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https://github.com/deepset-ai/haystack.git
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ce0917e586
* Add raise_on_failure to OpenAIDocumentEmbedder * Add reno * Add parameter to Azure Doc embedder as well * Fix bug * Update reno * PR comments * update reno
340 lines
14 KiB
Python
340 lines
14 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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import os
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import random
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from typing import List
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from unittest.mock import Mock, patch
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import pytest
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from openai import APIError
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from haystack import Document
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from haystack.components.embedders.openai_document_embedder import OpenAIDocumentEmbedder
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from haystack.utils.auth import Secret
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def mock_openai_response(input: List[str], model: str = "text-embedding-ada-002", **kwargs) -> dict:
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dict_response = {
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"object": "list",
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"data": [
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{"object": "embedding", "index": i, "embedding": [random.random() for _ in range(1536)]}
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for i in range(len(input))
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],
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"model": model,
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"usage": {"prompt_tokens": 4, "total_tokens": 4},
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}
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return dict_response
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class TestOpenAIDocumentEmbedder:
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def test_init_default(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key")
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embedder = OpenAIDocumentEmbedder()
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assert embedder.api_key.resolve_value() == "fake-api-key"
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assert embedder.model == "text-embedding-ada-002"
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assert embedder.organization is None
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assert embedder.prefix == ""
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assert embedder.suffix == ""
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assert embedder.batch_size == 32
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assert embedder.progress_bar is True
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assert embedder.meta_fields_to_embed == []
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assert embedder.embedding_separator == "\n"
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assert embedder.client.max_retries == 5
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assert embedder.client.timeout == 30.0
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def test_init_with_parameters(self, monkeypatch):
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monkeypatch.setenv("OPENAI_TIMEOUT", "100")
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monkeypatch.setenv("OPENAI_MAX_RETRIES", "10")
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embedder = OpenAIDocumentEmbedder(
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api_key=Secret.from_token("fake-api-key-2"),
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model="model",
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organization="my-org",
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prefix="prefix",
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suffix="suffix",
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batch_size=64,
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progress_bar=False,
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meta_fields_to_embed=["test_field"],
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embedding_separator=" | ",
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timeout=40.0,
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max_retries=1,
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)
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assert embedder.api_key.resolve_value() == "fake-api-key-2"
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assert embedder.organization == "my-org"
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assert embedder.model == "model"
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assert embedder.prefix == "prefix"
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assert embedder.suffix == "suffix"
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assert embedder.batch_size == 64
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assert embedder.progress_bar is False
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assert embedder.meta_fields_to_embed == ["test_field"]
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assert embedder.embedding_separator == " | "
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assert embedder.client.max_retries == 1
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assert embedder.client.timeout == 40.0
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def test_init_with_parameters_and_env_vars(self, monkeypatch):
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monkeypatch.setenv("OPENAI_TIMEOUT", "100")
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monkeypatch.setenv("OPENAI_MAX_RETRIES", "10")
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embedder = OpenAIDocumentEmbedder(
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api_key=Secret.from_token("fake-api-key-2"),
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model="model",
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organization="my-org",
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prefix="prefix",
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suffix="suffix",
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batch_size=64,
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progress_bar=False,
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meta_fields_to_embed=["test_field"],
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embedding_separator=" | ",
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)
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assert embedder.api_key.resolve_value() == "fake-api-key-2"
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assert embedder.organization == "my-org"
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assert embedder.model == "model"
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assert embedder.prefix == "prefix"
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assert embedder.suffix == "suffix"
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assert embedder.batch_size == 64
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assert embedder.progress_bar is False
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assert embedder.meta_fields_to_embed == ["test_field"]
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assert embedder.embedding_separator == " | "
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assert embedder.client.max_retries == 10
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assert embedder.client.timeout == 100.0
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def test_init_fail_wo_api_key(self, monkeypatch):
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monkeypatch.delenv("OPENAI_API_KEY", raising=False)
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with pytest.raises(ValueError, match="None of the .* environment variables are set"):
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OpenAIDocumentEmbedder()
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def test_to_dict(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key")
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component = OpenAIDocumentEmbedder()
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder",
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"init_parameters": {
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"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
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"api_base_url": None,
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"model": "text-embedding-ada-002",
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"dimensions": None,
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"organization": None,
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"http_client_kwargs": None,
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"prefix": "",
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"suffix": "",
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"batch_size": 32,
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"progress_bar": True,
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"meta_fields_to_embed": [],
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"embedding_separator": "\n",
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"timeout": None,
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"max_retries": None,
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"raise_on_failure": False,
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},
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}
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def test_to_dict_with_custom_init_parameters(self, monkeypatch):
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monkeypatch.setenv("ENV_VAR", "fake-api-key")
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component = OpenAIDocumentEmbedder(
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api_key=Secret.from_env_var("ENV_VAR", strict=False),
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model="model",
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organization="my-org",
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http_client_kwargs={"proxy": "http://localhost:8080"},
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prefix="prefix",
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suffix="suffix",
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batch_size=64,
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progress_bar=False,
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meta_fields_to_embed=["test_field"],
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embedding_separator=" | ",
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timeout=10.0,
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max_retries=2,
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raise_on_failure=True,
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)
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder",
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"init_parameters": {
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"api_key": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
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"api_base_url": None,
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"model": "model",
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"dimensions": None,
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"organization": "my-org",
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"http_client_kwargs": {"proxy": "http://localhost:8080"},
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"prefix": "prefix",
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"suffix": "suffix",
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"batch_size": 64,
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"progress_bar": False,
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"meta_fields_to_embed": ["test_field"],
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"embedding_separator": " | ",
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"timeout": 10.0,
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"max_retries": 2,
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"raise_on_failure": True,
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},
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}
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def test_prepare_texts_to_embed_w_metadata(self):
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documents = [
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Document(id=f"{i}", content=f"document number {i}:\ncontent", meta={"meta_field": f"meta_value {i}"})
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for i in range(5)
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]
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embedder = OpenAIDocumentEmbedder(
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api_key=Secret.from_token("fake-api-key"), meta_fields_to_embed=["meta_field"], embedding_separator=" | "
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)
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prepared_texts = embedder._prepare_texts_to_embed(documents)
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assert prepared_texts == {
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"0": "meta_value 0 | document number 0:\ncontent",
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"1": "meta_value 1 | document number 1:\ncontent",
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"2": "meta_value 2 | document number 2:\ncontent",
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"3": "meta_value 3 | document number 3:\ncontent",
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"4": "meta_value 4 | document number 4:\ncontent",
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}
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def test_prepare_texts_to_embed_w_suffix(self):
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documents = [Document(id=f"{i}", content=f"document number {i}") for i in range(5)]
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embedder = OpenAIDocumentEmbedder(
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api_key=Secret.from_token("fake-api-key"), prefix="my_prefix ", suffix=" my_suffix"
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)
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prepared_texts = embedder._prepare_texts_to_embed(documents)
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assert prepared_texts == {
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"0": "my_prefix document number 0 my_suffix",
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"1": "my_prefix document number 1 my_suffix",
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"2": "my_prefix document number 2 my_suffix",
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"3": "my_prefix document number 3 my_suffix",
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"4": "my_prefix document number 4 my_suffix",
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}
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def test_run_wrong_input_format(self):
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embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("fake-api-key"))
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# wrong formats
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string_input = "text"
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list_integers_input = [1, 2, 3]
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with pytest.raises(TypeError, match="OpenAIDocumentEmbedder expects a list of Documents as input"):
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embedder.run(documents=string_input)
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with pytest.raises(TypeError, match="OpenAIDocumentEmbedder expects a list of Documents as input"):
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embedder.run(documents=list_integers_input)
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def test_run_on_empty_list(self):
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embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("fake-api-key"))
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empty_list_input = []
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result = embedder.run(documents=empty_list_input)
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assert result["documents"] is not None
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assert not result["documents"] # empty list
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def test_embed_batch_handles_exceptions_gracefully(self, caplog):
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embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("fake_api_key"))
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fake_texts_to_embed = {"1": "text1", "2": "text2"}
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with patch.object(
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embedder.client.embeddings,
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"create",
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side_effect=APIError(message="Mocked error", request=Mock(), body=None),
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):
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embedder._embed_batch(texts_to_embed=fake_texts_to_embed, batch_size=2)
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assert len(caplog.records) == 1
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assert "Failed embedding of documents 1, 2 caused by Mocked error" in caplog.records[0].msg
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def test_run_handles_exceptions_gracefully(self, caplog):
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embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("fake_api_key"), batch_size=1)
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docs = [
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Document(content="I love cheese", meta={"topic": "Cuisine"}),
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Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
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]
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# Create a successful response for the second call
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successful_response = Mock()
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successful_response.data = [
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Mock(embedding=[0.4, 0.5, 0.6]) # Mock embedding for second doc
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]
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successful_response.model = "text-embedding-ada-002"
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successful_response.usage = {"prompt_tokens": 10, "total_tokens": 10}
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with patch.object(
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embedder.client.embeddings,
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"create",
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side_effect=[
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APIError(message="Mocked error", request=Mock(), body=None), # First call fails
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successful_response, # Second call succeeds
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],
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):
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result = embedder.run(documents=docs)
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assert len(result["documents"]) == 2
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assert result["documents"][0].embedding is None
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assert result["documents"][1].embedding == [0.4, 0.5, 0.6]
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def test_embed_batch_raises_exception_on_failure(self):
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embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("fake_api_key"), raise_on_failure=True)
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fake_texts_to_embed = {"1": "text1", "2": "text2"}
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with patch.object(
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embedder.client.embeddings,
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"create",
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side_effect=APIError(message="Mocked error", request=Mock(), body=None),
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):
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with pytest.raises(APIError, match="Mocked error"):
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embedder._embed_batch(texts_to_embed=fake_texts_to_embed, batch_size=2)
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@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY", "") == "", reason="OPENAI_API_KEY is not set")
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@pytest.mark.integration
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def test_run(self):
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docs = [
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Document(content="I love cheese", meta={"topic": "Cuisine"}),
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Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
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]
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model = "text-embedding-ada-002"
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embedder = OpenAIDocumentEmbedder(model=model, meta_fields_to_embed=["topic"], embedding_separator=" | ")
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result = embedder.run(documents=docs)
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documents_with_embeddings = result["documents"]
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assert isinstance(documents_with_embeddings, list)
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assert len(documents_with_embeddings) == len(docs)
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for doc in documents_with_embeddings:
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assert isinstance(doc, Document)
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assert isinstance(doc.embedding, list)
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assert len(doc.embedding) == 1536
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assert all(isinstance(x, float) for x in doc.embedding)
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assert "text" in result["meta"]["model"] and "ada" in result["meta"]["model"], (
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"The model name does not contain 'text' and 'ada'"
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)
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assert result["meta"]["usage"] == {"prompt_tokens": 15, "total_tokens": 15}, "Usage information does not match"
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@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY", "") == "", reason="OPENAI_API_KEY is not set")
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@pytest.mark.integration
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@pytest.mark.asyncio
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async def test_run_async(self):
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docs = [
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Document(content="I love cheese", meta={"topic": "Cuisine"}),
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Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
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]
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model = "text-embedding-ada-002"
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embedder = OpenAIDocumentEmbedder(model=model, meta_fields_to_embed=["topic"], embedding_separator=" | ")
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result = await embedder.run_async(documents=docs)
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documents_with_embeddings = result["documents"]
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assert isinstance(documents_with_embeddings, list)
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assert len(documents_with_embeddings) == len(docs)
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for doc in documents_with_embeddings:
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assert isinstance(doc, Document)
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assert isinstance(doc.embedding, list)
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assert len(doc.embedding) == 1536
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assert all(isinstance(x, float) for x in doc.embedding)
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assert "text" in result["meta"]["model"] and "ada" in result["meta"]["model"], (
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"The model name does not contain 'text' and 'ada'"
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)
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assert result["meta"]["usage"] == {"prompt_tokens": 15, "total_tokens": 15}, "Usage information does not match"
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