Update agent_workflow.py
Browse files- agent_workflow.py +317 -317
agent_workflow.py
CHANGED
@@ -1,317 +1,317 @@
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import os
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import logging
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from typing import List, Dict, Any, Optional, Tuple, Callable, Union
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from dotenv import load_dotenv
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from llm_providers import LLMProvider
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from langchain.schema import HumanMessage
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from tantivy_search_agent import TantivySearchAgent
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load_dotenv()
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class SearchAgent:
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def __init__(self, tantivy_agent: TantivySearchAgent, provider_name: str = "
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"""Initialize the search agent with Tantivy agent and LLM client"""
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self.tantivy_agent = tantivy_agent
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self.logger = logging.getLogger(__name__)
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# Initialize LLM provider
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self.llm_provider = LLMProvider()
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self.llm = None
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self.set_provider(provider_name)
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self.min_confidence_threshold = 0.5
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def set_provider(self, provider_name: str) -> None:
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self.llm = self.llm_provider.get_provider(provider_name)
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if not self.llm:
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raise ValueError(f"Provider {provider_name} not available")
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self.current_provider = provider_name
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def get_available_providers(self) -> list[str]:
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return self.llm_provider.get_available_providers()
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def get_query(self, query: str, failed_queries: List[Dict[str, str]] = []) -> str:
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"""Generate a Tantivy query using Claude, considering previously failed queries"""
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try:
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if not self.llm:
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raise ValueError("LLM provider not initialized")
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prompt = (
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"Create a query for this search request with the following restrictions:\n"+
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self.tantivy_agent.get_query_instructions()+
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"\n\nAdditional instructions: \n"
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"1. return only the search query without any other text\n"
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"2. Use only Hebrew terms for the search query\n"
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"3. the corpus to search in is an ancient Hebrew corpus - Tora and Talmud. so Try to use ancient Hebrew terms and or Talmudic expressions."
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"4. prevent modern words that are not common in talmudic texts \n"
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f"the search request: {query}"
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)
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if failed_queries:
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prompt += (
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f"\n\nPrevious failed queries:\n"+
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"------------------------\n"+
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'\n'.join(f"Query: {q['query']}, Reason: {q['reason']}" for q in failed_queries)+
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"\n\n"
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"Please generate an alternative query that:\n"
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"1. Uses different Hebrew synonyms or related terms\n"
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"2. Tries broader or more general terms\n"
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"3. Adjusts proximity values or uses wildcards\n"
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"4. Prevents using modern words that are not common in ancient hebrew and talmud texts\n"
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)
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response = self.llm.invoke([HumanMessage(content=prompt)])
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tantivy_query = response.content.strip()
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self.logger.info(f"Generated Tantivy query: {tantivy_query}")
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return tantivy_query
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except Exception as e:
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self.logger.error(f"Error generating query: {e}")
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# Fallback to basic quoted search
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return f'"{query}"'
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def _evaluate_results(self, results: List[Dict[str, Any]], query: str) -> Dict[str, Any]:
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"""Evaluate search results using Claude with confidence scoring"""
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if not self.llm:
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raise ValueError("LLM provider not initialized")
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# Prepare context from results
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context = "\n".join(f"Result {i}. Source: {r.get('reference',[])}\n Text: {r.get('text', [])}"
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for i, r in enumerate(results)
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)
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try:
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message = self.llm.invoke([HumanMessage(content=f"""Evaluate the search results for answering this question:
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Question: {query}
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Search Results:
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{context}
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Provide evaluation in this format (3 lines):
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Confidence score (0.0 to 1.0) indicating how well the results can answer the question. this line should include only the number return, don't include '[line 1]'
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ACCEPT if score >= {self.min_confidence_threshold}, REFINE if score < {self.min_confidence_threshold}. return only the word ACCEPT or REFINE.
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Detailed explanation of what information is present or missing, don't include '[line 3]'. it should be only in Hebrew
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""")])
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lines = message.content.strip().replace('\n\n', '\n').split('\n')
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confidence = float(lines[0])
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decision = lines[1].upper()
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explanation = lines[2]
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is_good = decision == 'ACCEPT'
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self.logger.info(f"Evaluation: Confidence={confidence}, Decision={decision}")
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self.logger.info(f"Explanation: {explanation}")
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return {
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"confidence": confidence,
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"is_sufficient": is_good,
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"explanation": explanation,
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}
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except Exception as e:
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self.logger.error(f"Error evaluating results: {e}")
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# Fallback to simple evaluation
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return {
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"confidence": 0.0,
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"is_sufficient": False,
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"explanation": "",
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}
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def _generate_answer(self, query: str, results: List[Dict[str, Any]]) -> str:
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"""Generate answer using Claude with improved context utilization"""
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if not self.llm:
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raise ValueError("LLM provider not initialized")
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if not results:
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return "ืื ื ืืฆืื ืชืืฆืืืช"
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# Prepare context from results
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context = "\n".join(f"Result {i+1}. Source: {r.get('reference',[])}\n Text: {r.get('text', [])}"
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for i, r in enumerate(results)
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)
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try:
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message = self.llm.invoke([HumanMessage(content=f"""Based on these search results, answer this question:
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Question: {query}
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Search Results:
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{context}
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Requirements for your answer:
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1. Use only information from the search results
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2. Be comprehensive but concise
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3. Structure the answer clearly
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4. If any aspect of the question cannot be fully answered, acknowledge this
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5. cite sources for each fact or information you use
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6. The answer should be only in Hebrew
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""")])
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return message.content.strip()
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except Exception as e:
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self.logger.error(f"Error generating answer: {e}")
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return f"I encountered an error generating the answer: {str(e)}"
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def search_and_answer(self, query: str, num_results: int = 10, max_iterations: int = 3,
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on_step: Optional[Callable[[Dict[str, Any]], None]] = None) -> Dict[str, Any]:
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"""Execute multi-step search process using Tantivy with streaming updates"""
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steps = []
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all_results = []
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# Step 1: Generate Tantivy query
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initial_query = self.get_query(query)
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step = {
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'action': 'ืืฆืืจืช ืฉืืืืชืช ืืืคืืฉ',
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'description': 'ื ืืฆืจื ืฉืืืืชืช ืืืคืืฉ ืขืืืจ ืื ืืข ืืืืคืืฉ',
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'results': [{'type': 'query', 'content': initial_query}]
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}
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steps.append(step)
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if on_step:
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on_step(step)
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# Step 2: Initial search with Tantivy query
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results = self.tantivy_agent.search(initial_query, num_results)
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step = {
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'action': 'ืืืคืืฉ ืืืืืจ',
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'description': f'ืืืคืืฉ ืืืืืจ ืขืืืจ ืฉืืืืชืช ืืืคืืฉ: {initial_query}',
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'results': [{'type': 'document', 'content': {
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'title': r['title'],
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'reference': r['reference'],
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'topics': r['topics'],
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'highlights': r['highlights'],
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'score': r['score']
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}} for r in results]
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}
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steps.append(step)
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if on_step:
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on_step(step)
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failed_queries = []
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if results.__len__() == 0:
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failed_queries.append({'query': initial_query, 'reason': 'no results'})
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is_sufficient = False
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else:
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all_results.extend(results)
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# Step 3: Evaluate results
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evaluation = self._evaluate_results(results, query)
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confidence = evaluation['confidence']
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is_sufficient = evaluation['is_sufficient']
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explanation = evaluation['explanation']
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step = {
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'action': 'ืืืจืื ืชืืฆืืืช',
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'description': 'ืืืจืื ืชืืฆืืืช ืืืคืืฉ',
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'results': [{
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'type': 'evaluation',
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'content': {
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'status': 'accepted' if is_sufficient else 'insufficient',
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'confidence': confidence,
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'explanation': explanation,
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}
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}]
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}
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steps.append(step)
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if on_step:
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on_step(step)
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if not is_sufficient:
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failed_queries.append({'query': initial_query, 'reason': explanation})
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# Step 4: Additional searches if needed
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attempt = 2
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while not is_sufficient and attempt < max_iterations:
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# Generate new query
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new_query = self.get_query(query, failed_queries)
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step = {
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'action': f'ืืฆืืจืช ืฉืืืืชื ืืืืฉ (ื ืืกืืื {attempt})',
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'description': 'ื ืืฆืจื ืฉืืืืชืช ืืืคืืฉ ื ืืกืคืช ืขืืืจ ืื ืืข ืืืืคืืฉ',
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'results': [
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{'type': 'new_query', 'content': new_query}
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]
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}
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steps.append(step)
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if on_step:
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on_step(step)
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# Search with new query
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results = self.tantivy_agent.search(new_query, num_results)
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step = {
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'action': f'ืืืคืืฉ ื ืืกืฃ (ื ืืกืืื {attempt}) ',
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'description': f'ืืืคืฉ ืืืืืจ ืขืืืจ ืฉืืืืชืช ืืืคืืฉ: {new_query}',
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'results': [{'type': 'document', 'content': {
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'title': r['title'],
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'reference': r['reference'],
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'topics': r['topics'],
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'highlights': r['highlights'],
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'score': r['score']
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}} for r in results]
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}
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steps.append(step)
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if on_step:
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on_step(step)
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if results.__len__() == 0:
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failed_queries.append({'query': new_query, 'reason': 'no results'})
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else:
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all_results.extend(results)
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# Re-evaluate with current results
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evaluation = self._evaluate_results(results, query)
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confidence = evaluation['confidence']
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is_sufficient = evaluation['is_sufficient']
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explanation = evaluation['explanation']
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step = {
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'action': f'ืืืจืื ืชืืฆืืืช (ื ืืกืืื {attempt})',
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'description': 'ืืืจืื ืชืืฆืืืช ืืืคืืฉ ืื ืืกืืื ืื',
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'explanation': explanation,
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'results': [{
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'type': 'evaluation',
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'content': {
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'status': 'accepted' if is_sufficient else 'insufficient',
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'confidence': confidence,
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'explanation': explanation,
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}
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}]
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}
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steps.append(step)
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if on_step:
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on_step(step)
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if not is_sufficient:
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failed_queries.append({'query': new_query, 'reason': explanation})
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attempt += 1
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# Step 5: Generate final answer
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answer = self._generate_answer(query, all_results)
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final_result = {
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'steps': steps,
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'answer': answer,
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'sources': [{
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'title': r['title'],
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'reference': r['reference'],
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'topics': r['topics'],
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'path': r['file_path'],
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'highlights': r['highlights'],
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'text': r['text'],
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'score': r['score']
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} for r in all_results]
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}
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# Send final result through callback
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if on_step:
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on_step({
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'action': 'ืกืืื',
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'description': 'ืืืืคืืฉ ืืืฉืื',
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'final_result': final_result
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})
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return final_result
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1 |
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import os
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2 |
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import logging
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3 |
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from typing import List, Dict, Any, Optional, Tuple, Callable, Union
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4 |
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from dotenv import load_dotenv
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5 |
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from llm_providers import LLMProvider
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from langchain.schema import HumanMessage
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from tantivy_search_agent import TantivySearchAgent
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load_dotenv()
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class SearchAgent:
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def __init__(self, tantivy_agent: TantivySearchAgent, provider_name: str = "Gemini"):
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"""Initialize the search agent with Tantivy agent and LLM client"""
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self.tantivy_agent = tantivy_agent
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self.logger = logging.getLogger(__name__)
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# Initialize LLM provider
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self.llm_provider = LLMProvider()
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self.llm = None
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self.set_provider(provider_name)
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+
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self.min_confidence_threshold = 0.5
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def set_provider(self, provider_name: str) -> None:
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self.llm = self.llm_provider.get_provider(provider_name)
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if not self.llm:
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raise ValueError(f"Provider {provider_name} not available")
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self.current_provider = provider_name
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def get_available_providers(self) -> list[str]:
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return self.llm_provider.get_available_providers()
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def get_query(self, query: str, failed_queries: List[Dict[str, str]] = []) -> str:
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34 |
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"""Generate a Tantivy query using Claude, considering previously failed queries"""
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35 |
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try:
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if not self.llm:
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raise ValueError("LLM provider not initialized")
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+
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prompt = (
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"Create a query for this search request with the following restrictions:\n"+
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self.tantivy_agent.get_query_instructions()+
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"\n\nAdditional instructions: \n"
|
43 |
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"1. return only the search query without any other text\n"
|
44 |
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"2. Use only Hebrew terms for the search query\n"
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45 |
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"3. the corpus to search in is an ancient Hebrew corpus - Tora and Talmud. so Try to use ancient Hebrew terms and or Talmudic expressions."
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46 |
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"4. prevent modern words that are not common in talmudic texts \n"
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f"the search request: {query}"
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)
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+
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if failed_queries:
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prompt += (
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f"\n\nPrevious failed queries:\n"+
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"------------------------\n"+
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'\n'.join(f"Query: {q['query']}, Reason: {q['reason']}" for q in failed_queries)+
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"\n\n"
|
56 |
+
"Please generate an alternative query that:\n"
|
57 |
+
"1. Uses different Hebrew synonyms or related terms\n"
|
58 |
+
"2. Tries broader or more general terms\n"
|
59 |
+
"3. Adjusts proximity values or uses wildcards\n"
|
60 |
+
"4. Prevents using modern words that are not common in ancient hebrew and talmud texts\n"
|
61 |
+
)
|
62 |
+
|
63 |
+
response = self.llm.invoke([HumanMessage(content=prompt)])
|
64 |
+
tantivy_query = response.content.strip()
|
65 |
+
self.logger.info(f"Generated Tantivy query: {tantivy_query}")
|
66 |
+
return tantivy_query
|
67 |
+
|
68 |
+
except Exception as e:
|
69 |
+
self.logger.error(f"Error generating query: {e}")
|
70 |
+
# Fallback to basic quoted search
|
71 |
+
return f'"{query}"'
|
72 |
+
|
73 |
+
def _evaluate_results(self, results: List[Dict[str, Any]], query: str) -> Dict[str, Any]:
|
74 |
+
"""Evaluate search results using Claude with confidence scoring"""
|
75 |
+
if not self.llm:
|
76 |
+
raise ValueError("LLM provider not initialized")
|
77 |
+
|
78 |
+
# Prepare context from results
|
79 |
+
context = "\n".join(f"Result {i}. Source: {r.get('reference',[])}\n Text: {r.get('text', [])}"
|
80 |
+
for i, r in enumerate(results)
|
81 |
+
)
|
82 |
+
|
83 |
+
try:
|
84 |
+
message = self.llm.invoke([HumanMessage(content=f"""Evaluate the search results for answering this question:
|
85 |
+
Question: {query}
|
86 |
+
|
87 |
+
Search Results:
|
88 |
+
{context}
|
89 |
+
|
90 |
+
Provide evaluation in this format (3 lines):
|
91 |
+
Confidence score (0.0 to 1.0) indicating how well the results can answer the question. this line should include only the number return, don't include '[line 1]'
|
92 |
+
ACCEPT if score >= {self.min_confidence_threshold}, REFINE if score < {self.min_confidence_threshold}. return only the word ACCEPT or REFINE.
|
93 |
+
Detailed explanation of what information is present or missing, don't include '[line 3]'. it should be only in Hebrew
|
94 |
+
""")])
|
95 |
+
lines = message.content.strip().replace('\n\n', '\n').split('\n')
|
96 |
+
confidence = float(lines[0])
|
97 |
+
decision = lines[1].upper()
|
98 |
+
explanation = lines[2]
|
99 |
+
|
100 |
+
is_good = decision == 'ACCEPT'
|
101 |
+
|
102 |
+
self.logger.info(f"Evaluation: Confidence={confidence}, Decision={decision}")
|
103 |
+
self.logger.info(f"Explanation: {explanation}")
|
104 |
+
|
105 |
+
return {
|
106 |
+
"confidence": confidence,
|
107 |
+
"is_sufficient": is_good,
|
108 |
+
"explanation": explanation,
|
109 |
+
|
110 |
+
}
|
111 |
+
|
112 |
+
except Exception as e:
|
113 |
+
self.logger.error(f"Error evaluating results: {e}")
|
114 |
+
# Fallback to simple evaluation
|
115 |
+
return {
|
116 |
+
"confidence": 0.0,
|
117 |
+
"is_sufficient": False,
|
118 |
+
"explanation": "",
|
119 |
+
}
|
120 |
+
|
121 |
+
def _generate_answer(self, query: str, results: List[Dict[str, Any]]) -> str:
|
122 |
+
"""Generate answer using Claude with improved context utilization"""
|
123 |
+
if not self.llm:
|
124 |
+
raise ValueError("LLM provider not initialized")
|
125 |
+
|
126 |
+
if not results:
|
127 |
+
return "ืื ื ืืฆืื ืชืืฆืืืช"
|
128 |
+
|
129 |
+
# Prepare context from results
|
130 |
+
context = "\n".join(f"Result {i+1}. Source: {r.get('reference',[])}\n Text: {r.get('text', [])}"
|
131 |
+
for i, r in enumerate(results)
|
132 |
+
)
|
133 |
+
|
134 |
+
try:
|
135 |
+
message = self.llm.invoke([HumanMessage(content=f"""Based on these search results, answer this question:
|
136 |
+
Question: {query}
|
137 |
+
|
138 |
+
Search Results:
|
139 |
+
{context}
|
140 |
+
|
141 |
+
Requirements for your answer:
|
142 |
+
1. Use only information from the search results
|
143 |
+
2. Be comprehensive but concise
|
144 |
+
3. Structure the answer clearly
|
145 |
+
4. If any aspect of the question cannot be fully answered, acknowledge this
|
146 |
+
5. cite sources for each fact or information you use
|
147 |
+
6. The answer should be only in Hebrew
|
148 |
+
""")])
|
149 |
+
return message.content.strip()
|
150 |
+
|
151 |
+
except Exception as e:
|
152 |
+
self.logger.error(f"Error generating answer: {e}")
|
153 |
+
return f"I encountered an error generating the answer: {str(e)}"
|
154 |
+
|
155 |
+
def search_and_answer(self, query: str, num_results: int = 10, max_iterations: int = 3,
|
156 |
+
on_step: Optional[Callable[[Dict[str, Any]], None]] = None) -> Dict[str, Any]:
|
157 |
+
"""Execute multi-step search process using Tantivy with streaming updates"""
|
158 |
+
steps = []
|
159 |
+
all_results = []
|
160 |
+
|
161 |
+
# Step 1: Generate Tantivy query
|
162 |
+
initial_query = self.get_query(query)
|
163 |
+
step = {
|
164 |
+
'action': 'ืืฆืืจืช ืฉืืืืชืช ืืืคืืฉ',
|
165 |
+
'description': 'ื ืืฆืจื ืฉืืืืชืช ืืืคืืฉ ืขืืืจ ืื ืืข ืืืืคืืฉ',
|
166 |
+
'results': [{'type': 'query', 'content': initial_query}]
|
167 |
+
}
|
168 |
+
steps.append(step)
|
169 |
+
if on_step:
|
170 |
+
on_step(step)
|
171 |
+
|
172 |
+
# Step 2: Initial search with Tantivy query
|
173 |
+
results = self.tantivy_agent.search(initial_query, num_results)
|
174 |
+
|
175 |
+
step = {
|
176 |
+
'action': 'ืืืคืืฉ ืืืืืจ',
|
177 |
+
'description': f'ืืืคืืฉ ืืืืืจ ืขืืืจ ืฉืืืืชืช ืืืคืืฉ: {initial_query}',
|
178 |
+
'results': [{'type': 'document', 'content': {
|
179 |
+
'title': r['title'],
|
180 |
+
'reference': r['reference'],
|
181 |
+
'topics': r['topics'],
|
182 |
+
'highlights': r['highlights'],
|
183 |
+
'score': r['score']
|
184 |
+
}} for r in results]
|
185 |
+
}
|
186 |
+
steps.append(step)
|
187 |
+
if on_step:
|
188 |
+
on_step(step)
|
189 |
+
|
190 |
+
failed_queries = []
|
191 |
+
|
192 |
+
if results.__len__() == 0:
|
193 |
+
failed_queries.append({'query': initial_query, 'reason': 'no results'})
|
194 |
+
is_sufficient = False
|
195 |
+
else:
|
196 |
+
all_results.extend(results)
|
197 |
+
|
198 |
+
# Step 3: Evaluate results
|
199 |
+
evaluation = self._evaluate_results(results, query)
|
200 |
+
confidence = evaluation['confidence']
|
201 |
+
is_sufficient = evaluation['is_sufficient']
|
202 |
+
explanation = evaluation['explanation']
|
203 |
+
|
204 |
+
step = {
|
205 |
+
'action': 'ืืืจืื ืชืืฆืืืช',
|
206 |
+
'description': 'ืืืจืื ืชืืฆืืืช ืืืคืืฉ',
|
207 |
+
'results': [{
|
208 |
+
'type': 'evaluation',
|
209 |
+
'content': {
|
210 |
+
'status': 'accepted' if is_sufficient else 'insufficient',
|
211 |
+
'confidence': confidence,
|
212 |
+
'explanation': explanation,
|
213 |
+
}
|
214 |
+
}]
|
215 |
+
}
|
216 |
+
steps.append(step)
|
217 |
+
if on_step:
|
218 |
+
on_step(step)
|
219 |
+
|
220 |
+
if not is_sufficient:
|
221 |
+
failed_queries.append({'query': initial_query, 'reason': explanation})
|
222 |
+
|
223 |
+
# Step 4: Additional searches if needed
|
224 |
+
attempt = 2
|
225 |
+
while not is_sufficient and attempt < max_iterations:
|
226 |
+
# Generate new query
|
227 |
+
new_query = self.get_query(query, failed_queries)
|
228 |
+
|
229 |
+
step = {
|
230 |
+
'action': f'ืืฆืืจืช ืฉืืืืชื ืืืืฉ (ื ืืกืืื {attempt})',
|
231 |
+
'description': 'ื ืืฆืจื ืฉืืืืชืช ืืืคืืฉ ื ืืกืคืช ืขืืืจ ืื ืืข ืืืืคืืฉ',
|
232 |
+
'results': [
|
233 |
+
{'type': 'new_query', 'content': new_query}
|
234 |
+
]
|
235 |
+
}
|
236 |
+
steps.append(step)
|
237 |
+
if on_step:
|
238 |
+
on_step(step)
|
239 |
+
|
240 |
+
# Search with new query
|
241 |
+
results = self.tantivy_agent.search(new_query, num_results)
|
242 |
+
|
243 |
+
step = {
|
244 |
+
'action': f'ืืืคืืฉ ื ืืกืฃ (ื ืืกืืื {attempt}) ',
|
245 |
+
'description': f'ืืืคืฉ ืืืืืจ ืขืืืจ ืฉืืืืชืช ืืืคืืฉ: {new_query}',
|
246 |
+
'results': [{'type': 'document', 'content': {
|
247 |
+
'title': r['title'],
|
248 |
+
'reference': r['reference'],
|
249 |
+
'topics': r['topics'],
|
250 |
+
'highlights': r['highlights'],
|
251 |
+
'score': r['score']
|
252 |
+
}} for r in results]
|
253 |
+
}
|
254 |
+
steps.append(step)
|
255 |
+
if on_step:
|
256 |
+
on_step(step)
|
257 |
+
|
258 |
+
if results.__len__() == 0:
|
259 |
+
failed_queries.append({'query': new_query, 'reason': 'no results'})
|
260 |
+
|
261 |
+
else:
|
262 |
+
all_results.extend(results)
|
263 |
+
|
264 |
+
# Re-evaluate with current results
|
265 |
+
evaluation = self._evaluate_results(results, query)
|
266 |
+
confidence = evaluation['confidence']
|
267 |
+
is_sufficient = evaluation['is_sufficient']
|
268 |
+
explanation = evaluation['explanation']
|
269 |
+
|
270 |
+
step = {
|
271 |
+
'action': f'ืืืจืื ืชืืฆืืืช (ื ืืกืืื {attempt})',
|
272 |
+
'description': 'ืืืจืื ืชืืฆืืืช ืืืคืืฉ ืื ืืกืืื ืื',
|
273 |
+
'explanation': explanation,
|
274 |
+
'results': [{
|
275 |
+
'type': 'evaluation',
|
276 |
+
'content': {
|
277 |
+
'status': 'accepted' if is_sufficient else 'insufficient',
|
278 |
+
'confidence': confidence,
|
279 |
+
'explanation': explanation,
|
280 |
+
}
|
281 |
+
}]
|
282 |
+
}
|
283 |
+
steps.append(step)
|
284 |
+
if on_step:
|
285 |
+
on_step(step)
|
286 |
+
|
287 |
+
if not is_sufficient:
|
288 |
+
failed_queries.append({'query': new_query, 'reason': explanation})
|
289 |
+
|
290 |
+
attempt += 1
|
291 |
+
|
292 |
+
# Step 5: Generate final answer
|
293 |
+
answer = self._generate_answer(query, all_results)
|
294 |
+
|
295 |
+
final_result = {
|
296 |
+
'steps': steps,
|
297 |
+
'answer': answer,
|
298 |
+
'sources': [{
|
299 |
+
'title': r['title'],
|
300 |
+
'reference': r['reference'],
|
301 |
+
'topics': r['topics'],
|
302 |
+
'path': r['file_path'],
|
303 |
+
'highlights': r['highlights'],
|
304 |
+
'text': r['text'],
|
305 |
+
'score': r['score']
|
306 |
+
} for r in all_results]
|
307 |
+
}
|
308 |
+
|
309 |
+
# Send final result through callback
|
310 |
+
if on_step:
|
311 |
+
on_step({
|
312 |
+
'action': 'ืกืืื',
|
313 |
+
'description': 'ืืืืคืืฉ ืืืฉืื',
|
314 |
+
'final_result': final_result
|
315 |
+
})
|
316 |
+
|
317 |
+
return final_result
|