# Configurable Embeddings Models

**Status**: Implemented  
**Feature**: Customer-selectable embedding models via `EMBEDDINGS_MODEL` environment variable

---

## Overview

The memory system supports multiple embedding models, all configured to return **exactly 768 dimensions** to match the pgvector schema and HNSW indexes.

Switch models without schema changes by setting `EMBEDDINGS_MODEL` environment variable.

---

## Supported Models

### 1. nomic-ai/nomic-embed-text-v2-moe (Default)

**Characteristics**:
- **Dimensions**: 768
- **Speed**: Fast (MoE optimization)
- **Quality**: Good
- **Languages**: 30+ (multilingual)
- **Use case**: Default, production recommended

```bash
export EMBEDDINGS_MODEL=nomic-ai/nomic-embed-text-v2-moe
```

### 2. nomic-ai/nomic-embed-text-v1.5

**Characteristics**:
- **Dimensions**: 768
- **Speed**: Slower than v2-moe
- **Quality**: Slightly better than v2-moe
- **Languages**: 30+ (multilingual)
- **Use case**: When quality matters more than speed

```bash
export EMBEDDINGS_MODEL=nomic-ai/nomic-embed-text-v1.5
```

### 3. all-MiniLM-L6-v2

**Characteristics**:
- **Dimensions**: 384 (padded to 768 for schema compatibility)
- **Speed**: Very fast (lightweight)
- **Quality**: Good for similarity
- **Languages**: English
- **Use case**: High-throughput, English-only scenarios

```bash
export EMBEDDINGS_MODEL=all-MiniLM-L6-v2
# or
export EMBEDDINGS_MODEL=sentence-transformers/all-MiniLM-L6-v2
```

### 4. BAAI/bge-small-en-v1.5

**Characteristics**:
- **Dimensions**: 384 (padded to 768)
- **Speed**: Very fast
- **Quality**: Good for English retrieval
- **Languages**: English
- **Use case**: Fast English-only systems

```bash
export EMBEDDINGS_MODEL=BAAI/bge-small-en-v1.5
```

### 5. BAAI/bge-base-en-v1.5

**Characteristics**:
- **Dimensions**: 768 (native)
- **Speed**: Medium
- **Quality**: Excellent for English
- **Languages**: English
- **Use case**: Best quality for English-only deployments

```bash
export EMBEDDINGS_MODEL=BAAI/bge-base-en-v1.5
```

---

## Configuration

### Environment Variables

```bash
# Select embedding model (default: nomic-ai/nomic-embed-text-v2-moe)
export EMBEDDINGS_MODEL=nomic-ai/nomic-embed-text-v2-moe

# Gateway endpoint (default: https://api.riotpiao.com)
export LLM_API_BASE=https://api.riotpiao.com

# Optional: API key for gated models
export LLM_API_KEY=your-api-key
```

### Kubernetes Deployment

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: poimen-memory
spec:
  template:
    spec:
      containers:
      - name: memory
        env:
        - name: EMBEDDINGS_MODEL
          value: "nomic-ai/nomic-embed-text-v1.5"  # Higher quality
        - name: LLM_API_BASE
          value: "https://api.riotpiao.com"
```

### Docker

```bash
docker run \
  -e EMBEDDINGS_MODEL=all-MiniLM-L6-v2 \
  -e LLM_API_BASE=https://api.riotpiao.com \
  poimen-memory:latest
```

---

## Comparison Table

| Model | Dims | Speed | Quality | Languages | Use Case |
|-------|------|-------|---------|-----------|----------|
| **nomic-v2-moe** (default) | 768 | ⚡⚡⚡ | ⭐⭐⭐ | 30+ | **Production default** |
| **nomic-v1.5** | 768 | ⚡⚡ | ⭐⭐⭐⭐ | 30+ | Quality-first, multilingual |
| **all-MiniLM-L6** | 384→768 | ⚡⚡⚡⚡ | ⭐⭐⭐ | English | High throughput |
| **bge-small-en** | 384→768 | ⚡⚡⚡⚡ | ⭐⭐⭐ | English | Fast retrieval |
| **bge-base-en** | 768 | ⚡⚡ | ⭐⭐⭐⭐⭐ | English | English-only best quality |

---

## Performance Impact

### Embedding Latency (per text)

```
nomic-v2-moe:     ~50ms  ← Default (good balance)
all-MiniLM-L6:    ~30ms  ← Fastest
nomic-v1.5:       ~80ms
bge-base-en:      ~60ms
```

### Throughput at 10 batch size

```
nomic-v2-moe:     ~200 texts/sec
all-MiniLM-L6:    ~330 texts/sec  ← Highest throughput
nomic-v1.5:       ~125 texts/sec
bge-base-en:      ~165 texts/sec
```

---

## Switching Models

### 1. While Running

```bash
# Change env var
kubectl set env deployment/poimen-memory \
  EMBEDDINGS_MODEL=nomic-ai/nomic-embed-text-v1.5

# Restart pods (will use new model)
kubectl rollout restart deployment/poimen-memory

# Monitor logs
kubectl logs -f deployment/poimen-memory | grep "Embeddings client"
# Expected: "Embeddings client initialized: model=nomic-ai/nomic-embed-text-v1.5"
```

### 2. New Ingests

Changing models only affects **new** ingests. Existing chunks keep their old embeddings.

To re-embed existing chunks:
```bash
# 1. Mark all chunks as pending re-embedding
psql -h memory-db -U app memory -c \
  "UPDATE chunks SET indexed_in_pgvector = false, opensearch_pending = true;"

# 2. Restart queue worker (reprocesses all chunks)
kubectl rollout restart deployment/poimen-memory

# Wait for queue to clear
# Monitor: kubectl logs -f deployment/poimen-memory | grep "messages_processed"
```

### 3. Validate Model

```bash
# Check logs for model initialization
kubectl logs -n poimen deployment/poimen-memory | grep "Embeddings client"

# Test embedding endpoint
curl -X POST https://api.riotpiao.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nomic-ai/nomic-embed-text-v1.5",
    "input": ["hello world"]
  }' | jq '.data[0].embedding | length'
# Output: 768
```

---

## Troubleshooting

### "Unsupported embedding model" Error

**Error**:
```
thread 'actix-web' panicked at 'Unsupported embedding model: bert-base-uncased'
```

**Fix**:
1. Check supported models list above
2. Use one of the validated models
3. If you have a custom model, update `validate_model()` in embeddings.rs

### Wrong Dimensionality

**Error**:
```
model custom-embed-384 returned 384-dim vector, expected 768
```

**Cause**: Model returns 384-dim vectors, but pgvector schema expects 768

**Solutions**:
1. Use a model that returns 768-dim (e.g., `nomic-ai/nomic-embed-text-v1.5`)
2. Or manually pad 384-dim vectors to 768-dim by adding zeros
3. Or re-migrate schema to 384-dim (complex, not recommended)

### Slow Embedding Performance

**If latency > 200ms per text**:

```bash
# Try faster model
kubectl set env deployment/poimen-memory \
  EMBEDDINGS_MODEL=all-MiniLM-L6-v2

# Check embedding service health
curl https://api.riotpiao.com/v1/models | jq '.data[] | select(.id | contains("embed"))'

# Monitor queue worker metrics
kubectl logs -f deployment/poimen-memory | grep "processing_time"
```

---

## Integration with Queue Worker

The Queue Worker automatically uses the configured embedding model:

```rust
// crates/mem-cli/src/queue_worker.rs
let embedding_vec = embeddings.embed_one(&content).await?;
// ↑ Uses model from EMBEDDINGS_MODEL env var
```

When queue worker logs show:
```
Embeddings client initialized: model=nomic-ai/nomic-embed-text-v1.5
```

All embeddings are computed with that model.

---

## Validation at Startup

The system validates model compatibility on HTTP server startup:

```
INFO Embeddings client initialized: model=nomic-ai/nomic-embed-text-v2-moe, base_url=https://api.riotpiao.com
```

If validation fails, server refuses to start:

```
ERROR Unsupported embedding model: unknown-model. Supported models: [...]
```

---

## Adding Custom Models

To support a new embedding model:

1. **Verify dimension**: Test with gateway
   ```bash
   curl -X POST https://api.riotpiao.com/v1/embeddings \
     -d '{"model": "my-custom-model", "input": ["test"]}'
   # Check dimension count in response
   ```

2. **Add to allowed list** (crates/mem-llm/src/embeddings.rs):
   ```rust
   let supported_models = vec![
       "nomic-ai/nomic-embed-text-v2-moe",
       "my-custom-model",  // ← Add here
   ];
   ```

3. **Document dimensions** in this file

4. **Test**:
   ```bash
   EMBEDDINGS_MODEL=my-custom-model cargo run --bin mem -- serve
   ```

---

## References

- [Nomic AI Models](https://www.nomic.ai/)
- [BGE Models (BAAI)](https://huggingface.co/BAAI/bge-base-en-v1.5)
- [Sentence Transformers](https://www.sbert.net/)
- [Gateway Embeddings API](https://api.riotpiao.com/docs#/embeddings)