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79 lines
No EOL
3.5 KiB
Python
79 lines
No EOL
3.5 KiB
Python
from typing import Dict, Any
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from langchain.schema import LLMResult, HumanMessage, SystemMessage
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from app.config import config
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class QueryService:
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"""
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Service for query-related operations, including reformulation and processing.
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"""
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@staticmethod
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async def reformulate_query(user_query: str) -> str:
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"""
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Reformulate the user query using the STRATEGIC_LLM to make it more
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effective for information retrieval and research purposes.
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Args:
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user_query: The original user query
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Returns:
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str: The reformulated query
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"""
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if not user_query or not user_query.strip():
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return user_query
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try:
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# Get the strategic LLM instance from config
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llm = config.strategic_llm_instance
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# Create system message with instructions
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system_message = SystemMessage(
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content="""
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You are an expert at reformulating user queries to optimize information retrieval.
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Your job is to take a user query and reformulate it to:
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1. Make it more specific and detailed
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2. Expand ambiguous terms
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3. Include relevant synonyms and alternative phrasings
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4. Break down complex questions into their core components
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5. Ensure it's comprehensive for research purposes
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The query will be used with the following data sources/connectors:
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- SERPER_API: Web search for retrieving current information from the internet
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- TAVILY_API: Research-focused search API for comprehensive information
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- SLACK_CONNECTOR: Retrieves information from indexed Slack workspace conversations
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- NOTION_CONNECTOR: Retrieves information from indexed Notion documents and databases
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- FILE: Searches through user's uploaded files
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- CRAWLED_URL: Searches through previously crawled web pages
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IMPORTANT: Keep the reformulated query as concise as possible while still being effective.
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Avoid unnecessary verbosity and limit the query to only essential terms and concepts.
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Please optimize the query to work effectively across these different data sources.
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Return ONLY the reformulated query without explanations, prefixes, or commentary.
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Do not include phrases like "Reformulated query:" or any other text except the query itself.
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"""
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)
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# Create human message with the user query
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human_message = HumanMessage(
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content=f"Reformulate this query for better research results: {user_query}"
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)
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# Get the response from the LLM
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response = await llm.agenerate(messages=[[system_message, human_message]])
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# Extract the reformulated query from the response
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reformulated_query = response.generations[0][0].text.strip()
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# Return the original query if the reformulation is empty
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if not reformulated_query:
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return user_query
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return reformulated_query
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except Exception as e:
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# Log the error and return the original query
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print(f"Error reformulating query: {e}")
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return user_query |