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## Overview
This guide helps diagnose and resolve issues with Atlas's search optimization features, including smart search decisions, caching problems, and performance issues.
## Common Issues
### Search Decision Problems
#### Issue: Too Many Unnecessary Searches
**Symptoms**:
- High search API costs
- Slow response times for follow-up questions
- `search_decision.should_search` is true for obvious follow-ups
**Diagnosis**:
```bash
# Check recent search decisions
curl http://localhost:7860/analytics/stats | jq '.search_usage_percentage'
# View specific decision details in responses
curl -X POST http://localhost:7860/chat -d '{
"prompt": "Tell me more about that",
"history": [{"role": "user", "content": "What is AI?"}]
}' | jq '.search_decision'
```
**Solutions**:
1. **Use Conservative Mode**:
```json
{
"prompt": "Follow-up question",
"search_decision_mode": "conservative"
}
```
2. **Check Conversation History Format**:
```javascript
// Correct format
history: [
{"role": "user", "content": "What is AI?"},
{"role": "assistant", "content": "AI is artificial intelligence..."}
]
// Alternative format
history: [
{"user": "What is AI?", "assistant": "AI is artificial intelligence..."}
]
// Incorrect - will cause unnecessary searches
history: [
{"message": "What is AI?", "response": "AI is..."} // Wrong keys
]
```
3. **Verify NLP Dependencies**:
```bash
# Check if spaCy model is loaded
python -c "import spacy; nlp = spacy.load('en_core_web_sm'); print('spaCy OK')"
# Check RAKE installation
python -c "from rake_nltk import Rake; print('RAKE OK')"
```
#### Issue: Missing Important Information
**Symptoms**:
- Outdated responses for current events
- `search_decision.should_search` is false for time-sensitive queries
- Users complaining about stale information
**Diagnosis**:
```bash
# Check search decision patterns
curl -X POST http://localhost:7860/chat -d '{
"prompt": "What are today'\''s tech news?",
"search_decision_mode": "balanced"
}' | jq '.search_decision'
```
**Solutions**:
1. **Use Aggressive Mode for Current Events**:
```json
{
"prompt": "Latest developments in AI",
"search_decision_mode": "aggressive"
}
```
2. **Force Search for Critical Updates**:
```json
{
"prompt": "Current stock price of AAPL",
"force_search": true
}
```
3. **Add Recency Keywords**:
```json
{
"prompt": "What are the latest news about climate change today?"
}
// Keywords like "latest", "today", "current" trigger searches
```
#### Issue: Inconsistent Search Decisions
**Symptoms**:
- Similar questions get different search decisions
- `search_decision.confidence` is very low (< 0.5)
- Decision method frequently falls back to "fallback_rule"
**Diagnosis**:
```bash
# Test decision consistency
for i in {1..5}; do
curl -X POST http://localhost:7860/chat -d '{
"prompt": "Tell me more about machine learning"
}' | jq '.search_decision.should_search'
done
```
**Solutions**:
1. **Check AI Model Availability**:
```python
# Verify Gemini API key
import os
print("GOOGLE_API_KEY:", "β" if os.getenv("GOOGLE_API_KEY") else "β")
```
2. **Monitor AI Decision Cache**:
```bash
# Clear AI decision cache if stale
curl -X POST http://localhost:7860/analytics/cache/clear?cache_type=all
```
3. **Review Conversation History Quality**:
```javascript
// Ensure meaningful history entries
const validHistory = history.filter(entry =>
entry.role && entry.content && entry.content.length > 10
);
```
### Cache-Related Issues
#### Issue: Poor Cache Hit Rates
**Symptoms**:
- `cache_info.cache_hit` is frequently false
- High response times despite caching
- Cache hit rate < 30% in analytics
**Diagnosis**:
```bash
# Check cache performance
curl http://localhost:7860/analytics/cache | jq '{
hit_rate: .cache_statistics.hit_rate_percentage,
cache_size: .cache_statistics.cache_size,
effectiveness: .cache_effectiveness
}'
```
**Solutions**:
1. **Check ChromaDB Configuration**:
```bash
# Verify ChromaDB dependencies
python -c "import chromadb; print('ChromaDB OK')"
python -c "from sentence_transformers import SentenceTransformer; print('SentenceTransformers OK')"
```
2. **Adjust Similarity Threshold**:
```python
# In cache configuration (environment variables)
CACHE_SIMILARITY_THRESHOLD=0.6 # Lower = more hits, less precision
CACHE_SIMILARITY_THRESHOLD=0.8 # Higher = fewer hits, more precision
```
3. **Monitor Query Patterns**:
```bash
# View popular queries
curl http://localhost:7860/analytics/cache | jq '.popular_queries'
```
#### Issue: Cache Storage Problems
**Symptoms**:
- `cache_info.stored_in_cache` is false
- Cache size not growing
- Persistent storage not working across restarts
**Diagnosis**:
```bash
# Check cache directory permissions
ls -la cache_db/
ls -la cache_results/
# Check disk space
df -h .
```
**Solutions**:
1. **Fix Directory Permissions**:
```bash
mkdir -p cache_db cache_results
chmod 755 cache_db cache_results
```
2. **Check Environment Variables**:
```bash
# Verify cache configuration
echo $CHROMADB_PATH
echo $CACHE_RESULTS_PATH
echo $CACHE_EMBEDDING_MODEL
```
3. **Clear Corrupted Cache**:
```bash
# Stop server, clear cache, restart
rm -rf cache_db cache_results
curl -X POST http://localhost:7860/analytics/cache/clear?cache_type=all
```
#### Issue: Memory Usage Issues
**Symptoms**:
- High memory consumption
- `cache_statistics.memory_usage_mb` increasing rapidly
- Server running out of memory
**Diagnosis**:
```bash
# Monitor cache memory usage
curl http://localhost:7860/analytics/cache | jq '.cache_statistics.memory_usage_mb'
# Check system memory
free -h
ps aux | grep python
```
**Solutions**:
1. **Adjust Cache Size Limits**:
```python
# In cache configuration
max_cache_size = 500 # Reduce from default 1000
```
2. **Implement Regular Cleanup**:
```bash
# Schedule cache cleanup
curl -X POST http://localhost:7860/analytics/cache/clear?cache_type=expired
```
3. **Monitor Cache Efficiency**:
```bash
# Check entries per MB ratio
curl http://localhost:7860/analytics/cache | jq '.memory_efficiency'
```
### Performance Issues
#### Issue: Slow Search Decisions
**Symptoms**:
- Response times > 5 seconds for simple questions
- `decision_method` frequently shows "hybrid" or AI usage
- High CPU usage during decision making
**Diagnosis**:
```bash
# Time search decision performance
time curl -X POST http://localhost:7860/chat -d '{
"prompt": "Simple question"
}' > /dev/null
```
**Solutions**:
1. **Optimize for Rule-Based Decisions**:
```json
{
"search_decision_mode": "balanced" // Uses more rules, less AI
}
```
2. **Check NLP Model Performance**:
```python
import time
import spacy
nlp = spacy.load("en_core_web_sm")
start = time.time()
doc = nlp("test sentence")
print(f"spaCy processing time: {time.time() - start:.3f}s")
```
3. **Monitor AI API Latency**:
```python
# Check Gemini API response times
import time
import google.generativeai as genai
start = time.time()
response = model.generate_content("test")
print(f"Gemini latency: {time.time() - start:.3f}s")
```
#### Issue: High Memory Usage
**Symptoms**:
- Gradual memory increase over time
- Server crashes with out-of-memory errors
- Slow performance after extended usage
**Diagnosis**:
```bash
# Monitor memory usage patterns
ps aux | grep -E 'python|atlas' | head -5
# Check for memory leaks
curl http://localhost:7860/analytics/cache | jq '.cache_statistics'
```
**Solutions**:
1. **Implement Cache Limits**:
```python
# Set maximum cache entries
MAX_CACHE_ENTRIES = 1000
MAX_MEMORY_MB = 100
```
2. **Regular Cache Cleanup**:
```bash
# Automated cleanup script
#!/bin/bash
while true; do
sleep 3600 # Every hour
curl -X POST http://localhost:7860/analytics/cache/clear?cache_type=expired
done
```
3. **Monitor Resource Usage**:
```bash
# Add monitoring script
watch 'curl -s http://localhost:7860/analytics/cache | jq ".cache_statistics.memory_usage_mb"'
```
## Diagnostic Tools
### Decision Analysis Script
```python
#!/usr/bin/env python3
"""Analyze search decision patterns"""
import requests
import json
def analyze_decisions(prompts):
results = []
for prompt in prompts:
response = requests.post('http://localhost:7860/chat',
json={'prompt': prompt}
)
data = response.json()
results.append({
'prompt': prompt,
'should_search': data['search_decision']['should_search'],
'reason': data['search_decision']['reason'],
'confidence': data['search_decision']['confidence'],
'method': data['search_decision'].get('decision_method')
})
return results
# Test cases
test_prompts = [
"What is AI?",
"Tell me more about that",
"What's the latest news?",
"Can you elaborate?",
"How does machine learning work?"
]
results = analyze_decisions(test_prompts)
for result in results:
print(f"'{result['prompt']}' -> {result['should_search']} ({result['confidence']:.2f}) - {result['reason']}")
```
### Cache Performance Monitor
```bash
#!/bin/bash
# Monitor cache performance over time
while true; do
timestamp=$(date '+%Y-%m-%d %H:%M:%S')
stats=$(curl -s http://localhost:7860/analytics/cache | jq '.cache_statistics')
hit_rate=$(echo $stats | jq '.hit_rate_percentage')
cache_size=$(echo $stats | jq '.cache_size')
memory_mb=$(echo $stats | jq '.memory_usage_mb')
echo "$timestamp - Hit Rate: ${hit_rate}%, Size: $cache_size, Memory: ${memory_mb}MB"
sleep 60
done
```
### Health Check Script
```python
#!/usr/bin/env python3
"""Comprehensive health check for search optimization"""
import requests
import json
import sys
def health_check():
issues = []
# Check basic connectivity
try:
response = requests.get('http://localhost:7860/')
if response.status_code != 200:
issues.append("Server not responding correctly")
except:
issues.append("Cannot connect to server")
return issues
# Check search decision functionality
try:
response = requests.post('http://localhost:7860/chat',
json={'prompt': 'Test question'}
)
data = response.json()
if 'search_decision' not in data:
issues.append("Search decision not in response")
except Exception as e:
issues.append(f"Search decision error: {e}")
# Check cache functionality
try:
response = requests.get('http://localhost:7860/analytics/cache')
if response.status_code != 200:
issues.append("Cache analytics not working")
else:
cache_data = response.json()
hit_rate = cache_data['cache_statistics']['hit_rate_percentage']
if hit_rate < 10:
issues.append(f"Very low cache hit rate: {hit_rate}%")
except Exception as e:
issues.append(f"Cache check error: {e}")
# Check NLP dependencies
try:
import spacy
nlp = spacy.load('en_core_web_sm')
except Exception as e:
issues.append(f"spaCy model error: {e}")
try:
from rake_nltk import Rake
except Exception as e:
issues.append(f"RAKE import error: {e}")
return issues
if __name__ == "__main__":
issues = health_check()
if issues:
print("β Issues found:")
for issue in issues:
print(f" - {issue}")
sys.exit(1)
else:
print("β
All health checks passed")
sys.exit(0)
```
## Configuration Troubleshooting
### Environment Variables
**Required Variables**:
```bash
# Essential for optimization
GOOGLE_API_KEY=your_key_here
# Cache configuration (optional)
CHROMADB_PATH=cache_db
CACHE_RESULTS_PATH=cache_results
CACHE_EMBEDDING_MODEL=all-MiniLM-L6-v2
```
**Validation Script**:
```bash
#!/bin/bash
echo "Checking environment variables..."
if [ -z "$GOOGLE_API_KEY" ]; then
echo "β GOOGLE_API_KEY not set"
else
echo "β
GOOGLE_API_KEY configured"
fi
if [ -d "$CHROMADB_PATH" ]; then
echo "β
ChromaDB path exists: $CHROMADB_PATH"
else
echo "β οΈ ChromaDB path not found: $CHROMADB_PATH"
fi
```
### Dependency Issues
**Check All Dependencies**:
```python
#!/usr/bin/env python3
"""Check all optimization dependencies"""
dependencies = [
('spacy', 'spaCy NLP processing'),
('rake_nltk', 'RAKE keyword extraction'),
('chromadb', 'ChromaDB vector database'),
('sentence_transformers', 'Sentence embeddings'),
('google.generativeai', 'Google Gemini API')
]
for module, description in dependencies:
try:
__import__(module)
print(f"β
{description}: OK")
except ImportError as e:
print(f"β {description}: {e}")
```
## Performance Optimization
### Recommended Settings
**For High Traffic (Cost Optimization)**:
```json
{
"search_decision_mode": "conservative",
"cache_similarity_threshold": 0.6,
"max_cache_size": 2000
}
```
**For Accuracy (Fresh Information)**:
```json
{
"search_decision_mode": "aggressive",
"cache_similarity_threshold": 0.8,
"force_search_for_news": true
}
```
**For Balanced Performance**:
```json
{
"search_decision_mode": "balanced",
"cache_similarity_threshold": 0.7,
"ai_decision_timeout": 5.0
}
```
### Monitoring Metrics
**Key Metrics to Track**:
- Search reduction percentage (target: 40-60%)
- Cache hit rate (target: >50%)
- Response time improvement (target: 20-30% faster)
- Decision confidence (target: >0.7 average)
- False positive rate (searches when not needed: <5%)
- False negative rate (no search when needed: <5%)
**Monitoring Setup**:
```bash
# Create monitoring dashboard
curl http://localhost:7860/analytics/dashboard
# Set up alerting thresholds
if [ $(curl -s http://localhost:7860/analytics/cache | jq '.cache_statistics.hit_rate_percentage') < 30 ]; then
echo "Alert: Low cache hit rate"
fi
```
## Getting Help
### Debug Information Collection
When reporting issues, include:
1. **System Information**:
```bash
python --version
pip list | grep -E 'spacy|chromadb|sentence|google'
df -h
free -h
```
2. **Configuration**:
```bash
env | grep -E 'GOOGLE|CACHE|CHROMADB'
ls -la cache_db/ cache_results/
```
3. **Recent Logs**:
```bash
# Server logs
tail -100 /var/log/atlas.log
# Decision patterns
curl http://localhost:7860/analytics/stats | jq '{
search_usage: .search_usage_percentage,
avg_response_time: .average_response_time_ms
}'
```
4. **Sample Requests**:
```bash
# Include problematic requests and responses
curl -X POST http://localhost:7860/chat -d '{
"prompt": "Your problem prompt here"
}' | jq .
```
### Support Resources
- **Documentation**: [Search Optimization Guide](../features/search-optimization.md)
- **Developer Guide**: [Search Optimizer Developer Guide](../developer/search-optimizer-guide.md)
- **API Reference**: [Integration Guide](../api/integration-guide.md)
- **Performance**: [Setup Guide](../setup/SETUP.md)
For complex issues, create a detailed issue report with the debug information above and specific reproduction steps. |