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Reverting the tools commit (#2404)

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This commit is contained in:
Parshva Daftari
2025-03-20 00:09:00 +05:30
committed by GitHub
parent 1aed611539
commit ee66e0c954
21 changed files with 990 additions and 475 deletions
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64
tests/llms/test_azure_openai.py
View File

@@ -20,10 +20,8 @@ def mock_openai_client():
yield mock_client
def test_generate_response(mock_openai_client):
config = BaseLlmConfig(
model=MODEL, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P
)
def test_generate_response_without_tools(mock_openai_client):
config = BaseLlmConfig(model=MODEL, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P)
llm = AzureOpenAILLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
@@ -31,21 +29,67 @@ def test_generate_response(mock_openai_client):
]
mock_response = Mock()
mock_response.choices = [
Mock(message=Mock(content="I'm doing well, thank you for asking!"))
]
mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
mock_openai_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages)
mock_openai_client.chat.completions.create.assert_called_once_with(
model=MODEL, messages=messages, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P
)
assert response == "I'm doing well, thank you for asking!"
def test_generate_response_with_tools(mock_openai_client):
config = BaseLlmConfig(model=MODEL, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P)
llm = AzureOpenAILLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Add a new memory: Today is a sunny day."},
]
tools = [
{
"type": "function",
"function": {
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
},
}
]
mock_response = Mock()
mock_message = Mock()
mock_message.content = "I've added the memory for you."
mock_tool_call = Mock()
mock_tool_call.function.name = "add_memory"
mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
mock_message.tool_calls = [mock_tool_call]
mock_response.choices = [Mock(message=mock_message)]
mock_openai_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages, tools=tools)
mock_openai_client.chat.completions.create.assert_called_once_with(
model=MODEL,
messages=messages,
temperature=TEMPERATURE,
max_tokens=MAX_TOKENS,
top_p=TOP_P,
tools=tools,
tool_choice="auto",
)
assert response == "I'm doing well, thank you for asking!"
assert response["content"] == "I've added the memory for you."
assert len(response["tool_calls"]) == 1
assert response["tool_calls"][0]["name"] == "add_memory"
assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
@pytest.mark.parametrize(
@@ -84,6 +128,4 @@ def test_generate_with_http_proxies(default_headers):
api_version=None,
default_headers=default_headers,
)
mock_http_client.assert_called_once_with(
proxies="http://testproxy.mem0.net:8000"
)
mock_http_client.assert_called_once_with(proxies="http://testproxy.mem0.net:8000")

96
tests/llms/test_deepseek.py
View File

@@ -16,47 +16,33 @@ def mock_deepseek_client():
def test_deepseek_llm_base_url():
# case1: default config with deepseek official base url
config = BaseLlmConfig(
model="deepseek-chat",
temperature=0.7,
max_tokens=100,
top_p=1.0,
api_key="api_key",
)
config = BaseLlmConfig(model="deepseek-chat", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
llm = DeepSeekLLM(config)
assert str(llm.client.base_url) == "https://api.deepseek.com"
# case2: with env variable DEEPSEEK_API_BASE
provider_base_url = "https://api.provider.com/v1/"
os.environ["DEEPSEEK_API_BASE"] = provider_base_url
config = BaseLlmConfig(
model="deepseek-chat",
temperature=0.7,
max_tokens=100,
top_p=1.0,
api_key="api_key",
)
config = BaseLlmConfig(model="deepseek-chat", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
llm = DeepSeekLLM(config)
assert str(llm.client.base_url) == provider_base_url
# case3: with config.deepseek_base_url
config_base_url = "https://api.config.com/v1/"
config = BaseLlmConfig(
model="deepseek-chat",
temperature=0.7,
max_tokens=100,
top_p=1.0,
api_key="api_key",
deepseek_base_url=config_base_url,
model="deepseek-chat",
temperature=0.7,
max_tokens=100,
top_p=1.0,
api_key="api_key",
deepseek_base_url=config_base_url
)
llm = DeepSeekLLM(config)
assert str(llm.client.base_url) == config_base_url
def test_generate_response(mock_deepseek_client):
config = BaseLlmConfig(
model="deepseek-chat", temperature=0.7, max_tokens=100, top_p=1.0
)
def test_generate_response_without_tools(mock_deepseek_client):
config = BaseLlmConfig(model="deepseek-chat", temperature=0.7, max_tokens=100, top_p=1.0)
llm = DeepSeekLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
@@ -64,18 +50,64 @@ def test_generate_response(mock_deepseek_client):
]
mock_response = Mock()
mock_response.choices = [
Mock(message=Mock(content="I'm doing well, thank you for asking!"))
]
mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
mock_deepseek_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages)
mock_deepseek_client.chat.completions.create.assert_called_once_with(
model="deepseek-chat",
messages=messages,
temperature=0.7,
max_tokens=100,
top_p=1.0,
model="deepseek-chat", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0
)
assert response == "I'm doing well, thank you for asking!"
def test_generate_response_with_tools(mock_deepseek_client):
config = BaseLlmConfig(model="deepseek-chat", temperature=0.7, max_tokens=100, top_p=1.0)
llm = DeepSeekLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Add a new memory: Today is a sunny day."},
]
tools = [
{
"type": "function",
"function": {
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
},
}
]
mock_response = Mock()
mock_message = Mock()
mock_message.content = "I've added the memory for you."
mock_tool_call = Mock()
mock_tool_call.function.name = "add_memory"
mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
mock_message.tool_calls = [mock_tool_call]
mock_response.choices = [Mock(message=mock_message)]
mock_deepseek_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages, tools=tools)
mock_deepseek_client.chat.completions.create.assert_called_once_with(
model="deepseek-chat",
messages=messages,
temperature=0.7,
max_tokens=100,
top_p=1.0,
tools=tools,
tool_choice="auto"
)
assert response["content"] == "I've added the memory for you."
assert len(response["tool_calls"]) == 1
assert response["tool_calls"][0]["name"] == "add_memory"
assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}

88
tests/llms/test_gemini_llm.py
View File

@@ -17,9 +17,7 @@ def mock_gemini_client():
def test_generate_response_without_tools(mock_gemini_client: Mock):
config = BaseLlmConfig(
model="gemini-1.5-flash-latest", temperature=0.7, max_tokens=100, top_p=1.0
)
config = BaseLlmConfig(model="gemini-1.5-flash-latest", temperature=0.7, max_tokens=100, top_p=1.0)
llm = GeminiLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
@@ -36,14 +34,86 @@ def test_generate_response_without_tools(mock_gemini_client: Mock):
mock_gemini_client.generate_content.assert_called_once_with(
contents=[
{
"parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.",
"role": "user",
},
{"parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.", "role": "user"},
{"parts": "Hello, how are you?", "role": "user"},
],
generation_config=GenerationConfig(
temperature=0.7, max_output_tokens=100, top_p=1.0
generation_config=GenerationConfig(temperature=0.7, max_output_tokens=100, top_p=1.0),
tools=None,
tool_config=content_types.to_tool_config(
{"function_calling_config": {"mode": "auto", "allowed_function_names": None}}
),
)
assert response == "I'm doing well, thank you for asking!"
def test_generate_response_with_tools(mock_gemini_client: Mock):
config = BaseLlmConfig(model="gemini-1.5-flash-latest", temperature=0.7, max_tokens=100, top_p=1.0)
llm = GeminiLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Add a new memory: Today is a sunny day."},
]
tools = [
{
"type": "function",
"function": {
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
},
}
]
mock_tool_call = Mock()
mock_tool_call.name = "add_memory"
mock_tool_call.args = {"data": "Today is a sunny day."}
mock_part = Mock()
mock_part.function_call = mock_tool_call
mock_part.text = "I've added the memory for you."
mock_content = Mock()
mock_content.parts = [mock_part]
mock_message = Mock()
mock_message.content = mock_content
mock_response = Mock(candidates=[mock_message])
mock_gemini_client.generate_content.return_value = mock_response
response = llm.generate_response(messages, tools=tools)
mock_gemini_client.generate_content.assert_called_once_with(
contents=[
{"parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.", "role": "user"},
{"parts": "Add a new memory: Today is a sunny day.", "role": "user"},
],
generation_config=GenerationConfig(temperature=0.7, max_output_tokens=100, top_p=1.0),
tools=[
{
"function_declarations": [
{
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
}
]
}
],
tool_config=content_types.to_tool_config(
{"function_calling_config": {"mode": "auto", "allowed_function_names": None}}
),
)
assert response["content"] == "I've added the memory for you."
assert len(response["tool_calls"]) == 1
assert response["tool_calls"][0]["name"] == "add_memory"
assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}

60
tests/llms/test_groq.py
View File

@@ -14,10 +14,8 @@ def mock_groq_client():
yield mock_client
def test_generate_response(mock_groq_client):
config = BaseLlmConfig(
model="llama3-70b-8192", temperature=0.7, max_tokens=100, top_p=1.0
)
def test_generate_response_without_tools(mock_groq_client):
config = BaseLlmConfig(model="llama3-70b-8192", temperature=0.7, max_tokens=100, top_p=1.0)
llm = GroqLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
@@ -25,18 +23,64 @@ def test_generate_response(mock_groq_client):
]
mock_response = Mock()
mock_response.choices = [
Mock(message=Mock(content="I'm doing well, thank you for asking!"))
]
mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
mock_groq_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages)
mock_groq_client.chat.completions.create.assert_called_once_with(
model="llama3-70b-8192", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0
)
assert response == "I'm doing well, thank you for asking!"
def test_generate_response_with_tools(mock_groq_client):
config = BaseLlmConfig(model="llama3-70b-8192", temperature=0.7, max_tokens=100, top_p=1.0)
llm = GroqLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Add a new memory: Today is a sunny day."},
]
tools = [
{
"type": "function",
"function": {
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
},
}
]
mock_response = Mock()
mock_message = Mock()
mock_message.content = "I've added the memory for you."
mock_tool_call = Mock()
mock_tool_call.function.name = "add_memory"
mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
mock_message.tool_calls = [mock_tool_call]
mock_response.choices = [Mock(message=mock_message)]
mock_groq_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages, tools=tools)
mock_groq_client.chat.completions.create.assert_called_once_with(
model="llama3-70b-8192",
messages=messages,
temperature=0.7,
max_tokens=100,
top_p=1.0,
tools=tools,
tool_choice="auto",
)
assert response == "I'm doing well, thank you for asking!"
assert response["content"] == "I've added the memory for you."
assert len(response["tool_calls"]) == 1
assert response["tool_calls"][0]["name"] == "add_memory"
assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}

62
tests/llms/test_litellm.py
View File

@@ -13,22 +13,17 @@ def mock_litellm():
def test_generate_response_with_unsupported_model(mock_litellm):
config = BaseLlmConfig(
model="unsupported-model", temperature=0.7, max_tokens=100, top_p=1
)
config = BaseLlmConfig(model="unsupported-model", temperature=0.7, max_tokens=100, top_p=1)
llm = litellm.LiteLLM(config)
messages = [{"role": "user", "content": "Hello"}]
mock_litellm.supports_function_calling.return_value = False
with pytest.raises(
ValueError,
match="Model 'unsupported-model' in LiteLLM does not support function calling.",
):
with pytest.raises(ValueError, match="Model 'unsupported-model' in litellm does not support function calling."):
llm.generate_response(messages)
def test_generate_response(mock_litellm):
def test_generate_response_without_tools(mock_litellm):
config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1)
llm = litellm.LiteLLM(config)
messages = [
@@ -37,9 +32,7 @@ def test_generate_response(mock_litellm):
]
mock_response = Mock()
mock_response.choices = [
Mock(message=Mock(content="I'm doing well, thank you for asking!"))
]
mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
mock_litellm.completion.return_value = mock_response
mock_litellm.supports_function_calling.return_value = True
@@ -49,3 +42,50 @@ def test_generate_response(mock_litellm):
model="gpt-4o", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0
)
assert response == "I'm doing well, thank you for asking!"
def test_generate_response_with_tools(mock_litellm):
config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1)
llm = litellm.LiteLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Add a new memory: Today is a sunny day."},
]
tools = [
{
"type": "function",
"function": {
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
},
}
]
mock_response = Mock()
mock_message = Mock()
mock_message.content = "I've added the memory for you."
mock_tool_call = Mock()
mock_tool_call.function.name = "add_memory"
mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
mock_message.tool_calls = [mock_tool_call]
mock_response.choices = [Mock(message=mock_message)]
mock_litellm.completion.return_value = mock_response
mock_litellm.supports_function_calling.return_value = True
response = llm.generate_response(messages, tools=tools)
mock_litellm.completion.assert_called_once_with(
model="gpt-4o", messages=messages, temperature=0.7, max_tokens=100, top_p=1, tools=tools, tool_choice="auto"
)
assert response["content"] == "I've added the memory for you."
assert len(response["tool_calls"]) == 1
assert response["tool_calls"][0]["name"] == "add_memory"
assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}

67
tests/llms/test_openai.py
View File

@@ -16,9 +16,7 @@ def mock_openai_client():
def test_openai_llm_base_url():
# case1: default config: with openai official base url
config = BaseLlmConfig(
model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key"
)
config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
llm = OpenAILLM(config)
# Note: openai client will parse the raw base_url into a URL object, which will have a trailing slash
assert str(llm.client.base_url) == "https://api.openai.com/v1/"
@@ -26,9 +24,7 @@ def test_openai_llm_base_url():
# case2: with env variable OPENAI_API_BASE
provider_base_url = "https://api.provider.com/v1"
os.environ["OPENAI_API_BASE"] = provider_base_url
config = BaseLlmConfig(
model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key"
)
config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
llm = OpenAILLM(config)
# Note: openai client will parse the raw base_url into a URL object, which will have a trailing slash
assert str(llm.client.base_url) == provider_base_url + "/"
@@ -36,19 +32,14 @@ def test_openai_llm_base_url():
# case3: with config.openai_base_url
config_base_url = "https://api.config.com/v1"
config = BaseLlmConfig(
model="gpt-4o",
temperature=0.7,
max_tokens=100,
top_p=1.0,
api_key="api_key",
openai_base_url=config_base_url,
model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key", openai_base_url=config_base_url
)
llm = OpenAILLM(config)
# Note: openai client will parse the raw base_url into a URL object, which will have a trailing slash
assert str(llm.client.base_url) == config_base_url + "/"
def test_generate_response(mock_openai_client):
def test_generate_response_without_tools(mock_openai_client):
config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0)
llm = OpenAILLM(config)
messages = [
@@ -57,9 +48,7 @@ def test_generate_response(mock_openai_client):
]
mock_response = Mock()
mock_response.choices = [
Mock(message=Mock(content="I'm doing well, thank you for asking!"))
]
mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
mock_openai_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages)
@@ -68,3 +57,49 @@ def test_generate_response(mock_openai_client):
model="gpt-4o", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0
)
assert response == "I'm doing well, thank you for asking!"
def test_generate_response_with_tools(mock_openai_client):
config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0)
llm = OpenAILLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Add a new memory: Today is a sunny day."},
]
tools = [
{
"type": "function",
"function": {
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
},
}
]
mock_response = Mock()
mock_message = Mock()
mock_message.content = "I've added the memory for you."
mock_tool_call = Mock()
mock_tool_call.function.name = "add_memory"
mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
mock_message.tool_calls = [mock_tool_call]
mock_response.choices = [Mock(message=mock_message)]
mock_openai_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages, tools=tools)
mock_openai_client.chat.completions.create.assert_called_once_with(
model="gpt-4o", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0, tools=tools, tool_choice="auto"
)
assert response["content"] == "I've added the memory for you."
assert len(response["tool_calls"]) == 1
assert response["tool_calls"][0]["name"] == "add_memory"
assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}

63
tests/llms/test_together.py
View File

@@ -14,13 +14,8 @@ def mock_together_client():
yield mock_client
def test_generate_response(mock_together_client):
config = BaseLlmConfig(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
temperature=0.7,
max_tokens=100,
top_p=1.0,
)
def test_generate_response_without_tools(mock_together_client):
config = BaseLlmConfig(model="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.7, max_tokens=100, top_p=1.0)
llm = TogetherLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
@@ -28,18 +23,64 @@ def test_generate_response(mock_together_client):
]
mock_response = Mock()
mock_response.choices = [
Mock(message=Mock(content="I'm doing well, thank you for asking!"))
]
mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
mock_together_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages)
mock_together_client.chat.completions.create.assert_called_once_with(
model="mistralai/Mixtral-8x7B-Instruct-v0.1", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0
)
assert response == "I'm doing well, thank you for asking!"
def test_generate_response_with_tools(mock_together_client):
config = BaseLlmConfig(model="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.7, max_tokens=100, top_p=1.0)
llm = TogetherLLM(config)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Add a new memory: Today is a sunny day."},
]
tools = [
{
"type": "function",
"function": {
"name": "add_memory",
"description": "Add a memory",
"parameters": {
"type": "object",
"properties": {"data": {"type": "string", "description": "Data to add to memory"}},
"required": ["data"],
},
},
}
]
mock_response = Mock()
mock_message = Mock()
mock_message.content = "I've added the memory for you."
mock_tool_call = Mock()
mock_tool_call.function.name = "add_memory"
mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
mock_message.tool_calls = [mock_tool_call]
mock_response.choices = [Mock(message=mock_message)]
mock_together_client.chat.completions.create.return_value = mock_response
response = llm.generate_response(messages, tools=tools)
mock_together_client.chat.completions.create.assert_called_once_with(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
messages=messages,
temperature=0.7,
max_tokens=100,
top_p=1.0,
tools=tools,
tool_choice="auto",
)
assert response == "I'm doing well, thank you for asking!"
assert response["content"] == "I've added the memory for you."
assert len(response["tool_calls"]) == 1
assert response["tool_calls"][0]["name"] == "add_memory"
assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}