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Merge branch 'feature/bm25'

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This commit is contained in:
Chris Coutinho
2025-11-16 07:57:36 +01:00
parent d1fb7eb633 fc6a2f14e4
commit 2aa82d849c
18 changed files with 1033 additions and 1099 deletions
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nextcloud_mcp_server/auth/userinfo_routes.py
+2 -8
View File
@@ -676,14 +676,11 @@ async def user_info_html(request: Request) -> HTMLResponse:
function vizApp() {{
return {{
query: '',
algorithm: 'hybrid',
algorithm: 'bm25_hybrid',
showAdvanced: false,
docTypes: [''], // Default to "All Types"
limit: 50,
scoreThreshold: 0.7,
semanticWeight: 0.5,
keywordWeight: 0.3,
fuzzyWeight: 0.2,
scoreThreshold: 0.0,
loading: false,
results: [],
@@ -697,9 +694,6 @@ async def user_info_html(request: Request) -> HTMLResponse:
algorithm: this.algorithm,
limit: this.limit,
score_threshold: this.scoreThreshold,
semantic_weight: this.semanticWeight,
keyword_weight: this.keywordWeight,
fuzzy_weight: this.fuzzyWeight,
}});
// Add doc_types parameter (filter out empty string for "All Types")
nextcloud_mcp_server/auth/viz_routes.py
+14 -41
View File
@@ -20,9 +20,7 @@ from starlette.responses import HTMLResponse, JSONResponse
from nextcloud_mcp_server.config import get_settings
from nextcloud_mcp_server.search import (
FuzzySearchAlgorithm,
HybridSearchAlgorithm,
KeywordSearchAlgorithm,
BM25HybridSearchAlgorithm,
SemanticSearchAlgorithm,
)
from nextcloud_mcp_server.vector.pca import PCA
@@ -209,10 +207,8 @@ async def vector_visualization_html(request: Request) -> HTMLResponse:
<div class="viz-control-group" style="margin-bottom: 0;">
<label>Algorithm</label>
<select x-model="algorithm">
<option value="semantic">Semantic (Vector Similarity)</option>
<option value="keyword">Keyword (Token Matching)</option>
<option value="fuzzy">Fuzzy (Character Overlap)</option>
<option value="hybrid" selected>Hybrid (RRF Fusion)</option>
<option value="semantic">Semantic (Dense Vectors)</option>
<option value="bm25_hybrid" selected>BM25 Hybrid (Dense + Sparse RRF)</option>
</select>
</div>
@@ -260,25 +256,13 @@ async def vector_visualization_html(request: Request) -> HTMLResponse:
</div>
</div>
<!-- Hybrid Weights (only when hybrid selected) -->
<div x-show="algorithm === 'hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
<label style="margin-bottom: 12px; display: block;">Hybrid Algorithm Weights</label>
<div style="margin-bottom: 8px;">
<label style="display: inline-block; width: 100px; font-weight: normal;">Semantic:</label>
<input type="range" x-model.number="semanticWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
<span class="viz-weight-display" x-text="semanticWeight.toFixed(1)"></span>
</div>
<div style="margin-bottom: 8px;">
<label style="display: inline-block; width: 100px; font-weight: normal;">Keyword:</label>
<input type="range" x-model.number="keywordWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
<span class="viz-weight-display" x-text="keywordWeight.toFixed(1)"></span>
</div>
<div>
<label style="display: inline-block; width: 100px; font-weight: normal;">Fuzzy:</label>
<input type="range" x-model.number="fuzzyWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
<span class="viz-weight-display" x-text="fuzzyWeight.toFixed(1)"></span>
</div>
<!-- Info: BM25 Hybrid uses native RRF fusion (no manual weights) -->
<div x-show="algorithm === 'bm25_hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
<p style="margin: 0; font-size: 14px; color: #666;">
<strong>BM25 Hybrid Search:</strong> Uses Qdrant's native Reciprocal Rank Fusion (RRF)
to automatically combine dense semantic vectors with sparse BM25 keyword vectors.
No manual weight tuning required.
</p>
</div>
</div>
</div>
@@ -365,12 +349,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
# Parse query parameters
query = request.query_params.get("query", "")
algorithm = request.query_params.get("algorithm", "hybrid")
algorithm = request.query_params.get("algorithm", "bm25_hybrid")
limit = int(request.query_params.get("limit", "50"))
score_threshold = float(request.query_params.get("score_threshold", "0.7"))
semantic_weight = float(request.query_params.get("semantic_weight", "0.5"))
keyword_weight = float(request.query_params.get("keyword_weight", "0.3"))
fuzzy_weight = float(request.query_params.get("fuzzy_weight", "0.2"))
score_threshold = float(request.query_params.get("score_threshold", "0.0"))
# Parse doc_types (comma-separated list, None = all types)
doc_types_param = request.query_params.get("doc_types", "")
@@ -395,16 +376,8 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
# Create search algorithm (no client needed - verification removed)
if algorithm == "semantic":
search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
elif algorithm == "keyword":
search_algo = KeywordSearchAlgorithm()
elif algorithm == "fuzzy":
search_algo = FuzzySearchAlgorithm()
elif algorithm == "hybrid":
search_algo = HybridSearchAlgorithm(
semantic_weight=semantic_weight,
keyword_weight=keyword_weight,
fuzzy_weight=fuzzy_weight,
)
elif algorithm == "bm25_hybrid":
search_algo = BM25HybridSearchAlgorithm(score_threshold=score_threshold)
else:
return JSONResponse(
{"success": False, "error": f"Unknown algorithm: {algorithm}"},