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362a075 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | # SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
import json
import os
import pytest
from haystack import Document, Pipeline
from haystack.components.builders.answer_builder import AnswerBuilder
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
from haystack.components.generators import OpenAIGenerator
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever, InMemoryEmbeddingRetriever
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
@pytest.mark.skipif(
not os.environ.get("OPENAI_API_KEY", None),
reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
)
def test_bm25_rag_pipeline(tmp_path):
# Create the RAG pipeline
prompt_template = """
Given these documents, answer the question.\nDocuments:
{% for doc in documents %}
{{ doc.content }}
{% endfor %}
\nQuestion: {{question}}
\nAnswer:
"""
rag_pipeline = Pipeline()
rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=InMemoryDocumentStore()), name="retriever")
rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")
rag_pipeline.connect("llm.replies", "answer_builder.replies")
rag_pipeline.connect("llm.meta", "answer_builder.meta")
rag_pipeline.connect("retriever", "answer_builder.documents")
# Draw the pipeline
rag_pipeline.draw(tmp_path / "test_bm25_rag_pipeline.png")
# Serialize the pipeline to YAML
with open(tmp_path / "test_bm25_rag_pipeline.yaml", "w") as f:
rag_pipeline.dump(f)
# Load the pipeline back
with open(tmp_path / "test_bm25_rag_pipeline.yaml", "r") as f:
rag_pipeline = Pipeline.load(f)
# Populate the document store
documents = [
Document(content="My name is Jean and I live in Paris."),
Document(content="My name is Mark and I live in Berlin."),
Document(content="My name is Giorgio and I live in Rome."),
]
rag_pipeline.get_component("retriever").document_store.write_documents(documents)
# Query and assert
questions = ["Who lives in Paris?", "Who lives in Berlin?", "Who lives in Rome?"]
answers_spywords = ["Jean", "Mark", "Giorgio"]
for question, spyword in zip(questions, answers_spywords):
result = rag_pipeline.run(
{
"retriever": {"query": question},
"prompt_builder": {"question": question},
"answer_builder": {"query": question},
}
)
assert len(result["answer_builder"]["answers"]) == 1
generated_answer = result["answer_builder"]["answers"][0]
assert spyword in generated_answer.data
assert generated_answer.query == question
assert hasattr(generated_answer, "documents")
assert hasattr(generated_answer, "meta")
@pytest.mark.skipif(
not os.environ.get("OPENAI_API_KEY", None),
reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
)
def test_embedding_retrieval_rag_pipeline(tmp_path):
# Create the RAG pipeline
prompt_template = """
Given these documents, answer the question.\nDocuments:
{% for doc in documents %}
{{ doc.content }}
{% endfor %}
\nQuestion: {{question}}
\nAnswer:
"""
rag_pipeline = Pipeline()
rag_pipeline.add_component(
instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
)
rag_pipeline.add_component(
instance=InMemoryEmbeddingRetriever(document_store=InMemoryDocumentStore()), name="retriever"
)
rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
rag_pipeline.connect("text_embedder", "retriever")
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")
rag_pipeline.connect("llm.replies", "answer_builder.replies")
rag_pipeline.connect("llm.meta", "answer_builder.meta")
rag_pipeline.connect("retriever", "answer_builder.documents")
# Draw the pipeline
rag_pipeline.draw(tmp_path / "test_embedding_rag_pipeline.png")
# Serialize the pipeline to JSON
with open(tmp_path / "test_embedding_rag_pipeline.json", "w") as f:
json.dump(rag_pipeline.to_dict(), f)
# Load the pipeline back
with open(tmp_path / "test_embedding_rag_pipeline.json", "r") as f:
rag_pipeline = Pipeline.from_dict(json.load(f))
# Populate the document store
documents = [
Document(content="My name is Jean and I live in Paris."),
Document(content="My name is Mark and I live in Berlin."),
Document(content="My name is Giorgio and I live in Rome."),
]
document_store = rag_pipeline.get_component("retriever").document_store
indexing_pipeline = Pipeline()
indexing_pipeline.add_component(
instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
name="document_embedder",
)
indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="document_writer")
indexing_pipeline.connect("document_embedder", "document_writer")
indexing_pipeline.run({"document_embedder": {"documents": documents}})
# Query and assert
questions = ["Who lives in Paris?", "Who lives in Berlin?", "Who lives in Rome?"]
answers_spywords = ["Jean", "Mark", "Giorgio"]
for question, spyword in zip(questions, answers_spywords):
result = rag_pipeline.run(
{
"text_embedder": {"text": question},
"prompt_builder": {"question": question},
"answer_builder": {"query": question},
}
)
assert len(result["answer_builder"]["answers"]) == 1
generated_answer = result["answer_builder"]["answers"][0]
assert spyword in generated_answer.data
assert generated_answer.query == question
assert hasattr(generated_answer, "documents")
assert hasattr(generated_answer, "meta")
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