Drop patch

src/crewai/knowledge/source/base_knowledge_source.py

@@ -46,4 +46,5 @@ class BaseKnowledgeSource(BaseModel, ABC):
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
+        print("CHUNKS: ", self.chunks)
         self.storage.save(self.chunks)

tests/agent_test.py

@@ -1592,36 +1592,36 @@ def test_agent_execute_task_with_ollama():
     assert "AI" in result or "artificial intelligence" in result.lower()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
+# @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources():
     # Create a knowledge source with some content
     content = "Brandon's favorite color is red and he likes Mexican food."
     string_source = StringKnowledgeSource(content=content)
 
-    with patch(
-        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
+    # with patch(
+    #     "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
+    # ) as MockKnowledge:
+    #     mock_knowledge_instance = MockKnowledge.return_value
+    #     mock_knowledge_instance.sources = [string_source]
+    #     mock_knowledge_instance.query.return_value = [{"content": content}]
 
-        agent = Agent(
-            role="Information Agent",
-            goal="Provide information based on knowledge sources",
-            backstory="You have access to specific knowledge sources.",
-            llm=LLM(model="gpt-4o-mini"),
-            knowledge_sources=[string_source],
-        )
+    agent = Agent(
+        role="Information Agent",
+        goal="Provide information based on knowledge sources",
+        backstory="You have access to specific knowledge sources.",
+        llm=LLM(model="gpt-4o-mini"),
+        knowledge_sources=[string_source],
+    )
 
-        # Create a task that requires the agent to use the knowledge
-        task = Task(
-            description="What is Brandon's favorite color?",
-            expected_output="Brandon's favorite color.",
-            agent=agent,
-        )
+    # Create a task that requires the agent to use the knowledge
+    task = Task(
+        description="What is Brandon's favorite color?",
+        expected_output="Brandon's favorite color.",
+        agent=agent,
+    )
 
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
+    crew = Crew(agents=[agent], tasks=[task])
+    result = crew.kickoff()
 
-        # Assert that the agent provides the correct information
-        assert "red" in result.raw.lower()
+    # Assert that the agent provides the correct information
+    assert "red" in result.raw.lower()