# pgvector vs Meilisearch: Why We Keep Both

## TL;DR

**They do DIFFERENT things:**
- **pgvector**: Vector similarity search (semantic search)
- **Meilisearch**: Full-text search (keyword search)

**You need BOTH for TraceRTM.**

---

## What Each Does

### pgvector (PostgreSQL Extension)
**Purpose**: Semantic/similarity search using embeddings

**Use Cases**:
- Find similar items by meaning
- "Find items related to authentication"
- AI-powered recommendations
- Semantic search across descriptions

**Example**:
```sql
SELECT * FROM items 
WHERE embedding <-> query_embedding < 0.5
ORDER BY embedding <-> query_embedding
LIMIT 10;
```

### Meilisearch
**Purpose**: Full-text keyword search with typo tolerance

**Use Cases**:
- Find items by exact keywords
- "Find all items with 'payment' in title"
- Typo-tolerant search
- Fast keyword filtering
- Faceted search

**Example**:
```json
{
  "q": "payment system",
  "filter": ["status = 'active'"],
  "sort": ["created_at:desc"]
}
```

---

## Why Both?

### Scenario 1: User searches "pay"
- **Meilisearch**: Finds "payment", "payroll", "payout" (typo tolerance)
- **pgvector**: Finds items about money, transactions, billing (semantic)

### Scenario 2: User searches "authentication"
- **Meilisearch**: Finds items with "auth", "login", "password" (keywords)
- **pgvector**: Finds items about security, access control, identity (meaning)

### Scenario 3: Complex search
- **Meilisearch**: Filter by status, type, date
- **pgvector**: Find semantically similar items

---

## TraceRTM Search Strategy

### Search Flow
1. **User enters query**: "payment processing"
2. **Meilisearch**: Fast keyword search → 50 results
3. **pgvector**: Semantic ranking → Top 10 by relevance
4. **Result**: Fast + Relevant

### Implementation
```go
// 1. Keyword search (fast)
results := meilisearch.Search("payment processing")

// 2. Semantic ranking (relevant)
for _, result := range results {
    score := pgvector.Similarity(result.Embedding, queryEmbedding)
    result.Score = score
}

// 3. Sort by score
sort.Slice(results, func(i, j int) bool {
    return results[i].Score > results[j].Score
})
```

---

## Cost Comparison

| Service | Cost | Capacity |
|---------|------|----------|
| **pgvector** | $0 (included in Supabase) | Unlimited |
| **Meilisearch** | $0 (free tier) | 100K documents |
| **Total** | $0 | Unlimited |

---

## Why NOT Replace pgvector with Meilisearch

### Meilisearch Limitations
- ❌ No semantic search (no embeddings)
- ❌ No similarity scoring
- ❌ No AI-powered recommendations
- ❌ Keyword-only search

### pgvector Limitations
- ❌ No typo tolerance
- ❌ No keyword filtering
- ❌ No faceted search
- ❌ Slower for exact matches

---

## Why NOT Replace Meilisearch with pgvector

### pgvector Limitations
- ❌ No typo tolerance
- ❌ No keyword filtering
- ❌ No faceted search
- ❌ Slower for exact matches

### Meilisearch Limitations
- ❌ No semantic search
- ❌ No similarity scoring
- ❌ No AI-powered recommendations
- ❌ Keyword-only search

---

## Recommendation

### ✅ **KEEP BOTH**

**Why:**
1. Different purposes (semantic vs keyword)
2. Complementary (fast + relevant)
3. Zero additional cost
4. Already integrated
5. Better user experience

### Search Quality Comparison

| Query | Meilisearch Only | pgvector Only | Both |
|-------|-----------------|---------------|------|
| "payment" | ✅ Fast | ⚠️ Slow | ✅ Fast + Relevant |
| "pay" (typo) | ✅ Finds it | ❌ Misses it | ✅ Finds it |
| "money" (semantic) | ❌ Misses it | ✅ Finds it | ✅ Finds it |
| Complex query | ⚠️ Limited | ⚠️ Limited | ✅ Perfect |

---

## Implementation Status

- ✅ pgvector: Integrated in Supabase
- ✅ Meilisearch: Docker container ready
- ✅ Both: Zero additional cost
- ✅ Both: Production-ready

**No changes needed. Your stack is optimal.**

