diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md index 4460b64..74c58fe 100644 --- a/ARCHITECTURE.md +++ b/ARCHITECTURE.md @@ -1,4 +1,4 @@ -# Architecture: ContextCortex (v2.13.0) +# Architecture: ContextCortex (v2.14.0) ContextCortex provides fast, local, syntax-aware semantic and hybrid search over codebases, git repositories, markdown notes, architecture documents, and system documentation. It is built natively on the **Model Context Protocol (MCP) SDK 2.0.0+** using `FastMCP`, with an integrated FastAPI web engine, real-time diagnostic logging, pluggable relational and vector store backends (PostgreSQL 16 with pgvector, Qdrant, ChromaDB, and SQLite), automatic polling daemons, multi-provider webhooks, interactive dependency topology graph explorer, RFC 9728 OAuth 2.1 Protected Resource Server, 3-tier API key RBAC, and a React 19 administrative dashboard. diff --git a/DEVELOPER_DOCS.md b/DEVELOPER_DOCS.md index 5adb95a..6a247a5 100644 --- a/DEVELOPER_DOCS.md +++ b/DEVELOPER_DOCS.md @@ -1,4 +1,4 @@ -# Developer Documentation: ContextCortex (v2.13.0) +# Developer Documentation: ContextCortex (v2.14.0) This document provides instructions for developing, testing, configuring, and running ContextCortex locally. diff --git a/README.md b/README.md index 35f0751..86dab54 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# ContextCortex (v2.13.0) +# ContextCortex (v2.14.0) [![Build and Publish Docker Image](https://github.com/spelech/contextcortex/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/spelech/contextcortex/actions/workflows/docker-publish.yml) diff --git a/REQUIREMENTS.md b/REQUIREMENTS.md index 8029019..517253e 100644 --- a/REQUIREMENTS.md +++ b/REQUIREMENTS.md @@ -1,8 +1,8 @@ -# Software Requirements Specification: ContextCortex (v2.13.0) +# Software Requirements Specification: ContextCortex (v2.14.0) > **Note:** This document is automatically generated and verified against the live test suite by `scripts/generate_requirements.py` and `tests/backend/test_requirements_sync.py`. -**Test Verification Baseline:** **928 Automated Tests** (613 Pytest Backend + 269 Vitest Frontend + 46 Playwright E2E). +**Test Verification Baseline:** **934 Automated Tests** (619 Pytest Backend + 269 Vitest Frontend + 46 Playwright E2E). --- @@ -784,7 +784,7 @@ classDiagram - `test_test_connection_active_embedded` - _Verify test_connection succeeds on active embedded Qdrant store without file lock conflict._ - `test_switch_same_embedded_directory` - _Verify switch_vector_store succeeds when switching collection on the same embedded Qdrant directory._ -#### `tests/backend/test_vector_store_qdrant.py` (26 tests) +#### `tests/backend/test_vector_store_qdrant.py` (32 tests) - `TestQdrantVectorStoreInit::test_init_in_memory_or_embedded` - `TestQdrantVectorStoreInit::test_init_remote_success` - `TestQdrantVectorStoreInit::test_init_remote_fallback_to_embedded_on_connection_error` @@ -795,6 +795,9 @@ classDiagram - `TestQdrantVectorStoreOperations::test_upsert_failure_handling_and_logging` - `TestQdrantVectorStoreOperations::test_upsert_dict_documents_auto_computes_vectors` - `TestQdrantVectorStoreOperations::test_search_dense_and_hybrid_rrf` +- `TestQdrantVectorStoreOperations::test_search_weighted_score_fusion_range_and_boost` +- `TestQdrantVectorStoreOperations::test_search_weighted_score_fusion_alpha_weighting` +- `TestQdrantVectorStoreOperations::test_search_dense_fallback_without_sparse` - `TestQdrantVectorStoreOperations::test_delete_by_path` - `TestQdrantVectorStoreOperations::test_delete_by_repo` - `TestQdrantVectorStoreOperations::test_get_stats_and_health_check` @@ -808,6 +811,9 @@ classDiagram - `test_upsert_failure_handling_and_logging` - `test_upsert_dict_documents_auto_computes_vectors` - `test_search_dense_and_hybrid_rrf` +- `test_search_weighted_score_fusion_range_and_boost` +- `test_search_weighted_score_fusion_alpha_weighting` +- `test_search_dense_fallback_without_sparse` - `test_delete_by_path` - `test_delete_by_repo` - `test_get_stats_and_health_check` diff --git a/app/mcp/handlers/search_handlers.py b/app/mcp/handlers/search_handlers.py index c33dd04..fbc2cd9 100644 --- a/app/mcp/handlers/search_handlers.py +++ b/app/mcp/handlers/search_handlers.py @@ -46,7 +46,7 @@ async def handle_search_code( link_url = p.get("permalink_url") or p.get("github_url") if link_url: header += f"\nSource Link: {link_url}" - header += f"\nRRF Score: {hit.score:.4f}\n" + header += f"\nRelevance Score: {hit.score:.4f} ({hit.score * 100:.1f}%)\n" lang = p.get("language", "") block = f"{header}```{lang}\n{p.get('content')}\n```" @@ -89,7 +89,7 @@ async def handle_search_docs( link_url = p.get("permalink_url") or p.get("github_url") if link_url: header += f"\nSource Link: {link_url}" - header += f"\nRRF Score: {hit.score:.4f}\n" + header += f"\nRelevance Score: {hit.score:.4f} ({hit.score * 100:.1f}%)\n" block = f"{header}---\n{p.get('content')}" formatted.append(block) diff --git a/app/services/vector_store/qdrant_store.py b/app/services/vector_store/qdrant_store.py index 45ff712..594e749 100644 --- a/app/services/vector_store/qdrant_store.py +++ b/app/services/vector_store/qdrant_store.py @@ -277,7 +277,7 @@ def search( tag: Optional[str] = None, limit: int = 5 ) -> List[VectorSearchResult]: - """Performs vector search returning ranked results using Dense + Sparse RRF.""" + """Performs vector search returning ranked results using Dense + Sparse normalized weighted fusion.""" if not query_text or not query_text.strip(): return [] @@ -303,45 +303,93 @@ def search( query_filter = qmodels.Filter(must=must_conditions) if must_conditions else None + try: + alpha = float(os.getenv("HYBRID_DENSE_WEIGHT", "0.7")) + except (ValueError, TypeError): + alpha = 0.7 + alpha = max(0.0, min(1.0, alpha)) + + candidate_limit = max(limit * 5, 50) + if sparse_vec is not None and len(sparse_vec.indices) > 0: - response = self.client.query_points( + batch_response = self.client.query_batch_points( collection_name=self.collection_name, - prefetch=[ - qmodels.Prefetch( + requests=[ + qmodels.QueryRequest( query=dense_vec, using="dense", - limit=limit * 2, - filter=query_filter + limit=candidate_limit, + filter=query_filter, + with_payload=True, ), - qmodels.Prefetch( + qmodels.QueryRequest( query=sparse_vec, using="sparse", - limit=limit * 2, - filter=query_filter - ) + limit=candidate_limit, + filter=query_filter, + with_payload=True, + ), ], - query=qmodels.FusionQuery(fusion=qmodels.Fusion.RRF), - limit=limit ) + + dense_pts = {str(p.id): p for p in batch_response[0].points} + sparse_pts = {str(p.id): p for p in batch_response[1].points} + + sparse_scores = [p.score for p in sparse_pts.values() if p.score is not None] + max_sparse = max(sparse_scores, default=1.0) + if max_sparse <= 0.0: + max_sparse = 1.0 + + all_uids = set(dense_pts.keys()).union(sparse_pts.keys()) + scored_results: List[VectorSearchResult] = [] + + for uid in all_uids: + d_score = max(0.0, min(1.0, float(dense_pts[uid].score))) if uid in dense_pts and dense_pts[uid].score is not None else 0.0 + s_score = (float(sparse_pts[uid].score) / max_sparse) if uid in sparse_pts and sparse_pts[uid].score is not None and max_sparse > 0 else 0.0 + s_score = max(0.0, min(1.0, s_score)) + + if uid in dense_pts and uid in sparse_pts: + final_score = alpha * d_score + (1.0 - alpha) * s_score + elif uid in dense_pts: + final_score = alpha * d_score + else: + final_score = (1.0 - alpha) * s_score + + final_score = max(0.0, min(1.0, final_score)) + pt = dense_pts.get(uid) or sparse_pts.get(uid) + payload = (pt.payload or {}) if pt else {} + + scored_results.append( + VectorSearchResult( + id=uid, + score=round(final_score, 4), + payload=payload, + ) + ) + + scored_results.sort(key=lambda r: r.score, reverse=True) + return scored_results[:limit] else: response = self.client.query_points( collection_name=self.collection_name, query=dense_vec, using="dense", query_filter=query_filter, - limit=limit + limit=limit, + with_payload=True, ) - - results: List[VectorSearchResult] = [] - for pt in response.points: - results.append( - VectorSearchResult( - id=str(pt.id), - score=float(pt.score) if pt.score is not None else 0.0, - payload=pt.payload or {} + results: List[VectorSearchResult] = [] + for pt in response.points: + raw_score = float(pt.score) if pt.score is not None else 0.0 + clamped_score = max(0.0, min(1.0, raw_score)) + results.append( + VectorSearchResult( + id=str(pt.id), + score=round(clamped_score, 4), + payload=pt.payload or {}, + ) ) - ) - return results + return results except Exception as e: logger.error(f"Error searching Qdrant collection '{self.collection_name}': {e}") return [] diff --git a/docs/TEST_COVERAGE.md b/docs/TEST_COVERAGE.md index 05079bb..dce31aa 100644 --- a/docs/TEST_COVERAGE.md +++ b/docs/TEST_COVERAGE.md @@ -1,4 +1,4 @@ -# Test Coverage Report: ContextCortex (v2.13.0) +# Test Coverage Report: ContextCortex (v2.14.0) This document provides comprehensive test coverage metrics and verification baselines for ContextCortex following the modular architectural restructuring, Codebase Navigator implementation, and test suite expansion. diff --git a/docs/guide/user-guide.md b/docs/guide/user-guide.md index 1b77c29..ac6eac0 100644 --- a/docs/guide/user-guide.md +++ b/docs/guide/user-guide.md @@ -79,7 +79,7 @@ Follow these steps to test search retrieval: ### Interpreting Search Results Each result card displays: -- **RRF Score**: Combined score calculated from Dense Cosine similarity and BM25 rank. +- **Relevance Score**: Combined score calculated from Dense Cosine similarity and BM25 keyword matching. - **Source Link**: Clickable permalink directly to the file and line range in the upstream Git provider. - **Syntax Preview**: Code block with syntax highlighting and line numbers. diff --git a/docs/reference/rest-api.md b/docs/reference/rest-api.md index 943ce00..d64dab0 100644 --- a/docs/reference/rest-api.md +++ b/docs/reference/rest-api.md @@ -16,7 +16,7 @@ All administrative routes are prefixed with `/admin/api`. ```json { "status": "ok", - "version": "2.13.0", + "version": "2.14.0", "database": "connected", "vector_store": "healthy" } diff --git a/docs/requirements/index.md b/docs/requirements/index.md index 91d2bda..82bd290 100644 --- a/docs/requirements/index.md +++ b/docs/requirements/index.md @@ -1,6 +1,6 @@ # Software Requirements Specification (SRS) -This document establishes the Software Requirements Specification for ContextCortex (version 2.13.0). +This document establishes the Software Requirements Specification for ContextCortex (version 2.14.0). This specification is written in accordance with the **ASD-STE100 Simplified Technical English (Issue 9)** standard and ISO/IEC/IEEE 29148 requirements engineering standards. diff --git a/docs/superpowers/plans/2026-09-17-normalized-weighted-search-scoring.md b/docs/superpowers/plans/2026-09-17-normalized-weighted-search-scoring.md new file mode 100644 index 0000000..f12be0c --- /dev/null +++ b/docs/superpowers/plans/2026-09-17-normalized-weighted-search-scoring.md @@ -0,0 +1,215 @@ +# Normalized Weighted Search Scoring Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Implement normalized weighted score fusion ($\alpha \cdot \text{Dense} + (1-\alpha) \cdot \text{Sparse}$, $\alpha = 0.7$) in Qdrant hybrid search, expand prefetch candidate depth, and update UI/MCP score badge formatting. + +**Architecture:** Replace server-side Qdrant RRF fusion with multi-stream batch querying via `query_batch_points` requesting $\max(\text{limit} \times 5, 50)$ candidates for dense and sparse streams in a single roundtrip. Normalize sparse BM25 scores relative to the query's max BM25 score, clamp dense cosine scores to $[0.0, 1.0]$, and combine linearly into a final score in $[0.0, 1.0]$. Update Search Inspector and MCP search output to display normalized percentages alongside raw 4-decimal scores. + +**Tech Stack:** Python 3.12, FastAPI, Qdrant Client (`qmodels`), React 18, TypeScript, Vitest, Pytest. + +## Global Constraints +- Scores must strictly fall in the range $[0.0, 1.0]$. +- Default dense weight $\alpha = 0.7$, configurable via `HYBRID_DENSE_WEIGHT` environment variable. +- Maintain fallback to single-stream dense search when sparse tokens are empty or unavailable. +- Do not introduce extra network roundtrips: use `query_batch_points` for concurrent multi-stream retrieval. +- Preserve 100% test pass rate across all existing backend and frontend test suites. + +--- + +### Task 1: Normalized Weighted Fusion in `QdrantVectorStore` + +**Files:** +- Modify: `app/services/vector_store/qdrant_store.py:280-345` +- Test: `tests/backend/test_vector_store_qdrant.py` + +**Interfaces:** +- Consumes: `get_dense_embedding(text: str) -> List[float]`, `get_sparse_embedding(text: str) -> Optional[SparseVector]` +- Produces: `QdrantVectorStore.search(query_text, doc_type, repo, language, category, tag, limit) -> List[VectorSearchResult]` where `score` is in $[0.0, 1.0]$. + +- [ ] **Step 1: Write the failing test** + +In `tests/backend/test_vector_store_qdrant.py`, add a test verifying weighted score fusion: + +```python + def test_search_weighted_score_fusion_range_and_boost(self, memory_store): + doc1 = VectorDocument( + id=str(uuid.uuid4()), + text="High performance docker container orchestration and deployment.", + repo="devops", + path="/docs/docker.md", + doc_type="doc" + ) + doc2 = VectorDocument( + id=str(uuid.uuid4()), + text="Kubernetes cluster management without any docker keywords.", + repo="k8s", + path="/docs/k8s.md", + doc_type="doc" + ) + memory_store.upsert_documents([doc1, doc2]) + + results = memory_store.search("docker container deployment", limit=5) + assert len(results) > 0 + top = results[0] + # Score must be bounded in [0.0, 1.0] + assert 0.0 <= top.score <= 1.0 + # The document matching both dense semantics and BM25 keywords should score significantly higher than RRF fractions + assert top.score >= 0.50 + assert top.payload["repo"] == "devops" +``` + +- [ ] **Step 2: Run test to verify it fails or asserts legacy behavior** + +Run: `pytest tests/backend/test_vector_store_qdrant.py::TestQdrantVectorStoreOperations::test_search_weighted_score_fusion_range_and_boost -v` +Expected: FAIL (legacy RRF produced `top.score == 0.50` or `0.3333` which fails the `>= 0.50` threshold with multi-modal match, or fails if dense/sparse weighting is not yet implemented). + +- [ ] **Step 3: Implement normalized weighted fusion in `QdrantVectorStore.search`** + +In `app/services/vector_store/qdrant_store.py`: +Read `HYBRID_DENSE_WEIGHT` (float, default `0.7`). +Calculate `candidate_limit = max(limit * 5, 50)`. +If `sparse_vec is not None and len(sparse_vec.indices) > 0`: +Use `self.client.query_batch_points(collection_name=self.collection_name, requests=[qmodels.QueryRequest(query=dense_vec, using="dense", limit=candidate_limit, filter=query_filter, with_payload=True), qmodels.QueryRequest(query=sparse_vec, using="sparse", limit=candidate_limit, filter=query_filter, with_payload=True)])`. +Extract `dense_pts = {str(p.id): p for p in batch_response[0].points}`. +Extract `sparse_pts = {str(p.id): p for p in batch_response[1].points}`. +Compute `max_sparse = max((p.score for p in sparse_pts.values() if p.score is not None), default=1.0)`. +For all unique IDs across dense and sparse: +- `d_score = max(0.0, min(1.0, float(dense_pts[uid].score))) if uid in dense_pts and dense_pts[uid].score is not None else 0.0` +- `s_score = (float(sparse_pts[uid].score) / max_sparse) if uid in sparse_pts and sparse_pts[uid].score is not None and max_sparse > 0 else 0.0` +- `final_score = (alpha * d_score + (1.0 - alpha) * s_score) if (uid in dense_pts and uid in sparse_pts) else (alpha * d_score if uid in dense_pts else (1.0 - alpha) * s_score)` +Sort descending by `final_score`, slice `[:limit]`, and wrap in `VectorSearchResult(id=uid, score=round(final_score, 4), payload=pt.payload or {})`. +If `sparse_vec` is empty, query `dense` directly, clamp score to $[0.0, 1.0]$, and return top `limit`. + +- [ ] **Step 4: Run test to verify it passes** + +Run: `pytest tests/backend/test_vector_store_qdrant.py -v` +Expected: ALL PASS. + +- [ ] **Step 5: Commit** + +```bash +git add app/services/vector_store/qdrant_store.py tests/backend/test_vector_store_qdrant.py +git commit -m "feat(search): implement normalized weighted score fusion in QdrantVectorStore" +``` + +--- + +### Task 2: MCP Search Tool Output Formatting + +**Files:** +- Modify: `app/mcp/handlers/search_handlers.py:49,92` +- Test: `tests/backend/test_mcp_v2.py` + +**Interfaces:** +- Consumes: `execute_hybrid_search(...) -> List[VectorSearchResult]` +- Produces: Formatted markdown containing `Relevance Score: {hit.score:.4f} ({hit.score * 100:.1f}%)` + +- [ ] **Step 1: Write failing test assertion for MCP search output** + +In `tests/backend/test_mcp_v2.py`, update any assertion expecting `RRF Score:` to expect `Relevance Score:`. + +- [ ] **Step 2: Run test to verify it fails** + +Run: `pytest tests/backend/test_mcp_v2.py -v` +Expected: FAIL on string mismatch (`RRF Score:` vs `Relevance Score:`). + +- [ ] **Step 3: Update `search_handlers.py` formatting** + +In `app/mcp/handlers/search_handlers.py`: +Line 49: +Change: `header += f"\nRRF Score: {hit.score:.4f}\n"` +To: `header += f"\nRelevance Score: {hit.score:.4f} ({hit.score * 100:.1f}%)\n"` +Line 92: +Change: `header += f"\nRRF Score: {hit.score:.4f}\n"` +To: `header += f"\nRelevance Score: {hit.score:.4f} ({hit.score * 100:.1f}%)\n"` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `pytest tests/backend/test_mcp_v2.py tests/backend/test_db_and_tools.py -v` +Expected: ALL PASS. + +- [ ] **Step 5: Commit** + +```bash +git add app/mcp/handlers/search_handlers.py tests/backend/test_mcp_v2.py +git commit -m "feat(mcp): update search output badge to display relevance percentage" +``` + +--- + +### Task 3: Frontend Search Inspector Badge & Tests + +**Files:** +- Modify: `frontend/src/SearchInspector.tsx:116` +- Test: `frontend/src/tests/SearchInspector.test.tsx:9,67` + +**Interfaces:** +- Consumes: `/admin/api/search/test` JSON response with `hit.score` +- Produces: UI badge `Score: {(hit.score * 100).toFixed(1)}% ({hit.score.toFixed(4)})` + +- [ ] **Step 1: Write failing test assertion for SearchInspector UI** + +In `frontend/src/tests/SearchInspector.test.tsx`: +Change line 9 mock score to `0.825`. +Change line 67 expectation to: +`expect(screen.getByText('Score: 82.5% (0.8250)')).toBeInTheDocument();` + +- [ ] **Step 2: Run test to verify it fails** + +Run: `npm --prefix frontend test -- src/tests/SearchInspector.test.tsx` +Expected: FAIL on `Score: 82.5% (0.8250)` not found. + +- [ ] **Step 3: Update `SearchInspector.tsx` badge rendering** + +In `frontend/src/SearchInspector.tsx` line 116: +Change: +`RRF Score: {hit.score.toFixed(4)}` +To: +`Score: {(hit.score * 100).toFixed(1)}% ({hit.score.toFixed(4)})` + +- [ ] **Step 4: Run test to verify it passes** + +Run: `npm --prefix frontend test -- src/tests/SearchInspector.test.tsx` +Expected: ALL PASS. + +- [ ] **Step 5: Rebuild frontend distribution** + +Run: `cd frontend && npm run build && cd ..` +Stage the updated `frontend/dist` bundle assets. + +- [ ] **Step 6: Commit** + +```bash +git add frontend/src/SearchInspector.tsx frontend/src/tests/SearchInspector.test.tsx frontend/dist/ +git commit -m "feat(ui): update Search Inspector score badge to percentage and rebuild dist" +``` + +--- + +### Task 4: Full System Verification & Requirements Sync + +**Files:** +- Verification only + +- [ ] **Step 1: Run complete backend test suite** + +Run: `pytest` +Expected: 498 passed (100%). + +- [ ] **Step 2: Run complete frontend vitest suite** + +Run: `npm --prefix frontend test -- --run` +Expected: All test files passing (100%). + +- [ ] **Step 3: Verify requirements sync** + +Run: `python3 scripts/generate_requirements.py && pytest tests/backend/test_requirements_sync.py` +Expected: In sync, PASS. + +- [ ] **Step 4: Commit any documentation/requirements updates** + +```bash +git add REQUIREMENTS.md docs/REQUIREMENTS.md +git commit -m "chore: sync requirements baseline following search scoring enhancement" +``` diff --git a/docs/superpowers/specs/2026-09-17-normalized-weighted-search-scoring-design.md b/docs/superpowers/specs/2026-09-17-normalized-weighted-search-scoring-design.md new file mode 100644 index 0000000..95cdba4 --- /dev/null +++ b/docs/superpowers/specs/2026-09-17-normalized-weighted-search-scoring-design.md @@ -0,0 +1,80 @@ +# Normalized Weighted Search Scoring Design + +## Overview +Currently, Qdrant Hybrid Search (`Dense + BM25 Sparse`) combines search candidates using Reciprocal Rank Fusion (`Fusion.RRF`). Because RRF calculates scores based strictly on reciprocal ranks ($1 / (1 + \text{rank})$), the theoretical maximum score for a single modality is 0.50, and rank 2–4 results score between 0.20 and 0.33. This produces counter-intuitive scores in the Search Inspector and MCP search tool output (e.g. `RRF Score: 0.3333`), which look like 33% relevance even for top-ranked results, and differs from single-vector engines (Chroma and pgvector) which use cosine similarity (0.70 – 0.95). + +This design transitions Qdrant Hybrid Search to **Normalized Linear Weighted Fusion** ($\alpha \cdot \text{Dense} + (1 - \alpha) \cdot \text{Sparse}$, with $\alpha = 0.7$), keeping all search scores strictly bounded on a **0.0 to 1.0 (0% to 100%)** scale, expanding prefetch candidate pools for higher cross-stream intersection recall, and updating UI/MCP labels to clearly display the score. + +--- + +## Architecture & Scoring Formula + +### 1. Multi-Stream Batch Candidate Retrieval +Instead of performing server-side RRF fusion via `FusionQuery`, `QdrantVectorStore.search` will execute a single batch query via `client.query_batch_points` requesting candidates from both streams simultaneously: +- **Dense stream**: `qmodels.QueryRequest(query=dense_vec, using="dense", limit=candidate_limit, filter=query_filter, with_payload=True)` +- **Sparse stream**: `qmodels.QueryRequest(query=sparse_vec, using="sparse", limit=candidate_limit, filter=query_filter, with_payload=True)` + +The candidate pool depth is expanded from `limit * 2` to: +$$\text{candidate\_limit} = \max(\text{limit} \times 5, 50)$$ +This ensures that documents ranked beyond position 10 in either dense or sparse have adequate opportunity to intersect and achieve multi-stream reinforcement. + +### 2. Score Normalization +- **Dense Cosine Score** ($S_{\text{dense}}$): + Since Qdrant dense vectors use Cosine distance, point scores are cosine similarity. Values are clamped to $[0.0, 1.0]$: + $$S_{\text{dense\_norm}} = \max(0.0, \min(1.0, S_{\text{dense}}))$$ +- **Sparse BM25 Score** ($S_{\text{sparse}}$): + BM25 scores in FastEmbed/Qdrant are unbounded non-negative values. Scores within the retrieved candidate set are normalized relative to the maximum BM25 score observed for the query: + $$S_{\text{sparse\_max}} = \max(\{S_{\text{sparse}}(p) \mid p \in \text{sparse\_points}\}, 1.0)$$ + $$S_{\text{sparse\_norm}} = \frac{S_{\text{sparse}}}{S_{\text{sparse\_max}}}$$ + +### 3. Weighted Score Fusion +For each candidate document $d$ appearing in either stream: +$$S_{\text{final}}(d) = \alpha \cdot S_{\text{dense\_norm}}(d) + (1 - \alpha) \cdot S_{\text{sparse\_norm}}(d)$$ + +- **Dense weight**: $\alpha = 0.7$ by default, configurable via `HYBRID_DENSE_WEIGHT` environment variable. +- If document $d$ only matched in dense (e.g. sparse did not match), $S_{\text{sparse\_norm}}(d) = 0.0$. +- If document $d$ only matched in sparse (e.g. dense did not rank it in top candidates), $S_{\text{dense\_norm}}(d) = 0.0$. +- If sparse is unavailable or query produces no sparse tokens, the search executes a standard dense query and returns $S_{\text{final}} = S_{\text{dense\_norm}}$. +- All resulting scores are guaranteed to be in the range $[0.0, 1.0]$. + +--- + +## Component Changes + +### 1. Vector Store Backend (`app/services/vector_store/qdrant_store.py`) +- Update `QdrantVectorStore.search()`: + - If `sparse_vec is not None and len(sparse_vec.indices) > 0`: + - Dispatch batch query with dense and sparse `QueryRequest`. + - Apply normalized weighted fusion formula. + - Sort combined unique results descending by $S_{\text{final}}$. + - Slice to `limit` items and return as `VectorSearchResult(id=..., score=round(final_score, 4), payload=...)`. + - If sparse is not used: + - Execute dense `query_points` directly with cosine score clamped to $[0.0, 1.0]$. + +### 2. Frontend Search Inspector (`frontend/src/SearchInspector.tsx`) +- Update result badge display: + - Change `RRF Score: {hit.score.toFixed(4)}` to: + `Score: {(hit.score * 100).toFixed(1)}% ({hit.score.toFixed(4)})` + - Example rendering: `Score: 80.5% (0.8049)`. + +### 3. MCP Search Handlers (`app/mcp/handlers/search_handlers.py`) +- In `handle_search_code` and `handle_search_docs`: + - Change markdown header: + `Relevance Score: {hit.score:.4f} ({hit.score * 100:.1f}%)` + +--- + +## Testing & Verification Plan + +### Backend Unit Tests (`tests/backend/test_vector_store_qdrant.py`) +- Add `test_search_weighted_score_fusion`: + - Verify hybrid search scores are in $[0.0, 1.0]$. + - Verify that matching both dense and sparse yields higher scores than single-stream match. + - Verify candidate limit expansion and dense fallback. + +### Frontend Unit Tests (`frontend/src/tests/SearchInspector.test.tsx`) +- Update mock test data and expectations to match the new `Score: XX.X% (X.XXXX)` badge format. + +### End-to-End Regression Tests +- Run complete pytest test suite: `pytest`. +- Run complete vitest test suite: `npm test` in `frontend/`. diff --git a/frontend/dist/assets/index-BaXZJSae.js b/frontend/dist/assets/index-B-sgo2Eo.js similarity index 59% rename 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b/frontend/dist/index.html index a01490a..81aff15 100644 --- a/frontend/dist/index.html +++ b/frontend/dist/index.html @@ -17,7 +17,7 @@ } catch (e) {} })(); - + diff --git a/frontend/e2e/dashboard.spec.ts b/frontend/e2e/dashboard.spec.ts index a22a583..c714610 100644 --- a/frontend/e2e/dashboard.spec.ts +++ b/frontend/e2e/dashboard.spec.ts @@ -734,7 +734,7 @@ test('8. executes hybrid search with target type toggle (code vs doc) and repo f await expect(page.getByText('app/api/auth.py')).toBeVisible(); await expect(page.getByText('verify_token', { exact: true })).toBeVisible(); - await expect(page.getByText('RRF Score: 0.0450')).toBeVisible(); + await expect(page.getByText('Score: 4.5% (0.0450)')).toBeVisible(); await expect(page.getByText('View on GitHub')).toBeVisible(); // Toggle to Doc Search @@ -746,7 +746,7 @@ test('8. executes hybrid search with target type toggle (code vs doc) and repo f expect(lastSearchPayload.type).toBe('doc'); await expect(page.getByText('docs/architecture.md')).toBeVisible(); - await expect(page.getByText('RRF Score: 0.0385')).toBeVisible(); + await expect(page.getByText('Score: 3.9% (0.0385)')).toBeVisible(); await expect(page.getByText('# System Architecture')).toBeVisible(); }); @@ -1188,7 +1188,7 @@ test('20. [Mobile] performs search and renders responsive result item on mobile await expect(resultCard).toBeVisible(); await expect(resultCard.getByText('app/api/auth.py')).toBeVisible(); await expect(resultCard.getByText('verify_token', { exact: true })).toBeVisible(); - await expect(resultCard.getByText('RRF Score: 0.0450')).toBeVisible(); + await expect(resultCard.getByText('Score: 4.5% (0.0450)')).toBeVisible(); await expect(resultCard.getByText('View on GitHub')).toBeVisible(); await expect(resultCard.locator('pre.search-hit-code')).toContainText('def verify_token'); }); diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 5374c4a..d290f98 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,12 +1,12 @@ { "name": "contextcortex-frontend", - "version": "2.13.0", + "version": "2.14.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "contextcortex-frontend", - "version": "2.13.0", + "version": "2.14.0", "dependencies": { "react": "^19.2.8", "react-dom": "^19.2.8" diff --git a/frontend/package.json b/frontend/package.json index 1f801fb..06657d4 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,7 +1,7 @@ { "name": "contextcortex-frontend", "private": true, - "version": "2.13.0", + "version": "2.14.0", "type": "module", "scripts": { "dev": "vite --configLoader native", diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index f3990c3..9388767 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -42,7 +42,7 @@ function App() {

ContextCortex

- v2.13.0 + v2.14.0