FSSCoding
10862583ad
- Created standalone batch_llm_classifier.py for custom email queries - Optimized all LLM prompts for caching (static instructions first, variables last) - Configured rtx3090 vLLM endpoint (qwen3-coder-30b) - Tested batch_size=4 optimal (100% success, 4.65 req/sec) - Added comprehensive documentation (tools/README.md, BATCH_LLM_QUICKSTART.md) Tool is completely separate from main ML pipeline - no interference. Prerequisite: vLLM server must be running at rtx3090.bobai.com.au
83 lines
1.6 KiB
YAML
83 lines
1.6 KiB
YAML
version: "1.0.0"
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calibration:
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sample_size: 250
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sample_strategy: "stratified"
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validation_size: 50
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min_confidence: 0.6
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processing:
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batch_size: 100
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llm_queue_size: 100
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parallel_workers: 4
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checkpoint_interval: 1000
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checkpoint_dir: "checkpoints"
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classification:
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default_threshold: 0.55
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min_threshold: 0.50
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max_threshold: 0.70
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adjustment_step: 0.05
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adjustment_frequency: 1000
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category_thresholds:
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junk: 0.55
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auth: 0.55
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transactional: 0.55
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newsletters: 0.55
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conversational: 0.55
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llm:
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provider: "ollama"
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fallback_enabled: true
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ollama:
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base_url: "http://localhost:11434"
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calibration_model: "qwen3:4b-instruct-2507-q8_0"
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consolidation_model: "qwen3:4b-instruct-2507-q8_0"
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classification_model: "qwen3:4b-instruct-2507-q8_0"
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temperature: 0.1
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max_tokens: 2000
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timeout: 30
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retry_attempts: 3
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openai:
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base_url: "https://rtx3090.bobai.com.au/v1"
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api_key: "rtx3090_foxadmin_10_8034ecb47841f45ba1d5f3f5d875c092"
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calibration_model: "qwen3-coder-30b"
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classification_model: "qwen3-coder-30b"
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temperature: 0.1
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max_tokens: 500
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email_providers:
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gmail:
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batch_size: 100
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microsoft:
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batch_size: 100
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imap:
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timeout: 30
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batch_size: 50
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features:
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text_features:
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max_vocab_size: 10000
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ngram_range: [1, 2]
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min_df: 2
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max_df: 0.95
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embedding_model: "all-MiniLM-L6-v2"
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embedding_batch_size: 32
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export:
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format: "json"
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include_confidence: true
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create_report: true
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output_dir: "results"
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logging:
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level: "INFO"
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file: "logs/email-sorter.log"
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cleanup:
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delete_temp_files: true
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delete_repo_after: false
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temp_dir: ".email-sorter-tmp"
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