7d720f5378
Implement a standalone Python package for processing customer reviews through a 4-stage pipeline using URT (Universal Review Taxonomy) v5.1: - Stage 1: Normalization (text cleaning, language detection, deduplication) - Stage 2: LLM Classification (OpenAI/Anthropic span extraction with URT codes) - Stage 3: Issue Routing (deterministic issue ID generation, span linking) - Stage 4: Fact Aggregation (time series metrics for dashboards) Package includes: - TypedDict contracts matching Pipeline-Contracts-v1.md - Async database layer with asyncpg and 5 SQL migrations - LLM client abstraction supporting both OpenAI and Anthropic - Sentence-transformers integration for embeddings - Validation rules V1.x through V4.x - CLI commands: migrate, run, validate, check - 55 unit and integration tests (all passing) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
98 lines
2.0 KiB
Markdown
98 lines
2.0 KiB
Markdown
# ReviewIQ Pipeline
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LLM-powered review classification and analysis pipeline using URT (Universal Review Taxonomy) v5.1.
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## Features
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- **Stage 1: Normalization** - Text cleaning, language detection, deduplication
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- **Stage 2: LLM Classification** - Span extraction with URT codes using OpenAI/Anthropic
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- **Stage 3: Issue Routing** - Route negative spans to issues for tracking
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- **Stage 4: Fact Aggregation** - Pre-aggregate metrics for dashboard queries
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## Installation
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```bash
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pip install reviewiq-pipeline
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```
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Or install from source:
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```bash
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pip install -e packages/reviewiq-pipeline
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```
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## Quick Start
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### Python API
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```python
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from reviewiq_pipeline import Pipeline, Config
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# Initialize
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config = Config(
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database_url="postgresql://...",
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llm_provider="openai",
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llm_api_key="sk-...",
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taxonomy_version="v5.1"
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)
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pipeline = Pipeline(config)
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# Run full pipeline
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result = await pipeline.process(scraper_output)
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# Or run individual stages
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stage1_result = await pipeline.normalize(scraper_output)
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stage2_result = await pipeline.classify(stage1_result)
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stage3_result = await pipeline.route(stage2_result)
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stage4_result = await pipeline.aggregate(business_id, date)
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# Validate
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validation = await pipeline.validate(job_id)
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```
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### CLI
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```bash
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# Run migrations
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reviewiq-pipeline migrate --database-url $DATABASE_URL
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# Process a job
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reviewiq-pipeline run --job-id <UUID> --stages 1,2,3,4
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# Validate pipeline output
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reviewiq-pipeline validate --job-id <UUID>
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```
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## Configuration
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Environment variables:
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- `DATABASE_URL` - PostgreSQL connection string
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- `LLM_PROVIDER` - `openai` or `anthropic`
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- `OPENAI_API_KEY` - OpenAI API key (if using OpenAI)
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- `ANTHROPIC_API_KEY` - Anthropic API key (if using Anthropic)
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- `TAXONOMY_VERSION` - URT taxonomy version (default: `v5.1`)
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## Development
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```bash
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# Install with dev dependencies
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pip install -e "packages/reviewiq-pipeline[dev]"
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# Run tests
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pytest
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# Run with coverage
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pytest --cov=reviewiq_pipeline
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# Type checking
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mypy src/reviewiq_pipeline
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# Linting
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ruff check src/reviewiq_pipeline
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```
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## License
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MIT
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