refactor: unify rag storage with instance-specific client support (#3455)

- ignore line length errors globally
- migrate knowledge/memory and crew query_knowledge to `SearchResult`
- remove legacy chromadb utils; fix empty metadata handling
- restore openai as default embedding provider; support instance-specific clients
- update and fix tests for `SearchResult` migration and rag changes

tests/utilities/test_knowledge_planning.py

@@ -29,13 +29,15 @@ def mock_knowledge_source():
     """
     return StringKnowledgeSource(content=content)
 
-@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
-def test_knowledge_included_in_planning(mock_chroma):
+
+@patch("crewai.rag.config.utils.get_rag_client")
+def test_knowledge_included_in_planning(mock_get_client):
     """Test that verifies knowledge sources are properly included in planning."""
-    # Mock ChromaDB collection
-    mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
-    mock_collection.add.return_value = None
-    
+    # Mock RAG client
+    mock_client = mock_get_client.return_value
+    mock_client.get_or_create_collection.return_value = None
+    mock_client.add_documents.return_value = None
+
     # Create an agent with knowledge
     agent = Agent(
         role="AI Researcher",
@@ -45,14 +47,14 @@ def test_knowledge_included_in_planning(mock_chroma):
             StringKnowledgeSource(
                 content="AI systems require careful training and validation."
             )
-        ]
+        ],
     )
 
     # Create a task for the agent
     task = Task(
         description="Explain the basics of AI systems",
         expected_output="A clear explanation of AI fundamentals",
-        agent=agent
+        agent=agent,
     )
 
     # Create a crew planner
@@ -62,23 +64,29 @@ def test_knowledge_included_in_planning(mock_chroma):
     task_summary = planner._create_tasks_summary()
 
     # Verify that knowledge is included in planning when present
-    assert "AI systems require careful training" in task_summary, \
+    assert "AI systems require careful training" in task_summary, (
         "Knowledge content should be present in task summary when knowledge exists"
-    assert '"agent_knowledge"' in task_summary, \
+    )
+    assert '"agent_knowledge"' in task_summary, (
         "agent_knowledge field should be present in task summary when knowledge exists"
+    )
 
     # Verify that knowledge is properly formatted
-    assert isinstance(task.agent.knowledge_sources, list), \
+    assert isinstance(task.agent.knowledge_sources, list), (
         "Knowledge sources should be stored in a list"
-    assert len(task.agent.knowledge_sources) > 0, \
+    )
+    assert len(task.agent.knowledge_sources) > 0, (
         "At least one knowledge source should be present"
-    assert task.agent.knowledge_sources[0].content in task_summary, \
+    )
+    assert task.agent.knowledge_sources[0].content in task_summary, (
         "Knowledge source content should be included in task summary"
+    )
 
     # Verify that other expected components are still present
-    assert task.description in task_summary, \
+    assert task.description in task_summary, (
         "Task description should be present in task summary"
-    assert task.expected_output in task_summary, \
+    )
+    assert task.expected_output in task_summary, (
         "Expected output should be present in task summary"
-    assert agent.role in task_summary, \
-        "Agent role should be present in task summary"
+    )
+    assert agent.role in task_summary, "Agent role should be present in task summary"