test: Skip integration tests if env var is missing

test/components/embedders/test_openai_document_embedder.py

@@ -1,3 +1,4 @@
+import os
 from typing import List
 
 import numpy as np
@@ -163,6 +164,7 @@ def test_run_on_empty_list(self):
         assert result["documents"] is not None
         assert not result["documents"]  # empty list
 
+    @pytest.mark.skipif("OPENAI_API_KEY" not in os.environ, reason="OPENAI_API_KEY is not set")
     @pytest.mark.integration
     def test_run(self):
         docs = [

test/components/embedders/test_openai_text_embedder.py

@@ -1,3 +1,5 @@
+import os
+
 import pytest
 from openai import OpenAIError
 
@@ -74,6 +76,7 @@ def test_run_wrong_input_format(self):
         with pytest.raises(TypeError, match="OpenAITextEmbedder expects a string as an input"):
             embedder.run(text=list_integers_input)
 
+    @pytest.mark.skipif("OPENAI_API_KEY" not in os.environ, reason="OPENAI_API_KEY is not set")
     @pytest.mark.integration
     def test_run(self):
         model = "text-similarity-ada-001"

test/pipelines/test_indexing_pipeline.py

@@ -1,3 +1,5 @@
+import os
+
 import pytest
 
 from haystack.pipeline_utils.indexing import build_indexing_pipeline
@@ -65,6 +67,7 @@ def test_embedding_model_not_found(self):
         with pytest.raises(ValueError, match="Could not find an embedder"):
             build_indexing_pipeline(document_store=document_store, embedding_model="invalid_model")
 
+    @pytest.mark.skipif("OPENAI_API_KEY" not in os.environ, reason="OPENAI_API_KEY is not set")
     @pytest.mark.integration
     def test_open_ai_embedding_model(self):
         document_store = InMemoryDocumentStore()

test/pipelines/test_rag_pipelines.py

@@ -1,3 +1,5 @@
+import os
+
 import pytest
 
 from haystack.dataclasses import Answer
@@ -6,6 +8,7 @@
 from haystack.testing.factory import document_store_class
 
 
+@pytest.mark.skipif("OPENAI_API_KEY" not in os.environ, reason="OPENAI_API_KEY is not set")
 @pytest.mark.integration
 def test_rag_pipeline(mock_chat_completion):
     rag_pipe = build_rag_pipeline(document_store=InMemoryDocumentStore())