forked from phoenix/litellm-mirror
e9aa492af3
* fix(ollama.py): fix get model info request Fixes https://github.com/BerriAI/litellm/issues/6703 * feat(anthropic/chat/transformation.py): support passing user id to anthropic via openai 'user' param * docs(anthropic.md): document all supported openai params for anthropic * test: fix tests * fix: fix tests * feat(jina_ai/): add rerank support Closes https://github.com/BerriAI/litellm/issues/6691 * test: handle service unavailable error * fix(handler.py): refactor together ai rerank call * test: update test to handle overloaded error * test: fix test * Litellm router trace (#6742) * feat(router.py): add trace_id to parent functions - allows tracking retry/fallbacks * feat(router.py): log trace id across retry/fallback logic allows grouping llm logs for the same request * test: fix tests * fix: fix test * fix(transformation.py): only set non-none stop_sequences * Litellm router disable fallbacks (#6743) * bump: version 1.52.6 → 1.52.7 * feat(router.py): enable dynamically disabling fallbacks Allows for enabling/disabling fallbacks per key * feat(litellm_pre_call_utils.py): support setting 'disable_fallbacks' on litellm key * test: fix test * fix(exception_mapping_utils.py): map 'model is overloaded' to internal server error * test: handle gemini error * test: fix test * fix: new run
732 lines
24 KiB
Python
732 lines
24 KiB
Python
import asyncio
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import os
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import sys
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import time
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import traceback
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import pytest
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from typing import List
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from litellm.types.utils import StreamingChoices, ChatCompletionAudioResponse
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def check_non_streaming_response(completion):
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assert completion.choices[0].message.audio is not None, "Audio response is missing"
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print("audio", completion.choices[0].message.audio)
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assert isinstance(
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completion.choices[0].message.audio, ChatCompletionAudioResponse
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), "Invalid audio response type"
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assert len(completion.choices[0].message.audio.data) > 0, "Audio data is empty"
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import os
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import dotenv
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from openai import OpenAI
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import litellm
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import stream_chunk_testdata
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from litellm import completion, stream_chunk_builder
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dotenv.load_dotenv()
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user_message = "What is the current weather in Boston?"
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messages = [{"content": user_message, "role": "user"}]
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function_schema = {
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"name": "get_weather",
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"description": "gets the current weather",
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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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},
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"required": ["location"],
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},
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}
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tools_schema = [
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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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# def test_stream_chunk_builder_tools():
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# try:
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# litellm.set_verbose = False
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# response = client.chat.completions.create(
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# model="gpt-3.5-turbo",
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# messages=messages,
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# tools=tools_schema,
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# # stream=True,
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# # complete_response=True # runs stream_chunk_builder under-the-hood
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# )
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# print(f"response: {response}")
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# print(f"response usage: {response.usage}")
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# except Exception as e:
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# pytest.fail(f"An exception occurred - {str(e)}")
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# test_stream_chunk_builder_tools()
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def test_stream_chunk_builder_litellm_function_call():
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try:
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litellm.set_verbose = False
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=messages,
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functions=[function_schema],
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# stream=True,
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# complete_response=True # runs stream_chunk_builder under-the-hood
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)
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print(f"response: {response}")
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except Exception as e:
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pytest.fail(f"An exception occurred - {str(e)}")
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# test_stream_chunk_builder_litellm_function_call()
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def test_stream_chunk_builder_litellm_tool_call():
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try:
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litellm.set_verbose = True
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=messages,
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tools=tools_schema,
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stream=True,
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complete_response=True,
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)
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print(f"complete response: {response}")
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print(f"complete response usage: {response.usage}")
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assert response.usage.completion_tokens > 0
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assert response.usage.prompt_tokens > 0
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assert (
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response.usage.total_tokens
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== response.usage.completion_tokens + response.usage.prompt_tokens
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)
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except Exception as e:
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pytest.fail(f"An exception occurred - {str(e)}")
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# test_stream_chunk_builder_litellm_tool_call()
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def test_stream_chunk_builder_litellm_tool_call_regular_message():
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try:
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messages = [{"role": "user", "content": "Hey, how's it going?"}]
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# litellm.set_verbose = True
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=messages,
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tools=tools_schema,
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stream=True,
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complete_response=True,
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)
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print(f"complete response: {response}")
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print(f"complete response usage: {response.usage}")
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assert response.usage.completion_tokens > 0
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assert response.usage.prompt_tokens > 0
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assert (
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response.usage.total_tokens
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== response.usage.completion_tokens + response.usage.prompt_tokens
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)
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# check provider is in hidden params
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print("hidden params", response._hidden_params)
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assert response._hidden_params["custom_llm_provider"] == "openai"
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except Exception as e:
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pytest.fail(f"An exception occurred - {str(e)}")
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# test_stream_chunk_builder_litellm_tool_call_regular_message()
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def test_stream_chunk_builder_litellm_usage_chunks():
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"""
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Checks if stream_chunk_builder is able to correctly rebuild with given metadata from streaming chunks
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"""
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from litellm.types.utils import Usage
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messages = [
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{"role": "user", "content": "Tell me the funniest joke you know."},
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{
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"role": "assistant",
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"content": "Why did the chicken cross the road?\nYou will not guess this one I bet\n",
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},
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{"role": "user", "content": "I do not know, why?"},
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{"role": "assistant", "content": "uhhhh\n\n\nhmmmm.....\nthinking....\n"},
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{"role": "user", "content": "\nI am waiting...\n\n...\n"},
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]
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usage: litellm.Usage = Usage(
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completion_tokens=27,
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prompt_tokens=55,
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total_tokens=82,
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completion_tokens_details=None,
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prompt_tokens_details=None,
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)
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gemini_pt = usage.prompt_tokens
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# make a streaming gemini call
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try:
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response = completion(
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model="gemini/gemini-1.5-flash",
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messages=messages,
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stream=True,
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complete_response=True,
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stream_options={"include_usage": True},
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)
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except litellm.InternalServerError as e:
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pytest.skip(f"Skipping test due to internal server error - {str(e)}")
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usage: litellm.Usage = response.usage
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stream_rebuilt_pt = usage.prompt_tokens
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# assert prompt tokens are the same
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assert gemini_pt == stream_rebuilt_pt
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def test_stream_chunk_builder_litellm_mixed_calls():
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response = stream_chunk_builder(stream_chunk_testdata.chunks)
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assert (
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response.choices[0].message.content
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== "To answer your question about how many rows are in the 'users' table, I'll need to run a SQL query. Let me do that for you."
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)
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print(response.choices[0].message.tool_calls[0].to_dict())
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assert len(response.choices[0].message.tool_calls) == 1
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assert response.choices[0].message.tool_calls[0].to_dict() == {
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"function": {
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"arguments": '{"query": "SELECT COUNT(*) FROM users;"}',
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"name": "sql_query",
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},
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"id": "toolu_01H3AjkLpRtGQrof13CBnWfK",
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"type": "function",
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}
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def test_stream_chunk_builder_litellm_empty_chunks():
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with pytest.raises(litellm.APIError):
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response = stream_chunk_builder(chunks=None)
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response = stream_chunk_builder(chunks=[])
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assert response is None
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def test_stream_chunk_builder_multiple_tool_calls():
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init_chunks = [
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"id": "call_X9P9B6STj7ze8OsJCGkfoN94",
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"function": {"arguments": "", "name": "exponentiate"},
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"type": "function",
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"index": 0,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": '{"ba'},
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"type": "function",
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"index": 0,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": 'se": '},
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"type": "function",
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"index": 0,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": '3, "ex'},
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"type": "function",
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"index": 0,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": "pone"},
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"type": "function",
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"index": 0,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": 'nt": '},
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"type": "function",
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"index": 0,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": "5}"},
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"type": "function",
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"index": 0,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"id": "call_Qq8yDeRx7v276abRcLrYORdW",
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"function": {"arguments": "", "name": "add"},
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"type": "function",
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"index": 1,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": '{"fi'},
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"type": "function",
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"index": 1,
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}
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],
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},
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}
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],
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"created": 1725932618,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": "rst_i"},
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"type": "function",
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"index": 1,
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}
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],
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},
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}
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],
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"created": 1725932618,
|
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"model": "gpt-4o-2024-08-06",
|
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
|
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
|
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"choices": [
|
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{
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"index": 0,
|
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"delta": {
|
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": 'nt": 1'},
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"type": "function",
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"index": 1,
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}
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],
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},
|
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}
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],
|
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"created": 1725932618,
|
|
"model": "gpt-4o-2024-08-06",
|
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"object": "chat.completion.chunk",
|
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"system_fingerprint": "fp_b2ffeb16ee",
|
|
},
|
|
{
|
|
"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
|
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"choices": [
|
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{
|
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"index": 0,
|
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"delta": {
|
|
"role": "assistant",
|
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"tool_calls": [
|
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{
|
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"function": {"arguments": '2, "'},
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"type": "function",
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"index": 1,
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}
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],
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},
|
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}
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],
|
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"created": 1725932618,
|
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"model": "gpt-4o-2024-08-06",
|
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_b2ffeb16ee",
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},
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{
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"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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"tool_calls": [
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{
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"function": {"arguments": "secon"},
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"type": "function",
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"index": 1,
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}
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],
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},
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}
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],
|
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"created": 1725932618,
|
|
"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
|
|
"system_fingerprint": "fp_b2ffeb16ee",
|
|
},
|
|
{
|
|
"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {
|
|
"role": "assistant",
|
|
"tool_calls": [
|
|
{
|
|
"function": {"arguments": 'd_int"'},
|
|
"type": "function",
|
|
"index": 1,
|
|
}
|
|
],
|
|
},
|
|
}
|
|
],
|
|
"created": 1725932618,
|
|
"model": "gpt-4o-2024-08-06",
|
|
"object": "chat.completion.chunk",
|
|
"system_fingerprint": "fp_b2ffeb16ee",
|
|
},
|
|
{
|
|
"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {
|
|
"role": "assistant",
|
|
"tool_calls": [
|
|
{
|
|
"function": {"arguments": ": 3}"},
|
|
"type": "function",
|
|
"index": 1,
|
|
}
|
|
],
|
|
},
|
|
}
|
|
],
|
|
"created": 1725932618,
|
|
"model": "gpt-4o-2024-08-06",
|
|
"object": "chat.completion.chunk",
|
|
"system_fingerprint": "fp_b2ffeb16ee",
|
|
},
|
|
{
|
|
"id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
|
|
"choices": [{"finish_reason": "tool_calls", "index": 0, "delta": {}}],
|
|
"created": 1725932618,
|
|
"model": "gpt-4o-2024-08-06",
|
|
"object": "chat.completion.chunk",
|
|
"system_fingerprint": "fp_b2ffeb16ee",
|
|
},
|
|
]
|
|
|
|
chunks = []
|
|
for chunk in init_chunks:
|
|
chunks.append(litellm.ModelResponse(**chunk, stream=True))
|
|
response = stream_chunk_builder(chunks=chunks)
|
|
|
|
print(f"Returned response: {response}")
|
|
completed_response = {
|
|
"id": "chatcmpl-A61mXjvcRX0Xr2IiojN9TPiy1P3Fm",
|
|
"choices": [
|
|
{
|
|
"finish_reason": "tool_calls",
|
|
"index": 0,
|
|
"message": {
|
|
"content": None,
|
|
"role": "assistant",
|
|
"tool_calls": [
|
|
{
|
|
"function": {
|
|
"arguments": '{"base": 3, "exponent": 5}',
|
|
"name": "exponentiate",
|
|
},
|
|
"id": "call_X9P9B6STj7ze8OsJCGkfoN94",
|
|
"type": "function",
|
|
},
|
|
{
|
|
"function": {
|
|
"arguments": '{"first_int": 12, "second_int": 3}',
|
|
"name": "add",
|
|
},
|
|
"id": "call_Qq8yDeRx7v276abRcLrYORdW",
|
|
"type": "function",
|
|
},
|
|
],
|
|
"function_call": None,
|
|
},
|
|
}
|
|
],
|
|
"created": 1726000181,
|
|
"model": "gpt-4o-2024-05-13",
|
|
"object": "chat.completion",
|
|
"system_fingerprint": "fp_25624ae3a5",
|
|
"usage": {"completion_tokens": 55, "prompt_tokens": 127, "total_tokens": 182},
|
|
"service_tier": None,
|
|
}
|
|
|
|
expected_response = litellm.ModelResponse(**completed_response)
|
|
|
|
print(f"\n\nexpected_response:\n{expected_response}\n\n")
|
|
assert (
|
|
expected_response.choices == response.choices
|
|
), "\nGot={}\n, Expected={}\n".format(response.choices, expected_response.choices)
|
|
|
|
|
|
def test_stream_chunk_builder_openai_prompt_caching():
|
|
from openai import OpenAI
|
|
from pydantic import BaseModel
|
|
|
|
client = OpenAI(
|
|
# This is the default and can be omitted
|
|
api_key=os.getenv("OPENAI_API_KEY"),
|
|
)
|
|
|
|
chat_completion = client.chat.completions.create(
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": "Say this is a test",
|
|
}
|
|
],
|
|
model="gpt-3.5-turbo",
|
|
stream=True,
|
|
stream_options={"include_usage": True},
|
|
)
|
|
chunks: List[litellm.ModelResponse] = []
|
|
usage_obj = None
|
|
for chunk in chat_completion:
|
|
chunks.append(litellm.ModelResponse(**chunk.model_dump(), stream=True))
|
|
|
|
print(f"chunks: {chunks}")
|
|
|
|
usage_obj: litellm.Usage = chunks[-1].usage # type: ignore
|
|
|
|
response = stream_chunk_builder(chunks=chunks)
|
|
print(f"response: {response}")
|
|
print(f"response usage: {response.usage}")
|
|
for k, v in usage_obj.model_dump(exclude_none=True).items():
|
|
print(k, v)
|
|
response_usage_value = getattr(response.usage, k) # type: ignore
|
|
print(f"response_usage_value: {response_usage_value}")
|
|
print(f"type: {type(response_usage_value)}")
|
|
if isinstance(response_usage_value, BaseModel):
|
|
assert response_usage_value.model_dump(exclude_none=True) == v
|
|
else:
|
|
assert response_usage_value == v
|
|
|
|
|
|
def test_stream_chunk_builder_openai_audio_output_usage():
|
|
from pydantic import BaseModel
|
|
from openai import OpenAI
|
|
from typing import Optional
|
|
|
|
client = OpenAI(
|
|
# This is the default and can be omitted
|
|
api_key=os.getenv("OPENAI_API_KEY"),
|
|
)
|
|
|
|
completion = client.chat.completions.create(
|
|
model="gpt-4o-audio-preview",
|
|
modalities=["text", "audio"],
|
|
audio={"voice": "alloy", "format": "pcm16"},
|
|
messages=[{"role": "user", "content": "response in 1 word - yes or no"}],
|
|
stream=True,
|
|
stream_options={"include_usage": True},
|
|
)
|
|
|
|
chunks = []
|
|
for chunk in completion:
|
|
chunks.append(litellm.ModelResponse(**chunk.model_dump(), stream=True))
|
|
|
|
usage_obj: Optional[litellm.Usage] = None
|
|
|
|
for index, chunk in enumerate(chunks):
|
|
if hasattr(chunk, "usage"):
|
|
usage_obj = chunk.usage
|
|
print(f"chunk usage: {chunk.usage}")
|
|
print(f"index: {index}")
|
|
print(f"len chunks: {len(chunks)}")
|
|
|
|
print(f"usage_obj: {usage_obj}")
|
|
response = stream_chunk_builder(chunks=chunks)
|
|
print(f"response usage: {response.usage}")
|
|
check_non_streaming_response(response)
|
|
print(f"response: {response}")
|
|
for k, v in usage_obj.model_dump(exclude_none=True).items():
|
|
print(k, v)
|
|
response_usage_value = getattr(response.usage, k) # type: ignore
|
|
print(f"response_usage_value: {response_usage_value}")
|
|
print(f"type: {type(response_usage_value)}")
|
|
if isinstance(response_usage_value, BaseModel):
|
|
assert response_usage_value.model_dump(exclude_none=True) == v
|
|
else:
|
|
assert response_usage_value == v
|