Task 8 Critical Fixes - Search and Retrieval System

Overview

Two critical bugs were identified after Task 8 implementation that broke core search functionality. Both have been resolved.

Bug 1: Missing Dataset in Qdrant Payload (HIGH PRIORITY)

Problem

Location: src/services/vector-db.service.ts:79-93

The storeEmbeddings() method was not including dataset or documentId in the Qdrant payload metadata. This caused:

All dataset filtering to fail silently (filters had no effect)
Document-specific searches (searchByDocument) to return unfiltered results
Complete failure of Task 8.2 (dataset scoping)

Root Cause

// BEFORE (BROKEN):
const points: QdrantPoint[] = embeddings.map((emb) => ({
  id: emb.chunkId,
  vector: emb.vector,
  payload: {
    chunkId: emb.chunkId,
    model: emb.model,
    dimensions: emb.dimensions,
    generatedAt: emb.generatedAt.toISOString(),
    // ❌ dataset and documentId missing!
  },
}));

When SearchService.vectorSearch() tried to filter by dataset:

filter = {
  should: query.datasets.map((dataset) => ({
    key: 'dataset',
    match: { value: dataset },
  })),
};

Qdrant would silently ignore the filter since the dataset field didn't exist in any payload.

Solution

Step 1: Update VectorDbService.storeEmbeddings() signature to accept dataset

// AFTER (FIXED):
async storeEmbeddings(
  embeddings: ChunkEmbedding[],
  documentId?: string,
  dataset?: string // ✅ Added dataset parameter
): Promise<void>

Step 2: Include dataset in payload

const points: QdrantPoint[] = embeddings.map((emb) => ({
  id: emb.chunkId,
  vector: emb.vector,
  payload: {
    chunkId: emb.chunkId,
    documentId: documentId || emb.chunkId.split('-')[0], // ✅ Include documentId
    dataset: dataset || 'default', // ✅ Include dataset for filtering
    model: emb.model,
    dimensions: emb.dimensions,
    generatedAt: emb.generatedAt.toISOString(),
  },
}));

Step 3: Update worker to retrieve and pass dataset name

// src/workers/document-processor.worker.ts
} else if (stage === 'embedding') {
  logger.info('Processing embedding stage', { documentId });
  
  await vectorDb.ensureCollection();
  
  // ✅ Get document dataset info
  const docResult = await db.query(
    'SELECT d.name as dataset FROM documents doc JOIN datasets d ON doc.dataset_id = d.id WHERE doc.id = $1',
    [documentId]
  );
  
  if (docResult.rows.length === 0) {
    throw new Error('Document not found');
  }
  
  const dataset = docResult.rows[0].dataset;
  
  const chunks = await chunkStorage.getChunksByDocument(documentId);
  const embeddings = await embeddingService.generateChunkEmbeddings(chunks);
  
  await embeddingStorage.saveEmbeddings(embeddings);
  
  // ✅ Pass dataset to vector database
  await vectorDb.storeEmbeddings(embeddings, documentId, dataset);
}

Step 4: Update test to verify dataset in payload

// src/services/vector-db.service.test.ts
it('should store embeddings with metadata', async () => {
  mockRequest.mockResolvedValue({ status: 200 });

  const embeddings: ChunkEmbedding[] = [
    {
      chunkId: 'chunk-1',
      vector: [0.1, 0.2, 0.3],
      model: 'test',
      dimensions: 3,
      generatedAt: new Date(),
    },
  ];

  await service.storeEmbeddings(embeddings, 'doc-123', 'legal-docs');

  expect(mockRequest).toHaveBeenCalledWith(
    expect.stringContaining('/points'),
    expect.objectContaining({
      points: expect.arrayContaining([
        expect.objectContaining({
          vector: [0.1, 0.2, 0.3],
          payload: expect.objectContaining({
            documentId: 'doc-123',
            dataset: 'legal-docs', // ✅ Verify dataset is included
          }),
        }),
      ]),
    })
  );
});

Impact

Before: All dataset filtering was non-functional
After: Dataset filtering works correctly
Before: searchByDocument() returned all chunks from all documents
After: searchByDocument() correctly scopes to specified document

Files Modified

src/services/vector-db.service.ts - Added dataset parameter
src/workers/document-processor.worker.ts - Query and pass dataset
src/services/vector-db.service.test.ts - Verify dataset in payload

Bug 2: Text Search Hardcoded to English (MEDIUM PRIORITY)

Problem

Location: src/services/search.service.ts:125-168

The textSearch() method hardcoded PostgreSQL full-text search to the 'english' configuration, ignoring the language parameter from the query. This caused:

Arabic searches to return zero results despite having Arabic data
Mixed-language searches to fail for non-English content
Complete violation of language-aware processing claims

Root Cause

// BEFORE (BROKEN):
const result = await db.query(
  `SELECT 
     tc.id as chunk_id,
     ts_rank(to_tsvector('english', tc.normalized_text), plainto_tsquery('english', $1)) as score
   FROM text_chunks tc
   WHERE to_tsvector('english', tc.normalized_text) @@ plainto_tsquery('english', $1)
   ...`
);
// ❌ Hardcoded to English!

PostgreSQL text search configurations use language-specific stemming, stop words, and tokenization:

'english' config: ignores Arabic text entirely
'arabic' config: properly handles Arabic morphology
'simple' config: language-agnostic for mixed content

Solution

Step 1: Detect language from query parameter

// src/services/search.service.ts
private async textSearch(
  queryText: string,
  limit: number,
  query: SearchQuery
): Promise<TextSearchResult[]> {
  logger.debug('Performing text search', { query: queryText, limit });

  // ✅ Select text search configuration based on language
  const language = query.language || 'english';
  let tsConfig = 'english'; // Default
  
  if (language === 'arabic') {
    tsConfig = 'arabic';
  } else if (language === 'mixed') {
    tsConfig = 'simple'; // Use simple for mixed language
  }

Step 2: Use language-specific configuration in SQL

  // ✅ Perform full-text search with language-specific configuration
  const result = await db.query(
    `SELECT 
       tc.id as chunk_id,
       ts_rank(to_tsvector('${tsConfig}', tc.normalized_text), plainto_tsquery('${tsConfig}', $1)) as score
     FROM text_chunks tc
     JOIN documents doc ON tc.document_id = doc.id
     JOIN datasets d ON doc.dataset_id = d.id
     WHERE to_tsvector('${tsConfig}', tc.normalized_text) @@ plainto_tsquery('${tsConfig}', $1)
     ${whereClause}
     ORDER BY score DESC
     LIMIT $${paramIndex}`,
    [...params, limit]
  );

Language Configuration Mapping

Query Language	PostgreSQL Config	Behavior
`english`	`english`	English stemming, stop words
`arabic`	`arabic`	Arabic morphology, RTL support
`mixed`	`simple`	No stemming, language-agnostic
(default)	`english`	Fallback to English

Impact

Before: Arabic queries "القانون" returned 0 results
After: Arabic queries return correctly ranked results
Before: Mixed-language documents poorly indexed
After: Mixed-language uses simple config for broader matching

Files Modified

src/services/search.service.ts - Language-aware text search configuration

Additional Fix: Qdrant Filter Format

Problem

Location: src/services/search.service.ts:85-121

Initial implementation used must for dataset filtering, which requires ALL datasets to match (impossible):

// WRONG:
filter = {
  must: query.datasets.map((dataset) => ({
    key: 'dataset',
    match: { value: dataset },
  })),
};
// ❌ "must" means AND - no document can be in ALL datasets

Solution

// CORRECT:
filter = {
  should: query.datasets.map((dataset) => ({
    key: 'dataset',
    match: { value: dataset },
  })),
};
// ✅ "should" means OR - match any of the datasets

Qdrant Filter Logic

must: ALL conditions must match (AND logic)
should: ANY condition can match (OR logic)
must_not: NONE of conditions can match (NOT logic)

Testing

Build Verification

npm run build
# ✅ Clean compilation, no TypeScript errors

Test Verification

npm test
# ✅ 145/145 tests passing
# ✅ Dataset payload test includes verification

Test Coverage

// Vector database test verifies dataset in payload
it('should store embeddings with metadata', async () => {
  await service.storeEmbeddings(embeddings, 'doc-123', 'legal-docs');
  
  expect(mockRequest).toHaveBeenCalledWith(
    expect.stringContaining('/points'),
    expect.objectContaining({
      points: expect.arrayContaining([
        expect.objectContaining({
          payload: expect.objectContaining({
            documentId: 'doc-123',
            dataset: 'legal-docs', // ✅ Verified
          }),
        }),
      ]),
    })
  );
});

Open Questions & Risks

Question 1: Backfilling Existing Vectors

Issue: If there are existing vectors in Qdrant without dataset/documentId fields, they won't be filterable.

Options:

Re-run embedding stage: Reset all documents to chunking stage and reprocess
Manual backfill: Script to update existing payloads via Qdrant API
Fresh start: Drop collection and reprocess all documents from scratch

Recommendation: For development, use fresh start (option 3). For production with existing data, implement backfill script (option 2).

Question 2: SQL Injection Risk

Issue: Using template literals with tsConfig variable in SQL query:

to_tsvector('${tsConfig}', tc.normalized_text)

Risk Assessment: LOW - tsConfig is derived from controlled enum ('english'/'arabic'/'simple'), not user input.

Mitigation: If additional paranoia required, use whitelist validation:

const allowedConfigs = ['english', 'arabic', 'simple'];
if (!allowedConfigs.includes(tsConfig)) {
  throw new Error(`Invalid text search configuration: ${tsConfig}`);
}

Question 3: Performance Impact

Issue: PostgreSQL full-text search with different configurations may have varying performance.

Consideration: Arabic configuration may be slower than English due to morphological analysis. Monitor query performance in production.

Summary

Bugs Fixed

✅ HIGH: Missing dataset in Qdrant payload - dataset filtering now works
✅ MEDIUM: Hardcoded English text search - language-aware search now works
✅ BONUS: Wrong Qdrant filter format - OR logic for dataset filtering

Functionality Restored

Dataset scoping (Task 8.2) now functional
Document-specific search works correctly
Arabic and mixed-language searches return results
Hybrid search properly combines vector + text results with language awareness

Tests

All 145 tests passing
Dataset payload verified in tests
Clean TypeScript compilation

Next Steps

Consider backfill strategy for existing vectors (if any)
Monitor text search performance with different language configurations
Proceed to Task 9: Administrative Interface

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Task 8 Critical Fixes - Search and Retrieval System

Overview

Bug 1: Missing Dataset in Qdrant Payload (HIGH PRIORITY)

Problem

Root Cause

Solution

Impact

Files Modified

Bug 2: Text Search Hardcoded to English (MEDIUM PRIORITY)

Problem

Root Cause

Solution

Language Configuration Mapping

Impact

Files Modified

Additional Fix: Qdrant Filter Format

Problem

Solution

Qdrant Filter Logic

Testing

Build Verification

Test Verification

Test Coverage

Open Questions & Risks

Question 1: Backfilling Existing Vectors

Question 2: SQL Injection Risk

Question 3: Performance Impact

Summary

Bugs Fixed

Functionality Restored

Tests

Next Steps

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TASK_8_FIXES.md

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Task 8 Critical Fixes - Search and Retrieval System

Overview

Bug 1: Missing Dataset in Qdrant Payload (HIGH PRIORITY)

Problem

Root Cause

Solution

Impact

Files Modified

Bug 2: Text Search Hardcoded to English (MEDIUM PRIORITY)

Problem

Root Cause

Solution

Language Configuration Mapping

Impact

Files Modified

Additional Fix: Qdrant Filter Format

Problem

Solution

Qdrant Filter Logic

Testing

Build Verification

Test Verification

Test Coverage

Open Questions & Risks

Question 1: Backfilling Existing Vectors

Question 2: SQL Injection Risk

Question 3: Performance Impact

Summary

Bugs Fixed

Functionality Restored

Tests

Next Steps