# SPDX-FileCopyrightText: 2022-present deepset GmbH # # SPDX-License-Identifier: Apache-2.0 import os import random from typing import List from unittest.mock import Mock, patch import pytest from openai import APIError from haystack import Document from haystack.components.embedders.openai_document_embedder import OpenAIDocumentEmbedder from haystack.utils.auth import Secret def mock_openai_response(input: List[str], model: str = "text-embedding-ada-002", **kwargs) -> dict: dict_response = { "object": "list", "data": [ {"object": "embedding", "index": i, "embedding": [random.random() for _ in range(1536)]} for i in range(len(input)) ], "model": model, "usage": {"prompt_tokens": 4, "total_tokens": 4}, } return dict_response class TestOpenAIDocumentEmbedder: def test_init_default(self, monkeypatch): monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key") embedder = OpenAIDocumentEmbedder() assert embedder.api_key.resolve_value() == "fake-api-key" assert embedder.model == "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.meta_fields_to_embed == [] assert embedder.embedding_separator == "\n" assert embedder.client.max_retries == 5 assert embedder.client.timeout == 30.0 def test_init_with_parameters(self, monkeypatch): monkeypatch.setenv("OPENAI_TIMEOUT", "100") monkeypatch.setenv("OPENAI_MAX_RETRIES", "10") embedder = OpenAIDocumentEmbedder( api_key=Secret.from_token("fake-api-key-2"), model="model", organization="my-org", prefix="prefix", suffix="suffix", batch_size=64, progress_bar=False, meta_fields_to_embed=["test_field"], embedding_separator=" | ", timeout=40.0, max_retries=1, ) assert embedder.api_key.resolve_value() == "fake-api-key-2" assert embedder.organization == "my-org" assert embedder.model == "model" assert embedder.prefix == "prefix" assert embedder.suffix == "suffix" assert embedder.batch_size == 64 assert embedder.progress_bar is False assert embedder.meta_fields_to_embed == ["test_field"] assert embedder.embedding_separator == " | " assert embedder.client.max_retries == 1 assert embedder.client.timeout == 40.0 def test_init_with_parameters_and_env_vars(self, monkeypatch): monkeypatch.setenv("OPENAI_TIMEOUT", "100") monkeypatch.setenv("OPENAI_MAX_RETRIES", "10") embedder = OpenAIDocumentEmbedder( api_key=Secret.from_token("fake-api-key-2"), model="model", organization="my-org", prefix="prefix", suffix="suffix", batch_size=64, progress_bar=False, meta_fields_to_embed=["test_field"], embedding_separator=" | ", ) assert embedder.api_key.resolve_value() == "fake-api-key-2" assert embedder.organization == "my-org" assert embedder.model == "model" assert embedder.prefix == "prefix" assert embedder.suffix == "suffix" assert embedder.batch_size == 64 assert embedder.progress_bar is False assert embedder.meta_fields_to_embed == ["test_field"] assert embedder.embedding_separator == " | " assert embedder.client.max_retries == 10 assert embedder.client.timeout == 100.0 def test_init_fail_wo_api_key(self, monkeypatch): monkeypatch.delenv("OPENAI_API_KEY", raising=False) with pytest.raises(ValueError, match="None of the .* environment variables are set"): OpenAIDocumentEmbedder() def test_to_dict(self, monkeypatch): monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key") component = OpenAIDocumentEmbedder() data = component.to_dict() assert data == { "type": "haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder", "init_parameters": { "api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"}, "api_base_url": None, "model": "text-embedding-ada-002", "dimensions": None, "organization": None, "http_client_kwargs": None, "prefix": "", "suffix": "", "batch_size": 32, "progress_bar": True, "meta_fields_to_embed": [], "embedding_separator": "\n", "timeout": None, "max_retries": None, }, } def test_to_dict_with_custom_init_parameters(self, monkeypatch): monkeypatch.setenv("ENV_VAR", "fake-api-key") component = OpenAIDocumentEmbedder( api_key=Secret.from_env_var("ENV_VAR", strict=False), model="model", organization="my-org", http_client_kwargs={"proxy": "http://localhost:8080"}, prefix="prefix", suffix="suffix", batch_size=64, progress_bar=False, meta_fields_to_embed=["test_field"], embedding_separator=" | ", timeout=10.0, max_retries=2, ) data = component.to_dict() assert data == { "type": "haystack.components.embedders.openai_document_embedder.OpenAIDocumentEmbedder", "init_parameters": { "api_key": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"}, "api_base_url": None, "model": "model", "dimensions": None, "organization": "my-org", "http_client_kwargs": {"proxy": "http://localhost:8080"}, "prefix": "prefix", "suffix": "suffix", "batch_size": 64, "progress_bar": False, "meta_fields_to_embed": ["test_field"], "embedding_separator": " | ", "timeout": 10.0, "max_retries": 2, }, } def test_prepare_texts_to_embed_w_metadata(self): documents = [ Document(id=f"{i}", content=f"document number {i}:\ncontent", meta={"meta_field": f"meta_value {i}"}) for i in range(5) ] embedder = OpenAIDocumentEmbedder( api_key=Secret.from_token("fake-api-key"), meta_fields_to_embed=["meta_field"], embedding_separator=" | " ) prepared_texts = embedder._prepare_texts_to_embed(documents) assert prepared_texts == { "0": "meta_value 0 | document number 0:\ncontent", "1": "meta_value 1 | document number 1:\ncontent", "2": "meta_value 2 | document number 2:\ncontent", "3": "meta_value 3 | document number 3:\ncontent", "4": "meta_value 4 | document number 4:\ncontent", } def test_prepare_texts_to_embed_w_suffix(self): documents = [Document(id=f"{i}", content=f"document number {i}") for i in range(5)] embedder = OpenAIDocumentEmbedder( api_key=Secret.from_token("fake-api-key"), prefix="my_prefix ", suffix=" my_suffix" ) prepared_texts = embedder._prepare_texts_to_embed(documents) assert prepared_texts == { "0": "my_prefix document number 0 my_suffix", "1": "my_prefix document number 1 my_suffix", "2": "my_prefix document number 2 my_suffix", "3": "my_prefix document number 3 my_suffix", "4": "my_prefix document number 4 my_suffix", } def test_run_wrong_input_format(self): embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("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=Secret.from_token("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 def test_embed_batch_handles_exceptions_gracefully(self, caplog): embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("fake_api_key")) fake_texts_to_embed = {"1": "text1", "2": "text2"} with patch.object( embedder.client.embeddings, "create", side_effect=APIError(message="Mocked error", request=Mock(), body=None), ): embedder._embed_batch(texts_to_embed=fake_texts_to_embed, batch_size=2) assert len(caplog.records) == 1 assert "Failed embedding of documents 1, 2 caused by Mocked error" in caplog.records[0].msg @pytest.mark.skipif(os.environ.get("OPENAI_API_KEY", "") == "", reason="OPENAI_API_KEY is not set") @pytest.mark.integration 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-embedding-ada-002" embedder = OpenAIDocumentEmbedder(model=model, meta_fields_to_embed=["topic"], embedding_separator=" | ") result = embedder.run(documents=docs) documents_with_embeddings = result["documents"] 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 "text" in result["meta"]["model"] and "ada" in result["meta"]["model"], ( "The model name does not contain 'text' and 'ada'" ) assert result["meta"]["usage"] == {"prompt_tokens": 15, "total_tokens": 15}, "Usage information does not match" @pytest.mark.skipif(os.environ.get("OPENAI_API_KEY", "") == "", reason="OPENAI_API_KEY is not set") @pytest.mark.integration @pytest.mark.asyncio async def test_run_async(self): docs = [ Document(content="I love cheese", meta={"topic": "Cuisine"}), Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}), ] model = "text-embedding-ada-002" embedder = OpenAIDocumentEmbedder(model=model, meta_fields_to_embed=["topic"], embedding_separator=" | ") result = await embedder.run_async(documents=docs) documents_with_embeddings = result["documents"] 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 "text" in result["meta"]["model"] and "ada" in result["meta"]["model"], ( "The model name does not contain 'text' and 'ada'" ) assert result["meta"]["usage"] == {"prompt_tokens": 15, "total_tokens": 15}, "Usage information does not match"