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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.
1.1 KiB
1.1 KiB
Haystack Deployment
Hayhooks
Hayhooks turns Haystack pipelines into REST APIs:
pip install hayhooks
hayhooks run # Starts server on port 1416
Deploy a pipeline:
# deploy.py
from hayhooks import deploy
deploy("my_pipeline.yaml") # Serialized pipeline YAML
# Then use curl:
# curl -X POST http://localhost:1416/my_pipeline \
# -H "Content-Type: application/json" \
# -d '{"text_embedder": {"text": "query"}}'
MCP Server
Hayhooks also exposes pipelines as MCP servers, enabling any MCP client to use your Haystack pipeline as a tool.
Containerization
FROM python:3.11-slim
RUN pip install haystack hayhooks
COPY pipelines/ /app/pipelines/
CMD ["hayhooks", "run", "--host", "0.0.0.0"]
Production Checklist
- Use a production document store (not InMemory)
- Separate indexing and query pipelines
- Set up Hayhooks for REST API access
- Add evaluation pipeline for monitoring
- Containerize with Docker
- Configure logging and error tracking
- Set up model caching to avoid reloading on every request