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(Feat) - Add /bedrock/meta.llama3-3-70b-instruct-v1:0 tool calling support + cost tracking + base llm unit test for tool calling (#8545)

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* Add support for bedrock meta.llama3-3-70b-instruct-v1:0 tool calling (#8512)

* fix(converse_transformation.py): fixing bedrock meta.llama3-3-70b tool calling

* test(test_bedrock_completion.py): adding llama3.3 tool compatibility check

* add TestBedrockTestSuite

* add bedrock llama 3.3 to base llm class

* us.meta.llama3-3-70b-instruct-v1:0

* test_basic_tool_calling

* TestAzureOpenAIO1

* test_basic_tool_calling

* test_basic_tool_calling

---------

Co-authored-by: miraclebakelaser <65143272+miraclebakelaser@users.noreply.github.com>
This commit is contained in:
Ishaan Jaff 2025-02-14 14:15:25 -08:00 • committed by GitHub
parent 3deefc8e2f
commit 1bbfdda84b
7 changed files with 154 additions and 2 deletions
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101
tests/llm_translation/base_llm_unit_tests.py
View file

@ -634,6 +634,107 @@ class BaseLLMChatTest(ABC):
return url
def test_basic_tool_calling(self):
try:
from litellm import completion, ModelResponse
litellm.set_verbose = True
litellm._turn_on_debug()
from litellm.utils import supports_function_calling
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
base_completion_call_args = self.get_base_completion_call_args()
if not supports_function_calling(base_completion_call_args["model"], None):
print("Model does not support function calling")
pytest.skip("Model does not support function calling")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["location"],
},
},
}
]
messages = [
{
"role": "user",
"content": "What's the weather like in Boston today in fahrenheit?",
}
]
request_args = {
"messages": messages,
"tools": tools,
}
request_args.update(self.get_base_completion_call_args())
response: ModelResponse = completion(**request_args) # type: ignore
print(f"response: {response}")
assert response is not None
# if the provider did not return any tool calls do not make a subsequent llm api call
if response.choices[0].message.tool_calls is None:
return
# Add any assertions here to check the response
assert isinstance(
response.choices[0].message.tool_calls[0].function.name, str
)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
messages.append(
response.choices[0].message.model_dump()
) # Add assistant tool invokes
tool_result = (
'{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
)
# Add user submitted tool results in the OpenAI format
messages.append(
{
"tool_call_id": response.choices[0].message.tool_calls[0].id,
"role": "tool",
"name": response.choices[0].message.tool_calls[0].function.name,
"content": tool_result,
}
)
# In the second response, Claude should deduce answer from tool results
request_2_args = {
"messages": messages,
"tools": tools,
}
request_2_args.update(self.get_base_completion_call_args())
second_response: ModelResponse = completion(**request_2_args) # type: ignore
print(f"second response: {second_response}")
assert second_response is not None
# either content or tool calls should be present
assert (
second_response.choices[0].message.content is not None
or second_response.choices[0].message.tool_calls is not None
)
except litellm.RateLimitError:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.asyncio
async def test_completion_cost(self):
from litellm import completion_cost