completed investors linkedin

.gitignore

@@ -13,4 +13,8 @@
 
 *.cypython
 
-nohup.out
+nohup.out
+
+server.log
+
+server.pid

app/db/db.py

@@ -1,5 +1,4 @@
 import os
-from pathlib import Path
 from typing import Annotated
 
 from fastapi import Depends

app/db/models.py

@@ -162,6 +162,7 @@ class InvestorMember(Base, TimestampMixin):
     role = Column(String, nullable=True)
     title = Column(String, nullable=True)  # Alternative to role
     email = Column(String, nullable=True)
+    linkedin = Column(String, nullable=True)  # LinkedIn profile URL
     source_url = Column(String, nullable=True)  # URL where member info was found
 
     investor_id = Column(Integer, ForeignKey("investors.id"))
@@ -215,6 +216,8 @@ class CompanyTable(Base, TimestampMixin):
     description = Column(String, nullable=True)
     founded_year = Column(Integer, nullable=True)
     website = Column(String, nullable=True)
+    product_service = Column(Text, nullable=True)  # Product/service description
+    clients = Column(JSON, nullable=True)  # List of client names or client information
 
     members = relationship(
         "CompanyMember", back_populates="company", cascade="all, delete-orphan"
@@ -296,9 +299,11 @@ class ProjectTable(Base, TimestampMixin):
 
     stage = Column(Enum(InvestmentStage), nullable=True)
     location = Column(String, nullable=True)
+    industry = Column(String, nullable=True)
     description = Column(Text, nullable=True)
     start_date = Column(DateTime, nullable=True)
     end_date = Column(DateTime, nullable=True)
+    is_archived = Column(Integer, default=0, nullable=False)  # 0 = active, 1 = archived
 
     sector = relationship(
         "SectorTable", secondary=project_sector_association, back_populates="projects"

app/linkedin_scraper.py

@@ -0,0 +1,730 @@
+"""
+LinkedIn Profile Scraper for Investor Members
+
+This module uses crawl4ai to scrape team pages and find LinkedIn profiles.
+Strategies:
+1. Crawl the source_url (team pages) to extract LinkedIn profile links
+2. Use LLM-powered web search to find LinkedIn profiles by name
+
+Key advantages of crawl4ai:
+- Handles JavaScript-rendered pages
+- Better at extracting content from modern websites
+- More reliable than simple requests
+"""
+
+import asyncio
+import logging
+import os
+import re
+from typing import Dict, List, Optional
+
+from crawl4ai import AsyncWebCrawler
+from ddgs import DDGS
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+
+# Setup logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
+)
+logger = logging.getLogger("linkedin_scraper")
+
+load_dotenv()
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
+
+
+class LinkedInProfileScraper:
+    """
+    LinkedIn profile finder using crawl4ai and LLM-powered web search.
+
+    Strategies:
+    1. Crawl source URLs (team pages) to extract LinkedIn links
+    2. Use LLM-powered web search to find profiles by name
+    """
+
+    def __init__(
+        self,
+        rate_limit_delay: float = 0.5,
+        use_cache: bool = True,
+        use_llm_search: bool = True,
+    ):
+        """
+        Initialize the scraper
+
+        Args:
+            rate_limit_delay: Delay between requests in seconds
+            use_cache: Whether to cache crawled pages
+            use_llm_search: Whether to use LLM-powered web search as fallback
+        """
+        self.rate_limit_delay = rate_limit_delay
+        self.use_cache = use_cache
+        self.use_llm_search = use_llm_search and OPENROUTER_API_KEY
+        self.page_cache: Dict[str, str] = {}  # Cache crawled pages by URL
+        self.html_cache: Dict[str, str] = {}  # Cache HTML separately
+        self.profile_cache: Dict[str, Dict] = {}  # Cache results by member
+
+        # Initialize LLM agent if API key available
+        if self.use_llm_search:
+            self._init_llm_agent()
+        else:
+            self.llm = None
+            self.agent = None
+            self.ddg_search = None
+            logger.info("LLM search disabled (no OPENROUTER_API_KEY)")
+
+    def _init_llm_agent(self):
+        """Initialize LLM agent for web search"""
+        try:
+            self.llm = ChatOpenAI(
+                api_key=OPENROUTER_API_KEY,
+                base_url="https://openrouter.ai/api/v1",
+                model="x-ai/grok-4.1-fast:free",
+                temperature=0,
+            )
+            self.ddg_search = DDGS()
+            logger.info("LLM search agent initialized")
+        except Exception as e:
+            logger.error(f"Failed to initialize LLM agent: {e}")
+            self.llm = None
+            self.ddg_search = None
+
+    def web_search(self, query: str) -> List[Dict]:
+        """Tool to search the web using DuckDuckGo"""
+        if not self.ddg_search:
+            return []
+        try:
+            results = list(self.ddg_search.text(query, max_results=10))
+            return results
+        except Exception as e:
+            logger.error(f"Web search error: {e}")
+            return []
+
+    async def crawl_page(self, url: str) -> Optional[str]:
+        """
+        Crawl a webpage and return its content.
+
+        Args:
+            url: URL to crawl
+
+        Returns:
+            Page content as markdown/text, or None if failed
+        """
+        if not url:
+            return None
+
+        # Check cache first
+        if self.use_cache and url in self.page_cache:
+            logger.debug(f"Using cached page for {url}")
+            return self.page_cache[url]
+
+        try:
+            logger.info(f"Crawling: {url}")
+            async with AsyncWebCrawler() as crawler:
+                result = await crawler.arun(url)
+
+                if result and result.markdown:
+                    content = result.markdown
+                    # Also get HTML for better link extraction
+                    html_content = result.html if hasattr(result, "html") else ""
+
+                    # Cache the results
+                    if self.use_cache:
+                        self.page_cache[url] = content
+                        self.html_cache[url] = html_content
+
+                    return content
+
+        except Exception as e:
+            logger.error(f"Error crawling {url}: {e}")
+
+        return None
+
+    def extract_linkedin_urls_from_content(self, content: str) -> List[Dict[str, str]]:
+        """
+        Extract all LinkedIn profile URLs from content (HTML or markdown).
+
+        Returns:
+            List of dicts with 'url', 'context', and 'username'
+        """
+        linkedin_links = []
+
+        # Pattern for LinkedIn profile URLs (handles country-specific domains)
+        linkedin_pattern = (
+            r"https?://(?:www\.)?(?:[a-z]{2,3}\.)?linkedin\.com/in/([a-zA-Z0-9_-]+)/?"
+        )
+
+        # Find all LinkedIn URLs
+        matches = list(re.finditer(linkedin_pattern, content, re.IGNORECASE))
+
+        for match in matches:
+            url = match.group(0).rstrip("/")
+            # Normalize URL
+            url = self._normalize_linkedin_url(url)
+
+            # Get surrounding context (200 chars before and after)
+            start = max(0, match.start() - 200)
+            end = min(len(content), match.end() + 200)
+            context = content[start:end]
+
+            # Clean up context (remove HTML tags for readability)
+            context = re.sub(r"<[^>]+>", " ", context)
+            context = " ".join(context.split())  # Normalize whitespace
+
+            linkedin_links.append(
+                {"url": url, "context": context, "username": match.group(1)}
+            )
+
+        # Remove duplicates while preserving order
+        seen_urls = set()
+        unique_links = []
+        for link in linkedin_links:
+            if link["url"] not in seen_urls:
+                seen_urls.add(link["url"])
+                unique_links.append(link)
+
+        return unique_links
+
+    def _normalize_linkedin_url(self, url: str) -> str:
+        """Normalize LinkedIn URL to standard format"""
+        # Remove trailing slashes
+        url = url.rstrip("/")
+
+        # Convert country-specific to www
+        url = re.sub(
+            r"https?://[a-z]{2,3}\.linkedin\.com", "https://www.linkedin.com", url
+        )
+
+        # Ensure https
+        if url.startswith("http://"):
+            url = url.replace("http://", "https://")
+
+        return url
+
+    def _name_matches_context(self, name: str, context: str) -> float:
+        """
+        Check if a person's name appears in the context around a LinkedIn URL.
+
+        Returns:
+            Confidence score 0-100
+        """
+        if not name or not context:
+            return 0
+
+        context_lower = context.lower()
+        name_lower = name.lower()
+
+        # Split name into parts (handle multiple spaces, titles like "Dr.", etc.)
+        name_parts = [p for p in name_lower.replace(".", " ").split() if len(p) > 1]
+
+        # Check for full name match
+        if name_lower in context_lower:
+            return 95
+
+        # Check for name parts in context
+        matches = sum(
+            1 for part in name_parts if part in context_lower and len(part) > 2
+        )
+
+        if len(name_parts) > 0:
+            if matches == len(name_parts):
+                return 90  # All name parts found
+            elif matches >= 2:
+                return 75  # At least 2 parts found (first + last typically)
+            elif matches == 1 and len(name_parts) <= 2:
+                return 50  # Only one part found but name is short
+            elif matches == 1:
+                return 35  # Only one part found
+
+        return 0
+
+    def _name_matches_username(self, name: str, username: str) -> float:
+        """
+        Check if LinkedIn username contains parts of the name.
+
+        Returns:
+            Confidence score 0-100
+        """
+        if not name or not username:
+            return 0
+
+        name_lower = name.lower()
+        username_lower = username.lower().replace("-", " ").replace("_", " ")
+
+        name_parts = [p for p in name_lower.replace(".", " ").split() if len(p) > 2]
+
+        matches = sum(1 for part in name_parts if part in username_lower)
+
+        if len(name_parts) > 0:
+            if matches == len(name_parts) and len(name_parts) >= 2:
+                return 85  # Full name in username
+            elif matches >= 2:
+                return 70  # Multiple parts match
+            elif matches == 1:
+                return 35  # Only one part matches
+
+        return 0
+
+    async def find_linkedin_from_source(
+        self, name: str, source_url: str, role: Optional[str] = None
+    ) -> Dict:
+        """
+        Find LinkedIn profile by crawling the source URL (team page).
+
+        Args:
+            name: Person's name
+            source_url: URL of the team/about page
+            role: Person's role (for additional context matching)
+
+        Returns:
+            Dict with linkedin_url, confidence, method, notes
+        """
+        if not source_url:
+            return {
+                "linkedin_url": None,
+                "confidence": 0,
+                "method": "source_crawl",
+                "notes": "No source URL provided",
+            }
+
+        # Crawl the page
+        content = await self.crawl_page(source_url)
+
+        if not content:
+            return {
+                "linkedin_url": None,
+                "confidence": 0,
+                "method": "source_crawl",
+                "notes": f"Failed to crawl {source_url}",
+            }
+
+        # Get HTML for better link extraction
+        html = self.html_cache.get(source_url, content)
+
+        # Extract all LinkedIn URLs from both HTML and markdown
+        linkedin_links = self.extract_linkedin_urls_from_content(html)
+        if not linkedin_links:
+            linkedin_links = self.extract_linkedin_urls_from_content(content)
+
+        if not linkedin_links:
+            return {
+                "linkedin_url": None,
+                "confidence": 0,
+                "method": "source_crawl",
+                "notes": f"No LinkedIn URLs found on {source_url}",
+            }
+
+        # Score each LinkedIn URL based on name matching
+        best_match = None
+        best_score = 0
+
+        for link in linkedin_links:
+            # Score based on context matching
+            context_score = self._name_matches_context(name, link["context"])
+
+            # Score based on username matching
+            username_score = self._name_matches_username(name, link["username"])
+
+            # Also check if role appears in context
+            role_bonus = 0
+            if role and role.lower() in link["context"].lower():
+                role_bonus = 10
+
+            # Combined score (take best of context or username, plus role bonus)
+            total_score = max(context_score, username_score) + role_bonus
+
+            logger.debug(
+                f"  {name} -> {link['url']}: context={context_score}, username={username_score}, role={role_bonus}, total={total_score}"
+            )
+
+            if total_score > best_score:
+                best_score = total_score
+                best_match = link
+
+        if best_match and best_score >= 30:  # Minimum threshold
+            return {
+                "linkedin_url": best_match["url"],
+                "confidence": min(best_score, 100),
+                "method": "source_crawl",
+                "notes": f"Found on {source_url}",
+            }
+
+        return {
+            "linkedin_url": None,
+            "confidence": 0,
+            "method": "source_crawl",
+            "notes": f'No matching LinkedIn profile found for "{name}" on {source_url}',
+        }
+
+    async def find_linkedin_via_search(
+        self, name: str, company: str, role: Optional[str] = None
+    ) -> Dict:
+        """
+        Find LinkedIn profile using web search.
+
+        Args:
+            name: Person's name
+            company: Company/investor name
+            role: Person's role (optional)
+
+        Returns:
+            Dict with linkedin_url, confidence, method, notes
+        """
+        if not self.ddg_search:
+            return {
+                "linkedin_url": None,
+                "confidence": 0,
+                "method": "web_search",
+                "notes": "Web search not available",
+            }
+
+        try:
+            # Build search query - search for LinkedIn profile
+            query = f"{name} {company} site:linkedin.com/in"
+            if role:
+                query = f"{name} {role} {company} site:linkedin.com/in"
+
+            logger.debug(f"Searching: {query}")
+            results = self.web_search(query)
+
+            if results:
+                # Look for LinkedIn profile URLs in results
+                linkedin_pattern = r"https?://(?:www\.)?(?:[a-z]{2,3}\.)?linkedin\.com/in/([a-zA-Z0-9_-]+)"
+
+                for result in results:
+                    url = result.get("href") or result.get("link") or ""
+                    title = result.get("title", "").lower()
+                    body = result.get("body", "").lower()
+
+                    match = re.search(linkedin_pattern, url, re.IGNORECASE)
+                    if match:
+                        linkedin_url = self._normalize_linkedin_url(match.group(0))
+                        username = match.group(1)
+
+                        # Score based on name matching in title/body and username
+                        context = f"{title} {body}"
+                        context_score = self._name_matches_context(name, context)
+                        username_score = self._name_matches_username(name, username)
+
+                        total_score = max(context_score, username_score)
+
+                        if total_score >= 30:
+                            return {
+                                "linkedin_url": linkedin_url,
+                                "confidence": min(
+                                    total_score, 90
+                                ),  # Cap at 90 for search results
+                                "method": "web_search",
+                                "notes": "Found via web search",
+                            }
+
+            return {
+                "linkedin_url": None,
+                "confidence": 0,
+                "method": "web_search",
+                "notes": "No matching profile found in search results",
+            }
+
+        except Exception as e:
+            logger.error(f"Web search error for {name}: {e}")
+            return {
+                "linkedin_url": None,
+                "confidence": 0,
+                "method": "web_search",
+                "notes": f"Search error: {str(e)}",
+            }
+
+    async def find_linkedin_profile(
+        self,
+        name: str,
+        company: str,
+        role: Optional[str] = None,
+        source_url: Optional[str] = None,
+    ) -> Dict:
+        """
+        Find LinkedIn profile for a person.
+
+        Primary strategy: Crawl source URL to find LinkedIn links.
+
+        Args:
+            name: Person's name
+            company: Company/investor name
+            role: Person's role/title (optional)
+            source_url: URL where person info was found (optional)
+
+        Returns:
+            Dict with:
+                - linkedin_url: Found LinkedIn URL or None
+                - confidence: Confidence score (0-100)
+                - method: Method used to find the profile
+                - notes: Additional information
+        """
+        cache_key = f"{name}|{company}"
+
+        # Check cache
+        if self.use_cache and cache_key in self.profile_cache:
+            logger.debug(f"Using cached result for {name}")
+            return self.profile_cache[cache_key]
+
+        result = {"linkedin_url": None, "confidence": 0, "method": "none", "notes": ""}
+
+        # Primary strategy: Crawl source URL
+        if source_url:
+            result = await self.find_linkedin_from_source(name, source_url, role)
+
+            if result["linkedin_url"]:
+                if self.use_cache:
+                    self.profile_cache[cache_key] = result
+                return result
+
+        # Fallback strategy: Web search (if enabled and no result from source crawl)
+        if self.use_llm_search and not result.get("linkedin_url"):
+            search_result = await self.find_linkedin_via_search(name, company, role)
+            if search_result["linkedin_url"]:
+                if self.use_cache:
+                    self.profile_cache[cache_key] = search_result
+                return search_result
+
+        # If no source URL or no match found
+        if not result["linkedin_url"]:
+            result = {
+                "linkedin_url": None,
+                "confidence": 0,
+                "method": "none",
+                "notes": "No source URL available"
+                if not source_url
+                else result.get("notes", "Not found"),
+            }
+
+        if self.use_cache:
+            self.profile_cache[cache_key] = result
+
+        return result
+
+    async def batch_find_profiles(
+        self, members: List[Dict], progress_callback=None, db_callback=None
+    ) -> List[Dict]:
+        """
+        Find LinkedIn profiles for multiple members efficiently.
+
+        Groups members by source_url to minimize crawling the same page multiple times.
+
+        Args:
+            members: List of dicts with 'name', 'company', 'role', 'source_url', 'id'
+            progress_callback: Optional callback function(current, total, result)
+            db_callback: Optional callback to save to database immediately when profile found
+                         Signature: db_callback(member_id, linkedin_url) -> bool
+
+        Returns:
+            List of results for each member
+        """
+        results = []
+        total = len(members)
+
+        # Group members by source_url for efficient crawling
+        url_groups: Dict[str, List[Dict]] = {}
+        no_url_members = []
+
+        for member in members:
+            url = member.get("source_url")
+            if url:
+                if url not in url_groups:
+                    url_groups[url] = []
+                url_groups[url].append(member)
+            else:
+                no_url_members.append(member)
+
+        logger.info(
+            f"Processing {len(url_groups)} unique source URLs for {total} members"
+        )
+        logger.info(f"Members with source URLs: {total - len(no_url_members)}")
+        logger.info(f"Members without source URLs: {len(no_url_members)}")
+        if self.use_llm_search:
+            logger.info("Web search fallback: ENABLED")
+        else:
+            logger.info("Web search fallback: DISABLED")
+
+        processed = 0
+
+        # Process members grouped by URL (efficient - one crawl per page)
+        for url, group_members in url_groups.items():
+            # Crawl the page once
+            content = await self.crawl_page(url)
+            html = self.html_cache.get(url, content or "")
+
+            # Extract all LinkedIn URLs from this page
+            linkedin_links = []
+            if content:
+                linkedin_links = self.extract_linkedin_urls_from_content(html)
+                if not linkedin_links:
+                    linkedin_links = self.extract_linkedin_urls_from_content(content)
+
+            # Match each member in this group
+            for member in group_members:
+                processed += 1
+                result = None
+                found_linkedin = False
+
+                if linkedin_links:
+                    # Find best matching LinkedIn for this member
+                    best_match = None
+                    best_score = 0
+
+                    for link in linkedin_links:
+                        context_score = self._name_matches_context(
+                            member["name"], link["context"]
+                        )
+                        username_score = self._name_matches_username(
+                            member["name"], link["username"]
+                        )
+                        role_bonus = (
+                            10
+                            if member.get("role")
+                            and member["role"].lower() in link["context"].lower()
+                            else 0
+                        )
+                        total_score = max(context_score, username_score) + role_bonus
+
+                        if total_score > best_score:
+                            best_score = total_score
+                            best_match = link
+
+                    if best_match and best_score >= 30:
+                        result = {
+                            "linkedin_url": best_match["url"],
+                            "confidence": min(best_score, 100),
+                            "method": "source_crawl",
+                            "notes": f"Found on {url}",
+                            "member_id": member.get("id"),
+                            "member_name": member["name"],
+                        }
+                        found_linkedin = True
+                        # Save to database immediately if callback provided
+                        if db_callback and member.get("id"):
+                            db_callback(member["id"], best_match["url"])
+
+                # If no result from source crawl, try web search IMMEDIATELY
+                if not found_linkedin and self.use_llm_search:
+                    search_result = await self.find_linkedin_via_search(
+                        member["name"], member["company"], member.get("role")
+                    )
+
+                    if search_result["linkedin_url"]:
+                        result = {
+                            "linkedin_url": search_result["linkedin_url"],
+                            "confidence": search_result["confidence"],
+                            "method": "web_search",
+                            "notes": search_result.get("notes", "Found via web search"),
+                            "member_id": member.get("id"),
+                            "member_name": member["name"],
+                        }
+                        found_linkedin = True
+                        # Save to database immediately
+                        if db_callback and member.get("id"):
+                            db_callback(member["id"], search_result["linkedin_url"])
+
+                # If still no result, record as not found
+                if not found_linkedin:
+                    result = {
+                        "linkedin_url": None,
+                        "confidence": 0,
+                        "method": "source_crawl" if content else "none",
+                        "notes": f"No match on {url}"
+                        if linkedin_links
+                        else (
+                            f"No LinkedIn URLs on {url}"
+                            if content
+                            else f"Failed to crawl {url}"
+                        ),
+                        "member_id": member.get("id"),
+                        "member_name": member["name"],
+                    }
+
+                results.append(result)
+
+                if progress_callback:
+                    progress_callback(processed, total, result)
+
+            # Small delay between different URLs
+            await asyncio.sleep(self.rate_limit_delay)
+
+        # Process members without source URLs - do web search immediately for each
+        for member in no_url_members:
+            processed += 1
+            result = None
+
+            # Try web search immediately
+            if self.use_llm_search:
+                search_result = await self.find_linkedin_via_search(
+                    member["name"], member["company"], member.get("role")
+                )
+
+                if search_result["linkedin_url"]:
+                    result = {
+                        "linkedin_url": search_result["linkedin_url"],
+                        "confidence": search_result["confidence"],
+                        "method": "web_search",
+                        "notes": search_result.get("notes", "Found via web search"),
+                        "member_id": member.get("id"),
+                        "member_name": member["name"],
+                    }
+                    # Save to database immediately
+                    if db_callback and member.get("id"):
+                        db_callback(member["id"], search_result["linkedin_url"])
+
+            # If no result from search
+            if not result:
+                result = {
+                    "linkedin_url": None,
+                    "confidence": 0,
+                    "method": "web_search" if self.use_llm_search else "none",
+                    "notes": "No LinkedIn profile found"
+                    if self.use_llm_search
+                    else "No source URL available",
+                    "member_id": member.get("id"),
+                    "member_name": member["name"],
+                }
+
+            results.append(result)
+
+            if progress_callback:
+                progress_callback(processed, total, result)
+
+            # Rate limit between searches
+            await asyncio.sleep(self.rate_limit_delay)
+
+        return results
+
+
+def format_linkedin_url(url: str) -> str:
+    """Normalize LinkedIn URL format"""
+    if not url:
+        return url
+
+    # Remove trailing slashes
+    url = url.rstrip("/")
+
+    # Ensure https and normalize to www
+    url = re.sub(r"https?://[a-z]{2,3}\.linkedin\.com", "https://www.linkedin.com", url)
+    if url.startswith("http://"):
+        url = url.replace("http://", "https://")
+
+    return url
+
+
+# Async wrapper for sync contexts
+def run_batch_scraper(
+    members: List[Dict], rate_limit: float = 0.5, progress_callback=None
+) -> List[Dict]:
+    """
+    Synchronous wrapper for batch_find_profiles.
+
+    Args:
+        members: List of member dicts
+        rate_limit: Delay between URL crawls
+        progress_callback: Optional progress callback
+
+    Returns:
+        List of results
+    """
+    scraper = LinkedInProfileScraper(rate_limit_delay=rate_limit)
+    return asyncio.run(scraper.batch_find_profiles(members, progress_callback))

app/main.py

@@ -1,11 +1,14 @@
 import io
+import logging
 
 import pandas as pd
 from db.db import Base, db_dependency, engine
 from dotenv import load_dotenv
-from fastapi import FastAPI, File, Form, UploadFile
+from fastapi import FastAPI, File, Form, HTTPException, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from routers import (
+    addition,
     companies,
     folk_crm,
     insight_route,
@@ -13,7 +16,8 @@ from routers import (
     projects,
     report_route,
 )
-from schemas.router_schemas import InvestmentResponse, PaginatedResponse
+from schemas.router_schemas import CompanyData, InvestmentResponse, PaginatedResponse
+from services.company_querying import CompanyQueryProcessor
 from services.llm_parser import InvestorProcessor
 from services.querying import QueryProcessor
 
@@ -25,10 +29,21 @@ def init_database():
     Base.metadata.create_all(bind=engine)
 
 
+logger = logging.getLogger(__name__)
+
 init_database()
 
 app = FastAPI()
 
+# Add CORS middleware to allow frontend requests
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # In production, replace with specific origins
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 
 # Request models
 class QueryRequest(BaseModel):
@@ -42,6 +57,17 @@ class QueryRequest(BaseModel):
         }
 
 
+class CompanyQueryRequest(BaseModel):
+    question: str
+
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "question": "Find me companies in the fintech sector located in San Francisco."
+            }
+        }
+
+
 @app.get("/")
 def health():
     return {"Hello": "World"}
@@ -61,16 +87,18 @@ async def parse_csv(
     - Handles AUM, fund sizes, and check sizes as integers
 
     **For companies:**
-    - Expected columns: Name, Website, Investor, Final Investor Profile (company profile)
+    - Expected columns: Name, Website, Perplexity Gap Output (or Final Investor Profile)
     - 100% manual JSON parsing - no LLM needed
-    - Extracts company details, executives, investors, and client categories
-    - Automatically links companies to investors in database
+    - **Only extracts:** founded_year and key_executives
+    - **Only updates companies already in the database** (syncs with existing records)
+    - Skips companies not found in the database
 
     **Benefits:**
     - Fast processing (5-10s per record)
     - Low cost (minimal or no LLM usage)
     - Accurate data extraction
     - Automatic database persistence
+    - Safe: won't create duplicate companies
     """
     # Read uploaded CSV with pandas
     content = await file.read()
@@ -95,21 +123,30 @@ async def parse_csv(
     "/query", response_model=PaginatedResponse[InvestmentResponse], tags=["Querying"]
 )
 async def query_investors(request: QueryRequest):
-    """
-    Query investors using natural language.
+    """Query investors/funds using natural language"""
+    try:
+        processor = QueryProcessor()
+        result = await processor.process_query(request.question)
+        logger.info(f"Query completed successfully with {result.total} results")
+        return result
+    except Exception as e:
+        logger.error(f"Error in query_investors: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
 
-    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()
-    results = processor.process_query(request.question)
-    return results
+@app.post(
+    "/query-companies", response_model=PaginatedResponse[CompanyData], tags=["Querying"]
+)
+async def query_companies(request: CompanyQueryRequest):
+    """Query companies using natural language"""
+    try:
+        processor = CompanyQueryProcessor()
+        result = await processor.process_query(request.question)
+        logger.info(f"Company query completed successfully with {result.total} results")
+        return result
+    except Exception as e:
+        logger.error(f"Error in query_companies: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
 
 
 app.include_router(investors.router)
@@ -118,6 +155,7 @@ app.include_router(projects.router)
 app.include_router(folk_crm.router)
 app.include_router(insight_route.router)
 app.include_router(report_route.router)
+app.include_router(addition.router)
 
 if __name__ == "__main__":
     import uvicorn

app/routers/addition.py

@@ -0,0 +1,370 @@
+from typing import Optional
+
+from db.db import get_db
+from db.models import FundTable, InvestorTable, SectorTable
+from fastapi import APIRouter, Depends
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+router = APIRouter(tags=["Additional Routes"])
+
+
+# Response schemas
+class SectorsResponse(BaseModel):
+    sectors: list[str]
+    total: int
+
+
+class CountryInfo(BaseModel):
+    name: str
+
+
+class ContinentInfo(BaseModel):
+    name: str
+    countries: list[str]
+
+
+class GeographyResponse(BaseModel):
+    continents: list[ContinentInfo]
+    total_continents: int
+    total_countries: int
+
+
+# Mapping of countries to continents
+COUNTRY_TO_CONTINENT = {
+    # Africa
+    "Algeria": "Africa",
+    "Angola": "Africa",
+    "Benin": "Africa",
+    "Botswana": "Africa",
+    "Burkina Faso": "Africa",
+    "Burundi": "Africa",
+    "Cameroon": "Africa",
+    "Cape Verde": "Africa",
+    "Central African Republic": "Africa",
+    "Chad": "Africa",
+    "Comoros": "Africa",
+    "Congo": "Africa",
+    "Democratic Republic of the Congo": "Africa",
+    "Djibouti": "Africa",
+    "Egypt": "Africa",
+    "Equatorial Guinea": "Africa",
+    "Eritrea": "Africa",
+    "Eswatini": "Africa",
+    "Ethiopia": "Africa",
+    "Gabon": "Africa",
+    "Gambia": "Africa",
+    "Ghana": "Africa",
+    "Guinea": "Africa",
+    "Guinea-Bissau": "Africa",
+    "Ivory Coast": "Africa",
+    "Kenya": "Africa",
+    "Lesotho": "Africa",
+    "Liberia": "Africa",
+    "Libya": "Africa",
+    "Madagascar": "Africa",
+    "Malawi": "Africa",
+    "Mali": "Africa",
+    "Mauritania": "Africa",
+    "Mauritius": "Africa",
+    "Morocco": "Africa",
+    "Mozambique": "Africa",
+    "Namibia": "Africa",
+    "Niger": "Africa",
+    "Nigeria": "Africa",
+    "Rwanda": "Africa",
+    "Sao Tome and Principe": "Africa",
+    "Senegal": "Africa",
+    "Seychelles": "Africa",
+    "Sierra Leone": "Africa",
+    "Somalia": "Africa",
+    "South Africa": "Africa",
+    "South Sudan": "Africa",
+    "Sudan": "Africa",
+    "Tanzania": "Africa",
+    "Togo": "Africa",
+    "Tunisia": "Africa",
+    "Uganda": "Africa",
+    "Zambia": "Africa",
+    "Zimbabwe": "Africa",
+    # Asia
+    "Afghanistan": "Asia",
+    "Armenia": "Asia",
+    "Azerbaijan": "Asia",
+    "Bahrain": "Asia",
+    "Bangladesh": "Asia",
+    "Bhutan": "Asia",
+    "Brunei": "Asia",
+    "Cambodia": "Asia",
+    "China": "Asia",
+    "Cyprus": "Asia",
+    "Georgia": "Asia",
+    "Hong Kong": "Asia",
+    "India": "Asia",
+    "Indonesia": "Asia",
+    "Iran": "Asia",
+    "Iraq": "Asia",
+    "Israel": "Asia",
+    "Japan": "Asia",
+    "Jordan": "Asia",
+    "Kazakhstan": "Asia",
+    "Kuwait": "Asia",
+    "Kyrgyzstan": "Asia",
+    "Laos": "Asia",
+    "Lebanon": "Asia",
+    "Malaysia": "Asia",
+    "Maldives": "Asia",
+    "Mongolia": "Asia",
+    "Myanmar": "Asia",
+    "Nepal": "Asia",
+    "North Korea": "Asia",
+    "Oman": "Asia",
+    "Pakistan": "Asia",
+    "Palestine": "Asia",
+    "Philippines": "Asia",
+    "Qatar": "Asia",
+    "Saudi Arabia": "Asia",
+    "Singapore": "Asia",
+    "South Korea": "Asia",
+    "Sri Lanka": "Asia",
+    "Syria": "Asia",
+    "Taiwan": "Asia",
+    "Tajikistan": "Asia",
+    "Thailand": "Asia",
+    "Timor-Leste": "Asia",
+    "Turkey": "Asia",
+    "Turkmenistan": "Asia",
+    "United Arab Emirates": "Asia",
+    "UAE": "Asia",
+    "Uzbekistan": "Asia",
+    "Vietnam": "Asia",
+    "Yemen": "Asia",
+    # Europe
+    "Albania": "Europe",
+    "Andorra": "Europe",
+    "Austria": "Europe",
+    "Belarus": "Europe",
+    "Belgium": "Europe",
+    "Bosnia and Herzegovina": "Europe",
+    "Bulgaria": "Europe",
+    "Croatia": "Europe",
+    "Czech Republic": "Europe",
+    "Czechia": "Europe",
+    "Denmark": "Europe",
+    "Estonia": "Europe",
+    "Finland": "Europe",
+    "France": "Europe",
+    "Germany": "Europe",
+    "Greece": "Europe",
+    "Hungary": "Europe",
+    "Iceland": "Europe",
+    "Ireland": "Europe",
+    "Italy": "Europe",
+    "Kosovo": "Europe",
+    "Latvia": "Europe",
+    "Liechtenstein": "Europe",
+    "Lithuania": "Europe",
+    "Luxembourg": "Europe",
+    "Malta": "Europe",
+    "Moldova": "Europe",
+    "Monaco": "Europe",
+    "Montenegro": "Europe",
+    "Netherlands": "Europe",
+    "North Macedonia": "Europe",
+    "Norway": "Europe",
+    "Poland": "Europe",
+    "Portugal": "Europe",
+    "Romania": "Europe",
+    "Russia": "Europe",
+    "San Marino": "Europe",
+    "Serbia": "Europe",
+    "Slovakia": "Europe",
+    "Slovenia": "Europe",
+    "Spain": "Europe",
+    "Sweden": "Europe",
+    "Switzerland": "Europe",
+    "Ukraine": "Europe",
+    "United Kingdom": "Europe",
+    "UK": "Europe",
+    "Vatican City": "Europe",
+    # North America
+    "Antigua and Barbuda": "North America",
+    "Bahamas": "North America",
+    "Barbados": "North America",
+    "Belize": "North America",
+    "Canada": "North America",
+    "Costa Rica": "North America",
+    "Cuba": "North America",
+    "Dominica": "North America",
+    "Dominican Republic": "North America",
+    "El Salvador": "North America",
+    "Grenada": "North America",
+    "Guatemala": "North America",
+    "Haiti": "North America",
+    "Honduras": "North America",
+    "Jamaica": "North America",
+    "Mexico": "North America",
+    "Nicaragua": "North America",
+    "Panama": "North America",
+    "Saint Kitts and Nevis": "North America",
+    "Saint Lucia": "North America",
+    "Saint Vincent and the Grenadines": "North America",
+    "Trinidad and Tobago": "North America",
+    "United States": "North America",
+    "USA": "North America",
+    "US": "North America",
+    # South America
+    "Argentina": "South America",
+    "Bolivia": "South America",
+    "Brazil": "South America",
+    "Chile": "South America",
+    "Colombia": "South America",
+    "Ecuador": "South America",
+    "Guyana": "South America",
+    "Paraguay": "South America",
+    "Peru": "South America",
+    "Suriname": "South America",
+    "Uruguay": "South America",
+    "Venezuela": "South America",
+    # Oceania
+    "Australia": "Oceania",
+    "Fiji": "Oceania",
+    "Kiribati": "Oceania",
+    "Marshall Islands": "Oceania",
+    "Micronesia": "Oceania",
+    "Nauru": "Oceania",
+    "New Zealand": "Oceania",
+    "Palau": "Oceania",
+    "Papua New Guinea": "Oceania",
+    "Samoa": "Oceania",
+    "Solomon Islands": "Oceania",
+    "Tonga": "Oceania",
+    "Tuvalu": "Oceania",
+    "Vanuatu": "Oceania",
+}
+
+# Valid continent names for direct matching
+VALID_CONTINENTS = {
+    "Africa",
+    "Asia",
+    "Europe",
+    "North America",
+    "South America",
+    "Oceania",
+    "Antarctica",
+}
+
+
+def extract_countries_from_geographic_focus(geographic_focus: str) -> set[str]:
+    """
+    Extract country names from a geographic_focus string.
+    Handles comma-separated values, slashes, and various formats.
+    """
+    if not geographic_focus:
+        return set()
+
+    countries = set()
+    # Split by common delimiters
+    parts = geographic_focus.replace("/", ",").replace(";", ",").split(",")
+
+    for part in parts:
+        cleaned = part.strip()
+        if cleaned:
+            # Check if it's a known country
+            if cleaned in COUNTRY_TO_CONTINENT:
+                countries.add(cleaned)
+            # Check for partial matches (e.g., "United States of America" -> "United States")
+            else:
+                for country in COUNTRY_TO_CONTINENT.keys():
+                    if country.lower() in cleaned.lower() or cleaned.lower() in country.lower():
+                        countries.add(country)
+                        break
+
+    return countries
+
+
+def organize_geography(geographic_data: list[str]) -> dict[str, set[str]]:
+    """
+    Organize geographic data into continents and their countries.
+    Returns a dict with continent names as keys and sets of countries as values.
+    """
+    continent_countries: dict[str, set[str]] = {}
+
+    for geo_focus in geographic_data:
+        if not geo_focus:
+            continue
+
+        # Extract countries from the geographic focus string
+        countries = extract_countries_from_geographic_focus(geo_focus)
+
+        for country in countries:
+            continent = COUNTRY_TO_CONTINENT.get(country)
+            if continent:
+                if continent not in continent_countries:
+                    continent_countries[continent] = set()
+                continent_countries[continent].add(country)
+
+        # Also check if the geographic focus itself is a continent
+        cleaned_geo = geo_focus.strip()
+        if cleaned_geo in VALID_CONTINENTS:
+            if cleaned_geo not in continent_countries:
+                continent_countries[cleaned_geo] = set()
+
+    return continent_countries
+
+
+@router.get("/sectors", response_model=SectorsResponse)
+def get_unique_sectors(db: Session = Depends(get_db)):
+    """
+    Get all unique sectors from the database.
+    Returns a list of sector names sorted alphabetically.
+    """
+    sectors = db.query(SectorTable.name).distinct().order_by(SectorTable.name).all()
+    sector_names = [s[0] for s in sectors if s[0]]
+
+    return SectorsResponse(sectors=sector_names, total=len(sector_names))
+
+
+@router.get("/geography", response_model=GeographyResponse)
+def get_arranged_geography(db: Session = Depends(get_db)):
+    """
+    Get all unique geographic locations arranged by continent and countries.
+    Extracts geography from both investors and funds tables.
+    Returns continents with their associated countries.
+    """
+    # Collect all geographic focus data from investors
+    investor_geo = (
+        db.query(InvestorTable.geographic_focus)
+        .filter(InvestorTable.geographic_focus.isnot(None))
+        .distinct()
+        .all()
+    )
+
+    # Collect all geographic focus data from funds
+    fund_geo = (
+        db.query(FundTable.geographic_focus)
+        .filter(FundTable.geographic_focus.isnot(None))
+        .distinct()
+        .all()
+    )
+
+    # Combine all geographic data
+    all_geo_data = [g[0] for g in investor_geo] + [g[0] for g in fund_geo]
+
+    # Organize into continents and countries
+    continent_countries = organize_geography(all_geo_data)
+
+    # Build response
+    continents = []
+    total_countries = 0
+
+    for continent_name in sorted(continent_countries.keys()):
+        countries = sorted(continent_countries[continent_name])
+        total_countries += len(countries)
+        continents.append(ContinentInfo(name=continent_name, countries=countries))
+
+    return GeographyResponse(
+        continents=continents,
+        total_continents=len(continents),
+        total_countries=total_countries,
+    )

app/routers/companies.py

@@ -63,11 +63,13 @@ def read_companies(
     # Transform CompanyTable objects to CompanyData format
     company_data_list = []
     for company in companies:
+        # Sort sectors alphabetically
+        sorted_sectors = sorted(company.sectors, key=lambda s: s.name) if company.sectors else []
         company_data = CompanyData(
             company=company,
             investors=company.investors,
             members=company.members,
-            sectors=company.sectors,
+            sectors=sorted_sectors,
         )
         company_data_list.append(company_data)
 
@@ -147,11 +149,13 @@ def filter_companies(
     # Transform to CompanyData format
     company_data_list = []
     for company in companies:
+        # Sort sectors alphabetically
+        sorted_sectors = sorted(company.sectors, key=lambda s: s.name) if company.sectors else []
         company_data = CompanyData(
             company=company,
             investors=company.investors,
             members=company.members,
-            sectors=company.sectors,
+            sectors=sorted_sectors,
         )
         company_data_list.append(company_data)
 
@@ -184,12 +188,15 @@ def read_company(company_id: int, db: Session = Depends(get_db)):
     if not company:
         raise HTTPException(status_code=404, detail="Company not found")
 
+    # Sort sectors alphabetically
+    sorted_sectors = sorted(company.sectors, key=lambda s: s.name) if company.sectors else []
+    
     # Transform to CompanyData format
     return CompanyData(
         company=company,
         investors=company.investors,
         members=company.members,
-        sectors=company.sectors,
+        sectors=sorted_sectors,
     )
 
 
@@ -250,12 +257,15 @@ def update_company(
         .first()
     )
 
+    # Sort sectors alphabetically
+    sorted_sectors = sorted(company_with_relations.sectors, key=lambda s: s.name) if company_with_relations.sectors else []
+    
     # Transform to CompanyData format
     return CompanyData(
         company=company_with_relations,
         investors=company_with_relations.investors,
         members=company_with_relations.members,
-        sectors=company_with_relations.sectors,
+        sectors=sorted_sectors,
     )
 
 

app/routers/folk_crm.py

@@ -1,15 +1,21 @@
+import os
 from typing import List
 
 from db.db import get_db
 from db.models import InvestorTable
 from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel
-from services.crm import folk
+from services.crm import FolkAPI
 from sqlalchemy.orm import Session, selectinload
 
 router = APIRouter(prefix="/folk", tags=["Folk CRM"])
 
 
+def get_folk_client():
+    """Get Folk API client with loaded environment variables"""
+    return FolkAPI(api_key=os.environ.get("FOLK_API_KEY", ""))
+
+
 class GroupResponse(BaseModel):
     id: str
     name: str
@@ -44,6 +50,7 @@ def get_folk_groups():
     to sync investors to Folk.
     """
     try:
+        folk = get_folk_client()
         groups_data = folk.get_groups()
         items = groups_data.get("data", {}).get("items", [])
 
@@ -71,6 +78,7 @@ def sync_investors_to_folk(
     Returns:
         Summary of sync operation including successes and errors
     """
+    folk = get_folk_client()
     # Fetch investors with their team members
     investors = (
         db.query(InvestorTable)
@@ -128,6 +136,11 @@ def sync_investors_to_folk(
                     if hasattr(member, "source_url") and member.source_url:
                         urls_list = [member.source_url]
 
+                    # Get LinkedIn URL if available
+                    linkedin_url = None
+                    if hasattr(member, "linkedin") and member.linkedin:
+                        linkedin_url = member.linkedin
+
                     # Build job title from title or role
                     job_title = None
                     if hasattr(member, "title") and member.title:
@@ -141,6 +154,7 @@ def sync_investors_to_folk(
                         email=member.email,
                         company_id=company_id,
                         group_id=request.group_id,
+                        linkedin_url=linkedin_url,
                         urls=urls_list,
                         jobTitle=job_title,
                     )

app/routers/investors.py

@@ -12,7 +12,10 @@ from schemas.router_schemas import (
     PaginatedResponse,
     SectorMinimal,
 )
-from services.compatibility_score import calculate_project_investor_compatibility
+from services.compatibility_score import (
+    _calculate_project_fund_compatibility,
+    _calculate_project_investor_direct_compatibility,
+)
 from sqlalchemy.orm import Session, selectinload
 
 router = APIRouter(tags=["Investor Routes"])
@@ -77,31 +80,46 @@ def read_investors(
         if not project:
             raise HTTPException(status_code=404, detail="Project not found")
 
-    # Get paginated results
-    investors = (
-        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),
+    # When project_id is provided, we need to get all investors first to sort by compatibility score
+    # Otherwise, we can paginate at the database level
+    if project is not None:
+        # Get all investors (we'll sort by compatibility score, then paginate)
+        all_investors = (
+            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),
+            )
+            .all()
+        )
+        # We'll paginate after sorting by compatibility score
+        investors = all_investors
+    else:
+        # Get paginated results (no sorting needed)
+        investors = (
+            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),
+            )
+            .offset(offset)
+            .limit(page_size)
+            .all()
         )
-        .offset(offset)
-        .limit(page_size)
-        .all()
-    )
 
     # Transform to InvestmentResponse format (one row per investor-fund combination)
     investment_responses = []
     for investor in investors:
-        # Calculate compatibility score if project provided
-        compatibility_score = 1.0
-        if project is not None:
-            compatibility_score = calculate_project_investor_compatibility(
-                project=project, investor=investor, use_funds=True
-            )
-
         # Get top 3 portfolio companies (id and name only)
         portfolio_companies = [
             CompanyMinimal(id=company.id, name=company.name)
@@ -111,6 +129,13 @@ def read_investors(
         # If investor has funds, create one entry per fund
         if investor.funds:
             for fund in investor.funds:
+                # Calculate compatibility score for this specific fund
+                compatibility_score = 1.0
+                if project is not None:
+                    compatibility_score = _calculate_project_fund_compatibility(
+                        project=project, fund=fund
+                    )
+
                 # Get stage focus as comma-separated string
                 stage_focus = (
                     ", ".join([stage.name for stage in fund.investment_stages])
@@ -118,10 +143,12 @@ def read_investors(
                     else None
                 )
 
-                # Get top 3 sectors from fund (id and name only)
+                # Get top 3 sectors from fund (id and name only) - sorted alphabetically
                 fund_sectors = [
                     SectorMinimal(id=sector.id, name=sector.name)
-                    for sector in (fund.sectors[:3] if fund.sectors else [])
+                    for sector in sorted(
+                        fund.sectors[:3] if fund.sectors else [], key=lambda s: s.name
+                    )
                 ]
 
                 investment_response = InvestmentResponse(
@@ -141,6 +168,13 @@ def read_investors(
                 investment_responses.append(investment_response)
         else:
             # If no funds, create one entry with null fund fields
+            # Calculate compatibility using investor-level data
+            compatibility_score = 1.0
+            if project is not None:
+                compatibility_score = _calculate_project_investor_direct_compatibility(
+                    project=project, investor=investor
+                )
+
             investment_response = InvestmentResponse(
                 id=investor.id,
                 name=investor.name,
@@ -155,6 +189,12 @@ def read_investors(
             )
             investment_responses.append(investment_response)
 
+    # Sort by compatibility score (descending) when project_id is provided
+    if project is not None:
+        investment_responses.sort(key=lambda x: x.compatibility_score, reverse=True)
+        # Apply pagination after sorting
+        investment_responses = investment_responses[offset : offset + page_size]
+
     # Calculate total pages
     total_pages = (total_count + page_size - 1) // page_size
 
@@ -246,20 +286,27 @@ def filter_investors(
     # 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()
+    # When project_id is provided, we need to get all funds first to sort by compatibility score
+    # Otherwise, we can paginate at the database level
+    if project is not None:
+        # Get all funds (we'll sort by compatibility score, then paginate)
+        all_funds = query.all()
+        funds = all_funds
+    else:
+        # Calculate offset and apply pagination (no sorting needed)
+        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
 
-        # Calculate compatibility score if project provided
+        # Calculate compatibility score for this specific fund
         compatibility_score = 1.0
         if project is not None:
-            compatibility_score = calculate_project_investor_compatibility(
-                project=project, investor=investor, use_funds=True
+            compatibility_score = _calculate_project_fund_compatibility(
+                project=project, fund=fund
             )
 
         # Get top 3 portfolio companies (id and name only)
@@ -275,10 +322,12 @@ def filter_investors(
             else None
         )
 
-        # Get top 3 sectors from fund (id and name only)
+        # Get top 3 sectors from fund (id and name only) - sorted alphabetically
         fund_sectors = [
             SectorMinimal(id=sector.id, name=sector.name)
-            for sector in (fund.sectors[:3] if fund.sectors else [])
+            for sector in sorted(
+                fund.sectors[:3] if fund.sectors else [], key=lambda s: s.name
+            )
         ]
 
         investment_response = InvestmentResponse(
@@ -297,6 +346,13 @@ def filter_investors(
         )
         investment_responses.append(investment_response)
 
+    # Sort by compatibility score (descending) when project_id is provided
+    if project is not None:
+        investment_responses.sort(key=lambda x: x.compatibility_score, reverse=True)
+        # Apply pagination after sorting
+        offset = (page - 1) * page_size
+        investment_responses = investment_responses[offset : offset + page_size]
+
     # Calculate total pages
     total_pages = (total_count + page_size - 1) // page_size
 

app/routers/projects.py

@@ -24,19 +24,29 @@ router = APIRouter(tags=["Project Routes"])
 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)"),
+    include_archived: bool = Query(False, description="Include archived projects"),
     db: Session = Depends(get_db),
 ):
-    """Get all projects with their related data (paginated)"""
+    """Get all projects with their related data (paginated)
+    
+    By default, archived projects are excluded. Set include_archived=True to include them.
+    """
     # Calculate offset
     offset = (page - 1) * page_size
 
+    # Start with base query
+    query = db.query(ProjectTable)
+    
+    # Filter out archived projects by default
+    if not include_archived:
+        query = query.filter(ProjectTable.is_archived == 0)
+
     # Get total count
-    total_count = db.query(ProjectTable).count()
+    total_count = query.count()
 
     # Get paginated results
     projects = (
-        db.query(ProjectTable)
-        .options(
+        query.options(
             selectinload(ProjectTable.sector),
             selectinload(ProjectTable.investors),
             selectinload(ProjectTable.companies),
@@ -162,7 +172,7 @@ def update_project(
 
 @router.delete("/projects/{project_id}")
 def delete_project(project_id: int, db: Session = Depends(get_db)):
-    """Delete a project"""
+    """Delete a project permanently"""
     db_project = db.query(ProjectTable).filter(ProjectTable.id == project_id).first()
 
     if not db_project:
@@ -174,6 +184,87 @@ def delete_project(project_id: int, db: Session = Depends(get_db)):
     return {"message": "Project deleted successfully"}
 
 
+@router.post("/projects/{project_id}/archive")
+def archive_project(project_id: int, db: Session = Depends(get_db)):
+    """Archive a project (soft delete)"""
+    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_project.is_archived = 1
+    db.commit()
+    db.refresh(db_project)
+
+    return {"message": "Project archived successfully", "project_id": project_id}
+
+
+@router.post("/projects/{project_id}/unarchive")
+def unarchive_project(project_id: int, db: Session = Depends(get_db)):
+    """Unarchive a project (restore from archive)"""
+    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_project.is_archived = 0
+    db.commit()
+    db.refresh(db_project)
+
+    return {"message": "Project unarchived successfully", "project_id": project_id}
+
+
+@router.get("/projects/archived", response_model=PaginatedResponse[ProjectData])
+def read_archived_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 archived projects (paginated)"""
+    # Calculate offset
+    offset = (page - 1) * page_size
+
+    # Query only archived projects
+    query = db.query(ProjectTable).filter(ProjectTable.is_archived == 1)
+
+    # Get total count
+    total_count = query.count()
+
+    # Get paginated results
+    projects = (
+        query.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/filter", response_model=PaginatedResponse[ProjectData])
 def filter_projects(
     stage: Optional[InvestmentStage] = Query(
@@ -182,6 +273,7 @@ def filter_projects(
     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)"),
+    industry: Optional[str] = Query(None, description="Industry (partial match)"),
     sector: Optional[str] = Query(None, description="Sector name (partial match)"),
     investor_name: Optional[str] = Query(
         None, description="Investor name (partial match)"
@@ -215,6 +307,9 @@ def filter_projects(
     if location:
         query = query.filter(ProjectTable.location.ilike(f"%{location}%"))
 
+    if industry:
+        query = query.filter(ProjectTable.industry.ilike(f"%{industry}%"))
+
     if sector:
         query = query.join(ProjectTable.sector).filter(
             SectorTable.name.ilike(f"%{sector}%")

app/routers/report_route.py

@@ -52,7 +52,6 @@ async def generate_investor_report(
         "website": investor.website,
         "headquarters": investor.headquarters,
         "aum": investor.aum,
-        "geographic_focus": investor.geographic_focus,
         "portfolio_highlights": investor.portfolio_highlights or [],
         "investment_thesis": investor.investment_thesis or [],
         "sectors": [sector.name for sector in investor.sectors],
@@ -65,24 +64,22 @@ async def generate_investor_report(
             }
             for member in investor.team_members
         ],
-        "check_size_lower": None,
-        "check_size_upper": None,
-        "investment_stages": [],
+        "funds": [],
     }
 
-    # Get check sizes and stages from funds
+    # Get all funds with their data
     if investor.funds:
-        # Use the first fund's data or aggregate
-        fund = investor.funds[0]
-        investor_data["check_size_lower"] = fund.check_size_lower
-        investor_data["check_size_upper"] = fund.check_size_upper
-
-        # Aggregate all investment stages from all funds
-        stages = set()
         for fund in investor.funds:
-            for stage in fund.investment_stages:
-                stages.add(stage.name)
-        investor_data["investment_stages"] = list(stages)
+            fund_data = {
+                "fund_name": fund.fund_name,
+                "fund_size": fund.fund_size,
+                "check_size_lower": fund.check_size_lower,
+                "check_size_upper": fund.check_size_upper,
+                "geographic_focus": fund.geographic_focus,
+                "investment_stages": [stage.name for stage in fund.investment_stages],
+                "sectors": [sector.name for sector in fund.sectors],
+            }
+            investor_data["funds"].append(fund_data)
 
     # Fetch project data if project_id is provided
     project_data = None
@@ -109,7 +106,7 @@ async def generate_investor_report(
     # Generate PDF report
     report_generator = ReportGenerator()
     pdf_bytes = await report_generator.generate_investor_report(
-        investor_data, project_data
+        investor_data, project_data, investor_model=investor, project_model=project
     )
 
     # Return PDF as downloadable file

app/schemas/project_schemas.py

@@ -60,6 +60,7 @@ class ProjectSchema(BaseModel):
     valuation: int | None
     stage: InvestmentStage | None
     location: str | None
+    industry: str | None
     description: Optional[str]
     start_date: Optional[datetime]
     end_date: Optional[datetime]
@@ -75,6 +76,7 @@ class ProjectCreate(BaseModel):
     valuation: Optional[int] = None
     stage: Optional[InvestmentStage] = None
     location: Optional[str] = None
+    industry: Optional[str] = None
     description: Optional[str] = None
     start_date: Optional[datetime] = None
     end_date: Optional[datetime] = None
@@ -85,6 +87,7 @@ class ProjectUpdate(BaseModel):
     valuation: Optional[int] = None
     stage: Optional[InvestmentStage] = None
     location: Optional[str] = None
+    industry: Optional[str] = None
     description: Optional[str] = None
     start_date: Optional[datetime] = None
     end_date: Optional[datetime] = None

app/schemas/router_schemas.py

@@ -38,6 +38,7 @@ class InvestorMemberSchema(BaseModel):
     name: str
     role: str | None
     email: str | None
+    linkedin: str | None
 
     class Config:
         from_attributes = True
@@ -168,6 +169,7 @@ class InvestorFundData(BaseModel):
     class Config:
         from_attributes = True
 
+
 class InvestorMinimal(BaseModel):
     """Minimal investor info with just id and name"""
 
@@ -177,6 +179,7 @@ class InvestorMinimal(BaseModel):
     class Config:
         from_attributes = True
 
+
 class CompanySchemaMinimal(BaseModel):
     id: int
     name: str
@@ -188,9 +191,12 @@ class CompanySchemaMinimal(BaseModel):
     class Config:
         from_attributes = True
 
+
 class CompanyData(BaseModel):  # Renamed from CompaniesData for consistency
     company: CompanySchemaMinimal
     investors: List[InvestorMinimal]
+    members: List[CompanyMemberSchema] = []
+    sectors: List[SectorSchema] = []
 
     class Config:
         from_attributes = True

app/services/company_querying.py

@@ -0,0 +1,228 @@
+import asyncio
+import hashlib
+import logging
+import os
+from typing import List
+
+from db.db import get_db
+from db.models import CompanyTable
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from schemas.router_schemas import CompanyData, PaginatedResponse
+from sqlalchemy import text
+from sqlalchemy.orm import selectinload
+
+logger = logging.getLogger(__name__)
+
+
+class CompanyQueryProcessor:
+    def __init__(self):
+        self.llm = ChatOpenAI(
+            api_key=os.getenv("OPENROUTER_API_KEY"),
+            base_url="https://openrouter.ai/api/v1",
+            model="openai/gpt-4o-mini",
+            temperature=0,
+        )
+
+        # Query cache for performance
+        self.query_cache = {}
+
+        # SQL generation prompt
+        self.sql_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a SQL expert. Generate a SQLite query to find company IDs based on user requirements.
+
+Database Schema:
+- companies: id, name, industry, location, description, founded_year, website
+- company_sector: company_id, sector_id
+- sectors: id, name
+- investor_companies: investor_id, company_id
+- investors: id, name, aum
+- team_members: id, company_id, name, title
+
+IMPORTANT RULES:
+1. ALWAYS return ONLY company IDs (companies.id) - use SELECT DISTINCT c.id
+2. For industry: Check BOTH industry field AND sectors table with synonyms
+   - Use LEFT JOIN for sectors so companies without sector tags still match
+   - Include related terms: 'Fintech' → c.industry LIKE '%Fintech%' OR c.industry LIKE '%Finance%' OR sec.name LIKE '%Fintech%' OR sec.name LIKE '%Financial%'
+   - 'AI' → c.industry LIKE '%AI%' OR c.industry LIKE '%Artificial Intelligence%' OR c.industry LIKE '%Machine Learning%' OR sec.name LIKE '%AI%' OR sec.name LIKE '%ML%'
+3. For location: Be FLEXIBLE with variations and abbreviations
+   - 'San Francisco' → c.location LIKE '%San Francisco%' OR c.location LIKE '%SF%' OR c.location LIKE '%Bay Area%'
+   - 'New York' → c.location LIKE '%New York%' OR c.location LIKE '%NYC%' OR c.location LIKE '%NY%'
+   - 'Europe' → c.location LIKE '%Europe%' OR c.location LIKE '%UK%' OR c.location LIKE '%London%' OR c.location LIKE '%Berlin%' OR c.location LIKE '%Paris%'
+4. For sectors: Use LEFT JOIN and include multiple synonyms
+   - 'Healthcare' → sec.name LIKE '%Healthcare%' OR sec.name LIKE '%Health%' OR sec.name LIKE '%Medical%' OR sec.name LIKE '%Biotech%' OR c.industry LIKE '%Health%'
+5. For founding year filters (include NULL to be inclusive):
+   - "founded after 2020" → WHERE (founded_year >= 2020 OR founded_year IS NULL)
+   - "founded before 2018" → WHERE (founded_year <= 2018 OR founded_year IS NULL)
+   - "founded in 2020" → WHERE founded_year = 2020
+6. For investor-related queries: Use JOIN investor_companies
+7. Use LEFT JOIN for sectors so companies without tags still match
+8. Use DISTINCT to avoid duplicates from joins
+9. Be INCLUSIVE - use OR conditions with synonyms and variations
+10. Return a single, complete SELECT query
+
+Example Queries:
+Q: "Fintech companies founded in 2020"
+A: SELECT DISTINCT c.id FROM companies c 
+   LEFT JOIN company_sector cs ON c.id = cs.company_id 
+   LEFT JOIN sectors sec ON cs.sector_id = sec.id 
+   WHERE (c.industry LIKE '%Fintech%' OR c.industry LIKE '%Finance%' OR c.industry LIKE '%Financial%' OR sec.name LIKE '%Fintech%' OR sec.name LIKE '%Financial Services%') 
+   AND c.founded_year = 2020
+
+Q: "AI companies in San Francisco"
+A: SELECT DISTINCT c.id FROM companies c 
+   LEFT JOIN company_sector cs ON c.id = cs.company_id 
+   LEFT JOIN sectors sec ON cs.sector_id = sec.id 
+   WHERE (c.industry LIKE '%AI%' OR c.industry LIKE '%Artificial Intelligence%' OR c.industry LIKE '%Machine Learning%' OR sec.name LIKE '%AI%' OR sec.name LIKE '%Machine Learning%' OR sec.name LIKE '%ML%') 
+   AND (c.location LIKE '%San Francisco%' OR c.location LIKE '%SF%' OR c.location LIKE '%Bay Area%')
+
+Q: "Healthcare companies"
+A: SELECT DISTINCT c.id FROM companies c 
+   LEFT JOIN company_sector cs ON c.id = cs.company_id 
+   LEFT JOIN sectors sec ON cs.sector_id = sec.id 
+   WHERE c.industry LIKE '%Healthcare%' OR c.industry LIKE '%Health%' OR c.industry LIKE '%Medical%' OR sec.name LIKE '%Healthcare%' OR sec.name LIKE '%Medical%' OR sec.name LIKE '%Biotech%' OR sec.name LIKE '%Pharma%'
+
+Q: "Companies funded by Sequoia"
+A: SELECT DISTINCT c.id FROM companies c 
+   JOIN investor_companies ic ON c.id = ic.company_id 
+   JOIN investors i ON ic.investor_id = i.id 
+   WHERE i.name LIKE '%Sequoia%'
+
+Q: "European startups founded after 2019"
+A: SELECT DISTINCT c.id FROM companies c 
+   WHERE (c.location LIKE '%Europe%' OR c.location LIKE '%UK%' OR c.location LIKE '%London%' OR c.location LIKE '%Germany%' OR c.location LIKE '%Berlin%' OR c.location LIKE '%France%' OR c.location LIKE '%Paris%') 
+   AND (c.founded_year > 2019 OR c.founded_year IS NULL)
+
+Q: "SaaS companies"
+A: SELECT DISTINCT c.id FROM companies c 
+   LEFT JOIN company_sector cs ON c.id = cs.company_id 
+   LEFT JOIN sectors sec ON cs.sector_id = sec.id 
+   WHERE c.industry LIKE '%SaaS%' OR c.industry LIKE '%Software%' OR c.industry LIKE '%Cloud%' OR sec.name LIKE '%SaaS%' OR sec.name LIKE '%Software%'
+
+IMPORTANT: 
+- Use LEFT JOIN so companies without sector tags still match via industry field
+- Use OR conditions with related keywords/synonyms to cast a wider net
+- Include NULL checks for optional filters to avoid excluding companies with missing data
+
+Return ONLY the SQL query, no explanations or markdown.""",
+                ),
+                ("user", "{question}"),
+            ]
+        )
+
+    def _get_cache_key(self, question: str) -> str:
+        """Generate cache key from normalized question."""
+        return hashlib.md5(question.lower().strip().encode()).hexdigest()
+
+    # synchronous helper is provided below as `_process_query_sync` and an
+    # async wrapper `process_query` runs it in a thread. This keeps the
+    # FastAPI event loop non-blocking while reusing the existing sync code.
+    async def process_query(self, question: str) -> PaginatedResponse[CompanyData]:
+        """Async wrapper for process_query. Runs blocking work in a thread to avoid
+        blocking the event loop.
+        """
+        return await asyncio.to_thread(self._process_query_sync, question)
+
+    def _process_query_sync(self, question: str) -> PaginatedResponse[CompanyData]:
+        """Synchronous implementation of process_query. This is run in a thread by
+        the async wrapper above.
+        """
+        cache_key = self._get_cache_key(question)
+
+        # Check cache first
+        if cache_key in self.query_cache:
+            sql_query = self.query_cache[cache_key]
+            logger.info(f"Using cached SQL: {sql_query}")
+        else:
+            # Generate SQL query
+            messages = self.sql_prompt.format_messages(question=question)
+            response = self.llm.invoke(messages)
+            sql_query = response.content.strip()
+
+            # Clean up SQL (remove markdown code blocks if present)
+            sql_query = sql_query.replace("```sql", "").replace("```", "").strip()
+
+            # Cache the query
+            self.query_cache[cache_key] = sql_query
+            logger.info(f"Generated SQL: {sql_query}")
+
+        # Execute query to get company IDs
+        db_session = next(get_db())
+        try:
+            result = db_session.execute(text(sql_query))
+            company_ids = [row[0] for row in result.fetchall()]
+            logger.info(
+                f"Found {len(company_ids)} company IDs: {company_ids[:10]}{'...' if len(company_ids) > 10 else ''}"
+            )
+
+            return self._fetch_companies_by_ids(company_ids)
+        except Exception as e:
+            logger.error(f"SQL execution error: {e}")
+            logger.error(f"Failed SQL: {sql_query}")
+            # Return empty result
+            return PaginatedResponse(
+                items=[], total=0, page=1, page_size=10, total_pages=0
+            )
+        finally:
+            db_session.close()
+
+    def _fetch_companies_by_ids(
+        self, company_ids: List[int]
+    ) -> PaginatedResponse[CompanyData]:
+        """Fetch companies with all their relationships from the database using company IDs.
+
+        Args:
+            company_ids: List of company IDs to fetch
+        """
+        if not company_ids:
+            return PaginatedResponse(
+                items=[],
+                total=0,
+                page=1,
+                page_size=10,
+                total_pages=0,
+            )
+
+        # Get database session
+        db_session = next(get_db())
+
+        try:
+            # Query companies with all necessary relationships loaded
+            companies = (
+                db_session.query(CompanyTable)
+                .options(
+                    selectinload(CompanyTable.investors),
+                    selectinload(CompanyTable.members),
+                    selectinload(CompanyTable.sectors),
+                )
+                .filter(CompanyTable.id.in_(company_ids))
+                .all()
+            )
+
+            # Transform to CompanyData format
+            company_data_list = []
+            for company in companies:
+                company_data = CompanyData(
+                    company=company,
+                    investors=company.investors,
+                    members=company.members,
+                    sectors=company.sectors,
+                )
+                company_data_list.append(company_data)
+
+            total_count = len(company_data_list)
+            total_pages = 1 if total_count > 0 else 0
+
+            return PaginatedResponse(
+                items=company_data_list,
+                total=total_count,
+                page=1,
+                page_size=total_count,
+                total_pages=total_pages,
+            )
+
+        finally:
+            db_session.close()

app/services/compatibility_score.py

@@ -6,6 +6,7 @@ The scoring system evaluates multiple dimensions to determine how well a project
 matches with an investor's investment criteria.
 """
 
+from difflib import SequenceMatcher
 from typing import List, Optional, Tuple
 
 from db.models import FundTable, InvestorTable, ProjectTable
@@ -99,12 +100,16 @@ def _calculate_project_fund_compatibility(
             else str(project.stage)
         )
 
-        if project_stage_name in fund_stage_names:
+        # Normalize both for case-insensitive comparison
+        project_stage_normalized = project_stage_name.upper().strip()
+        fund_stages_normalized = {name.upper().strip() for name in fund_stage_names}
+
+        if project_stage_normalized in fund_stages_normalized:
             stage_score = 30
         else:
             # Partial credit for adjacent stages
             stage_score = _calculate_stage_proximity(
-                project_stage_name, fund_stage_names
+                project_stage_normalized, fund_stages_normalized
             )
 
     total_score += stage_score
@@ -112,22 +117,53 @@ def _calculate_project_fund_compatibility(
     # 2. Sector Overlap (30 points)
     sector_score = 0
     if project.sector and fund.sectors:
-        project_sector_ids = {sector.id for sector in project.sector}
-        fund_sector_ids = {sector.id for sector in fund.sectors}
+        project_sectors = [s for s in project.sector if hasattr(s, "name")]
+        fund_sectors = [s for s in fund.sectors if hasattr(s, "name")]
 
-        if project_sector_ids and fund_sector_ids:
-            common_sectors = project_sector_ids.intersection(fund_sector_ids)
-            # Score based on what percentage of project sectors are covered by fund
-            overlap_ratio = len(common_sectors) / len(project_sector_ids)
-            sector_score = int(30 * overlap_ratio)
+        if project_sectors and fund_sectors:
+            # Use fuzzy matching to account for similar but not identical sector names
+            match_count = 0
+            total_matches = 0
+
+            for proj_sector in project_sectors:
+                best_match_score = 0
+                proj_name = proj_sector.name.lower().strip()
+
+                for fund_sector in fund_sectors:
+                    fund_name = fund_sector.name.lower().strip()
+
+                    # Exact match
+                    if proj_name == fund_name:
+                        best_match_score = 1.0
+                        break
+
+                    # Fuzzy match using sequence matcher
+                    similarity = SequenceMatcher(None, proj_name, fund_name).ratio()
+
+                    # Also check if one contains the other (substring match)
+                    if proj_name in fund_name or fund_name in proj_name:
+                        similarity = max(similarity, 0.8)
+
+                    best_match_score = max(best_match_score, similarity)
+
+                # Count matches with threshold
+                # Perfect match (1.0), strong match (>0.75), partial match (>0.6)
+                if best_match_score >= 0.6:
+                    total_matches += best_match_score
+                    match_count += 1
+
+            if match_count > 0:
+                # Calculate overlap ratio based on fuzzy matches
+                overlap_ratio = total_matches / len(project_sectors)
+                sector_score = int(30 * overlap_ratio)
 
     total_score += sector_score
 
     # 3. Geographic Match (20 points)
     geo_score = 0
     if project.location and fund.geographic_focus:
-        project_location_lower = project.location.lower()
-        fund_geo_lower = (fund.geographic_focus or "").lower()
+        project_location_lower = project.location.lower().strip()
+        fund_geo_lower = (fund.geographic_focus or "").lower().strip()
 
         # Exact match
         if project_location_lower == fund_geo_lower:
@@ -137,10 +173,11 @@ def _calculate_project_fund_compatibility(
             project_location_lower in fund_geo_lower
             or fund_geo_lower in project_location_lower
         ):
-            geo_score = 10
-        # Check for common geographic terms
+            geo_score = 15
+        # Check for common geographic terms or regional overlap (continent/country matching)
         elif _check_geographic_overlap(project_location_lower, fund_geo_lower):
-            geo_score = 5
+            # Give higher score for continent/country matches (e.g., Germany -> Europe)
+            geo_score = 18
 
     total_score += geo_score
 
@@ -209,13 +246,44 @@ def _calculate_project_investor_direct_compatibility(
     # 2. Sector Overlap (30 points)
     sector_score = 0
     if project.sector and investor.sectors:
-        project_sector_ids = {sector.id for sector in project.sector}
-        investor_sector_ids = {sector.id for sector in investor.sectors}
+        project_sectors = [s for s in project.sector if hasattr(s, "name")]
+        investor_sectors = [s for s in investor.sectors if hasattr(s, "name")]
 
-        if project_sector_ids and investor_sector_ids:
-            common_sectors = project_sector_ids.intersection(investor_sector_ids)
-            overlap_ratio = len(common_sectors) / len(project_sector_ids)
-            sector_score = int(30 * overlap_ratio)
+        if project_sectors and investor_sectors:
+            # Use fuzzy matching to account for similar but not identical sector names
+            match_count = 0
+            total_matches = 0
+
+            for proj_sector in project_sectors:
+                best_match_score = 0
+                proj_name = proj_sector.name.lower().strip()
+
+                for inv_sector in investor_sectors:
+                    inv_name = inv_sector.name.lower().strip()
+
+                    # Exact match
+                    if proj_name == inv_name:
+                        best_match_score = 1.0
+                        break
+
+                    # Fuzzy match using sequence matcher
+                    similarity = SequenceMatcher(None, proj_name, inv_name).ratio()
+
+                    # Also check if one contains the other (substring match)
+                    if proj_name in inv_name or inv_name in proj_name:
+                        similarity = max(similarity, 0.8)
+
+                    best_match_score = max(best_match_score, similarity)
+
+                # Count matches with threshold
+                if best_match_score >= 0.6:
+                    total_matches += best_match_score
+                    match_count += 1
+
+            if match_count > 0:
+                # Calculate overlap ratio based on fuzzy matches
+                overlap_ratio = total_matches / len(project_sectors)
+                sector_score = int(30 * overlap_ratio)
 
     total_score += sector_score
 
@@ -231,9 +299,10 @@ def _calculate_project_investor_direct_compatibility(
             project_location_lower in investor_geo_lower
             or investor_geo_lower in project_location_lower
         ):
-            geo_score = 10
+            geo_score = 15
         elif _check_geographic_overlap(project_location_lower, investor_geo_lower):
-            geo_score = 5
+            # Give higher score for continent/country matches (e.g., Germany -> Europe)
+            geo_score = 18
 
     total_score += geo_score
 
@@ -278,8 +347,11 @@ def _calculate_stage_proximity(project_stage: str, fund_stages: set) -> int:
     """
     stage_order = ["SEED", "SERIES_A", "SERIES_B", "SERIES_C", "GROWTH", "LATE_STAGE"]
 
+    # Normalize project stage for comparison
+    project_stage_normalized = project_stage.upper().strip()
+
     try:
-        project_idx = stage_order.index(project_stage)
+        project_idx = stage_order.index(project_stage_normalized)
     except ValueError:
         return 0
 
@@ -290,8 +362,10 @@ def _calculate_stage_proximity(project_stage: str, fund_stages: set) -> int:
     if project_idx < len(stage_order) - 1:
         adjacent_stages.append(stage_order[project_idx + 1])
 
+    # Normalize fund stages and check for matches
     for stage in fund_stages:
-        if stage in adjacent_stages:
+        stage_normalized = stage.upper().strip()
+        if stage_normalized in adjacent_stages:
             return 15  # Half credit for adjacent stage
 
     return 0
@@ -305,25 +379,90 @@ def _check_geographic_overlap(location1: str, location2: str) -> bool:
         - "San Francisco, CA" and "California" -> True
         - "New York" and "USA" -> True (if both contain USA/US)
         - "London, UK" and "United Kingdom" -> True
+        - "Germany" and "Europe" -> True
     """
-    # Common geographic groupings
+    # Normalize inputs
+    loc1 = location1.lower().strip()
+    loc2 = location2.lower().strip()
+
+    # Common geographic groupings with broader regional mappings
     geo_groups = [
-        ["usa", "us", "united states", "america"],
-        ["uk", "united kingdom", "britain"],
-        ["california", "ca"],
-        ["new york", "ny"],
+        # North America
+        ["usa", "us", "united states", "america", "u.s.", "u.s.a"],
+        ["canada", "canadian"],
+        ["mexico", "mexican"],
+        # Europe and countries
+        [
+            "europe",
+            "european",
+            "eu",
+            "germany",
+            "france",
+            "uk",
+            "united kingdom",
+            "britain",
+            "spain",
+            "italy",
+            "netherlands",
+            "belgium",
+            "sweden",
+            "denmark",
+            "norway",
+            "finland",
+            "poland",
+            "portugal",
+            "austria",
+            "switzerland",
+            "ireland",
+            "greece",
+            "czech",
+            "romania",
+        ],
+        # UK specific
+        ["uk", "united kingdom", "britain", "england", "scotland", "wales", "london"],
+        # US states
+        ["california", "ca", "san francisco", "los angeles", "silicon valley"],
+        ["new york", "ny", "nyc"],
         ["texas", "tx"],
-        ["europe", "eu"],
-        ["asia", "asian"],
-        ["africa", "african"],
+        ["massachusetts", "ma", "boston"],
+        ["washington", "seattle"],
+        # Asia
+        [
+            "asia",
+            "asian",
+            "china",
+            "japan",
+            "korea",
+            "singapore",
+            "hong kong",
+            "india",
+            "indonesia",
+            "thailand",
+            "vietnam",
+            "malaysia",
+            "philippines",
+        ],
+        # Middle East
+        ["middle east", "israel", "uae", "dubai", "saudi arabia"],
+        # Latin America
+        ["latin america", "brazil", "argentina", "chile", "colombia", "mexico"],
+        # Africa
+        ["africa", "african", "south africa", "nigeria", "kenya", "egypt"],
+        # Oceania
+        ["australia", "australian", "new zealand"],
     ]
 
+    # Check if both locations match any group
     for group in geo_groups:
-        found_in_1 = any(term in location1 for term in group)
-        found_in_2 = any(term in location2 for term in group)
+        found_in_1 = any(term in loc1 for term in group)
+        found_in_2 = any(term in loc2 for term in group)
         if found_in_1 and found_in_2:
             return True
 
+    # Check for direct substring match (one contains the other)
+    if loc1 in loc2 or loc2 in loc1:
+        return True
+
     return False
 
 

app/services/crm.py

@@ -1,14 +1,24 @@
+import logging
 import os
 import sys
 
 import requests
 
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[logging.StreamHandler()],
+)
+logger = logging.getLogger(__name__)
+
 
 class FolkAPI:
     BASE_URL = "https://api.folk.app/v1"
 
     def __init__(self, api_key: str):
+        api_key = os.environ.get("FOLK_API_KEY", api_key)
         self.headers = {"Authorization": f"Bearer {api_key}"}
+        logger.info(f"FolkAPI initialized with API key: {api_key[:4]}***")
 
     def get_groups(self):
         """Fetch all groups from Folk."""
@@ -109,6 +119,7 @@ class FolkAPI:
         email: str = None,
         company_id: str = None,
         group_id: str = None,
+        linkedin_url: str = None,
         companies=None,
         emails=None,
         phones=None,
@@ -174,7 +185,9 @@ class FolkAPI:
         addresses_list = _to_list(addresses)
         if addresses_list:
             data["addresses"] = addresses_list
-        urls_list = _to_list(urls)
+        urls_list = _to_list(urls) or []
+        if linkedin_url:
+            urls_list.append(linkedin_url)
         if urls_list:
             data["urls"] = urls_list
 
@@ -190,71 +203,3 @@ class FolkAPI:
         response.raise_for_status()
         return response.json()
 
-
-# Prefer getting the API key from the environment. If not set, fall back to the
-# existing (hard-coded) key so behavior is unchanged for now.
-DEFAULT_API_KEY = "FOLKfIGXuv74ML9EAajxyiUR39ePaNrZ"
-api_key = os.environ.get("FOLK_API_KEY", DEFAULT_API_KEY)
-
-folk = FolkAPI(api_key=api_key)
-
-
-def example_flow():
-    # Step 1: Get groups
-    groups = folk.get_groups()
-    print(groups)
-
-    # Safely dig into the returned structure. The API returns groups under
-    # groups['data']['items'] (not groups['data'][0]). Handle missing/empty.
-    items = groups.get("data", {}).get("items", [])
-    if not items:
-        print("No groups returned by Folk API.")
-        sys.exit(1)
-
-    # Choose the first group as an example
-    group_id = items[0].get("id")
-    if not group_id:
-        print("No id found for the first group item.")
-        sys.exit(1)
-
-    # Step 2: Choose a group_id and create a company
-    company = folk.create_company(
-        name="2050 Investment Partners",
-        group_id=group_id,
-        website="https://2050.com",
-        linkedin_url="https://linkedin.com/company/2050-investments",
-    )
-
-    # Step 3: Add a person to the same group or company
-    person = folk.create_person(
-        first_name="John",
-        last_name="Doe",
-        email="john@2050.com",
-        company_id=company.get("data", {}).get("id"),
-        group_id=group_id,
-    )
-
-    print("Created company:", company)
-    print("Created person:", person)
-
-
-if __name__ == "__main__":
-    try:
-        example_flow()
-    except requests.HTTPError as e:
-        # Try to include response body for easier debugging if available
-        resp = getattr(e, "response", None)
-        if resp is not None:
-            try:
-                body = resp.text
-            except Exception:
-                body = "<unreadable response body>"
-            print("HTTP error while talking to Folk API:", e)
-            print("Response status:", resp.status_code)
-            print("Response body:", body)
-        else:
-            print("HTTP error while talking to Folk API:", e)
-        sys.exit(1)
-    except Exception as e:  # pragma: no cover - top-level safety
-        print("Unexpected error:", e)
-        sys.exit(1)

app/services/insight.py

@@ -49,7 +49,7 @@ class QueryProcessor:
         """Tool to search the web using google, provide the relevant query to get the information"""
         logger.info(f"\nWeb Search Tool Called with query: {query}")
         if query:
-            result = self.ddg_search.text(query, max_results=10, backend="google")
+            result = self.ddg_search.text(query, max_results=10)
             return result
         return "No query provided."
 
@@ -87,11 +87,15 @@ class QueryProcessor:
             context_parts.append(f"Location: {investor_headquarters}")
         if investor_description:
             context_parts.append(f"Description: {investor_description}")
-        if investment_thesis:
-            thesis_str = ", ".join(investment_thesis[:3])  # Limit to first 3
+        if investment_thesis and isinstance(investment_thesis, list):
+            thesis_str = ", ".join(
+                str(item) for item in investment_thesis[:3]
+            )  # Limit to first 3
             context_parts.append(f"Investment Focus: {thesis_str}")
-        if portfolio_highlights:
-            portfolio_str = ", ".join(portfolio_highlights[:5])  # Limit to first 5
+        if portfolio_highlights and isinstance(portfolio_highlights, list):
+            portfolio_str = ", ".join(
+                str(item) for item in portfolio_highlights[:5]
+            )  # Limit to first 5
             context_parts.append(f"Notable Portfolio Companies: {portfolio_str}")
 
         context = "\n".join(context_parts)

app/services/llm_parser.py

@@ -145,16 +145,74 @@ Return the lower and upper bounds in USD."""
         """
         Manually parse the JSON profile from the CSV.
         Returns a cleaned dictionary with the investor profile data.
+        Handles JSON wrapped in markdown code blocks (```json ... ```).
+        Handles trailing quotes and extra data after JSON.
         """
         if not json_str or pd.isna(json_str):
             return None
 
         try:
+            # Clean the JSON string
+            cleaned_json = json_str.strip()
+
+            # Check if it's plain text (no JSON structure)
+            if not cleaned_json.startswith(("{", "```", "'")):
+                print("   ⚠️  No JSON structure found - skipping")
+                return None
+
+            # Remove markdown code block markers if present
+            if cleaned_json.startswith("```"):
+                # Remove opening marker (```json or ```Json or ```)
+                lines = cleaned_json.split("\n")
+                if lines[0].startswith("```"):
+                    lines = lines[1:]  # Remove first line
+                # Remove closing marker (``` or ```')
+                if lines and lines[-1].strip() in ("```", "```'", '```"'):
+                    lines = lines[:-1]  # Remove last line
+                cleaned_json = "\n".join(lines).strip()
+
+            # Remove trailing quotes that might be left over
+            if cleaned_json.endswith(("'", '"')):
+                cleaned_json = cleaned_json[:-1].strip()
+
+            # Try to find JSON boundaries if there's extra data
+            # Look for the first { and the last }
+            start_idx = cleaned_json.find("{")
+            if start_idx == -1:
+                print("   ⚠️  No opening brace found - not valid JSON")
+                return None
+
+            # Find the matching closing brace
+            # We need to count braces to find the actual end
+            brace_count = 0
+            end_idx = -1
+            for i in range(start_idx, len(cleaned_json)):
+                if cleaned_json[i] == "{":
+                    brace_count += 1
+                elif cleaned_json[i] == "}":
+                    brace_count -= 1
+                    if brace_count == 0:
+                        end_idx = i + 1
+                        break
+
+            if end_idx == -1:
+                print("   ⚠️  No matching closing brace found")
+                return None
+
+            # Extract just the JSON part
+            cleaned_json = cleaned_json[start_idx:end_idx]
+
             # Parse JSON string
-            profile = json.loads(json_str)
+            profile = json.loads(cleaned_json)
             return profile
         except json.JSONDecodeError as e:
-            print(f"Error parsing JSON: {e}")
+            print(f"   ❌ JSON parsing error: {e}")
+            # Print first 200 chars for debugging
+            preview = json_str[:200] if len(json_str) > 200 else json_str
+            print(f"   Preview: {preview}...")
+            return None
+        except Exception as e:
+            print(f"   ❌ Unexpected error: {e}")
             return None
 
     async def process_investor_profile(
@@ -338,34 +396,45 @@ Return the lower and upper bounds in USD."""
             if existing_company:
                 # Update only founded_year on existing company
                 company = existing_company
+                updated_fields = []
+
                 if company_data.get("founded_year"):
                     company.founded_year = company_data["founded_year"]
+                    updated_fields.append(
+                        f"founded_year: {company_data['founded_year']}"
+                    )
+
+                # Add/update company members (key executives)
+                # First, remove existing members if updating
+                db.query(CompanyMember).filter_by(company_id=company.id).delete()
+
+                exec_count = 0
+                for exec_data in company_data.get("key_executives", []):
+                    member = CompanyMember(
+                        name=exec_data.get("name"),
+                        role=exec_data.get("title"),
+                        linkedin=exec_data.get(
+                            "source_url"
+                        ),  # Store source URL in linkedin field
+                        company_id=company.id,
+                    )
+                    db.add(member)
+                    exec_count += 1
+
+                if exec_count > 0:
+                    updated_fields.append(f"{exec_count} executives")
+
+                if updated_fields:
+                    print(f"      📝 Updated: {', '.join(updated_fields)}")
+
+                return company
             else:
-                # Company should already be in base database, but if not found, skip
-                print(
-                    f"⚠️  Company '{company_data['name']}' not found in base database - skipping"
-                )
+                # Company not found in base database, skip
+                print("      ⚠️  Not in database - skipping")
                 return None
 
-            # Add/update company members (key executives)
-            # First, remove existing members if updating
-            db.query(CompanyMember).filter_by(company_id=company.id).delete()
-
-            for exec_data in company_data.get("key_executives", []):
-                member = CompanyMember(
-                    name=exec_data.get("name"),
-                    role=exec_data.get("title"),
-                    linkedin=exec_data.get(
-                        "source_url"
-                    ),  # Store source URL in linkedin field
-                    company_id=company.id,
-                )
-                db.add(member)
-
-            return company
-
         except Exception as e:
-            print(f"Error saving company to database: {e}")
+            print(f"      ❌ Error saving: {e}")
             db.rollback()
             return None
 
@@ -789,8 +858,11 @@ Return the lower and upper bounds in USD."""
                 if pd.notna(row.get("Investor"))
                 else None
             )
+            # Try both column names for flexibility
             profile_json = (
-                row.get("Final Investor Profile", "")
+                row.get("Perplexity Gap Output", "")
+                if pd.notna(row.get("Perplexity Gap Output"))
+                else row.get("Final Investor Profile", "")
                 if pd.notna(row.get("Final Investor Profile"))
                 else None
             )

app/services/querying.py

@@ -1,29 +1,25 @@
-import json
+import asyncio
+import hashlib
 import logging
 import os
 from typing import List, Optional
 
-from db.db import DATABASE_URL, get_db
+from db.db import get_db
 from db.models import FundTable, InvestorTable, ProjectTable
-from langchain import hub
-from langchain_community.agent_toolkits import SQLDatabaseToolkit
-from langchain_community.utilities import SQLDatabase
+from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
 from schemas.router_schemas import (
     CompanyMinimal,
     InvestmentResponse,
     PaginatedResponse,
     SectorMinimal,
 )
+from sqlalchemy import text
 from sqlalchemy.orm import selectinload
 
 from services.compatibility_score import calculate_project_investor_compatibility
 
 logger = logging.getLogger(__name__)
-# Connect to SQLite
-prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
-db = SQLDatabase.from_uri(DATABASE_URL)
 
 
 class QueryProcessor:
@@ -34,78 +30,155 @@ class QueryProcessor:
             model="openai/gpt-4o-mini",
             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\n=== IMPORTANT TERMINOLOGY ==="
-            + "\n- When users say 'investors' or 'find me investors', they mean FUNDS"
-            + "\n- Always query the 'funds' table for investment opportunities"
-            + "\n- The 'investors' table is for parent company information only"
-            + "\n- Relationship: investors (1) -> (many) funds"
-            + "\n\n=== YOUR TASK ==="
-            + "\nReturn ONLY fund IDs (funds.id) that match the user's criteria."
-            + "\nFormat: comma-separated numbers only (e.g., 1, 5, 12, 23)"
-            + "\nNo explanations, no other data."
-            + "\n\n=== QUERY GUIDELINES ==="
-            + "\n1. For geographic searches: use funds.geographic_focus"
-            + "\n2. For sector searches: JOIN with fund_sectors table"
-            + "\n3. For stage searches: JOIN with fund_investment_stages table"
-            + "\n4. If no results: respond with 'NO_RESULTS'"
-            + "\n5. Never repeat the same failed query"
-        )
-        self.agent = create_react_agent(
-            model=self.llm,
-            tools=self.toolkit.get_tools(),
-            prompt=system_message_updated,
+
+        # Query cache for performance
+        self.query_cache = {}
+
+        # SQL generation prompt
+        self.sql_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a SQL expert. Generate a SQLite query to find fund IDs based on user requirements.
+
+Database Schema:
+- funds: id, fund_name, investor_id, check_size_lower, check_size_upper, geographic_focus
+- fund_sectors: fund_id, sector_id
+- fund_investment_stages: fund_id, stage_id
+- sectors: id, name
+- investment_stages: id, name
+- investors: id, name, aum
+
+IMPORTANT RULES:
+1. ALWAYS return ONLY fund IDs (funds.id) - use SELECT DISTINCT f.id
+2. For geography: Be FLEXIBLE - use OR with variations and partial matches
+   - 'Europe' → WHERE geographic_focus LIKE '%Europe%' OR geographic_focus LIKE '%European%'
+   - 'America' → WHERE geographic_focus LIKE '%America%' OR geographic_focus LIKE '%US%' OR geographic_focus LIKE '%United States%'
+   - 'Asia' → WHERE geographic_focus LIKE '%Asia%' OR geographic_focus LIKE '%Asian%'
+   - If no geography specified, DON'T filter by geography
+3. For stages: Use LEFT JOIN and LIKE for flexible matching with synonyms
+   - 'Seed' → s.name LIKE '%Seed%' OR s.name LIKE '%Pre-Seed%' OR s.name LIKE '%Early%'
+   - 'Series A' → s.name LIKE '%Series A%' OR s.name LIKE '%A%'
+   - 'Growth' → s.name LIKE '%Growth%' OR s.name LIKE '%Late%' OR s.name LIKE '%Expansion%'
+   - If stage not specified, include ALL funds
+4. For sectors: Use LEFT JOIN and include related terms with OR
+   - 'Fintech' → sec.name LIKE '%Fintech%' OR sec.name LIKE '%Finance%' OR sec.name LIKE '%Financial%'
+   - 'AI' → sec.name LIKE '%AI%' OR sec.name LIKE '%Artificial Intelligence%' OR sec.name LIKE '%Machine Learning%' OR sec.name LIKE '%ML%'
+   - 'Healthcare' → sec.name LIKE '%Healthcare%' OR sec.name LIKE '%Health%' OR sec.name LIKE '%Medical%' OR sec.name LIKE '%Biotech%'
+5. For check size filters (be flexible with ranges):
+   - "under X" → WHERE (check_size_upper <= X OR check_size_upper IS NULL)
+   - "over X" → WHERE (check_size_lower >= X OR check_size_lower IS NULL)
+   - "between X and Y" → WHERE check_size_lower >= X AND check_size_upper <= Y
+6. Use LEFT JOIN for stages and sectors so funds without tags still match
+7. Use DISTINCT to avoid duplicates from joins
+8. Be INCLUSIVE - use OR conditions to cast a wider net
+9. If query is very simple (e.g., just "seed stage"), don't add unnecessary filters
+10. Return a single, complete SELECT query
+
+Example Queries:
+Q: "Seed stage investors in Europe"
+A: SELECT DISTINCT f.id FROM funds f 
+   LEFT JOIN fund_investment_stages fis ON f.id = fis.fund_id 
+   LEFT JOIN investment_stages s ON fis.stage_id = s.id 
+   WHERE (s.name LIKE '%Seed%' OR s.name LIKE '%Pre-Seed%' OR s.name LIKE '%Early%' OR s.id IS NULL)
+   AND (f.geographic_focus LIKE '%Europe%' OR f.geographic_focus LIKE '%European%')
+
+Q: "Fintech investors with check size under 5 million"
+A: SELECT DISTINCT f.id FROM funds f 
+   LEFT JOIN fund_sectors fs ON f.id = fs.fund_id 
+   LEFT JOIN sectors sec ON fs.sector_id = sec.id 
+   WHERE (sec.name LIKE '%Fintech%' OR sec.name LIKE '%Finance%' OR sec.name LIKE '%Financial%' OR sec.id IS NULL)
+   AND (f.check_size_upper <= 5000000 OR f.check_size_upper IS NULL)
+
+Q: "Seed stage investors"
+A: SELECT DISTINCT f.id FROM funds f 
+   LEFT JOIN fund_investment_stages fis ON f.id = fis.fund_id 
+   LEFT JOIN investment_stages s ON fis.stage_id = s.id 
+   WHERE s.name LIKE '%Seed%' OR s.name LIKE '%Pre-Seed%' OR s.name LIKE '%Early%'
+
+Q: "Growth stage investors"
+A: SELECT DISTINCT f.id FROM funds f 
+   LEFT JOIN fund_investment_stages fis ON f.id = fis.fund_id 
+   LEFT JOIN investment_stages s ON fis.stage_id = s.id 
+   WHERE s.name LIKE '%Growth%' OR s.name LIKE '%Late%' OR s.name LIKE '%Expansion%' OR s.name LIKE '%Series C%' OR s.name LIKE '%Series D%'
+
+Q: "AI investors in America"
+A: SELECT DISTINCT f.id FROM funds f 
+   LEFT JOIN fund_sectors fs ON f.id = fs.fund_id 
+   LEFT JOIN sectors sec ON fs.sector_id = sec.id 
+   WHERE (sec.name LIKE '%AI%' OR sec.name LIKE '%Artificial Intelligence%' OR sec.name LIKE '%Machine Learning%' OR sec.name LIKE '%ML%')
+   AND (f.geographic_focus LIKE '%America%' OR f.geographic_focus LIKE '%US%' OR f.geographic_focus LIKE '%United States%' OR f.geographic_focus LIKE '%USA%')
+
+Q: "Healthcare investors"
+A: SELECT DISTINCT f.id FROM funds f 
+   LEFT JOIN fund_sectors fs ON f.id = fs.fund_id 
+   LEFT JOIN sectors sec ON fs.sector_id = sec.id 
+   WHERE sec.name LIKE '%Healthcare%' OR sec.name LIKE '%Health%' OR sec.name LIKE '%Medical%' OR sec.name LIKE '%Biotech%' OR sec.name LIKE '%Pharma%'
+
+IMPORTANT: Use LEFT JOIN so funds without sector/stage tags can still match. Include synonym terms with OR for better recall.
+
+Return ONLY the SQL query, no explanations or markdown.""",
+                ),
+                ("user", "{question}"),
+            ]
         )
 
-    def process_query(
+    def _get_cache_key(self, question: str) -> str:
+        """Generate cache key from normalized question."""
+        return hashlib.md5(question.lower().strip().encode()).hexdigest()
+
+    async def process_query(
         self, question: str, project_id: Optional[int] = None
     ) -> PaginatedResponse[InvestmentResponse]:
-        """Process a query using the LLM and return investment response data.
-
-        Args:
-            question: The natural language query to process
-            project_id: Optional project ID for compatibility scoring
+        """Async wrapper for process_query. Runs blocking work in a thread to avoid
+        blocking the event loop.
         """
-        # Let the LLM handle all database interactions and filtering to get fund IDs
-        response = self.agent.invoke(
-            {"messages": [("user", question)]},
-            config={"recursion_limit": 50},
-        )
+        return await asyncio.to_thread(self._process_query_sync, question, project_id)
 
-        # Extract the actual message content
-        logger.info(f"{response}")
-        final_message_content = response["messages"][-1].content
-        logger.info(f"AI Response: \n{final_message_content}")
-        # Extract fund IDs from the AI response
-        fund_ids = self._extract_fund_ids_from_response(final_message_content)
+    def _process_query_sync(
+        self, question: str, project_id: Optional[int] = None
+    ) -> PaginatedResponse[InvestmentResponse]:
+        """Synchronous implementation of process_query. This is run in a thread by
+        the async wrapper above.
+        """
+        cache_key = self._get_cache_key(question)
 
-        # Fetch full fund data with investor relationships using the IDs
-        return self._fetch_funds_by_ids(fund_ids, project_id)
+        # Check cache first
+        if cache_key in self.query_cache:
+            sql_query = self.query_cache[cache_key]
+            logger.info(f"Using cached SQL: {sql_query}")
+        else:
+            # Generate SQL query
+            messages = self.sql_prompt.format_messages(question=question)
+            response = self.llm.invoke(messages)
+            sql_query = response.content.strip()
 
-    def _extract_fund_ids_from_response(self, ai_response: str) -> List[int]:
-        """Extract fund IDs from AI response."""
-        import re
+            # Clean up SQL (remove markdown code blocks if present)
+            sql_query = sql_query.replace("```sql", "").replace("```", "").strip()
 
-        fund_ids = []
+            # Cache the query
+            self.query_cache[cache_key] = sql_query
+            logger.info(f"Generated SQL: {sql_query}")
+
+        # Execute query to get fund IDs
+        db_session = next(get_db())
         try:
-            # Try multiple patterns to extract IDs from the response
-            # Pattern 1: Simple numbers (assuming they are IDs)
-            numbers = re.findall(r"\b\d+\b", ai_response)
-            fund_ids = [int(num) for num in numbers]
-
-            # Pattern 2: If response contains explicit ID references
-            id_matches = re.findall(r"\bid[:\s]*(\d+)", ai_response.lower())
-            if id_matches:
-                fund_ids = [int(id_str) for id_str in id_matches]
+            result = db_session.execute(text(sql_query))
+            fund_ids = [row[0] for row in result.fetchall()]
+            logger.info(
+                f"Found {len(fund_ids)} fund IDs: {fund_ids[:10]}{'...' if len(fund_ids) > 10 else ''}"
+            )
 
+            return self._fetch_funds_by_ids(fund_ids, project_id)
         except Exception as e:
-            print(f"Error extracting IDs from response: {e}")
-            return []
-
-        return fund_ids
+            logger.error(f"SQL execution error: {e}")
+            logger.error(f"Failed SQL: {sql_query}")
+            # Return empty result
+            return PaginatedResponse(
+                items=[], total=0, page=1, page_size=10, total_pages=0
+            )
+        finally:
+            db_session.close()
 
     def _fetch_funds_by_ids(
         self, fund_ids: List[int], project_id: Optional[int] = None
@@ -185,10 +258,10 @@ class QueryProcessor:
                     else None
                 )
 
-                # Get top 3 sectors from fund (id and name only)
+                # Get top 3 sectors from fund (id and name only) - sorted alphabetically
                 fund_sectors = [
                     SectorMinimal(id=sector.id, name=sector.name)
-                    for sector in (fund.sectors[:3] if fund.sectors else [])
+                    for sector in sorted(fund.sectors[:3] if fund.sectors else [], key=lambda s: s.name)
                 ]
 
                 investment_response = InvestmentResponse(

app/services/report_gen.py

@@ -1,9 +1,13 @@
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
+# Import database models and compatibility score service
+from db.models import InvestorTable, ProjectTable
 from jinja2 import Environment, FileSystemLoader
 from playwright.async_api import async_playwright
 
+from services.compatibility_score import calculate_project_investor_compatibility
+
 
 class ReportGenerator:
     """Service for generating PDF reports from HTML templates"""
@@ -17,6 +21,8 @@ class ReportGenerator:
         self,
         investor_data: Dict[str, Any],
         project_data: Optional[Dict[str, Any]] = None,
+        investor_model: Optional[InvestorTable] = None,
+        project_model: Optional[ProjectTable] = None,
     ) -> bytes:
         """
         Generate a PDF report for an investor profile.
@@ -24,12 +30,16 @@ class ReportGenerator:
         Args:
             investor_data: Dictionary containing investor information
             project_data: Optional dictionary containing project information for compatibility analysis
+            investor_model: Optional database model for investor (used for compatibility scoring)
+            project_model: Optional database model for project (used for compatibility scoring)
 
         Returns:
             bytes: PDF file content
         """
         # Prepare template context
-        context = self._prepare_context(investor_data, project_data)
+        context = self._prepare_context(
+            investor_data, project_data, investor_model, project_model
+        )
 
         # Render HTML from template
         template = self.env.get_template("report.html")
@@ -43,6 +53,8 @@ class ReportGenerator:
         self,
         investor_data: Dict[str, Any],
         project_data: Optional[Dict[str, Any]] = None,
+        investor_model: Optional[InvestorTable] = None,
+        project_model: Optional[ProjectTable] = None,
     ) -> Dict[str, Any]:
         """Prepare the context dictionary for template rendering"""
         context = {
@@ -55,9 +67,20 @@ class ReportGenerator:
 
         # If project data is provided, calculate compatibility
         if project_data:
-            context["compatibility_score"] = self._calculate_compatibility_score(
-                investor_data, project_data
-            )
+            # Use the compatibility_score service if models are provided
+            if investor_model and project_model:
+                # Calculate using the standardized compatibility score service
+                # Returns score between 0 and 1, convert to percentage (0-100)
+                score_decimal = calculate_project_investor_compatibility(
+                    project=project_model, investor=investor_model, use_funds=True
+                )
+                context["compatibility_score"] = int(score_decimal * 100)
+            else:
+                # Fallback to old calculation method if models not provided
+                context["compatibility_score"] = self._calculate_compatibility_score(
+                    investor_data, project_data
+                )
+
             context["match_criteria"] = self._generate_match_criteria(
                 investor_data, project_data
             )
@@ -76,43 +99,75 @@ class ReportGenerator:
             "sector": 30,
             "stage": 30,
             "geography": 20,
-            "check_size": 15,
-            "thesis": 5,
+            "check_size": 20,
         }
 
+        # Aggregate data from all funds
+        all_sectors = set(investor_data.get("sectors", []))
+        all_stages = set()
+        all_geographies = []
+        check_ranges = []
+
+        for fund in investor_data.get("funds", []):
+            all_sectors.update(fund.get("sectors", []))
+            all_stages.update(fund.get("investment_stages", []))
+            if fund.get("geographic_focus"):
+                all_geographies.append(fund["geographic_focus"])
+            if fund.get("check_size_lower") and fund.get("check_size_upper"):
+                check_ranges.append(
+                    {
+                        "lower": fund["check_size_lower"],
+                        "upper": fund["check_size_upper"],
+                    }
+                )
+
         # Sector match
-        investor_sectors = set(investor_data.get("sectors", []))
         project_sectors = set(project_data.get("sectors", []))
-        if investor_sectors and project_sectors:
-            if investor_sectors & project_sectors:
+        if all_sectors and project_sectors:
+            if all_sectors & project_sectors:
                 score += weights["sector"]
 
-        # Stage match
-        investor_stages = set(investor_data.get("investment_stages", []))
+        # Stage match - case insensitive comparison
         project_stage = project_data.get("stage")
-        if project_stage and project_stage in investor_stages:
-            score += weights["stage"]
+        if project_stage and all_stages:
+            # Normalize stage names for comparison (case-insensitive)
+            normalized_stages = {
+                stage.lower().replace("_", " ") for stage in all_stages
+            }
+            project_stage_normalized = project_stage.lower().replace("_", " ")
+            if project_stage_normalized in normalized_stages:
+                score += weights["stage"]
 
-        # Geography match
-        investor_geo = (investor_data.get("geographic_focus") or "").lower()
+        # Geography match - check if any fund matches
         project_geo = (project_data.get("location") or "").lower()
-        if investor_geo and project_geo and investor_geo in project_geo:
+        geo_match = False
+        if all_geographies:
+            for geo in all_geographies:
+                if geo:
+                    geo_lower = geo.lower()
+                    # Match if investor geography is "global" or if there's a location overlap
+                    if "global" in geo_lower or "worldwide" in geo_lower:
+                        geo_match = True
+                        break
+                    if project_geo and (
+                        geo_lower in project_geo or project_geo in geo_lower
+                    ):
+                        geo_match = True
+                        break
+        if geo_match:
             score += weights["geography"]
 
-        # Check size match
+        # Check size match - check if any fund's range matches
         project_valuation = project_data.get("valuation", 0)
-        check_lower = investor_data.get("check_size_lower") or 0
-        check_upper = investor_data.get("check_size_upper") or float("inf")
-        if (
-            check_lower
-            and check_upper
-            and check_lower <= project_valuation <= check_upper
-        ):
+        check_match = False
+        if project_valuation and check_ranges:
+            for check_range in check_ranges:
+                if check_range["lower"] <= project_valuation <= check_range["upper"]:
+                    check_match = True
+                    break
+        if check_match:
             score += weights["check_size"]
 
-        # Thesis alignment (simplified)
-        score += weights["thesis"]
-
         return min(score, 100)
 
     def _generate_match_criteria(
@@ -121,86 +176,124 @@ class ReportGenerator:
         """Generate detailed match criteria table"""
         criteria = []
 
+        # Aggregate data from all funds
+        all_sectors = set(investor_data.get("sectors", []))
+        all_stages = set()
+        all_geographies = []
+        check_ranges = []
+
+        for fund in investor_data.get("funds", []):
+            all_sectors.update(fund.get("sectors", []))
+            all_stages.update(fund.get("investment_stages", []))
+            if fund.get("geographic_focus"):
+                all_geographies.append(fund["geographic_focus"])
+            if fund.get("check_size_lower") and fund.get("check_size_upper"):
+                check_ranges.append(
+                    {
+                        "lower": fund["check_size_lower"],
+                        "upper": fund["check_size_upper"],
+                        "fund_name": fund.get("fund_name", "Unnamed Fund"),
+                    }
+                )
+
         # Sector criterion
-        investor_sectors = investor_data.get("sectors", [])
         project_sectors = project_data.get("sectors", [])
-        sector_match = (
-            "Perfect" if set(investor_sectors) & set(project_sectors) else "Mismatch"
-        )
+        sector_match = "Perfect" if all_sectors & set(project_sectors) else "Mismatch"
         criteria.append(
             {
                 "name": "Sector",
-                "requirement": "Cybersecurity, B2B SaaS" if project_sectors else "N/A",
-                "evidence": ", ".join(investor_sectors[:3])
-                if investor_sectors
-                else "N/A",
+                "requirement": ", ".join(project_sectors) if project_sectors else "N/A",
+                "evidence": ", ".join(list(all_sectors)[:3]) if all_sectors else "N/A",
                 "match": sector_match,
                 "weight": "30%",
             }
         )
 
-        # Stage criterion
-        investor_stages = investor_data.get("investment_stages", [])
+        # Stage criterion - case insensitive comparison
         project_stage = project_data.get("stage", "N/A")
-        stage_match = "Perfect" if project_stage in investor_stages else "Mismatch"
+        stage_match = "Mismatch"
+        if project_stage != "N/A" and all_stages:
+            # Normalize stage names for comparison
+            normalized_stages = {
+                stage.lower().replace("_", " ") for stage in all_stages
+            }
+            project_stage_normalized = project_stage.lower().replace("_", " ")
+            stage_match = (
+                "Perfect"
+                if project_stage_normalized in normalized_stages
+                else "Mismatch"
+            )
+        elif project_stage == "N/A":
+            stage_match = "N/A"
+
         criteria.append(
             {
                 "name": "Stage",
                 "requirement": str(project_stage),
-                "evidence": ", ".join(investor_stages) if investor_stages else "N/A",
+                "evidence": ", ".join(all_stages) if all_stages else "N/A",
                 "match": stage_match,
                 "weight": "30%",
             }
         )
 
         # Geography criterion
-        investor_geo = investor_data.get("geographic_focus") or "N/A"
         project_geo = project_data.get("location") or "N/A"
+        investor_geo_display = ", ".join(all_geographies) if all_geographies else "N/A"
+
+        # Safe comparison handling None values and "Global" matches
+        geo_match = "Mismatch"
+        if project_geo != "N/A" and all_geographies:
+            for geo in all_geographies:
+                if geo:
+                    geo_lower = geo.lower()
+                    # Match if investor geography is "global" or if there's a location overlap
+                    if "global" in geo_lower or "worldwide" in geo_lower:
+                        geo_match = "Perfect"
+                        break
+                    if (
+                        geo_lower in project_geo.lower()
+                        or project_geo.lower() in geo_lower
+                    ):
+                        geo_match = "Strong"
+                        break
+        elif not all_geographies and project_geo == "N/A":
+            geo_match = "N/A"
 
-        # Safe comparison handling None values
-        if investor_geo == "N/A" or project_geo == "N/A":
-            geo_match = (
-                "N/A" if investor_geo == "N/A" and project_geo == "N/A" else "Mismatch"
-            )
-        else:
-            investor_geo_lower = investor_geo.lower()
-            project_geo_lower = project_geo.lower()
-            geo_match = (
-                "Strong"
-                if investor_geo_lower in project_geo_lower
-                or project_geo_lower in investor_geo_lower
-                else "Mismatch"
-            )
         criteria.append(
             {
                 "name": "Geography",
                 "requirement": project_geo,
-                "evidence": investor_geo,
+                "evidence": investor_geo_display,
                 "match": geo_match,
                 "weight": "20%",
             }
         )
 
         # Check Size criterion
-        check_lower = investor_data.get("check_size_lower") or 0
-        check_upper = investor_data.get("check_size_upper") or 0
         project_val = project_data.get("valuation", 0)
 
+        # Build evidence string from all fund ranges
         check_evidence = "N/A"
-        if check_lower and check_upper:
-            check_evidence = (
-                f"€{check_lower / 1000000:.0f}M - €{check_upper / 1000000:.0f}M"
-            )
-        elif check_lower:
-            check_evidence = f"€{check_lower / 1000000:.0f}M+"
+        if check_ranges:
+            evidence_parts = []
+            for cr in check_ranges[:3]:  # Show up to 3 funds
+                range_str = (
+                    f"€{cr['lower'] / 1000000:.0f}M - €{cr['upper'] / 1000000:.0f}M"
+                )
+                if cr["fund_name"]:
+                    evidence_parts.append(f"{cr['fund_name']}: {range_str}")
+                else:
+                    evidence_parts.append(range_str)
+            check_evidence = "; ".join(evidence_parts)
+
+        # Check if project valuation matches any fund
+        check_match = "N/A"
+        if project_val > 0 and check_ranges:
+            match_found = any(
+                cr["lower"] <= project_val <= cr["upper"] for cr in check_ranges
+            )
+            check_match = "Perfect" if match_found else "Mismatch"
 
-        check_match = (
-            "Perfect"
-            if check_lower and check_upper and check_lower <= project_val <= check_upper
-            else "Strong"
-            if project_val > 0
-            else "N/A"
-        )
         criteria.append(
             {
                 "name": "Check Size",
@@ -209,19 +302,7 @@ class ReportGenerator:
                 else "N/A",
                 "evidence": check_evidence,
                 "match": check_match,
-                "weight": "15%",
-            }
-        )
-
-        # Thesis criterion
-        thesis = investor_data.get("investment_thesis", [])
-        criteria.append(
-            {
-                "name": "Thesis",
-                "requirement": "Founder-led, ESG focus",
-                "evidence": ", ".join(thesis[:2]) if thesis else "Entrepreneur-led",
-                "match": "Strong",
-                "weight": "5%",
+                "weight": "20%",
             }
         )
 

app/templates/report.html

@@ -161,13 +161,6 @@
                                 </p>
                             </div>
 
-                            <div>
-                                <p class="text-xs text-gray-600">DACH Region:</p>
-                                <p class="font-semibold text-gray-900">
-                                    {{ investor.geographic_focus or 'N/A' }}
-                                </p>
-                            </div>
-
                             <div>
                                 <p class="text-xs text-gray-600">AUM (EUR million):</p>
                                 <p class="font-semibold text-gray-900">
@@ -179,33 +172,47 @@
                                 </p>
                             </div>
 
-                    <div class="mb-4">
-                        <p class="text-xs text-gray-600 mb-1">
-                            Investment Stage:
-                        </p>
-                        <p class="text-sm font-semibold text-gray-900">
-                            {% if investor.investment_stages %} {{
-                            investor.investment_stages | join(', ') }} {% else
-                            %} N/A {% endif %}
-                        </p>
-                    </div>
-
-                    <div class="mb-4">
-                        <p class="text-xs text-gray-600 mb-1">
-                            Est. Investment Size:
-                        </p>
-                        <p class="text-sm font-semibold text-gray-900">
-                            {% if investor.check_size_lower and
-                            investor.check_size_upper %} €{{
-                            '{:,.0f}'.format(investor.check_size_lower /
-                            1000000) }}M - €{{
-                            '{:,.0f}'.format(investor.check_size_upper /
-                            1000000) }}M {% elif investor.check_size_lower %}
-                            €{{ '{:,.0f}'.format(investor.check_size_lower /
-                            1000000) }}M+ {% else %} N/A {% endif %}
+                            <div>
+                                <p class="text-xs text-gray-600 mb-1">Number of Funds:</p>
+                                <p class="font-semibold text-gray-900">
+                                    {{ investor.funds | length if investor.funds else 'N/A' }}
                                 </p>
                             </div>
                         </div>
+
+                        <div class="mt-4">
+                            <h3 class="text-xs font-bold text-gray-900 uppercase mb-2">
+                                Fund Details
+                            </h3>
+                            {% if investor.funds %}
+                            {% for fund in investor.funds %}
+                            <div class="mb-3 pb-3 border-b border-gray-200">
+                                <p class="text-sm font-semibold text-gray-900 mb-1">
+                                    {{ fund.fund_name or 'Fund ' + loop.index|string }}
+                                </p>
+                                <div class="text-xs text-gray-700 space-y-1">
+                                    {% if fund.fund_size %}
+                                    <p>Fund Size: €{{ '{:,.0f}'.format(fund.fund_size / 1000000) }}M</p>
+                                    {% endif %}
+                                    {% if fund.check_size_lower and fund.check_size_upper %}
+                                    <p>Check Size: €{{ '{:,.0f}'.format(fund.check_size_lower / 1000000) }}M - €{{ '{:,.0f}'.format(fund.check_size_upper / 1000000) }}M</p>
+                                    {% endif %}
+                                    {% if fund.geographic_focus %}
+                                    <p>Geography: {{ fund.geographic_focus }}</p>
+                                    {% endif %}
+                                    {% if fund.investment_stages %}
+                                    <p>Stages: {{ fund.investment_stages | join(', ') }}</p>
+                                    {% endif %}
+                                    {% if fund.sectors %}
+                                    <p>Sectors: {{ fund.sectors[:3] | join(', ') }}</p>
+                                    {% endif %}
+                                </div>
+                            </div>
+                            {% endfor %}
+                            {% else %}
+                            <p class="text-xs text-gray-500">No fund information available</p>
+                            {% endif %}
+                        </div>
                     </div>
                 </div>
 

migrations/add_feedback_fixes_2025_01_07.py

@@ -0,0 +1,117 @@
+"""
+Migration: Add fields from feedback fixes
+Date: 2025-01-07
+
+Adds the following fields:
+- projects.is_archived (INTEGER, default 0)
+- companies.product_service (TEXT, nullable)
+- companies.clients (TEXT, nullable - stored as JSON string)
+- investor_members.linkedin (VARCHAR, nullable)
+"""
+
+import sys
+from pathlib import Path
+
+# Add parent directory to path to import app modules
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from sqlalchemy import text
+
+from app.db.db import engine
+
+
+def check_column_exists(conn, table_name, column_name):
+    """Check if a column exists in a table"""
+    result = conn.execute(text(f"PRAGMA table_info({table_name})"))
+    columns = [row[1] for row in result]
+    return column_name in columns
+
+
+def upgrade():
+    """Add new columns to tables"""
+    print("Running migration: Add feedback fixes fields")
+    print("=" * 60)
+
+    with engine.begin() as conn:  # Use begin() for transaction management
+        # 1. Add is_archived to projects table
+        print("\n1. Adding 'is_archived' column to projects table...")
+        if check_column_exists(conn, "projects", "is_archived"):
+            print("   ✓ Column 'is_archived' already exists. Skipping.")
+        else:
+            conn.execute(
+                text(
+                    "ALTER TABLE projects ADD COLUMN is_archived INTEGER DEFAULT 0 NOT NULL"
+                )
+            )
+            # Set default value for existing rows
+            conn.execute(
+                text("UPDATE projects SET is_archived = 0 WHERE is_archived IS NULL")
+            )
+            print("   ✓ Successfully added 'is_archived' column to projects table")
+
+        # 2. Add product_service to companies table
+        print("\n2. Adding 'product_service' column to companies table...")
+        if check_column_exists(conn, "companies", "product_service"):
+            print("   ✓ Column 'product_service' already exists. Skipping.")
+        else:
+            conn.execute(text("ALTER TABLE companies ADD COLUMN product_service TEXT"))
+            print("   ✓ Successfully added 'product_service' column to companies table")
+
+        # 3. Add clients to companies table
+        print("\n3. Adding 'clients' column to companies table...")
+        if check_column_exists(conn, "companies", "clients"):
+            print("   ✓ Column 'clients' already exists. Skipping.")
+        else:
+            conn.execute(text("ALTER TABLE companies ADD COLUMN clients TEXT"))
+            print("   ✓ Successfully added 'clients' column to companies table")
+
+        # 4. Add linkedin to investor_members table
+        print("\n4. Adding 'linkedin' column to investor_members table...")
+        if check_column_exists(conn, "investor_members", "linkedin"):
+            print("   ✓ Column 'linkedin' already exists. Skipping.")
+        else:
+            conn.execute(
+                text("ALTER TABLE investor_members ADD COLUMN linkedin VARCHAR")
+            )
+            print("   ✓ Successfully added 'linkedin' column to investor_members table")
+
+    print("\n" + "=" * 60)
+    print("Migration completed successfully!")
+
+
+def downgrade():
+    """Remove added columns from tables"""
+    print("Running downgrade: Remove feedback fixes fields")
+    print("=" * 60)
+
+    # Note: SQLite doesn't support DROP COLUMN directly
+    print("\nWarning: SQLite doesn't support DROP COLUMN directly.")
+    print("To remove these columns, you would need to:")
+    print("1. Create new tables without the columns")
+    print("2. Copy data from old tables to new tables")
+    print("3. Drop old tables and rename new tables")
+    print("\nColumns to remove:")
+    print("  - projects.is_archived")
+    print("  - companies.product_service")
+    print("  - companies.clients")
+    print("  - investor_members.linkedin")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Run database migration")
+    parser.add_argument(
+        "direction",
+        choices=["upgrade", "downgrade"],
+        default="upgrade",
+        nargs="?",
+        help="Migration direction (default: upgrade)",
+    )
+
+    args = parser.parse_args()
+
+    if args.direction == "upgrade":
+        upgrade()
+    else:
+        downgrade()

migrations/add_industry_to_projects.py

@@ -0,0 +1,67 @@
+"""
+Migration: Add industry column to projects table
+Date: 2025-10-23
+"""
+
+import os
+import sys
+from pathlib import Path
+
+# Add parent directory to path to import app modules
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from sqlalchemy import create_engine, text
+from app.db.db import DATABASE_URL, engine
+
+
+def upgrade():
+    """Add industry column to projects table"""
+    print("Running migration: Add industry column to projects table")
+    
+    with engine.connect() as conn:
+        # Check if column already exists
+        result = conn.execute(text("PRAGMA table_info(projects)"))
+        columns = [row[1] for row in result]
+        
+        if 'industry' in columns:
+            print("Column 'industry' already exists in projects table. Skipping migration.")
+            return
+        
+        # Add the industry column
+        conn.execute(text("ALTER TABLE projects ADD COLUMN industry VARCHAR"))
+        conn.commit()
+        
+        print("Successfully added 'industry' column to projects table")
+
+
+def downgrade():
+    """Remove industry column from projects table"""
+    print("Running downgrade: Remove industry column from projects table")
+    
+    # Note: SQLite doesn't support DROP COLUMN directly
+    # This is a simplified version - in production you'd need to recreate the table
+    print("Warning: SQLite doesn't support DROP COLUMN.")
+    print("To remove the column, you would need to:")
+    print("1. Create a new table without the industry column")
+    print("2. Copy data from old table to new table")
+    print("3. Drop old table and rename new table")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Run database migration")
+    parser.add_argument(
+        "direction",
+        choices=["upgrade", "downgrade"],
+        default="upgrade",
+        nargs="?",
+        help="Migration direction (default: upgrade)"
+    )
+    
+    args = parser.parse_args()
+    
+    if args.direction == "upgrade":
+        upgrade()
+    else:
+        downgrade()

server_manager.sh

@@ -0,0 +1,68 @@
+#!/bin/bash
+
+# Server management script for app/main.py
+# Usage: ./server_manager.sh start|stop|restart
+
+PID_FILE="server.pid"
+LOG_FILE="server.log"
+
+start() {
+    if [ -f "$PID_FILE" ] && kill -0 $(cat "$PID_FILE") 2>/dev/null; then
+        echo "Server is already running (PID: $(cat "$PID_FILE"))"
+        return 1
+    fi
+    echo "Starting server..."
+    nohup uv run app/main.py > "$LOG_FILE" 2>&1 &
+    echo $! > "$PID_FILE"
+    echo "Server started (PID: $(cat "$PID_FILE"))"
+}
+
+stop() {
+    if [ ! -f "$PID_FILE" ]; then
+        echo "Server is not running (no PID file found)"
+        return 1
+    fi
+    PID=$(cat "$PID_FILE")
+    if ! kill -0 "$PID" 2>/dev/null; then
+        echo "Server is not running (PID $PID not found)"
+        rm -f "$PID_FILE"
+        return 1
+    fi
+    echo "Stopping server (PID: $PID)..."
+    kill "$PID"
+    # Wait for process to stop
+    for i in {1..10}; do
+        if ! kill -0 "$PID" 2>/dev/null; then
+            break
+        fi
+        sleep 1
+    done
+    if kill -0 "$PID" 2>/dev/null; then
+        echo "Force killing server..."
+        kill -9 "$PID"
+    fi
+    rm -f "$PID_FILE"
+    echo "Server stopped"
+}
+
+restart() {
+    stop
+    sleep 2
+    start
+}
+
+case "$1" in
+    start)
+        start
+        ;;
+    stop)
+        stop
+        ;;
+    restart)
+        restart
+        ;;
+    *)
+        echo "Usage: $0 {start|stop|restart}"
+        exit 1
+        ;;
+esac

update_linkedin_profiles.py

@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+"""
+Update Investor Members LinkedIn Profiles Script
+
+This script finds and updates LinkedIn profile URLs for investor members in the database.
+Uses crawl4ai to efficiently scrape team pages and extract LinkedIn URLs.
+
+Usage:
+    python update_linkedin_profiles.py [--test] [--limit N] [--skip-existing]
+
+Options:
+    --test          Test mode: process only 10 records and don't update database
+    --limit N       Process only N records (default: all)
+    --skip-existing Skip members that already have LinkedIn URLs
+    --start-from N  Start from record N (for resuming)
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import sys
+from datetime import datetime
+
+# Add app to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "app"))
+
+from db.db import get_db_session
+from db.models import InvestorMember, InvestorTable
+from linkedin_scraper import LinkedInProfileScraper, format_linkedin_url
+
+
+def progress_callback(current, total, result):
+    """Print progress updates"""
+    percent = (current / total) * 100
+    status = "✓" if result["linkedin_url"] else "✗"
+    print(f"[{current}/{total} - {percent:.1f}%] {status} {result['member_name']}")
+    if result["linkedin_url"]:
+        print(
+            f"  → {result['linkedin_url']} (confidence: {result['confidence']}%, method: {result['method']})"
+        )
+
+
+def create_db_callback(test_mode=False):
+    """
+    Create a callback function that saves LinkedIn profiles to the database immediately.
+    This allows stopping and resuming without losing progress.
+    """
+    saved_count = {"count": 0}  # Use dict to allow modification in closure
+
+    def db_callback(member_id: int, linkedin_url: str) -> bool:
+        """Save LinkedIn URL to database immediately"""
+        if test_mode:
+            print(f"  [TEST] Would save to DB: member {member_id}")
+            saved_count["count"] += 1
+            return True
+
+        try:
+            db = get_db_session()
+            member = db.query(InvestorMember).filter_by(id=member_id).first()
+            if member:
+                member.linkedin = format_linkedin_url(linkedin_url)
+                db.commit()
+                saved_count["count"] += 1
+                return True
+        except Exception as e:
+            print(f"  ⚠️  DB Error for member {member_id}: {e}")
+            try:
+                db.rollback()
+            except Exception:
+                pass
+            return False
+        finally:
+            try:
+                db.close()
+            except Exception:
+                pass
+        return False
+
+    return db_callback, saved_count
+
+
+def update_database(members_data, test_mode=False):
+    """Update database with found LinkedIn profiles"""
+    db = get_db_session()
+
+    try:
+        updated_count = 0
+        for data in members_data:
+            if data["linkedin_url"] and data["member_id"]:
+                if not test_mode:
+                    member = (
+                        db.query(InvestorMember).filter_by(id=data["member_id"]).first()
+                    )
+                    if member:
+                        member.linkedin = format_linkedin_url(data["linkedin_url"])
+                        updated_count += 1
+                else:
+                    print(
+                        f"  [TEST MODE] Would update member {data['member_id']}: {data['linkedin_url']}"
+                    )
+                    updated_count += 1
+
+        if not test_mode:
+            db.commit()
+            print(f"\n✓ Successfully updated {updated_count} records in database")
+        else:
+            print(f"\n[TEST MODE] Would have updated {updated_count} records")
+
+        return updated_count
+
+    except Exception as e:
+        db.rollback()
+        print(f"\n✗ Error updating database: {e}")
+        raise
+    finally:
+        db.close()
+
+
+def save_results(results, filename="linkedin_scraping_results.json"):
+    """Save results to JSON file for backup/analysis"""
+    output = {
+        "timestamp": datetime.now().isoformat(),
+        "total_processed": len(results),
+        "found_count": sum(1 for r in results if r["linkedin_url"]),
+        "results": results,
+    }
+
+    with open(filename, "w") as f:
+        json.dump(output, f, indent=2)
+
+    print(f"\n✓ Results saved to {filename}")
+
+
+def print_summary(results):
+    """Print summary statistics"""
+    total = len(results)
+    found = sum(1 for r in results if r["linkedin_url"])
+    not_found = total - found
+
+    # Count by method
+    methods = {}
+    for r in results:
+        if r["linkedin_url"]:
+            method = r["method"]
+            methods[method] = methods.get(method, 0) + 1
+
+    # Average confidence for found profiles
+    avg_confidence = (
+        sum(r["confidence"] for r in results if r["linkedin_url"]) / found
+        if found > 0
+        else 0
+    )
+
+    print("\n" + "=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    print(f"Total processed:        {total}")
+    print(f"LinkedIn found:         {found} ({found / total * 100:.1f}%)")
+    print(f"Not found:              {not_found} ({not_found / total * 100:.1f}%)")
+    print(f"\nAverage confidence:     {avg_confidence:.1f}%")
+    print("\nMethods used:")
+    for method, count in sorted(methods.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {method:20s} {count:5d} ({count / found * 100:.1f}%)")
+    print("=" * 60)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Update LinkedIn profiles for investor members"
+    )
+    parser.add_argument(
+        "--test",
+        action="store_true",
+        help="Test mode: process only 10 records without updating database",
+    )
+    parser.add_argument("--limit", type=int, help="Limit number of records to process")
+    parser.add_argument(
+        "--skip-existing",
+        action="store_true",
+        help="Skip members that already have LinkedIn URLs",
+    )
+    parser.add_argument(
+        "--start-from",
+        type=int,
+        default=0,
+        help="Start from record N (for resuming interrupted runs)",
+    )
+    parser.add_argument(
+        "--rate-limit",
+        type=float,
+        default=0.5,
+        help="Delay between URL crawls in seconds (default: 0.5)",
+    )
+
+    args = parser.parse_args()
+
+    # Test mode overrides limit
+    if args.test and not args.limit:
+        args.limit = 10
+
+    print("=" * 60)
+    print("LinkedIn Profile Scraper for Investor Members (crawl4ai)")
+    print("=" * 60)
+
+    if args.test:
+        print("\n⚠️  TEST MODE - No database changes will be made")
+
+    # Initialize database and scraper
+    db = get_db_session()
+
+    try:
+        # Build query
+        query = db.query(InvestorMember, InvestorTable).join(
+            InvestorTable, InvestorMember.investor_id == InvestorTable.id
+        )
+
+        # Filter existing if requested
+        if args.skip_existing:
+            query = query.filter(
+                (InvestorMember.linkedin.is_(None)) | (InvestorMember.linkedin == "")
+            )
+            print("\n✓ Filtering to members without LinkedIn profiles")
+
+        # Get total count
+        total_available = query.count()
+        print(f"\n✓ Found {total_available} members to process")
+
+        # Apply offset and limit
+        if args.start_from > 0:
+            query = query.offset(args.start_from)
+            print(f"✓ Starting from record {args.start_from}")
+
+        if args.limit:
+            query = query.limit(args.limit)
+            print(f"✓ Processing {args.limit} records")
+
+        # Fetch members
+        members_data = []
+        for member, investor in query.all():
+            members_data.append(
+                {
+                    "id": member.id,
+                    "name": member.name,
+                    "company": investor.name,
+                    "role": member.role,
+                    "source_url": member.source_url,
+                }
+            )
+
+        if not members_data:
+            print("\n⚠️  No members to process")
+            return
+
+        # Count unique source URLs
+        unique_urls = len(set(m["source_url"] for m in members_data if m["source_url"]))
+        with_urls = sum(1 for m in members_data if m["source_url"])
+
+        print(f"\n✓ Loaded {len(members_data)} members")
+        print(
+            f"✓ {with_urls} members have source URLs ({unique_urls} unique pages to crawl)"
+        )
+        print(f"✓ {len(members_data) - with_urls} members without source URLs")
+        print(f"✓ Rate limit: {args.rate_limit}s between page crawls")
+        print("\nStarting LinkedIn profile search using crawl4ai...\n")
+
+    finally:
+        db.close()
+
+    # Initialize scraper
+    scraper = LinkedInProfileScraper(rate_limit_delay=args.rate_limit, use_cache=True)
+
+    print("ℹ️  Using crawl4ai to scrape team pages and extract LinkedIn URLs")
+    print(
+        "ℹ️  Profiles are saved to database IMMEDIATELY when found - safe to stop anytime!\n"
+    )
+
+    # Create database callback for real-time saving
+    db_callback, saved_count = create_db_callback(test_mode=args.test)
+
+    # Process members asynchronously with real-time DB saving
+    results = asyncio.run(
+        scraper.batch_find_profiles(
+            members_data, progress_callback=progress_callback, db_callback=db_callback
+        )
+    )
+
+    # Print summary
+    print_summary(results)
+
+    # Save results
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    results_file = f"linkedin_results_{timestamp}.json"
+    save_results(results, results_file)
+
+    # Show database update summary
+    if not args.test:
+        print(
+            f"\n✓ Database updated in real-time: {saved_count['count']} profiles saved"
+        )
+    else:
+        print(
+            f"\n[TEST MODE] Would have saved {saved_count['count']} profiles to database"
+        )
+
+    print("\n✓ Done! You can resume anytime with --skip-existing")
+
+
+if __name__ == "__main__":
+    main()