fixed querying

app/main.py

@@ -44,6 +44,15 @@ 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():
@@ -120,7 +129,7 @@ async def query_investors(request: QueryRequest):
 @app.post(
     "/query-companies", response_model=PaginatedResponse[CompanyData], tags=["Querying"]
 )
-async def query_companies(request: QueryRequest):
+async def query_companies(request: CompanyQueryRequest):
     """
     Query companies using natural language.
 

app/services/company_querying.py

@@ -1,21 +1,17 @@
+import hashlib
 import logging
 import os
 from typing import List
 
-from db.db import DATABASE_URL, get_db
+from db.db import get_db
 from db.models import CompanyTable
-from langchain import hub
+from langchain_core.prompts import ChatPromptTemplate
-from langchain_community.agent_toolkits import SQLDatabaseToolkit
-from langchain_community.utilities import SQLDatabase
 from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
 from schemas.router_schemas import CompanyData, PaginatedResponse
+from sqlalchemy import text
 from sqlalchemy.orm import selectinload
 
 logger = logging.getLogger(__name__)
-# Connect to SQLite
-prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
-db = SQLDatabase.from_uri(DATABASE_URL)
 
 
 class CompanyQueryProcessor:
@@ -26,96 +22,144 @@ class CompanyQueryProcessor:
             model="openai/gpt-4o-mini",
             temperature=0,
         )
-        self.toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
+
-        # Update system message to specifically request only company IDs
+        # Query cache for performance
-        system_message_updated = (
+        self.query_cache = {}
-            prompt_template.format(dialect="SQLite", top_k=5)
+
-            + "\n\n=== CRITICAL INSTRUCTIONS ==="
+        # SQL generation prompt
-            + "\n- Your ONLY task is to run SQL queries and extract company IDs"
+        self.sql_prompt = ChatPromptTemplate.from_messages(
-            + "\n- When you get SQL results with company IDs, return them EXACTLY as shown"
+            [
-            + "\n- If the SQL query returns rows with company IDs like [(1,), (5,), (9,)], return all those IDs"
+                (
-            + "\n- Do NOT add any explanations, just list the IDs"
+                    "system",
-            + "\n- If a query returns NO ROWS (empty result), then respond with 'NO_RESULTS'"
+                    """You are a SQL expert. Generate a SQLite query to find company IDs based on user requirements.
-            + "\n\n=== QUERY GUIDELINES ==="
+
-            + "\n1. For sector searches: SELECT companies.id FROM companies JOIN company_sector ON companies.id = company_sector.company_id JOIN sectors ON company_sector.sector_id = sectors.id WHERE sectors.name LIKE '%sector_name%'"
+Database Schema:
-            + "\n2. For industry searches: WHERE companies.industry LIKE '%search_term%'"
+- companies: id, name, industry, location, description, founded_year, website
-            + "\n3. For location searches: WHERE companies.location LIKE '%location%'"
+- company_sector: company_id, sector_id
-            + "\n4. For founding year searches: WHERE companies.founded_year >= year"
+- sectors: id, name
-            + "\n5. For investor-related: JOIN investor_companies table"
+- investor_companies: investor_id, company_id
-        )
+- investors: id, name, aum
-        self.agent = create_react_agent(
+- team_members: id, company_id, name, title
-            model=self.llm,
+
-            tools=self.toolkit.get_tools(),
+IMPORTANT RULES:
-            prompt=system_message_updated,
+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()
+
     def process_query(self, question: str) -> PaginatedResponse[CompanyData]:
-        """Process a query using the LLM and return company response data.
+        """Process a query by generating and executing SQL directly.
 
         Args:
             question: The natural language query to process
         """
-        # Let the LLM handle all database interactions and filtering to get company IDs
+        cache_key = self._get_cache_key(question)
-        response = self.agent.invoke(
-            {"messages": [("user", question)]},
-            config={"recursion_limit": 50},
-        )
 
-        # Extract the actual message content
+        # Check cache first
-        logger.info(f"{response}")
+        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()
 
-        # Look through all messages to find the SQL query results (ToolMessage with actual data)
+            # Clean up SQL (remove markdown code blocks if present)
-        company_ids = []
+            sql_query = sql_query.replace("```sql", "").replace("```", "").strip()
-        for message in response["messages"]:
-            if hasattr(message, "content") and message.content:
-                # Check if this looks like SQL results (contains tuples with numbers)
-                if "(" in str(message.content) and "," in str(message.content):
-                    company_ids = self._extract_company_ids_from_response(
-                        str(message.content)
-                    )
-                    if company_ids:
-                        logger.info(
-                            f"Extracted {len(company_ids)} company IDs from results"
-                        )
-                        break
 
-        # If no IDs found from ToolMessage, check the final AI message
+            # Cache the query
-        if not company_ids:
+            self.query_cache[cache_key] = sql_query
-            final_message_content = response["messages"][-1].content
+            logger.info(f"Generated SQL: {sql_query}")
-            logger.info(f"AI Response: \n{final_message_content}")
-            company_ids = self._extract_company_ids_from_response(final_message_content)
-
-        # Fetch full company data with relationships using the IDs
-        return self._fetch_companies_by_ids(company_ids)
-
-    def _extract_company_ids_from_response(self, ai_response: str) -> List[int]:
-        """Extract company IDs from AI response."""
-        import re
-
-        company_ids = []
-
-        # Check if response is NO_RESULTS
-        if "NO_RESULTS" in ai_response.upper():
-            return []
 
+        # Execute query to get company IDs
+        db_session = next(get_db())
         try:
-            # The response contains tuples like (1,), (5,), etc.
+            result = db_session.execute(text(sql_query))
-            # Extract numbers between parentheses
+            company_ids = [row[0] for row in result.fetchall()]
-            pattern = r"\((\d+),?\)"
+            logger.info(
-            matches = re.findall(pattern, ai_response)
+                f"Found {len(company_ids)} company IDs: {company_ids[:10]}{'...' if len(company_ids) > 10 else ''}"
-            if matches:
+            )
-                company_ids = [int(match) for match in matches]
-            else:
-                # Fallback: extract all numbers
-                numbers = re.findall(r"\b\d+\b", ai_response)
-                # Filter out very large numbers that might be tokens or timestamps
-                company_ids = [int(num) for num in numbers if int(num) < 100000]
 
+            return self._fetch_companies_by_ids(company_ids)
         except Exception as e:
-            logger.error(f"Error extracting IDs from response: {e}")
+            logger.error(f"SQL execution error: {e}")
-            return []
+            logger.error(f"Failed SQL: {sql_query}")
-
+            # Return empty result
-        return company_ids
+            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]
@@ -130,7 +174,7 @@ class CompanyQueryProcessor:
                 items=[],
                 total=0,
                 page=1,
-                page_size=len(company_ids) if company_ids else 10,
+                page_size=10,
                 total_pages=0,
             )
 

app/services/querying.py

@@ -1,29 +1,24 @@
-import json
+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_core.prompts import ChatPromptTemplate
-from langchain_community.agent_toolkits import SQLDatabaseToolkit
-from langchain_community.utilities import SQLDatabase
 from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
 from schemas.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:
@@ -34,89 +29,150 @@ 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
+        # Query cache for performance
-        system_message_updated = (
+        self.query_cache = {}
-            prompt_template.format(dialect="SQLite", top_k=100)
+
-            + "\n\n=== IMPORTANT TERMINOLOGY ==="
+        # SQL generation prompt
-            + "\n- When users say 'investors' or 'find me investors', they mean FUNDS"
+        self.sql_prompt = ChatPromptTemplate.from_messages(
-            + "\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"
+                    "system",
-            + "\n\n=== YOUR TASK ==="
+                    """You are a SQL expert. Generate a SQLite query to find fund IDs based on user requirements.
-            + "\nReturn ONLY fund IDs (funds.id) that match the user's criteria."
+
-            + "\nFormat: comma-separated numbers only (e.g., 1, 5, 12, 23)"
+Database Schema:
-            + "\nNo explanations, no other data."
+- funds: id, fund_name, investor_id, check_size_lower, check_size_upper, geographic_focus
-            + "\n\n=== QUERY GUIDELINES ==="
+- fund_sectors: fund_id, sector_id
-            + "\n1. For geographic searches: use funds.geographic_focus"
+- fund_investment_stages: fund_id, stage_id
-            + "\n2. For sector searches: JOIN with fund_sectors table"
+- sectors: id, name
-            + "\n3. For stage searches: JOIN with fund_investment_stages table"
+- investment_stages: id, name
-            + "\n4. Return ALL matching fund IDs, not just the first few"
+- investors: id, name, aum
-            + "\n5. If no results: respond with 'NO_RESULTS'"
+
-            + "\n6. Never repeat the same failed query"
+IMPORTANT RULES:
-            + "\n\n=== GEOGRAPHIC SEARCH RULES (VERY IMPORTANT) ==="
+1. ALWAYS return ONLY fund IDs (funds.id) - use SELECT DISTINCT f.id
-            + "\n- ALWAYS use LIKE '%keyword%' for geographic searches, NEVER use exact equality (=)"
+2. For geography: Be FLEXIBLE - use OR with variations and partial matches
-            + "\n- When user says 'Europe', match ANY location containing 'Europe' (e.g., 'Northern Europe', 'Western Europe', 'Europe', 'Central Europe')"
+   - 'Europe' → WHERE geographic_focus LIKE '%Europe%' OR geographic_focus LIKE '%European%'
-            + "\n- When user says 'America', match locations like 'North America', 'South America', 'Latin America', 'United States'"
+   - 'America' → WHERE geographic_focus LIKE '%America%' OR geographic_focus LIKE '%US%' OR geographic_focus LIKE '%United States%'
-            + "\n- When user says 'Asia', match 'Asia', 'Southeast Asia', 'East Asia', etc."
+   - 'Asia' → WHERE geographic_focus LIKE '%Asia%' OR geographic_focus LIKE '%Asian%'
-            + "\n- Examples:"
+   - If no geography specified, DON'T filter by geography
-            + "\n  * User: 'Europe' → SQL: WHERE geographic_focus LIKE '%Europe%'"
+3. For stages: Use LEFT JOIN and LIKE for flexible matching with synonyms
-            + "\n  * User: 'America' → SQL: WHERE geographic_focus LIKE '%America%'"
+   - 'Seed' → s.name LIKE '%Seed%' OR s.name LIKE '%Pre-Seed%' OR s.name LIKE '%Early%'
-            + "\n  * User: 'UK' → SQL: WHERE geographic_focus LIKE '%UK%' OR geographic_focus LIKE '%United Kingdom%'"
+   - 'Series A' → s.name LIKE '%Series A%' OR s.name LIKE '%A%'
-            + "\n- Be INCLUSIVE: capture all relevant regional variations"
+   - 'Growth' → s.name LIKE '%Growth%' OR s.name LIKE '%Late%' OR s.name LIKE '%Expansion%'
-        )
+   - If stage not specified, include ALL funds
-        self.agent = create_react_agent(
+4. For sectors: Use LEFT JOIN and include related terms with OR
-            model=self.llm,
+   - 'Fintech' → sec.name LIKE '%Fintech%' OR sec.name LIKE '%Finance%' OR sec.name LIKE '%Financial%'
-            tools=self.toolkit.get_tools(),
+   - 'AI' → sec.name LIKE '%AI%' OR sec.name LIKE '%Artificial Intelligence%' OR sec.name LIKE '%Machine Learning%' OR sec.name LIKE '%ML%'
-            prompt=system_message_updated,
+   - '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 _get_cache_key(self, question: str) -> str:
+        """Generate cache key from normalized question."""
+        return hashlib.md5(question.lower().strip().encode()).hexdigest()
+
     def process_query(
         self, question: str, project_id: Optional[int] = None
     ) -> PaginatedResponse[InvestmentResponse]:
-        """Process a query using the LLM and return investment response data.
+        """Process a query by generating and executing SQL directly.
 
         Args:
             question: The natural language query to process
             project_id: Optional project ID for compatibility scoring
         """
-        # Let the LLM handle all database interactions and filtering to get fund IDs
+        cache_key = self._get_cache_key(question)
-        response = self.agent.invoke(
-            {"messages": [("user", question)]},
-            config={"recursion_limit": 50},
-        )
 
-        # Extract the actual message content
+        # Check cache first
-        logger.info(f"{response}")
+        if cache_key in self.query_cache:
-        final_message_content = response["messages"][-1].content
+            sql_query = self.query_cache[cache_key]
-        logger.info(f"AI Response: \n{final_message_content}")
+            logger.info(f"Using cached SQL: {sql_query}")
-        # Extract fund IDs from the AI response
+        else:
-        fund_ids = self._extract_fund_ids_from_response(final_message_content)
+            # Generate SQL query
+            messages = self.sql_prompt.format_messages(question=question)
+            response = self.llm.invoke(messages)
+            sql_query = response.content.strip()
 
-        # Fetch full fund data with investor relationships using the IDs
+            # Clean up SQL (remove markdown code blocks if present)
-        return self._fetch_funds_by_ids(fund_ids, project_id)
+            sql_query = sql_query.replace("```sql", "").replace("```", "").strip()
 
-    def _extract_fund_ids_from_response(self, ai_response: str) -> List[int]:
+            # Cache the query
-        """Extract fund IDs from AI response."""
+            self.query_cache[cache_key] = sql_query
-        import re
+            logger.info(f"Generated SQL: {sql_query}")
 
-        fund_ids = []
+        # Execute query to get fund IDs
+        db_session = next(get_db())
         try:
-            # Try multiple patterns to extract IDs from the response
+            result = db_session.execute(text(sql_query))
-            # Pattern 1: Simple numbers (assuming they are IDs)
+            fund_ids = [row[0] for row in result.fetchall()]
-            numbers = re.findall(r"\b\d+\b", ai_response)
+            logger.info(
-            fund_ids = [int(num) for num in numbers]
+                f"Found {len(fund_ids)} fund IDs: {fund_ids[:10]}{'...' if len(fund_ids) > 10 else ''}"
-
+            )
-            # 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]
 
+            return self._fetch_funds_by_ids(fund_ids, project_id)
         except Exception as e:
-            print(f"Error extracting IDs from response: {e}")
+            logger.error(f"SQL execution error: {e}")
-            return []
+            logger.error(f"Failed SQL: {sql_query}")
-
+            # Return empty result
-        return fund_ids
+            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