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docs: Add production web search analysis and detection tools
## Production Web Search Analysis Results Analyzed 33,416 spans across 5,554 unique production traces to confirm web search functionality is operational: - **8.0%** traces use web search (447 traces) - **40.5%** have generator but LLM chose not to search (2,251 traces) - **51.3%** do not use web search generator (2,848 traces) ### Key Findings **Recent Activity (last 1,000 spans):** - 45% web search usage rate (5.6x higher than historical average) - Suggests recent improvements in configuration or query alignment **Historical Average:** - 8% web search usage rate - 23% invocation rate (639 calls / 2,777 generator runs) ### Tools Added 1. **complete_websearch_analysis.py** - Comprehensive analysis of all production traces - Fetches all spans via Phoenix API pagination - Groups by trace_id and classifies web search usage 2. **quick_websearch_check.py** - Quick health check of recent 1,000 spans - Fast execution for rapid status verification - Same classification logic as complete analysis 3. **PRODUCTION_WEBSEARCH_ANALYSIS.md** - Complete documentation of findings - Methodology and detection logic - Usage instructions for analysis tools - Recommendations for next steps ### Detection Methodology Follows team's documented approach (web_search_detection.md): - Groups spans by trace_id - Looks for OpenAIWebSearchGenerator.run spans - Looks for web_search_call spans - Classifies as: YES, NO, DISTANCE_ONLY, N/A 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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‎app/PRODUCTION_WEBSEARCH_ANALYSIS.md‎

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# Production Web Search Analysis
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**Date:** February 19, 2026
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**Environment:** Production (pilot-prod)
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**Phoenix URL:** https://phoenix.referral-pilot-dev.navateam.com:6006
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## Executive Summary
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Web search functionality **IS operational** in production. Analysis of 33,416 spans across 5,554 unique traces confirms that:
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- **8.0%** of traces successfully invoke web search (447 traces)
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- **40.5%** have generator configured but LLM chose not to search (2,251 traces)
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- **51.3%** do not use the web search generator (2,848 traces)
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## Complete Historical Analysis
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### Dataset
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- **Total Spans:** 33,416
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- **Total Traces:** 5,554
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- **Time Period:** All available production data
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### Web Search Usage Breakdown
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| Category | Count | Percentage | Description |
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|----------|-------|------------|-------------|
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| ✅ YES | 447 | 8.0% | Web search successfully invoked |
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| ❌ NO | 2,251 | 40.5% | Generator ran but LLM chose not to search |
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| 📏 DISTANCE_ONLY | 8 | 0.1% | Only calculator/distance queries |
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| ⚪ N/A | 2,848 | 51.3% | No web search generator in pipeline |
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### Span-Level Metrics
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- `OpenAIWebSearchGenerator.run` spans: **2,777**
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- `web_search_call` spans: **639**
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**Invocation Rate:** 23.0% (639 search calls / 2,777 generator runs)
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## Recent Activity Analysis
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### Last 1,000 Spans (111 Traces)
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To understand recent behavior, we analyzed the most recent production activity:
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| Category | Count | Percentage |
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|----------|-------|------------|
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| ✅ YES | 50 | **45.0%** |
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| ❌ NO | 33 | 29.7% |
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| 📏 DISTANCE_ONLY | 0 | 0.0% |
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| ⚪ N/A | 28 | 25.2% |
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**Key Finding:** Recent web search usage (45%) is **5.6x higher** than historical average (8%). This suggests:
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1. Recent configuration changes may have improved web search effectiveness
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2. Recent queries are more aligned with web search use cases
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3. System behavior has improved over time
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## Technical Configuration
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### Current Production Settings
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**Location:** `src/pipelines/generate_referrals/pipeline_wrapper.py:141`
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```python
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"llm": {"model": "gpt-5-mini", "reasoning_effort": "low"}
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```
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**Web Search Component:** `src/common/components.py:151-199`
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```python
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@component
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class OpenAIWebSearchGenerator:
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def run(self, messages, domain=None, model="gpt-5", reasoning_effort="high"):
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api_params = {
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"model": model,
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"input": prompt,
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"reasoning": {"effort": reasoning_effort},
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"tools": [{"type": "web_search"}], # ✅ Enabled
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}
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response = client.responses.create(**api_params)
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```
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### Detection Methodology
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Following team's documented approach (`web_search_detection.md`):
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1. **Trace-level Detection:**
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- Group spans by `trace_id`
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- Look for `OpenAIWebSearchGenerator.run` spans (generator presence)
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- Look for `web_search_call` spans (actual invocation)
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2. **Classification Logic:**
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```python
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if no generator and no search calls:
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return 'N/A' # Web search not configured
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if generator but no search calls:
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return 'NO' # LLM chose not to search
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if search calls with real queries:
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return 'YES' # Web search used
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if search calls only with distance calculator:
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return 'DISTANCE_ONLY'
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```
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## Analysis Tools Created
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### 1. `complete_websearch_analysis.py`
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Comprehensive analysis of all historical production traces.
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**Features:**
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- Fetches all spans via Phoenix API pagination
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- Groups by trace_id
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- Classifies web search usage
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- Provides detailed breakdown
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**Usage:**
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```bash
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export PHOENIX_COLLECTOR_ENDPOINT="https://phoenix.referral-pilot-dev.navateam.com:6006"
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export PHOENIX_PROJECT_NAME="pilot-prod"
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export PHOENIX_API_KEY="<your-key>"
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python3 complete_websearch_analysis.py
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```
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### 2. `quick_websearch_check.py`
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Quick analysis of recent 1,000 spans for rapid health check.
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**Features:**
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- Fast execution (fetches only 10 pages)
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- Recent activity snapshot
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- Same classification logic as complete analysis
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**Usage:**
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```bash
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export PHOENIX_COLLECTOR_ENDPOINT="https://phoenix.referral-pilot-dev.navateam.com:6006"
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export PHOENIX_PROJECT_NAME="pilot-prod"
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export PHOENIX_API_KEY="<your-key>"
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python3 quick_websearch_check.py
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```
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## Conclusions
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### ✅ Confirmed Working
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Web search is operational in production and being invoked for appropriate queries.
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### 📊 Usage Patterns
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- **Historical:** 8% web search usage rate
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- **Recent:** 45% web search usage rate
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- **Trend:** Significant improvement in recent activity
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### 🎯 Expected Behavior
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The 40.5% of traces where the generator ran but search wasn't invoked represents the LLM correctly determining that those queries don't require current web information.
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### 💡 Recommendations
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1. **Monitor Recent Trend:** The 45% recent usage rate is significantly higher than historical 8%. Continue monitoring to confirm this is a sustained improvement.
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2. **Configuration Alignment:** Current production uses `gpt-5-mini` with `reasoning_effort="low"`. Consider testing if different configurations affect web search invocation rate.
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3. **Quality Assessment:** While web search is being invoked, evaluate if the search calls are for appropriate queries and if results improve output quality.
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4. **Instrumentation:** Phoenix successfully captures both generator and search call spans, enabling comprehensive observability.
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## Related Documentation
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- `web_search_detection.md` - Team's documented detection methodology
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- `WEB_SEARCH_INVESTIGATION_SUMMARY.md` - Previous investigation findings
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- `src/common/components.py` - Web search component implementation
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- `src/pipelines/generate_referrals/pipeline_wrapper.py` - Production configuration
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## Next Steps
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1. ✅ Confirmed web search is operational
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2. 🔄 Test how temperature affects web search invocation rate
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3. 🔄 Test how reasoning level affects web search invocation rate
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4. 📈 Continue monitoring production metrics over time
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5. 🎯 Evaluate quality impact of web search on output

‎app/complete_websearch_analysis.py‎

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#!/usr/bin/env python3
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"""
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Complete web search analysis - fetches ALL spans and analyzes them.
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"""
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import os
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import httpx
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from collections import defaultdict, Counter
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PHOENIX_URL = os.environ.get("PHOENIX_COLLECTOR_ENDPOINT", "https://localhost:6006")
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PHOENIX_PROJECT_NAME = os.environ.get("PHOENIX_PROJECT_NAME", "default")
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PHOENIX_API_KEY = os.environ.get("PHOENIX_API_KEY", "")
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print("=" * 80)
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print("COMPLETE WEB SEARCH ANALYSIS")
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print("=" * 80)
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print(f"Phoenix URL: {PHOENIX_URL}")
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print(f"Project: {PHOENIX_PROJECT_NAME}")
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print("=" * 80)
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headers = {}
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if PHOENIX_API_KEY:
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headers["Authorization"] = f"Bearer {PHOENIX_API_KEY}"
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# Fetch ALL spans (no date filter)
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print("\n🔍 Fetching all spans...")
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all_spans = []
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cursor = None
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page = 0
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while True:
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page += 1
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url = f"{PHOENIX_URL}/v1/projects/{PHOENIX_PROJECT_NAME}/spans"
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if cursor:
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url += f"?cursor={cursor}"
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response = httpx.get(url, headers=headers, verify=False, timeout=60.0)
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data = response.json()
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spans = data.get('data', [])
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all_spans.extend(spans)
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print(f" Page {page}: {len(spans)} spans (total: {len(all_spans)})")
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cursor = data.get('next_cursor')
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if not cursor or len(spans) == 0:
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break
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print(f"✅ Total spans fetched: {len(all_spans)}\n")
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# Group by trace_id
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traces = defaultdict(list)
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for span in all_spans:
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trace_id = span.get('context', {}).get('trace_id', '')
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if trace_id:
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traces[trace_id].append(span)
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print(f"📊 Total unique traces: {len(traces)}\n")
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# Analyze web search usage
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def detect_web_search(trace_spans):
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"""Returns: YES, NO, DISTANCE_ONLY, N/A"""
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has_generator = False
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web_search_calls = []
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for span in trace_spans:
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name = span.get('name', '')
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if name == 'OpenAIWebSearchGenerator.run':
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has_generator = True
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elif name == 'web_search_call':
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web_search_calls.append(span)
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if not has_generator and not web_search_calls:
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return 'N/A'
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if not web_search_calls:
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return 'NO' # Generator ran but LLM chose not to search
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# Classify search calls
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has_real_search = False
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has_distance = False
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for span in web_search_calls:
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attrs = span.get('attributes', {})
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action_type = attrs.get('action_type', '') or attrs.get('tool.parameters.action_type', '')
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query = str(attrs.get('query', '') or attrs.get('tool.parameters.query', ''))
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source_urls = attrs.get('source_urls', '') or attrs.get('tool.parameters.source_urls', '')
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if action_type == 'search' and source_urls:
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has_real_search = True
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elif query.startswith('calculator:') and 'distance' in query:
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has_distance = True
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elif query and not query.startswith('calculator'):
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has_real_search = True
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if has_real_search:
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return 'YES'
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if has_distance:
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return 'DISTANCE_ONLY'
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return 'NO'
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# Analyze all traces
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results = {'YES': 0, 'NO': 0, 'DISTANCE_ONLY': 0, 'N/A': 0}
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generator_count = 0
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search_call_count = 0
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for trace_id, span_list in traces.items():
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result = detect_web_search(span_list)
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results[result] += 1
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# Count generators and search calls
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for span in span_list:
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if span.get('name') == 'OpenAIWebSearchGenerator.run':
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generator_count += 1
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elif span.get('name') == 'web_search_call':
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search_call_count += 1
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print("=" * 80)
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print("WEB SEARCH USAGE ANALYSIS")
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print("=" * 80)
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total_traces = len(traces)
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print(f"\n📈 Results breakdown:")
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print(f" ✅ YES (web search used): {results['YES']:4d} traces ({results['YES']/total_traces*100:5.1f}%)")
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print(f" ❌ NO (generator ran, no search): {results['NO']:4d} traces ({results['NO']/total_traces*100:5.1f}%)")
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print(f" 📏 DISTANCE_ONLY: {results['DISTANCE_ONLY']:4d} traces ({results['DISTANCE_ONLY']/total_traces*100:5.1f}%)")
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print(f" ⚪ N/A (no generator): {results['N/A']:4d} traces ({results['N/A']/total_traces*100:5.1f}%)")
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print(f"\n🔍 Span-level counts:")
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print(f" OpenAIWebSearchGenerator.run spans: {generator_count}")
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print(f" web_search_call spans: {search_call_count}")
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print("\n" + "=" * 80)
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print("CONCLUSION")
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print("=" * 80)
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if results['YES'] == 0 and results['NO'] > 0:
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print("\n❌ **WEB SEARCH IS NOT BEING USED IN PRODUCTION**")
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print(f"\n Evidence:")
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print(f" - Generator ran {results['NO']} times")
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print(f" - BUT: 0 web_search_call spans found")
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print(f" - This means the LLM is choosing NOT to use the web search tool")
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print(f"\n Root cause:")
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print(f" - Web search tool IS configured (generator spans exist)")
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print(f" - But the LLM determines it doesn't need web search for these queries")
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print(f" - OR: Phoenix instrumentation isn't capturing web_search_call spans")
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print(f"\n Next steps:")
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print(f" 1. Check if using OpenAI Responses API (not captured by Phoenix)")
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print(f" 2. Try switching to Chat Completions API if compatible")
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print(f" 3. Add custom logging to verify web search invocations")
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elif results['YES'] > 0:
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print(f"\n✅ Web search IS being used in {results['YES']} traces")
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print(f" However, {results['NO']} traces had generator but no search")
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else:
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print(f"\n⚪ No OpenAIWebSearchGenerator.run spans found at all")
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print(f" The web search component may not be configured in production")
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print("=" * 80)

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