# Epic 010 Retrospective - Full-Text Search

**Date:** 2025-11-29
**Epic:** Epic 010 - Full-Text Search
**Facilitator:** Bob (Scrum Master)
**Participants:** Alice (PO), Charlie (Senior Dev), Dana (QA), Ricardo_Leon1 (Project Lead)

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## Executive Summary

Epic 010 successfully delivered a **comprehensive Full-Text Search (FTS) capability** for the TestIO MCP Server. By leveraging SQLite's FTS5 extension and BM25 ranking, we enabled intuitive, google-like discovery of bugs, tests, and features. The execution was notably smooth, benefiting heavily from architectural patterns established in previous epics.

**Key Achievements:**
- **Unified Search Tool:** Single entry point (`search`) for all entity types, available via both MCP and REST.
- **Rich Indexing:** Denormalized critical bug fields (`actual_result`, `expected_result`, `steps`) to make them searchable.
- **Zero-Maintenance Index:** Implemented SQLite triggers to keep the search index in sync with core tables automatically.
- **Dual Transport:** Delivered MCP tool and REST endpoint in parallel with shared business logic.

**Project Lead Highlight:**
> "It was indeed quite smooth: our previous architecture work seems to be paying off." - Ricardo_Leon1

**Next Epic Focus:** Showcase & Polish (Epic 011) - Crafting deep, context-aware use cases for CSMs and Executives.

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## Epic 010 Delivery Metrics

**Completion:**
- **Stories Completed:** 3/3 (100%)
- **Duration:** 2025-11-29 (Rapid execution)
- **Quality:** High (Minimal bugs, clean architecture)

**Stories Delivered:**
| Story | Title | Outcome |
|-------|-------|---------|
| STORY-063 | Bug Field Denormalization | Successfully backfilled `actual_result`, `expected_result`, and `steps` from JSON to searchable columns. |
| STORY-064 | FTS5 Infrastructure | Implemented `search_index` virtual table with robust trigger maintenance. |
| STORY-065 | Search MCP Tool | Delivered `search` tool with BM25 ranking, entity filtering, and REST parity. |

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## Major Achievements

### 1. The "Zero-Maintenance" Search Index
**Delivered:** A unified `search_index` FTS5 virtual table that updates automatically via SQLite triggers.
**Impact:** No complex application-side sync logic required. The index is always consistent with the core data, even after background syncs or manual updates.

### 2. Service Layer Reuse (ADR-006)
**Delivered:** `SearchService` handles all business logic, while `SearchTool` (MCP) and `api.py` (REST) act as thin transport layers.
**Impact:** We achieved "REST Parity" almost for free. The same validation, error handling, and query logic serves both AI agents and web clients.

### 3. Pattern Reuse (STORY-054)
**Delivered:** Reused the JSON-to-Column denormalization pattern from Story 054 for Story 063.
**Impact:** Reduced implementation risk and effort. We knew exactly how to handle the Alembic migration and backfill logic.

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## Lessons Learned

### Lesson #1: Single Unified Index > Per-Entity Tables
**What We Learned:** Using a single `search_index` table with an `entity_type` column simplified the query logic immensely compared to managing separate FTS tables for Bugs, Tests, etc.
**Benefit:** Cross-entity search ("find everything about 'login'") works out of the box.

### Lesson #2: Trigger Stability (UPDATE vs DELETE+INSERT)
**What We Learned:** For the `UPDATE` triggers, it was critical to use the `UPDATE` SQL command rather than a `DELETE` followed by an `INSERT`. This preserves the `rowid` stability in the FTS index, which is important for performance and fragmentation.

### Lesson #3: Integration Testing FTS5
**What We Learned:** Standard `SQLModel.metadata.create_all` does not create FTS5 virtual tables (since they are raw SQL). Integration tests required manual execution of the FTS5 creation SQL in the test fixture (`conftest.py`).

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## Epic 011: Showcase & Polish (Planned)

We are shifting focus from "Feature Building" to "Showcase & Polish". The goal is to demonstrate the **Context-Aware** power of the system through two deep, authentic scenarios.

### The Strategy
Instead of a generic feature list, we will implement:

**1. The Tactical Detective (Operational)**
- **User:** CSM (Reactive)
- **Scenario:** Diagnosing a "noisy" or "low value" test cycle.
- **Action:** AI correlates test instructions, feature descriptions, and bug rejection reasons to find the root cause.
- **Requirement:** `testio://knowledge/playbook` resource with "Tactical" signals.

**2. The Strategic Analyst (EBR/QBR)**
- **User:** CSM (Proactive)
- **Scenario:** Preparing for an Executive Business Review.
- **Action:** AI aggregates quality trends over time (Quarterly) to demonstrate ROI and long-term improvements.
- **Requirement:** `testio://knowledge/playbook` resource with "Strategic" templates and robust Analytics aggregation.

### Action Items for Epic 011
1.  **Create Playbook Resource:** `docs/resources/playbook.md` (or similar) exposed via MCP.
2.  **Refine Analytics:** Ensure `query_metrics` supports the specific aggregations needed for EBRs.
3.  **Enhance Test Details:** Ensure `get_test_details` returns full instruction text for the "Detective" scenario.
4.  **Develop Demo Scripts:** "Golden path" transcripts for both scenarios.

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**Retrospective Completed:** 2025-11-29
**Next Epic:** Epic 011 - Showcase & Polish
