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Magnus Hedemark
fe5b275d00
Greenfield SkillOpt: 3 epochs for deepset Haystack skill. Pipeline DAG model, document stores, retrievers, evaluation, deployment. Epoch 1 — Prominence: Hard-gate on Pipeline DAG vs LCEL pipe model Epoch 2 — Decision Guidance: Where to Start, Framework Routing Guide Epoch 3 — Pattern Expansion: Hybrid RAG pattern, evaluation pipeline, deployment 11 files: SKILL.md, 6 references, 3 templates, 1 script.
46 lines
1.1 KiB
Markdown
46 lines
1.1 KiB
Markdown
# Haystack Deployment
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## Hayhooks
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Hayhooks turns Haystack pipelines into REST APIs:
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```bash
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pip install hayhooks
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hayhooks run # Starts server on port 1416
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```
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Deploy a pipeline:
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```python
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# deploy.py
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from hayhooks import deploy
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deploy("my_pipeline.yaml") # Serialized pipeline YAML
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# Then use curl:
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# curl -X POST http://localhost:1416/my_pipeline \
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# -H "Content-Type: application/json" \
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# -d '{"text_embedder": {"text": "query"}}'
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```
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## MCP Server
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Hayhooks also exposes pipelines as MCP servers, enabling any MCP client to use your Haystack pipeline as a tool.
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## Containerization
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```dockerfile
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FROM python:3.11-slim
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RUN pip install haystack hayhooks
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COPY pipelines/ /app/pipelines/
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CMD ["hayhooks", "run", "--host", "0.0.0.0"]
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```
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## Production Checklist
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- [ ] Use a production document store (not InMemory)
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- [ ] Separate indexing and query pipelines
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- [ ] Set up Hayhooks for REST API access
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- [ ] Add evaluation pipeline for monitoring
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- [ ] Containerize with Docker
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- [ ] Configure logging and error tracking
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- [ ] Set up model caching to avoid reloading on every request
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