# Kubeflow Pipelines

Kubeflow Pipeline definitions for the DaviesTechLabs AI/ML platform.

## Pipelines

| Pipeline | Description | Output |
|----------|-------------|--------|
| `voice_pipeline.py` | STT → RAG → LLM → TTS | voice_pipeline.yaml |
| `document_ingestion_pipeline.py` | Ingest docs → chunk → embed → Milvus | document_ingestion.yaml |
| `document_ingestion_mlflow_pipeline.py` | Same with MLflow tracking | document_ingestion_mlflow.yaml |
| `evaluation_pipeline.py` | Evaluate models against benchmarks | evaluation.yaml |

## Usage

### Compile Pipelines

```bash
pip install kfp==2.12.1

# Compile all
python voice_pipeline.py
python document_ingestion_pipeline.py
python evaluation_pipeline.py

# Or compile individually
python -c "from kfp import compiler; from voice_pipeline import voice_assistant_pipeline; compiler.Compiler().compile(voice_assistant_pipeline, 'voice.yaml')"
```

### Upload to Kubeflow

Upload the generated `.yaml` files to Kubeflow Pipelines UI or use the SDK:

```python
import kfp
client = kfp.Client(host='http://kubeflow.ai-ml.svc.cluster.local/pipeline')
client.upload_pipeline('voice_pipeline.yaml', pipeline_name='Voice Assistant')
```

## Pipeline Details

### voice_pipeline

Full voice assistant with RAG:
1. Transcribe audio (Whisper)
2. Generate embeddings (BGE)
3. Search Milvus
4. Rerank documents (BGE Reranker)
5. Generate response (vLLM)
6. Synthesize speech (XTTS)

### document_ingestion_pipeline

Ingest documents into vector DB:
1. Extract text (PDF, DOCX, HTML, TXT)
2. Chunk with overlap (tiktoken)
3. Generate embeddings
4. Store in Milvus collection

### evaluation_pipeline

Benchmark model quality:
1. Load eval dataset (MMLU, etc.)
2. Run inference
3. Calculate metrics (accuracy, F1)
4. Log to MLflow

## Integration

### kfp-sync-job.yaml

Kubernetes Job to sync compiled pipelines to Kubeflow:

```bash
kubectl apply -f kfp-sync-job.yaml
```

### From Argo Workflows

Pipelines can be triggered from Argo via the kfp-integration workflow in the [argo](https://git.daviestechlabs.io/daviestechlabs/argo) repo.

## Service Endpoints

| Service | Endpoint |
|---------|----------|
| Whisper STT | `http://whisper-predictor.ai-ml.svc.cluster.local` |
| Embeddings | `http://embeddings-predictor.ai-ml.svc.cluster.local` |
| Reranker | `http://reranker-predictor.ai-ml.svc.cluster.local` |
| vLLM | `http://llm-draft.ai-ml.svc.cluster.local:8000` |
| TTS | `http://tts-predictor.ai-ml.svc.cluster.local` |
| Milvus | `milvus.ai-ml.svc.cluster.local:19530` |

## Related

- [argo](https://git.daviestechlabs.io/daviestechlabs/argo) - Argo Workflows for training
- [voice-assistant](https://git.daviestechlabs.io/daviestechlabs/voice-assistant) - Real-time voice handler
- [homelab-design](https://git.daviestechlabs.io/daviestechlabs/homelab-design) - Architecture docs