mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-10-31 09:49:48 +00:00
271 lines
10 KiB
Python
271 lines
10 KiB
Python
from unittest.mock import patch
|
|
from typing import List, cast
|
|
|
|
import pytest
|
|
import numpy as np
|
|
import openai
|
|
from openai.util import convert_to_openai_object
|
|
from openai.openai_object import OpenAIObject
|
|
|
|
from haystack import Document
|
|
from haystack.components.embedders.openai_document_embedder import OpenAIDocumentEmbedder
|
|
|
|
|
|
def mock_openai_response(input: List[str], model: str = "text-embedding-ada-002", **kwargs) -> OpenAIObject:
|
|
dict_response = {
|
|
"object": "list",
|
|
"data": [
|
|
{"object": "embedding", "index": i, "embedding": np.random.rand(1536).tolist()} for i in range(len(input))
|
|
],
|
|
"model": model,
|
|
"usage": {"prompt_tokens": 4, "total_tokens": 4},
|
|
}
|
|
|
|
return cast(OpenAIObject, convert_to_openai_object(dict_response))
|
|
|
|
|
|
class TestOpenAIDocumentEmbedder:
|
|
def test_init_default(self, monkeypatch):
|
|
openai.api_key = None
|
|
monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key")
|
|
embedder = OpenAIDocumentEmbedder()
|
|
|
|
assert openai.api_key == "fake-api-key"
|
|
|
|
assert embedder.model_name == "text-embedding-ada-002"
|
|
assert embedder.organization is None
|
|
assert embedder.prefix == ""
|
|
assert embedder.suffix == ""
|
|
assert embedder.batch_size == 32
|
|
assert embedder.progress_bar is True
|
|
assert embedder.metadata_fields_to_embed == []
|
|
assert embedder.embedding_separator == "\n"
|
|
|
|
def test_init_with_parameters(self):
|
|
embedder = OpenAIDocumentEmbedder(
|
|
api_key="fake-api-key",
|
|
model_name="model",
|
|
organization="my-org",
|
|
prefix="prefix",
|
|
suffix="suffix",
|
|
batch_size=64,
|
|
progress_bar=False,
|
|
metadata_fields_to_embed=["test_field"],
|
|
embedding_separator=" | ",
|
|
)
|
|
assert openai.api_key == "fake-api-key"
|
|
assert openai.organization == "my-org"
|
|
|
|
assert embedder.organization == "my-org"
|
|
assert embedder.model_name == "model"
|
|
assert embedder.prefix == "prefix"
|
|
assert embedder.suffix == "suffix"
|
|
assert embedder.batch_size == 64
|
|
assert embedder.progress_bar is False
|
|
assert embedder.metadata_fields_to_embed == ["test_field"]
|
|
assert embedder.embedding_separator == " | "
|
|
|
|
def test_init_fail_wo_api_key(self, monkeypatch):
|
|
openai.api_key = None
|
|
monkeypatch.delenv("OPENAI_API_KEY", raising=False)
|
|
with pytest.raises(ValueError, match="OpenAIDocumentEmbedder expects an OpenAI API key"):
|
|
OpenAIDocumentEmbedder()
|
|
|
|
def test_to_dict(self):
|
|
component = OpenAIDocumentEmbedder(api_key="fake-api-key")
|
|
data = component.to_dict()
|
|
assert data == {
|
|
"type": "haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder",
|
|
"init_parameters": {
|
|
"model_name": "text-embedding-ada-002",
|
|
"organization": None,
|
|
"prefix": "",
|
|
"suffix": "",
|
|
"batch_size": 32,
|
|
"progress_bar": True,
|
|
"metadata_fields_to_embed": [],
|
|
"embedding_separator": "\n",
|
|
},
|
|
}
|
|
|
|
def test_to_dict_with_custom_init_parameters(self):
|
|
component = OpenAIDocumentEmbedder(
|
|
api_key="fake-api-key",
|
|
model_name="model",
|
|
organization="my-org",
|
|
prefix="prefix",
|
|
suffix="suffix",
|
|
batch_size=64,
|
|
progress_bar=False,
|
|
metadata_fields_to_embed=["test_field"],
|
|
embedding_separator=" | ",
|
|
)
|
|
data = component.to_dict()
|
|
assert data == {
|
|
"type": "haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder",
|
|
"init_parameters": {
|
|
"model_name": "model",
|
|
"organization": "my-org",
|
|
"prefix": "prefix",
|
|
"suffix": "suffix",
|
|
"batch_size": 64,
|
|
"progress_bar": False,
|
|
"metadata_fields_to_embed": ["test_field"],
|
|
"embedding_separator": " | ",
|
|
},
|
|
}
|
|
|
|
def test_prepare_texts_to_embed_w_metadata(self):
|
|
documents = [
|
|
Document(content=f"document number {i}:\ncontent", meta={"meta_field": f"meta_value {i}"}) for i in range(5)
|
|
]
|
|
|
|
embedder = OpenAIDocumentEmbedder(
|
|
api_key="fake-api-key", metadata_fields_to_embed=["meta_field"], embedding_separator=" | "
|
|
)
|
|
|
|
prepared_texts = embedder._prepare_texts_to_embed(documents)
|
|
|
|
# note that newline is replaced by space
|
|
assert prepared_texts == [
|
|
"meta_value 0 | document number 0: content",
|
|
"meta_value 1 | document number 1: content",
|
|
"meta_value 2 | document number 2: content",
|
|
"meta_value 3 | document number 3: content",
|
|
"meta_value 4 | document number 4: content",
|
|
]
|
|
|
|
def test_prepare_texts_to_embed_w_suffix(self):
|
|
documents = [Document(content=f"document number {i}") for i in range(5)]
|
|
|
|
embedder = OpenAIDocumentEmbedder(api_key="fake-api-key", prefix="my_prefix ", suffix=" my_suffix")
|
|
|
|
prepared_texts = embedder._prepare_texts_to_embed(documents)
|
|
|
|
assert prepared_texts == [
|
|
"my_prefix document number 0 my_suffix",
|
|
"my_prefix document number 1 my_suffix",
|
|
"my_prefix document number 2 my_suffix",
|
|
"my_prefix document number 3 my_suffix",
|
|
"my_prefix document number 4 my_suffix",
|
|
]
|
|
|
|
def test_embed_batch(self):
|
|
texts = ["text 1", "text 2", "text 3", "text 4", "text 5"]
|
|
|
|
with patch("haystack.components.embedders.openai_document_embedder.openai.Embedding") as openai_embedding_patch:
|
|
openai_embedding_patch.create.side_effect = mock_openai_response
|
|
embedder = OpenAIDocumentEmbedder(api_key="fake-api-key", model_name="model")
|
|
|
|
embeddings, metadata = embedder._embed_batch(texts_to_embed=texts, batch_size=2)
|
|
|
|
assert openai_embedding_patch.create.call_count == 3
|
|
|
|
assert isinstance(embeddings, list)
|
|
assert len(embeddings) == len(texts)
|
|
for embedding in embeddings:
|
|
assert isinstance(embedding, list)
|
|
assert len(embedding) == 1536
|
|
assert all(isinstance(x, float) for x in embedding)
|
|
|
|
# openai.Embedding.create is called 3 times
|
|
assert metadata == {"model": "model", "usage": {"prompt_tokens": 3 * 4, "total_tokens": 3 * 4}}
|
|
|
|
def test_run(self):
|
|
docs = [
|
|
Document(content="I love cheese", meta={"topic": "Cuisine"}),
|
|
Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
|
|
]
|
|
|
|
model = "text-similarity-ada-001"
|
|
with patch("haystack.components.embedders.openai_document_embedder.openai.Embedding") as openai_embedding_patch:
|
|
openai_embedding_patch.create.side_effect = mock_openai_response
|
|
embedder = OpenAIDocumentEmbedder(
|
|
api_key="fake-api-key",
|
|
model_name=model,
|
|
prefix="prefix ",
|
|
suffix=" suffix",
|
|
metadata_fields_to_embed=["topic"],
|
|
embedding_separator=" | ",
|
|
)
|
|
|
|
result = embedder.run(documents=docs)
|
|
|
|
openai_embedding_patch.create.assert_called_once_with(
|
|
model=model,
|
|
input=[
|
|
"prefix Cuisine | I love cheese suffix",
|
|
"prefix ML | A transformer is a deep learning architecture suffix",
|
|
],
|
|
)
|
|
documents_with_embeddings = result["documents"]
|
|
metadata = result["metadata"]
|
|
|
|
assert isinstance(documents_with_embeddings, list)
|
|
assert len(documents_with_embeddings) == len(docs)
|
|
for doc in documents_with_embeddings:
|
|
assert isinstance(doc, Document)
|
|
assert isinstance(doc.embedding, list)
|
|
assert len(doc.embedding) == 1536
|
|
assert all(isinstance(x, float) for x in doc.embedding)
|
|
assert metadata == {"model": model, "usage": {"prompt_tokens": 4, "total_tokens": 4}}
|
|
|
|
def test_run_custom_batch_size(self):
|
|
docs = [
|
|
Document(content="I love cheese", meta={"topic": "Cuisine"}),
|
|
Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
|
|
]
|
|
|
|
model = "text-similarity-ada-001"
|
|
with patch("haystack.components.embedders.openai_document_embedder.openai.Embedding") as openai_embedding_patch:
|
|
openai_embedding_patch.create.side_effect = mock_openai_response
|
|
embedder = OpenAIDocumentEmbedder(
|
|
api_key="fake-api-key",
|
|
model_name=model,
|
|
prefix="prefix ",
|
|
suffix=" suffix",
|
|
metadata_fields_to_embed=["topic"],
|
|
embedding_separator=" | ",
|
|
batch_size=1,
|
|
)
|
|
|
|
result = embedder.run(documents=docs)
|
|
|
|
assert openai_embedding_patch.create.call_count == 2
|
|
|
|
documents_with_embeddings = result["documents"]
|
|
metadata = result["metadata"]
|
|
|
|
assert isinstance(documents_with_embeddings, list)
|
|
assert len(documents_with_embeddings) == len(docs)
|
|
for doc in documents_with_embeddings:
|
|
assert isinstance(doc, Document)
|
|
assert isinstance(doc.embedding, list)
|
|
assert len(doc.embedding) == 1536
|
|
assert all(isinstance(x, float) for x in doc.embedding)
|
|
|
|
# openai.Embedding.create is called 2 times
|
|
assert metadata == {"model": model, "usage": {"prompt_tokens": 2 * 4, "total_tokens": 2 * 4}}
|
|
|
|
def test_run_wrong_input_format(self):
|
|
embedder = OpenAIDocumentEmbedder(api_key="fake-api-key")
|
|
|
|
# wrong formats
|
|
string_input = "text"
|
|
list_integers_input = [1, 2, 3]
|
|
|
|
with pytest.raises(TypeError, match="OpenAIDocumentEmbedder expects a list of Documents as input"):
|
|
embedder.run(documents=string_input)
|
|
|
|
with pytest.raises(TypeError, match="OpenAIDocumentEmbedder expects a list of Documents as input"):
|
|
embedder.run(documents=list_integers_input)
|
|
|
|
def test_run_on_empty_list(self):
|
|
embedder = OpenAIDocumentEmbedder(api_key="fake-api-key")
|
|
|
|
empty_list_input = []
|
|
result = embedder.run(documents=empty_list_input)
|
|
|
|
assert result["documents"] is not None
|
|
assert not result["documents"] # empty list
|