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Magnus Hedemark fe5b275d00 feat: add haystack — expert skill for production search pipelines
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.
2026-07-09 14:53:43 -04:00

36 lines
1.7 KiB
Python

#!/usr/bin/env python3
"""Hybrid RAG pipeline — BM25 + embedding in parallel."""
from haystack import Pipeline
from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever, InMemoryBM25Retriever
from haystack.components.joiners import DocumentJoiner
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack.document_stores.in_memory import InMemoryDocumentStore
document_store = InMemoryDocumentStore()
pipeline = Pipeline()
pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
pipeline.add_component("bm25_retriever", InMemoryBM25Retriever(document_store=document_store, top_k=5))
pipeline.add_component("embedding_retriever", InMemoryEmbeddingRetriever(document_store=document_store, top_k=5))
pipeline.add_component("joiner", DocumentJoiner(join_mode="merge"))
pipeline.add_component("prompt_builder", PromptBuilder(
template="Context:\n{{documents}}\n\nQuestion: {{question}}\nAnswer:"
))
pipeline.add_component("generator", OpenAIGenerator())
pipeline.connect("text_embedder.embedding", "embedding_retriever.query_embedding")
pipeline.connect("bm25_retriever.documents", "joiner.documents")
pipeline.connect("embedding_retriever.documents", "joiner.documents")
pipeline.connect("joiner.documents", "prompt_builder.documents")
pipeline.connect("prompt_builder", "generator")
result = pipeline.run({
"text_embedder": {"text": "hybrid search query"},
"bm25_retriever": {"query": "hybrid search query"},
"prompt_builder": {"question": "hybrid search query"}
})
print(result["generator"]["replies"][0])