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fix: Respect use_system_prompt in Converter regeneration
Fixes #2571 Co-authored-by: Devin AI <devin-ai-integration[bot]@users.noreply.github.com> Co-Authored-By: Joe Moura <joao@crewai.com>
This commit is contained in:
Devin AI
File diff suppressed because it is too large
Load Diff
@@ -1,864 +0,0 @@
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heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting,
|
||||
and eating disorders\\n 13. Any content intended to incite or promote violence,
|
||||
abuse, or any infliction of bodily harm to an individual\\n3. Intentionally
|
||||
deceive or mislead others, including use of Llama 3.2 related to the following:\\n
|
||||
\ 14. Generating, promoting, or furthering fraud or the creation or promotion
|
||||
of disinformation\\n 15. Generating, promoting, or furthering defamatory
|
||||
content, including the creation of defamatory statements, images, or other content\\n
|
||||
\ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating
|
||||
another individual without consent, authorization, or legal right\\n 18.
|
||||
Representing that the use of Llama 3.2 or outputs are human-generated\\n 19.
|
||||
Generating or facilitating false online engagement, including fake reviews and
|
||||
other means of fake online engagement\\n4. Fail to appropriately disclose to
|
||||
end users any known dangers of your AI system\\n5. Interact with third party
|
||||
tools, models, or software designed to generate unlawful content or engage in
|
||||
unlawful or harmful conduct and/or represent that the outputs of such tools,
|
||||
models, or software are associated with Meta or Llama 3.2\\n\\nWith respect
|
||||
to any multimodal models included in Llama 3.2, the rights granted under Section
|
||||
1(a) of the Llama 3.2 Community License Agreement are not being granted to you
|
||||
if you are an individual domiciled in, or a company with a principal place of
|
||||
business in, the European Union. This restriction does not apply to end users
|
||||
of a product or service that incorporates any such multimodal models.\\n\\nPlease
|
||||
report any violation of this Policy, software \u201Cbug,\u201D or other problems
|
||||
that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n*
|
||||
Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
|
||||
Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
|
||||
Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n*
|
||||
Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
|
||||
3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop
|
||||
\ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting
|
||||
Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{-
|
||||
if .Tools }}When you receive a tool call response, use the output to format
|
||||
an answer to the orginal user question.\\n\\nYou are a helpful assistant with
|
||||
tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i,
|
||||
$_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{-
|
||||
if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{-
|
||||
if and $.Tools $last }}\\n\\nGiven the following functions, please respond with
|
||||
a JSON for a function call with its proper arguments that best answers the given
|
||||
prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\":
|
||||
dictionary of argument name and its value}. Do not use variables.\\n\\n{{ range
|
||||
$.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{-
|
||||
else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
|
||||
end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{-
|
||||
if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name
|
||||
}}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else }}\\n\\n{{
|
||||
.Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ end }}\\n{{-
|
||||
else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{
|
||||
.Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
|
||||
end }}\\n{{- end }}\\n{{- end }}\",\"details\":{\"parent_model\":\"\",\"format\":\"gguf\",\"family\":\"llama\",\"families\":[\"llama\"],\"parameter_size\":\"3.2B\",\"quantization_level\":\"Q4_K_M\"},\"model_info\":{\"general.architecture\":\"llama\",\"general.basename\":\"Llama-3.2\",\"general.file_type\":15,\"general.finetune\":\"Instruct\",\"general.languages\":[\"en\",\"de\",\"fr\",\"it\",\"pt\",\"hi\",\"es\",\"th\"],\"general.parameter_count\":3212749888,\"general.quantization_version\":2,\"general.size_label\":\"3B\",\"general.tags\":[\"facebook\",\"meta\",\"pytorch\",\"llama\",\"llama-3\",\"text-generation\"],\"general.type\":\"model\",\"llama.attention.head_count\":24,\"llama.attention.head_count_kv\":8,\"llama.attention.key_length\":128,\"llama.attention.layer_norm_rms_epsilon\":0.00001,\"llama.attention.value_length\":128,\"llama.block_count\":28,\"llama.context_length\":131072,\"llama.embedding_length\":3072,\"llama.feed_forward_length\":8192,\"llama.rope.dimension_count\":128,\"llama.rope.freq_base\":500000,\"llama.vocab_size\":128256,\"tokenizer.ggml.bos_token_id\":128000,\"tokenizer.ggml.eos_token_id\":128009,\"tokenizer.ggml.merges\":null,\"tokenizer.ggml.model\":\"gpt2\",\"tokenizer.ggml.pre\":\"llama-bpe\",\"tokenizer.ggml.token_type\":null,\"tokenizer.ggml.tokens\":null},\"modified_at\":\"2025-02-20T18:55:09.150577031-08:00\"}"
|
||||
headers:
|
||||
Content-Type:
|
||||
- application/json; charset=utf-8
|
||||
Date:
|
||||
- Fri, 21 Feb 2025 02:57:55 GMT
|
||||
Transfer-Encoding:
|
||||
- chunked
|
||||
http_version: HTTP/1.1
|
||||
status_code: 200
|
||||
version: 1
|
||||
@@ -197,6 +197,101 @@ def test_convert_with_instructions_success(
|
||||
assert output.age == 50
|
||||
|
||||
|
||||
@patch("crewai.utilities.converter.get_conversion_instructions")
|
||||
@patch("crewai.utilities.converter.create_converter")
|
||||
def test_convert_with_instructions_respects_use_system_prompt_false(
|
||||
mock_create_converter, mock_get_instructions, mock_agent
|
||||
):
|
||||
"""
|
||||
Test that convert_with_instructions does not use a system prompt
|
||||
when agent.use_system_prompt is False and the LLM doesn't support function calling.
|
||||
"""
|
||||
mock_agent.use_system_prompt = False
|
||||
mock_llm = MagicMock()
|
||||
mock_llm.supports_function_calling.return_value = False
|
||||
mock_agent.llm = mock_llm
|
||||
mock_agent.function_calling_llm = None # Ensure fallback to agent.llm
|
||||
|
||||
mock_get_instructions.return_value = "Test Instructions"
|
||||
|
||||
mock_converter_instance = MagicMock(spec=Converter)
|
||||
mock_converter_instance.agent = mock_agent # Set the agent on the mock converter
|
||||
mock_converter_instance.llm = mock_llm
|
||||
mock_converter_instance.instructions = "Test Instructions"
|
||||
mock_converter_instance.text = "Some text"
|
||||
mock_converter_instance.model = SimpleModel
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=mock_llm,
|
||||
text="Some text",
|
||||
model=SimpleModel,
|
||||
instructions="Test Instructions",
|
||||
)
|
||||
mock_create_converter.return_value = (
|
||||
converter # This instance will be used by convert_with_instructions
|
||||
)
|
||||
|
||||
converter.llm.call = MagicMock(return_value='{"name": "Mock Name", "age": 99}')
|
||||
|
||||
convert_with_instructions("Some text", SimpleModel, False, mock_agent)
|
||||
|
||||
converter.llm.call.assert_called_once()
|
||||
call_args = converter.llm.call.call_args[0][0] # Get the 'messages' list argument
|
||||
|
||||
assert not any(msg.get("role") == "system" for msg in call_args)
|
||||
user_message = next((msg for msg in call_args if msg.get("role") == "user"), None)
|
||||
assert user_message is not None
|
||||
assert "Test Instructions" in user_message["content"]
|
||||
assert "Some text" in user_message["content"]
|
||||
assert user_message["content"].startswith("Test Instructions\n\n")
|
||||
|
||||
|
||||
@patch("crewai.utilities.converter.get_conversion_instructions")
|
||||
@patch("crewai.utilities.converter.create_converter")
|
||||
def test_convert_with_instructions_respects_use_system_prompt_true(
|
||||
mock_create_converter, mock_get_instructions, mock_agent
|
||||
):
|
||||
"""
|
||||
Test that convert_with_instructions uses a system prompt
|
||||
when agent.use_system_prompt is True and the LLM doesn't support function calling.
|
||||
"""
|
||||
mock_agent.use_system_prompt = True # Explicitly True
|
||||
mock_llm = MagicMock()
|
||||
mock_llm.supports_function_calling.return_value = False
|
||||
mock_agent.llm = mock_llm
|
||||
mock_agent.function_calling_llm = None
|
||||
|
||||
mock_get_instructions.return_value = "Test Instructions"
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=mock_llm,
|
||||
text="Some text",
|
||||
model=SimpleModel,
|
||||
instructions="Test Instructions",
|
||||
)
|
||||
mock_create_converter.return_value = (
|
||||
converter # This instance will be used by convert_with_instructions
|
||||
)
|
||||
|
||||
converter.llm.call = MagicMock(return_value='{"name": "Mock Name", "age": 99}')
|
||||
|
||||
convert_with_instructions("Some text", SimpleModel, False, mock_agent)
|
||||
|
||||
converter.llm.call.assert_called_once()
|
||||
call_args = converter.llm.call.call_args[0][0]
|
||||
|
||||
system_message = next(
|
||||
(msg for msg in call_args if msg.get("role") == "system"), None
|
||||
)
|
||||
assert system_message is not None
|
||||
assert system_message["content"] == "Test Instructions"
|
||||
user_message = next((msg for msg in call_args if msg.get("role") == "user"), None)
|
||||
assert user_message is not None
|
||||
assert user_message["content"] == "Some text"
|
||||
|
||||
|
||||
@patch("crewai.utilities.converter.create_converter")
|
||||
@patch("crewai.utilities.converter.get_conversion_instructions")
|
||||
def test_convert_with_instructions_failure(
|
||||
@@ -334,7 +429,10 @@ def test_convert_with_instructions():
|
||||
sample_text = "Name: Alice, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
mock_agent = Mock() # Add mock agent if not available
|
||||
mock_agent.use_system_prompt = True # Default or set as needed
|
||||
converter = Converter(
|
||||
agent=mock_agent, # Add agent argument
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
@@ -362,7 +460,10 @@ def test_converter_with_llama3_2_model():
|
||||
llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
|
||||
sample_text = "Name: Alice Llama, Age: 30"
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
mock_agent = Mock() # Add mock agent if not available
|
||||
mock_agent.use_system_prompt = True # Default or set as needed
|
||||
converter = Converter(
|
||||
agent=mock_agent, # Add agent argument
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
@@ -380,7 +481,10 @@ def test_converter_with_llama3_1_model():
|
||||
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
|
||||
sample_text = "Name: Alice Llama, Age: 30"
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
mock_agent = Mock() # Add mock agent if not available
|
||||
mock_agent.use_system_prompt = True # Default or set as needed
|
||||
converter = Converter(
|
||||
agent=mock_agent, # Add agent argument
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
@@ -405,7 +509,11 @@ def test_converter_with_nested_model():
|
||||
sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
|
||||
|
||||
instructions = get_conversion_instructions(Person, llm)
|
||||
mock_agent = Mock()
|
||||
mock_agent.use_system_prompt = True
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=Person,
|
||||
@@ -431,7 +539,10 @@ def test_converter_error_handling():
|
||||
sample_text = "Name: Alice, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
mock_agent = Mock() # Add mock agent if not available
|
||||
mock_agent.use_system_prompt = True # Default or set as needed
|
||||
converter = Converter(
|
||||
agent=mock_agent, # Add agent argument
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
@@ -456,7 +567,11 @@ def test_converter_retry_logic():
|
||||
sample_text = "Name: Retry Alice, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
mock_agent = Mock()
|
||||
mock_agent.use_system_prompt = True
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
@@ -485,7 +600,11 @@ def test_converter_with_optional_fields():
|
||||
sample_text = "Name: Bob, age: None"
|
||||
|
||||
instructions = get_conversion_instructions(OptionalModel, llm)
|
||||
mock_agent = Mock()
|
||||
mock_agent.use_system_prompt = True
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=OptionalModel,
|
||||
@@ -510,7 +629,11 @@ def test_converter_with_list_field():
|
||||
sample_text = "Items: 1, 2, 3"
|
||||
|
||||
instructions = get_conversion_instructions(ListModel, llm)
|
||||
mock_agent = Mock()
|
||||
mock_agent.use_system_prompt = True
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=ListModel,
|
||||
@@ -543,7 +666,11 @@ def test_converter_with_enum():
|
||||
sample_text = "Name: Alice, Color: Red"
|
||||
|
||||
instructions = get_conversion_instructions(EnumModel, llm)
|
||||
mock_agent = Mock()
|
||||
mock_agent.use_system_prompt = True
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=EnumModel,
|
||||
@@ -565,7 +692,11 @@ def test_converter_with_ambiguous_input():
|
||||
sample_text = "Charlie is thirty years old"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
mock_agent = Mock()
|
||||
mock_agent.use_system_prompt = True
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
@@ -586,7 +717,11 @@ def test_converter_with_function_calling():
|
||||
instructor = Mock()
|
||||
instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
|
||||
|
||||
mock_agent = Mock()
|
||||
mock_agent.use_system_prompt = True
|
||||
|
||||
converter = Converter(
|
||||
agent=mock_agent,
|
||||
llm=llm,
|
||||
text="Name: Eve, Age: 35",
|
||||
model=SimpleModel,
|
||||
|
||||
@@ -29,13 +29,14 @@ def mock_knowledge_source():
|
||||
"""
|
||||
return StringKnowledgeSource(content=content)
|
||||
|
||||
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
|
||||
|
||||
@patch("crewai.knowledge.storage.knowledge_storage.chromadb")
|
||||
def test_knowledge_included_in_planning(mock_chroma):
|
||||
"""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
|
||||
|
||||
|
||||
# Create an agent with knowledge
|
||||
agent = Agent(
|
||||
role="AI Researcher",
|
||||
@@ -45,14 +46,14 @@ 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 +63,29 @@ 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, \
|
||||
"Knowledge content should be present in task summary when knowledge exists"
|
||||
assert '"agent_knowledge"' in task_summary, \
|
||||
"agent_knowledge field should be present in task summary when knowledge exists"
|
||||
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
|
||||
), "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), \
|
||||
"Knowledge sources should be stored in a list"
|
||||
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, \
|
||||
"Knowledge source content should be included in task summary"
|
||||
assert isinstance(
|
||||
task.agent.knowledge_sources, list
|
||||
), "Knowledge sources should be stored in a list"
|
||||
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
|
||||
), "Knowledge source content should be included in task summary"
|
||||
|
||||
# Verify that other expected components are still present
|
||||
assert task.description in task_summary, \
|
||||
"Task description should be present 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 (
|
||||
task.description in task_summary
|
||||
), "Task description should be present 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"
|
||||
|
||||
@@ -100,7 +100,7 @@ class InternalCrewPlanner:
|
||||
# Knowledge field should not be present when empty
|
||||
assert '"agent_knowledge"' not in tasks_summary
|
||||
|
||||
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
|
||||
@patch("crewai.knowledge.storage.knowledge_storage.chromadb")
|
||||
def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma):
|
||||
"""Test task summary generation with both knowledge and tools present."""
|
||||
# Mock ChromaDB collection
|
||||
@@ -146,8 +146,8 @@ class InternalCrewPlanner:
|
||||
tools=[tool1, tool2],
|
||||
knowledge_sources=[
|
||||
StringKnowledgeSource(content="Test knowledge content")
|
||||
]
|
||||
)
|
||||
],
|
||||
),
|
||||
)
|
||||
|
||||
# Create planner with the new task
|
||||
|
||||
Reference in New Issue
Block a user