mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
fix(utils.py): ensure argument is always a string
This commit is contained in:
Krrish Dholakia
parent
673e9f0703
commit
b6017115e3
2 changed files with 147 additions and 46 deletions
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@ -4,6 +4,7 @@
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import sys, os, asyncio
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import traceback
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import time, pytest
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from pydantic import BaseModel
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sys.path.insert(
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0, os.path.abspath("../..")
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@ -92,7 +93,7 @@ def validate_second_format(chunk):
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for choice in chunk["choices"]:
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assert isinstance(choice["index"], int), "'index' should be an integer."
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assert "role" not in choice["delta"], "'role' should be a string."
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assert hasattr(choice["delta"], "role"), "'role' should be a string."
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# openai v1.0.0 returns content as None
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assert (choice["finish_reason"] is None) or isinstance(
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choice["finish_reason"], str
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@ -1455,8 +1456,8 @@ first_openai_function_call_example = {
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def validate_first_function_call_chunk_structure(item):
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if not isinstance(item, dict):
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raise Exception("Incorrect format")
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if not (isinstance(item, dict) or isinstance(item, litellm.ModelResponse)):
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raise Exception(f"Incorrect format, type of item: {type(item)}")
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required_keys = {"id", "object", "created", "model", "choices"}
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for key in required_keys:
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@ -1468,27 +1469,42 @@ def validate_first_function_call_chunk_structure(item):
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required_keys_in_choices_array = {"index", "delta", "finish_reason"}
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for choice in item["choices"]:
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if not isinstance(choice, dict):
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raise Exception("Incorrect format")
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if not (
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isinstance(choice, dict)
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or isinstance(choice, litellm.utils.StreamingChoices)
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):
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raise Exception(f"Incorrect format, type of choice: {type(choice)}")
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for key in required_keys_in_choices_array:
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if key not in choice:
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raise Exception("Incorrect format")
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if not isinstance(choice["delta"], dict):
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raise Exception("Incorrect format")
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if not (
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isinstance(choice["delta"], dict)
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or isinstance(choice["delta"], litellm.utils.Delta)
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):
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raise Exception(
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f"Incorrect format, type of choice: {type(choice['delta'])}"
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)
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required_keys_in_delta = {"role", "content", "function_call"}
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for key in required_keys_in_delta:
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if key not in choice["delta"]:
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raise Exception("Incorrect format")
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if not isinstance(choice["delta"]["function_call"], dict):
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raise Exception("Incorrect format")
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if not (
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isinstance(choice["delta"]["function_call"], dict)
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or isinstance(choice["delta"]["function_call"], BaseModel)
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):
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raise Exception(
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f"Incorrect format, type of function call: {type(choice['delta']['function_call'])}"
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)
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required_keys_in_function_call = {"name", "arguments"}
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for key in required_keys_in_function_call:
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if key not in choice["delta"]["function_call"]:
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raise Exception("Incorrect format")
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if not hasattr(choice["delta"]["function_call"], key):
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raise Exception(
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f"Incorrect format, expected key={key}; actual keys: {choice['delta']['function_call']}, eval: {hasattr(choice['delta']['function_call'], key)}"
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)
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return True
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@ -1547,7 +1563,7 @@ final_function_call_chunk_example = {
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def validate_final_function_call_chunk_structure(data):
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if not isinstance(data, dict):
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if not (isinstance(data, dict) or isinstance(data, litellm.ModelResponse)):
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raise Exception("Incorrect format")
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required_keys = {"id", "object", "created", "model", "choices"}
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@ -1560,7 +1576,9 @@ def validate_final_function_call_chunk_structure(data):
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required_keys_in_choices_array = {"index", "delta", "finish_reason"}
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for choice in data["choices"]:
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if not isinstance(choice, dict):
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if not (
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isinstance(choice, dict) or isinstance(choice["delta"], litellm.utils.Delta)
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):
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raise Exception("Incorrect format")
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for key in required_keys_in_choices_array:
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if key not in choice:
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@ -1592,37 +1610,88 @@ def streaming_and_function_calling_format_tests(idx, chunk):
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return extracted_chunk, finished
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# def test_openai_streaming_and_function_calling():
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# function1 = [
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# {
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# "name": "get_current_weather",
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# "description": "Get the current weather in a given location",
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# "parameters": {
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# "type": "object",
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# "properties": {
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# "location": {
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# "type": "string",
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# "description": "The city and state, e.g. San Francisco, CA",
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# },
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# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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# },
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# "required": ["location"],
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# },
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# }
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# ]
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# messages=[{"role": "user", "content": "What is the weather like in Boston?"}]
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# try:
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# response = completion(
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# model="gpt-3.5-turbo", functions=function1, messages=messages, stream=True,
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# )
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# # Add any assertions here to check the response
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# for idx, chunk in enumerate(response):
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# streaming_and_function_calling_format_tests(idx=idx, chunk=chunk)
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# raise e
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def test_openai_streaming_and_function_calling():
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tools = [
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{
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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}
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]
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messages = [{"role": "user", "content": "What is the weather like in Boston?"}]
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try:
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response = completion(
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model="gpt-3.5-turbo",
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tools=tools,
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messages=messages,
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stream=True,
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)
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# Add any assertions here to check the response
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for idx, chunk in enumerate(response):
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streaming_and_function_calling_format_tests(idx=idx, chunk=chunk)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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raise e
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# test_openai_streaming_and_function_calling()
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def test_azure_streaming_and_function_calling():
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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},
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}
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]
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messages = [{"role": "user", "content": "What is the weather like in Boston?"}]
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try:
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response = completion(
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model="azure/gpt-4-nov-release",
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tools=tools,
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tool_choice="auto",
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messages=messages,
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stream=True,
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api_base=os.getenv("AZURE_FRANCE_API_BASE"),
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api_key=os.getenv("AZURE_FRANCE_API_KEY"),
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api_version="2024-02-15-preview",
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)
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# Add any assertions here to check the response
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for idx, chunk in enumerate(response):
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if idx == 0:
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assert (
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chunk.choices[0].delta.tool_calls[0].function.arguments is not None
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)
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assert isinstance(
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chunk.choices[0].delta.tool_calls[0].function.arguments, str
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)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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raise e
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# test_azure_streaming_and_function_calling()
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def test_success_callback_streaming():
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@ -258,11 +258,14 @@ class Message(OpenAIObject):
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class Delta(OpenAIObject):
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def __init__(self, content=None, role=None, **params):
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def __init__(
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self, content=None, role=None, function_call=None, tool_calls=None, **params
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):
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super(Delta, self).__init__(**params)
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self.content = content
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if role is not None:
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self.role = role
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self.function_call = function_call
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self.tool_calls = tool_calls
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def __contains__(self, key):
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# Define custom behavior for the 'in' operator
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@ -8675,8 +8678,37 @@ class CustomStreamWrapper:
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):
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try:
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delta = dict(original_chunk.choices[0].delta)
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## AZURE - check if arguments is not None
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if (
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original_chunk.choices[0].delta.function_call
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is not None
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):
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if (
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getattr(
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original_chunk.choices[0].delta.function_call,
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"arguments",
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)
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is None
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):
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original_chunk.choices[
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0
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].delta.function_call.arguments = ""
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elif original_chunk.choices[0].delta.tool_calls is not None:
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if isinstance(
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original_chunk.choices[0].delta.tool_calls, list
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):
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for t in original_chunk.choices[0].delta.tool_calls:
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if (
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getattr(
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t.function,
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"arguments",
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)
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is None
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):
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t.function.arguments = ""
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model_response.choices[0].delta = Delta(**delta)
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except Exception as e:
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traceback.print_exc()
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model_response.choices[0].delta = Delta()
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else:
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return
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