78 lines
2.0 KiB
Markdown
78 lines
2.0 KiB
Markdown
# Job Configuration Files
|
|
|
|
This directory contains batch job configuration files for content generation.
|
|
|
|
## Usage
|
|
|
|
Run a batch job using the CLI:
|
|
|
|
```bash
|
|
python main.py generate-batch --job-file jobs/example_tier1_batch.json -u admin -p password
|
|
```
|
|
|
|
## Job Configuration Structure
|
|
|
|
```json
|
|
{
|
|
"job_name": "Descriptive name",
|
|
"project_id": 1,
|
|
"description": "Optional description",
|
|
"tiers": [
|
|
{
|
|
"tier": 1,
|
|
"article_count": 15,
|
|
"models": {
|
|
"title": "model-id",
|
|
"outline": "model-id",
|
|
"content": "model-id"
|
|
},
|
|
"anchor_text_config": {
|
|
"mode": "default|override|append",
|
|
"custom_text": ["optional", "custom", "anchors"],
|
|
"additional_text": ["optional", "additions"]
|
|
},
|
|
"validation_attempts": 3
|
|
}
|
|
],
|
|
"failure_config": {
|
|
"max_consecutive_failures": 5,
|
|
"skip_on_failure": true
|
|
},
|
|
"interlinking": {
|
|
"links_per_article_min": 2,
|
|
"links_per_article_max": 4,
|
|
"include_home_link": true
|
|
}
|
|
}
|
|
```
|
|
|
|
## Available Models
|
|
|
|
- `anthropic/claude-3.5-sonnet` - Best for high-quality content
|
|
- `anthropic/claude-3-haiku` - Fast and cost-effective
|
|
- `openai/gpt-4o` - Excellent quality
|
|
- `openai/gpt-4o-mini` - Good for titles/outlines
|
|
- `meta-llama/llama-3.1-70b-instruct` - Open source alternative
|
|
- `google/gemini-pro-1.5` - Google's offering
|
|
|
|
## Anchor Text Modes
|
|
|
|
- **default**: Use CORA rules (keyword, entities, related searches)
|
|
- **override**: Replace default with custom_text list
|
|
- **append**: Add additional_text to default anchor text
|
|
|
|
## Example Files
|
|
|
|
- `example_tier1_batch.json` - Single tier 1 with 15 articles
|
|
- `example_multi_tier_batch.json` - Three tiers with 165 total articles
|
|
- `example_custom_anchors.json` - Custom anchor text demo
|
|
|
|
## Tips
|
|
|
|
1. Start with tier 1 to ensure quality
|
|
2. Use faster/cheaper models for tier 2+
|
|
3. Set `skip_on_failure: true` to continue on errors
|
|
4. Adjust `max_consecutive_failures` based on model reliability
|
|
5. Test with small batches first
|
|
|