fixed querying
This commit is contained in:
bolade
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@@ -44,6 +44,15 @@ class QueryRequest(BaseModel):
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}
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}
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class CompanyQueryRequest(BaseModel):
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question: str
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class Config:
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json_schema_extra = {
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"example": {
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"question": "Find me companies in the fintech sector located in San Francisco."
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}
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}
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@app.get("/")
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def health():
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@@ -120,7 +129,7 @@ async def query_investors(request: QueryRequest):
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@app.post(
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"/query-companies", response_model=PaginatedResponse[CompanyData], tags=["Querying"]
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)
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async def query_companies(request: QueryRequest):
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async def query_companies(request: CompanyQueryRequest):
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"""
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Query companies using natural language.
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@@ -1,21 +1,17 @@
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import hashlib
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import logging
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import os
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from typing import List
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from db.db import DATABASE_URL, get_db
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from db.db import get_db
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from db.models import CompanyTable
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from langchain import hub
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from langchain_community.agent_toolkits import SQLDatabaseToolkit
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from langchain_community.utilities import SQLDatabase
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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from schemas.router_schemas import CompanyData, PaginatedResponse
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from sqlalchemy import text
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from sqlalchemy.orm import selectinload
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logger = logging.getLogger(__name__)
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# Connect to SQLite
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prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
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db = SQLDatabase.from_uri(DATABASE_URL)
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class CompanyQueryProcessor:
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@@ -26,96 +22,144 @@ class CompanyQueryProcessor:
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model="openai/gpt-4o-mini",
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temperature=0,
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)
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self.toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
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# Update system message to specifically request only company IDs
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system_message_updated = (
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prompt_template.format(dialect="SQLite", top_k=5)
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+ "\n\n=== CRITICAL INSTRUCTIONS ==="
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+ "\n- Your ONLY task is to run SQL queries and extract company IDs"
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+ "\n- When you get SQL results with company IDs, return them EXACTLY as shown"
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+ "\n- If the SQL query returns rows with company IDs like [(1,), (5,), (9,)], return all those IDs"
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+ "\n- Do NOT add any explanations, just list the IDs"
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+ "\n- If a query returns NO ROWS (empty result), then respond with 'NO_RESULTS'"
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+ "\n\n=== QUERY GUIDELINES ==="
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+ "\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%'"
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+ "\n2. For industry searches: WHERE companies.industry LIKE '%search_term%'"
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+ "\n3. For location searches: WHERE companies.location LIKE '%location%'"
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+ "\n4. For founding year searches: WHERE companies.founded_year >= year"
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+ "\n5. For investor-related: JOIN investor_companies table"
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)
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self.agent = create_react_agent(
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model=self.llm,
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tools=self.toolkit.get_tools(),
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prompt=system_message_updated,
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# Query cache for performance
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self.query_cache = {}
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# SQL generation prompt
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self.sql_prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"""You are a SQL expert. Generate a SQLite query to find company IDs based on user requirements.
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Database Schema:
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- companies: id, name, industry, location, description, founded_year, website
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- company_sector: company_id, sector_id
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- sectors: id, name
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- investor_companies: investor_id, company_id
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- investors: id, name, aum
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- team_members: id, company_id, name, title
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IMPORTANT RULES:
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1. ALWAYS return ONLY company IDs (companies.id) - use SELECT DISTINCT c.id
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2. For industry: Check BOTH industry field AND sectors table with synonyms
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- Use LEFT JOIN for sectors so companies without sector tags still match
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- Include related terms: 'Fintech' → c.industry LIKE '%Fintech%' OR c.industry LIKE '%Finance%' OR sec.name LIKE '%Fintech%' OR sec.name LIKE '%Financial%'
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- '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%'
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3. For location: Be FLEXIBLE with variations and abbreviations
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- 'San Francisco' → c.location LIKE '%San Francisco%' OR c.location LIKE '%SF%' OR c.location LIKE '%Bay Area%'
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- 'New York' → c.location LIKE '%New York%' OR c.location LIKE '%NYC%' OR c.location LIKE '%NY%'
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- '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%'
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4. For sectors: Use LEFT JOIN and include multiple synonyms
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- '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%'
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5. For founding year filters (include NULL to be inclusive):
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- "founded after 2020" → WHERE (founded_year >= 2020 OR founded_year IS NULL)
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- "founded before 2018" → WHERE (founded_year <= 2018 OR founded_year IS NULL)
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- "founded in 2020" → WHERE founded_year = 2020
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6. For investor-related queries: Use JOIN investor_companies
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7. Use LEFT JOIN for sectors so companies without tags still match
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8. Use DISTINCT to avoid duplicates from joins
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9. Be INCLUSIVE - use OR conditions with synonyms and variations
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10. Return a single, complete SELECT query
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Example Queries:
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Q: "Fintech companies founded in 2020"
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A: SELECT DISTINCT c.id FROM companies c
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LEFT JOIN company_sector cs ON c.id = cs.company_id
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LEFT JOIN sectors sec ON cs.sector_id = sec.id
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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%')
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AND c.founded_year = 2020
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Q: "AI companies in San Francisco"
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A: SELECT DISTINCT c.id FROM companies c
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LEFT JOIN company_sector cs ON c.id = cs.company_id
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LEFT JOIN sectors sec ON cs.sector_id = sec.id
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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%')
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AND (c.location LIKE '%San Francisco%' OR c.location LIKE '%SF%' OR c.location LIKE '%Bay Area%')
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Q: "Healthcare companies"
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A: SELECT DISTINCT c.id FROM companies c
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LEFT JOIN company_sector cs ON c.id = cs.company_id
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LEFT JOIN sectors sec ON cs.sector_id = sec.id
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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%'
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Q: "Companies funded by Sequoia"
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A: SELECT DISTINCT c.id FROM companies c
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JOIN investor_companies ic ON c.id = ic.company_id
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JOIN investors i ON ic.investor_id = i.id
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WHERE i.name LIKE '%Sequoia%'
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Q: "European startups founded after 2019"
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A: SELECT DISTINCT c.id FROM companies c
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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%')
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AND (c.founded_year > 2019 OR c.founded_year IS NULL)
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Q: "SaaS companies"
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A: SELECT DISTINCT c.id FROM companies c
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LEFT JOIN company_sector cs ON c.id = cs.company_id
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LEFT JOIN sectors sec ON cs.sector_id = sec.id
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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%'
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IMPORTANT:
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- Use LEFT JOIN so companies without sector tags still match via industry field
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- Use OR conditions with related keywords/synonyms to cast a wider net
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- Include NULL checks for optional filters to avoid excluding companies with missing data
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Return ONLY the SQL query, no explanations or markdown.""",
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),
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("user", "{question}"),
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]
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)
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def _get_cache_key(self, question: str) -> str:
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"""Generate cache key from normalized question."""
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return hashlib.md5(question.lower().strip().encode()).hexdigest()
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def process_query(self, question: str) -> PaginatedResponse[CompanyData]:
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"""Process a query using the LLM and return company response data.
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"""Process a query by generating and executing SQL directly.
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Args:
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question: The natural language query to process
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"""
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# Let the LLM handle all database interactions and filtering to get company IDs
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response = self.agent.invoke(
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{"messages": [("user", question)]},
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config={"recursion_limit": 50},
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)
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cache_key = self._get_cache_key(question)
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# Extract the actual message content
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logger.info(f"{response}")
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# Look through all messages to find the SQL query results (ToolMessage with actual data)
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company_ids = []
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for message in response["messages"]:
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if hasattr(message, "content") and message.content:
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# Check if this looks like SQL results (contains tuples with numbers)
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if "(" in str(message.content) and "," in str(message.content):
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company_ids = self._extract_company_ids_from_response(
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str(message.content)
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)
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if company_ids:
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logger.info(
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f"Extracted {len(company_ids)} company IDs from results"
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)
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break
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# If no IDs found from ToolMessage, check the final AI message
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if not company_ids:
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final_message_content = response["messages"][-1].content
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logger.info(f"AI Response: \n{final_message_content}")
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company_ids = self._extract_company_ids_from_response(final_message_content)
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# Fetch full company data with relationships using the IDs
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return self._fetch_companies_by_ids(company_ids)
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def _extract_company_ids_from_response(self, ai_response: str) -> List[int]:
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"""Extract company IDs from AI response."""
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import re
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company_ids = []
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# Check if response is NO_RESULTS
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if "NO_RESULTS" in ai_response.upper():
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return []
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try:
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# The response contains tuples like (1,), (5,), etc.
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# Extract numbers between parentheses
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pattern = r"\((\d+),?\)"
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matches = re.findall(pattern, ai_response)
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if matches:
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company_ids = [int(match) for match in matches]
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# Check cache first
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if cache_key in self.query_cache:
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sql_query = self.query_cache[cache_key]
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logger.info(f"Using cached SQL: {sql_query}")
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else:
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# Fallback: extract all numbers
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numbers = re.findall(r"\b\d+\b", ai_response)
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# Filter out very large numbers that might be tokens or timestamps
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company_ids = [int(num) for num in numbers if int(num) < 100000]
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# Generate SQL query
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messages = self.sql_prompt.format_messages(question=question)
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response = self.llm.invoke(messages)
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sql_query = response.content.strip()
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# Clean up SQL (remove markdown code blocks if present)
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sql_query = sql_query.replace("```sql", "").replace("```", "").strip()
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# Cache the query
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self.query_cache[cache_key] = sql_query
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logger.info(f"Generated SQL: {sql_query}")
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# Execute query to get company IDs
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db_session = next(get_db())
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try:
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result = db_session.execute(text(sql_query))
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company_ids = [row[0] for row in result.fetchall()]
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logger.info(
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f"Found {len(company_ids)} company IDs: {company_ids[:10]}{'...' if len(company_ids) > 10 else ''}"
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)
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return self._fetch_companies_by_ids(company_ids)
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except Exception as e:
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logger.error(f"Error extracting IDs from response: {e}")
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return []
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return company_ids
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logger.error(f"SQL execution error: {e}")
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logger.error(f"Failed SQL: {sql_query}")
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# Return empty result
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return PaginatedResponse(
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items=[], total=0, page=1, page_size=10, total_pages=0
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)
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finally:
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db_session.close()
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def _fetch_companies_by_ids(
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self, company_ids: List[int]
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@@ -130,7 +174,7 @@ class CompanyQueryProcessor:
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items=[],
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total=0,
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page=1,
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page_size=len(company_ids) if company_ids else 10,
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page_size=10,
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total_pages=0,
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)
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+134
-78
@@ -1,29 +1,24 @@
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import json
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import hashlib
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import logging
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import os
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from typing import List, Optional
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from db.db import DATABASE_URL, get_db
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from db.db import get_db
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from db.models import FundTable, InvestorTable, ProjectTable
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from langchain import hub
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from langchain_community.agent_toolkits import SQLDatabaseToolkit
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from langchain_community.utilities import SQLDatabase
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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from schemas.router_schemas import (
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CompanyMinimal,
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InvestmentResponse,
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PaginatedResponse,
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SectorMinimal,
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)
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from sqlalchemy import text
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from sqlalchemy.orm import selectinload
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from services.compatibility_score import calculate_project_investor_compatibility
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logger = logging.getLogger(__name__)
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# Connect to SQLite
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prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
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db = SQLDatabase.from_uri(DATABASE_URL)
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class QueryProcessor:
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@@ -34,89 +29,150 @@ class QueryProcessor:
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model="openai/gpt-4o-mini",
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temperature=0,
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)
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self.toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
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# Update system message to specifically request only fund IDs
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system_message_updated = (
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prompt_template.format(dialect="SQLite", top_k=100)
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+ "\n\n=== IMPORTANT TERMINOLOGY ==="
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+ "\n- When users say 'investors' or 'find me investors', they mean FUNDS"
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+ "\n- Always query the 'funds' table for investment opportunities"
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+ "\n- The 'investors' table is for parent company information only"
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+ "\n- Relationship: investors (1) -> (many) funds"
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+ "\n\n=== YOUR TASK ==="
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+ "\nReturn ONLY fund IDs (funds.id) that match the user's criteria."
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+ "\nFormat: comma-separated numbers only (e.g., 1, 5, 12, 23)"
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+ "\nNo explanations, no other data."
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+ "\n\n=== QUERY GUIDELINES ==="
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+ "\n1. For geographic searches: use funds.geographic_focus"
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+ "\n2. For sector searches: JOIN with fund_sectors table"
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+ "\n3. For stage searches: JOIN with fund_investment_stages table"
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+ "\n4. Return ALL matching fund IDs, not just the first few"
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+ "\n5. If no results: respond with 'NO_RESULTS'"
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+ "\n6. Never repeat the same failed query"
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+ "\n\n=== GEOGRAPHIC SEARCH RULES (VERY IMPORTANT) ==="
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+ "\n- ALWAYS use LIKE '%keyword%' for geographic searches, NEVER use exact equality (=)"
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+ "\n- When user says 'Europe', match ANY location containing 'Europe' (e.g., 'Northern Europe', 'Western Europe', 'Europe', 'Central Europe')"
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+ "\n- When user says 'America', match locations like 'North America', 'South America', 'Latin America', 'United States'"
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+ "\n- When user says 'Asia', match 'Asia', 'Southeast Asia', 'East Asia', etc."
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+ "\n- Examples:"
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+ "\n * User: 'Europe' → SQL: WHERE geographic_focus LIKE '%Europe%'"
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+ "\n * User: 'America' → SQL: WHERE geographic_focus LIKE '%America%'"
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+ "\n * User: 'UK' → SQL: WHERE geographic_focus LIKE '%UK%' OR geographic_focus LIKE '%United Kingdom%'"
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+ "\n- Be INCLUSIVE: capture all relevant regional variations"
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)
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self.agent = create_react_agent(
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model=self.llm,
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tools=self.toolkit.get_tools(),
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prompt=system_message_updated,
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# Query cache for performance
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self.query_cache = {}
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# SQL generation prompt
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self.sql_prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"""You are a SQL expert. Generate a SQLite query to find fund IDs based on user requirements.
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Database Schema:
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- funds: id, fund_name, investor_id, check_size_lower, check_size_upper, geographic_focus
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- fund_sectors: fund_id, sector_id
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- fund_investment_stages: fund_id, stage_id
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- sectors: id, name
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- investment_stages: id, name
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- investors: id, name, aum
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IMPORTANT RULES:
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1. ALWAYS return ONLY fund IDs (funds.id) - use SELECT DISTINCT f.id
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2. For geography: Be FLEXIBLE - use OR with variations and partial matches
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- 'Europe' → WHERE geographic_focus LIKE '%Europe%' OR geographic_focus LIKE '%European%'
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- 'America' → WHERE geographic_focus LIKE '%America%' OR geographic_focus LIKE '%US%' OR geographic_focus LIKE '%United States%'
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- 'Asia' → WHERE geographic_focus LIKE '%Asia%' OR geographic_focus LIKE '%Asian%'
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- If no geography specified, DON'T filter by geography
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3. For stages: Use LEFT JOIN and LIKE for flexible matching with synonyms
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- 'Seed' → s.name LIKE '%Seed%' OR s.name LIKE '%Pre-Seed%' OR s.name LIKE '%Early%'
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- 'Series A' → s.name LIKE '%Series A%' OR s.name LIKE '%A%'
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- 'Growth' → s.name LIKE '%Growth%' OR s.name LIKE '%Late%' OR s.name LIKE '%Expansion%'
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- If stage not specified, include ALL funds
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4. For sectors: Use LEFT JOIN and include related terms with OR
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- 'Fintech' → sec.name LIKE '%Fintech%' OR sec.name LIKE '%Finance%' OR sec.name LIKE '%Financial%'
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- 'AI' → sec.name LIKE '%AI%' OR sec.name LIKE '%Artificial Intelligence%' OR sec.name LIKE '%Machine Learning%' OR sec.name LIKE '%ML%'
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- 'Healthcare' → sec.name LIKE '%Healthcare%' OR sec.name LIKE '%Health%' OR sec.name LIKE '%Medical%' OR sec.name LIKE '%Biotech%'
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5. For check size filters (be flexible with ranges):
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- "under X" → WHERE (check_size_upper <= X OR check_size_upper IS NULL)
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- "over X" → WHERE (check_size_lower >= X OR check_size_lower IS NULL)
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- "between X and Y" → WHERE check_size_lower >= X AND check_size_upper <= Y
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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
|
||||
response = self.agent.invoke(
|
||||
{"messages": [("user", question)]},
|
||||
config={"recursion_limit": 50},
|
||||
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 fund IDs
|
||||
db_session = next(get_db())
|
||||
try:
|
||||
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 ''}"
|
||||
)
|
||||
|
||||
# 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)
|
||||
|
||||
# Fetch full fund data with investor relationships using the IDs
|
||||
return self._fetch_funds_by_ids(fund_ids, project_id)
|
||||
|
||||
def _extract_fund_ids_from_response(self, ai_response: str) -> List[int]:
|
||||
"""Extract fund IDs from AI response."""
|
||||
import re
|
||||
|
||||
fund_ids = []
|
||||
try:
|
||||
# Try multiple patterns to extract IDs from the response
|
||||
# Pattern 1: Simple numbers (assuming they are IDs)
|
||||
numbers = re.findall(r"\b\d+\b", ai_response)
|
||||
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]
|
||||
|
||||
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
|
||||
|
||||
Reference in New Issue
Block a user