund_investment_stages_association,
+        back_populates="investment_stages",
+    )
+
+
+class CompanyTable(Base, TimestampMixin):
+    __tablename__ = "companies"
+
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False)
+    industry = Column(String, nullable=True)
+    location = Column(String, nullable=True)
+    description = Column(String, nullable=True)
+    founded_year = Column(Integer, nullable=True)
+    website = Column(String, nullable=True)
+
+    members = relationship(
+        "CompanyMember", back_populates="company", cascade="all, delete-orphan"
+    )
+    # Relationship back to investors
+    investors = relationship(
+        "InvestorTable",
+        secondary=investor_company_association,
+        back_populates="portfolio_companies",
+    )
+
+    sectors = relationship(
+        "SectorTable", secondary=company_sector_association, back_populates="companies"
+    )
+
+    projects = relationship(
+        "ProjectTable",
+        secondary=project_company_association,
+        back_populates="companies",
+    )
+
+
+class CompanyMember(Base, TimestampMixin):
+    __tablename__ = "company_members"
+    id = Column(Integer, primary_key=True)
+    name = Column(String)
+    linkedin = Column(String, nullable=True)
+    role = Column(String, nullable=True)
+    company_id = Column(Integer, ForeignKey("companies.id"), nullable=False)
+
+    company = relationship("CompanyTable", back_populates="members")
+
+
+class SectorTable(Base, TimestampMixin):
+    __tablename__ = "sectors"
+
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False)
+
+    # Relationships
+    investors = relationship(
+        "InvestorTable",
+        secondary=investor_sector_association,
+        back_populates="sectors",
+    )
+    companies = relationship(
+        "CompanyTable", secondary=company_sector_association, back_populates="sectors"
+    )
+    projects = relationship(
+        "ProjectTable", secondary=project_sector_association, back_populates="sector"
+    )
+    funds = relationship(
+        "FundTable",
+        secondary=fund_sectors_association,
+        back_populates="sectors",
+    )
+
+
+class ProjectTable(Base, TimestampMixin):
+    __tablename__ = "projects"
+
+    id = Column(Integer, primary_key=True, index=True)
+    name = Column(String, nullable=False)
+    valuation = Column(Integer, nullable=True)
+
+    stage = Column(Enum(InvestmentStage), nullable=True)
+    location = Column(String, nullable=True)
+    description = Column(Text, nullable=True)
+    start_date = Column(DateTime, nullable=True)
+    end_date = Column(DateTime, nullable=True)
+
+    sector = relationship(
+        "SectorTable", secondary=project_sector_association, back_populates="projects"
+    )
+    investors = relationship(
+        "InvestorTable",
+        secondary=project_investor_association,
+        back_populates="projects",
+    )
+    companies = relationship(
+        "CompanyTable", secondary=project_company_association, back_populates="projects"
+    )