2 Commits

Author	SHA1	Message	Date
Alejandro Gutiérrez	824634aa76	feat: Add extensible multi-pipeline integration system ... This commit implements a plugin-like pipeline architecture with: Pipeline Core Package (packages/pipeline-core/): - BasePipeline abstract class all pipelines implement - PipelineRegistry for database-backed discovery/management - PipelineRunner for execution with status tracking - DashboardConfig contracts for dynamic widget definitions Database Migration (006_pipeline_registry.sql): - pipeline.registry table for registered pipelines - pipeline.executions table for execution history - Views for execution stats and monitoring ReviewIQ Pipeline Refactor: - Implements BasePipeline interface - Adds get_dashboard_config() with widget definitions - Adds get_widget_data() methods for all dashboard widgets - Maintains backward compatibility with Pipeline alias Generic Pipeline API (api/routes/pipelines.py): - GET /api/pipelines - List all registered pipelines - GET /api/pipelines/{id} - Pipeline details - POST /api/pipelines/{id}/execute - Execute pipeline - GET /api/pipelines/{id}/dashboard - Dashboard config - GET /api/pipelines/{id}/widgets/{w} - Widget data - GET /api/pipelines/{id}/executions - Execution history Frontend Dynamic Dashboard System: - DynamicDashboard component renders from config - WidgetRegistry maps types to components - Widget components: StatCard, LineChart, BarChart, PieChart, DataTable, Heatmap - Pipeline API client library Frontend Pipeline Pages: - /pipelines - List all registered pipelines - /pipelines/[id] - Dynamic dashboard for pipeline - /pipelines/[id]/executions - Execution history - Pipelines nav item in Sidebar Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-01-24 19:05:38 +00:00
Alejandro Gutiérrez	7d720f5378	feat: Add reviewiq-pipeline package for LLM-powered review classification ... 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>	2026-01-24 18:07:11 +00:00