248 lines
6.2 KiB
Markdown
248 lines
6.2 KiB
Markdown
# Cost Tracking Implementation Summary
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## ✅ What Was Added
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### 1. Database Schema
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- New `runs` table tracks each execution
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- Fields: articles_fetched, articles_processed, articles_included, costs, timestamps
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- Indexes for efficient queries
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### 2. Cost Extraction
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- OpenRouter API responses include cost in `usage.cost` field
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- AI client accumulates costs during session
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- Automatic tracking without manual intervention
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### 3. Database Methods
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- `save_run()` - Store run statistics and costs
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- `get_total_cost()` - Calculate cumulative spending
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- `get_run_stats()` - Retrieve recent run history
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### 4. Email Display
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- **HTML Email**: Nice formatted cost box at bottom
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- **Plain Text**: Cost information in footer
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- Shows both session and cumulative costs
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### 5. Cost Viewer Script
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- `python -m src.view_costs` - View statistics
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- Shows recent runs, averages, totals
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- Export capability for analysis
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## 📊 What You'll See
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### In Your Email
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Bottom of every digest:
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```
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💰 Cost Information
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This digest: $0.0234 | Total spent: $1.2456
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```
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### In Cost Viewer
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```bash
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python -m src.view_costs
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```
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Output:
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```
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============================================================
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News Agent Cost Statistics
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============================================================
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💰 Total Cumulative Cost: $1.2456
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Recent Runs (last 20):
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------------------------------------------------------------
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Date Articles Included Cost
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------------------------------------------------------------
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2026-01-26 152 15 $0.0234
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2026-01-25 143 12 $0.0198
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...
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Averages (last 20 runs):
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Cost per run: $0.0226
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Articles per digest: 14.2
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Cost per article: $0.0016
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```
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## 🔍 How It Works
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1. **During Filtering**: AI scores each article
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- OpenRouter returns cost per API call
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- Client tracks: `self.total_cost += cost`
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2. **During Summarization**: AI summarizes selected articles
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- More API calls, more cost
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- Accumulated in same session
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3. **After Processing**: Save to database
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```python
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await db.save_run(
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articles_fetched=152,
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articles_processed=25,
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articles_included=15,
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total_cost=0.0234,
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)
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```
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4. **Before Email**: Calculate totals
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```python
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session_cost = ai_client.get_session_cost() # This run
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cumulative_cost = await db.get_total_cost() # All time
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```
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5. **In Email**: Display both values
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- Session cost: Just this digest
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- Cumulative cost: Total since start
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## 💰 Expected Costs
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### With `openai/gpt-4o-mini`
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| Scenario | Cost/Run | Monthly | Yearly |
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|----------|----------|---------|--------|
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| 150 articles, 15 selected | $0.02-0.03 | $0.60-0.90 | $7-11 |
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| 200 articles, 15 selected | $0.03-0.04 | $0.90-1.20 | $11-15 |
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| 100 articles, 10 selected | $0.01-0.02 | $0.30-0.60 | $4-7 |
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**Note:** Costs vary based on article length and model used.
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## 📁 Files Modified
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### Core Tracking
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- `src/storage/database.py` - Added runs table and cost methods
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- `src/ai/client.py` - Track costs from API responses
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- `src/main.py` - Save costs and pass to email generator
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### Email Display
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- `src/email/generator.py` - Accept cost parameters
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- `src/email/templates/daily_digest.html` - Display costs nicely
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### Utilities
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- `src/view_costs.py` - NEW: Cost statistics viewer
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- `COST_TRACKING.md` - NEW: Complete documentation
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## 🎯 Benefits
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1. **Transparency**: Know exactly what you're spending
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2. **Budgeting**: Track costs over time
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3. **Optimization**: Identify expensive runs
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4. **Accountability**: See if changes save/cost money
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5. **Planning**: Estimate future costs accurately
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## 🔧 Usage
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### View Costs Anytime
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```bash
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python -m src.view_costs
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```
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### Query Database
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```bash
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sqlite3 data/articles.db
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-- Total spending
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SELECT SUM(total_cost) FROM runs;
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-- Last 10 runs
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SELECT run_date, articles_included, total_cost
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FROM runs ORDER BY run_date DESC LIMIT 10;
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-- This month
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SELECT SUM(total_cost) FROM runs
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WHERE run_date >= date('now', 'start of month');
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```
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### Export to CSV
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```bash
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sqlite3 -header -csv data/articles.db \
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"SELECT * FROM runs ORDER BY run_date" > costs.csv
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```
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## ⚙️ Cost Optimization
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### Reduce Costs by:
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1. **Higher filter threshold** (fewer articles):
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```yaml
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ai:
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filtering:
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min_score: 7.0 # Was 5.5
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```
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2. **Fewer max articles**:
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```yaml
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ai:
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filtering:
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max_articles: 10 # Was 15
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```
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3. **Cheaper model**:
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```yaml
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ai:
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model: "google/gemini-2.0-flash-exp:free" # FREE
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```
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4. **Fewer RSS sources** (less to process)
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## 🐛 Troubleshooting
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### Cost Shows $0.0000
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**Likely causes:**
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- Using free model (doesn't report costs)
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- OpenRouter API response doesn't include cost field
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- First run (no history yet)
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**Check logs:**
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```bash
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grep -i "cost" data/logs/news-agent.log
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```
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### Cost Seems High
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**Review:**
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1. Number of articles being processed
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2. Model being used (check pricing at openrouter.ai)
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3. Article content length
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4. Recent runs: `python -m src.view_costs`
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## 📝 Notes
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- Costs stored in cents/dollars (OpenRouter credits = USD)
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- Database never deleted (unless you manually delete it)
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- Cumulative cost includes ALL runs since database creation
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- Costs shown with 4 decimal places ($0.0234)
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- Free models may show $0.00 (rate limited instead)
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## 🚀 Future Enhancements
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Possible additions:
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1. **Budget alerts** - Email if cost exceeds threshold
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2. **Cost breakdown** - Separate filtering vs summarization
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3. **Cost predictions** - Estimate before running
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4. **Monthly reports** - Summary email at month end
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5. **Cost per category** - Track which topics cost most
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6. **Web dashboard** - Visualize trends
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## 📚 Documentation
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See full details in:
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- **COST_TRACKING.md** - Complete documentation
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- **README.md** - Updated with cost tracking feature
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- **src/view_costs.py** - Source code for viewer
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## ✨ Example Output
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The cost information appears at the bottom of every email:
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```
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┌─────────────────────────────────────┐
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│ 💰 Cost Information │
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│ This digest: $0.0234 │
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│ Total spent: $1.2456 │
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└─────────────────────────────────────┘
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```
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Clean, simple, and informative!
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