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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.
31 lines
1.3 KiB
Python
31 lines
1.3 KiB
Python
#!/usr/bin/env python3
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"""Haystack query pipeline — retrieve, prompt, generate."""
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from haystack import Pipeline
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from haystack.components.embedders import SentenceTransformersTextEmbedder
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from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
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from haystack.components.builders import PromptBuilder
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from haystack.components.generators import OpenAIGenerator
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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# Assume document_store already has documents
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document_store = InMemoryDocumentStore()
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pipeline = Pipeline()
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pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
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pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store, top_k=5))
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pipeline.add_component("prompt_builder", PromptBuilder(
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template="Answer based on the context.\n\nContext: {{documents}}\n\nQuestion: {{question}}\nAnswer:"
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))
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pipeline.add_component("generator", OpenAIGenerator())
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pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
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pipeline.connect("retriever.documents", "prompt_builder.documents")
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pipeline.connect("prompt_builder", "generator")
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result = pipeline.run({
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"text_embedder": {"text": "What is Haystack?"},
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"prompt_builder": {"question": "What is Haystack?"}
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})
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print(result["generator"]["replies"][0])
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