AI Web Researcher: Added Exa answer and Tavily answer to the metaphor search results
Added AI insights to the metaphor search results Better display of AI search results
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ajaysi
277
lib/alwrity_ui/display_google_serp_results.py
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277
lib/alwrity_ui/display_google_serp_results.py
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import streamlit as st
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import logging
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from datetime import datetime
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from typing import Dict, Optional, Any
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# Configure module logger
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logger = logging.getLogger(__name__)
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def display_research_results(results: Dict[str, Any]) -> None:
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"""
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Display research results in a structured format with tabs.
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Args:
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results (dict): Processed research results containing summary and data
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"""
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if not results:
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st.warning("No results to display")
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return
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# Create tabs for different result sections
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tabs = st.tabs(["📊 Summary", "🔍 Results", "📈 Statistics"])
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with tabs[0]:
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display_summary_section(results)
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with tabs[1]:
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if results['source'] == 'gemini':
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display_gemini_results(results)
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else:
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display_serp_results(results)
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with tabs[2]:
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display_statistics(results)
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def process_research_results(results: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""Process and format research results."""
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logger.info("Processing research results")
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try:
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if not results:
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return None
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processed = {
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'timestamp': str(datetime.now()),
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'source': results.get('source', 'unknown'),
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'summary': {},
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'data': {}
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}
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if results.get('source') == 'gemini':
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processed.update(process_gemini_results(results))
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else:
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processed.update(process_serp_results(results))
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logger.info("Results processing completed")
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return processed
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except Exception as err:
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logger.error(f"Failed to process results: {err}", exc_info=True)
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return None
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def process_search_results(search_results: Dict[str, Any], search_type: str = "general") -> Optional[Dict[str, Any]]:
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"""Process search results and prepare for display."""
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logger.info(f"Processing {search_type} search results")
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try:
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if not search_results:
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return None
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processed = {
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'organic': process_organic_results(search_results.get('organic', [])),
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'peopleAlsoAsk': process_paa_results(search_results.get('peopleAlsoAsk', [])),
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'relatedSearches': process_related_searches(search_results.get('relatedSearches', [])),
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'metadata': {
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'timestamp': str(datetime.now()),
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'type': search_type
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}
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}
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return processed
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except Exception as err:
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logger.error(f"Error processing search results: {err}", exc_info=True)
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return None
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# Helper functions for result processing
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def process_organic_results(results):
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"""Process organic search results."""
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return [{
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'title': result.get('title', 'No Title'),
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'link': result.get('link', '#'),
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'snippet': result.get('snippet', 'No snippet available'),
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'position': result.get('position', 'N/A')
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} for result in results]
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def process_paa_results(results):
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"""Process People Also Ask results."""
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return [{
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'question': result.get('title', ''),
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'answer': result.get('snippet', 'No answer available'),
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'link': result.get('link', '#')
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} for result in results]
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def process_related_searches(results):
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"""Process related searches."""
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return [query.get('query', '') for query in results]
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def process_gemini_results(results: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Process Gemini API research results.
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Args:
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results (dict): Raw Gemini research results
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Returns:
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dict: Processed results with summary and data
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"""
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gemini_data = results.get('results', {})
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return {
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'summary': {
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'main_findings': gemini_data.get('main_response', ''),
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'sources': gemini_data.get('grounding_data', []),
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'processing_time': gemini_data.get('metadata', {}).get('timestamp'),
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'total_sources': len(gemini_data.get('grounding_data', [])),
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'model': gemini_data.get('metadata', {}).get('model', 'unknown')
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},
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'data': gemini_data
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}
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def process_serp_results(results: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Process SERP search results.
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Args:
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results (dict): Raw SERP results
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Returns:
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dict: Processed results with summary and data
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"""
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organic_results = results.get('organic', [])
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paa_results = results.get('peopleAlsoAsk', [])
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related_searches = results.get('relatedSearches', [])
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return {
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'summary': {
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'total_results': len(organic_results),
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'sources': [result.get('link') for result in organic_results],
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'titles': [result.get('title') for result in organic_results],
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'total_questions': len(paa_results),
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'total_related': len(related_searches)
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},
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'data': {
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'organic': process_organic_results(organic_results),
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'peopleAlsoAsk': process_paa_results(paa_results),
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'relatedSearches': process_related_searches(related_searches)
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}
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}
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# Display helper functions
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def display_summary_section(results):
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"""Display summary section of results."""
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st.markdown("### 📋 Research Summary")
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st.markdown(f"""
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- **Source**: {results['source'].title()}
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- **Time**: {results['timestamp']}
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- **Total Sources**: {len(results.get('summary', {}).get('sources', []))}
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""")
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def display_gemini_results(results):
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"""Display Gemini-specific results."""
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st.markdown("### 🤖 Gemini Research Findings")
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st.write(results['summary']['main_findings'])
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with st.expander("🌐 Sources and References", expanded=False):
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st.write(results['data'].get('grounding_data', 'No sources available'))
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def display_serp_results(results):
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"""Display SERP-specific results."""
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st.markdown("### 🔍 Search Results")
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for result in results['data'].get('organic', []):
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with st.expander(f"📄 {result['title']}", expanded=False):
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st.markdown(f"""
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**Rank:** {result['position']}
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**Link:** [{result['link']}]({result['link']})
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**Snippet:**
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{result['snippet']}
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""")
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def display_statistics(results: Dict[str, Any]) -> None:
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"""
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Display statistical information about search results.
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Args:
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results (dict): Processed research results
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"""
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st.markdown("### 📈 Research Statistics")
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# Source-specific metrics
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if results['source'] == 'gemini':
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col1, col2 = st.columns(2)
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with col1:
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st.metric(
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"Sources Analyzed",
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results.get('summary', {}).get('total_sources', 0)
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)
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with col2:
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st.metric(
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"Model Used",
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results.get('summary', {}).get('model', 'Unknown')
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)
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else: # SERP results
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric(
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"Organic Results",
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results.get('summary', {}).get('total_results', 0)
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)
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with col2:
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st.metric(
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"Related Questions",
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results.get('summary', {}).get('total_questions', 0)
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)
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with col3:
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st.metric(
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"Related Searches",
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results.get('summary', {}).get('total_related', 0)
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)
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# Common metrics
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st.markdown("#### 🕒 Timing Information")
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st.info(f"Research completed at: {results['timestamp']}")
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# Display data quality metrics
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st.markdown("#### 📊 Data Quality")
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quality_metrics = calculate_quality_metrics(results)
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col1, col2 = st.columns(2)
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with col1:
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st.progress(quality_metrics['completeness'])
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st.caption("Data Completeness")
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with col2:
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st.progress(quality_metrics['relevance'])
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st.caption("Estimated Relevance")
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def calculate_quality_metrics(results: Dict[str, Any]) -> Dict[str, float]:
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"""
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Calculate quality metrics for the research results.
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Args:
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results (dict): Processed research results
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Returns:
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dict: Quality metrics including completeness and relevance scores
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"""
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try:
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if results['source'] == 'gemini':
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completeness = 1.0 if results['summary']['main_findings'] else 0.0
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relevance = 0.8 if results['summary']['sources'] else 0.4
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else:
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organic_results = results.get('summary', {}).get('total_results', 0)
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completeness = min(organic_results / 10, 1.0) # Normalize to 0-1
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has_paa = bool(results.get('summary', {}).get('total_questions', 0))
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has_related = bool(results.get('summary', {}).get('total_related', 0))
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relevance = (0.6 + (0.2 if has_paa else 0) + (0.2 if has_related else 0))
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return {
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'completeness': completeness,
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'relevance': relevance
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}
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except Exception as err:
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logger.error(f"Error calculating quality metrics: {err}")
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return {'completeness': 0.0, 'relevance': 0.0}
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@@ -323,7 +323,8 @@ def do_web_research():
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"num_results": 10,
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"time_range": "past month",
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"include_domains": "",
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"similar_url": ""
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"similar_url": "",
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"search_mode": "google" # Default search mode
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}
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# Define the research options dialog function
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@@ -425,6 +426,12 @@ def do_web_research():
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horizontal=True,
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help="Choose your preferred research method"
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)
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# Map the selected option to the search_mode value
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for mode, label, _, _ in search_options:
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if label == selected_option:
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st.session_state.research_options["search_mode"] = mode
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break
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else:
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st.warning("No search methods available. Please configure API keys.")
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@@ -439,7 +446,7 @@ def do_web_research():
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st.rerun()
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# Main interface
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st.title("Keyword Research Assistant")
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st.title("ALwrity Web Researcher")
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# Primary search area with help popover
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with st.popover("ℹ️ Keyword Research Tips"):
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@@ -450,6 +457,7 @@ def do_web_research():
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3. **Search Depth**: Higher depth = more comprehensive but slower
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4. **Target Audience**: Affects content recommendations
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5. **Content Type**: Influences research focus
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6. **Search Mode**: Choose between traditional web research(Google), AI-powered search(Tavily and Metaphor) and Deep Researcher
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""")
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col1, col2 = st.columns([3, 1])
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@@ -484,6 +492,7 @@ def do_web_research():
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# Execute search with all parameters
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web_research_result = gpt_web_researcher(
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search_keywords=st.session_state.research_options["primary_keywords"],
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search_mode=st.session_state.research_options["search_mode"],
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related_keywords=st.session_state.research_options["related_keywords"],
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target_audience=st.session_state.research_options["target_audience"],
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content_type=st.session_state.research_options["content_type"],
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