query decomp is closer to working

src/services/llm/context/services/vector_search_service.ts

@@ -376,6 +376,76 @@ export class VectorSearchService {
       return '';
     }
   }
+
+  /**
+   * Find notes that are semantically relevant to multiple queries
+   * Combines results from multiple queries, deduplicates them, and returns the most relevant ones
+   *
+   * @param queries - Array of search queries
+   * @param contextNoteId - Optional note ID to restrict search to a branch
+   * @param options - Search options including result limit and summarization preference
+   * @returns Array of relevant notes with similarity scores, deduplicated and sorted
+   */
+  async findRelevantNotesMultiQuery(
+    queries: string[],
+    contextNoteId: string | null = null,
+    options: VectorSearchOptions = {}
+  ): Promise<NoteSearchResult[]> {
+    if (!queries || queries.length === 0) {
+      log.info('No queries provided to findRelevantNotesMultiQuery');
+      return [];
+    }
+
+    log.info(`VectorSearchService: Finding relevant notes for ${queries.length} queries`);
+    log.info(`Multi-query parameters: contextNoteId=${contextNoteId || 'global'}, queries=${JSON.stringify(queries.map(q => q.substring(0, 20) + '...'))}`);
+
+    try {
+      // Create a Map to deduplicate results across queries
+      const allResults = new Map<string, NoteSearchResult>();
+
+      // For each query, adjust maxResults to avoid getting too many total results
+      const adjustedMaxResults = options.maxResults ?
+        Math.ceil(options.maxResults / queries.length) :
+        Math.ceil(SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_MAX_RESULTS / queries.length);
+
+      // Search for each query and combine results
+      for (const query of queries) {
+        try {
+          const queryOptions = {
+            ...options,
+            maxResults: adjustedMaxResults,
+            useEnhancedQueries: false // We're already using enhanced queries
+          };
+
+          const results = await this.findRelevantNotes(query, contextNoteId, queryOptions);
+
+          // Merge results, keeping the highest similarity score for duplicates
+          for (const note of results) {
+            if (!allResults.has(note.noteId) ||
+                (allResults.has(note.noteId) && note.similarity > (allResults.get(note.noteId)?.similarity || 0))) {
+              allResults.set(note.noteId, note);
+            }
+          }
+
+          log.info(`Found ${results.length} results for query: "${query.substring(0, 30)}..."`);
+        } catch (error) {
+          log.error(`Error searching for query "${query}": ${error}`);
+        }
+      }
+
+      // Convert map to array and sort by similarity
+      const combinedResults = Array.from(allResults.values())
+        .sort((a, b) => b.similarity - a.similarity)
+        .slice(0, options.maxResults || SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_MAX_RESULTS);
+
+      log.info(`VectorSearchService: Found ${combinedResults.length} total deduplicated results across ${queries.length} queries`);
+
+      return combinedResults;
+    } catch (error) {
+      log.error(`Error in findRelevantNotesMultiQuery: ${error}`);
+      return [];
+    }
+  }
 }
 
 // Export a singleton instance

src/services/llm/pipeline/chat_pipeline.ts

@@ -202,21 +202,23 @@ export class ChatPipeline {
             const vectorSearchStartTime = Date.now();
             log.info(`========== STAGE 3: VECTOR SEARCH ==========`);
             log.info('Using VectorSearchStage pipeline component to find relevant notes');
+            log.info(`Searching with ${searchQueries.length} queries from decomposition`);
 
+            // Use the vectorSearchStage with multiple queries
             const vectorSearchResult = await this.stages.vectorSearch.execute({
-                query: userQuery,
+                query: userQuery, // Original query as fallback
+                queries: searchQueries, // All decomposed queries
                 noteId: input.noteId || 'global',
                 options: {
                     maxResults: SEARCH_CONSTANTS.CONTEXT.MAX_SIMILAR_NOTES,
-                    useEnhancedQueries: true,
+                    useEnhancedQueries: false, // We're already using enhanced queries from decomposition
                     threshold: SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_THRESHOLD,
                     llmService: llmService || undefined
                 }
             });
 
             this.updateStageMetrics('vectorSearch', vectorSearchStartTime);
-
+            log.info(`Vector search found ${vectorSearchResult.searchResults.length} relevant notes across ${searchQueries.length} queries`);
-            log.info(`Vector search found ${vectorSearchResult.searchResults.length} relevant notes`);
 
             // Extract context from search results
             log.info(`========== SEMANTIC CONTEXT EXTRACTION ==========`);

src/services/llm/pipeline/stages/vector_search_stage.ts

@@ -13,6 +13,7 @@ import { SEARCH_CONSTANTS } from '../../constants/search_constants.js';
 
 export interface VectorSearchInput {
   query: string;
+  queries?: string[];
   noteId?: string;
   options?: {
     maxResults?: number;
@@ -42,6 +43,7 @@ export class VectorSearchStage {
   async execute(input: VectorSearchInput): Promise<VectorSearchOutput> {
     const {
       query,
+      queries = [],
       noteId = 'global',
       options = {}
     } = input;
@@ -53,6 +55,42 @@ export class VectorSearchStage {
       llmService = undefined
     } = options;
 
+    // If queries array is provided, use multi-query search
+    if (queries && queries.length > 0) {
+      log.info(`VectorSearchStage: Searching with ${queries.length} queries`);
+      log.info(`Parameters: noteId=${noteId}, maxResults=${maxResults}, threshold=${threshold}`);
+
+      try {
+        // Use the new multi-query method
+        const searchResults = await vectorSearchService.findRelevantNotesMultiQuery(
+          queries,
+          noteId === 'global' ? null : noteId,
+          {
+            maxResults,
+            threshold,
+            llmService: llmService || null
+          }
+        );
+
+        log.info(`VectorSearchStage: Found ${searchResults.length} relevant notes from multi-query search`);
+
+        return {
+          searchResults,
+          originalQuery: query,
+          noteId
+        };
+      } catch (error) {
+        log.error(`Error in vector search stage multi-query: ${error}`);
+        // Return empty results on error
+        return {
+          searchResults: [],
+          originalQuery: query,
+          noteId
+        };
+      }
+    }
+
+    // Fallback to single query search
     log.info(`VectorSearchStage: Searching for "${query.substring(0, 50)}..."`);
     log.info(`Parameters: noteId=${noteId}, maxResults=${maxResults}, threshold=${threshold}`);
 
@@ -64,7 +102,7 @@ export class VectorSearchStage {
         {
           maxResults,
           threshold,
-          llmService
+          llmService: llmService || null
         }
       );