mirror of
https://github.com/hwchase17/langchain
synced 2024-11-20 03:25:56 +00:00
5f839beab9
This is technically a breaking change because it'll switch out default models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI is shutting off those endpoints on 1/4 anyways. Feels less disruptive to switch out the default instead.
333 lines
11 KiB
Python
333 lines
11 KiB
Python
"""Test ChatOpenAI wrapper."""
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from typing import Any, Optional
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import pytest
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from langchain_core.callbacks import CallbackManager
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
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from langchain_core.outputs import (
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ChatGeneration,
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ChatResult,
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LLMResult,
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)
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_community.chat_models.openai import ChatOpenAI
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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@pytest.mark.scheduled
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def test_chat_openai() -> None:
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"""Test ChatOpenAI wrapper."""
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chat = ChatOpenAI(
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temperature=0.7,
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base_url=None,
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organization=None,
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openai_proxy=None,
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timeout=10.0,
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max_retries=3,
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http_client=None,
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n=1,
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max_tokens=10,
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default_headers=None,
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default_query=None,
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)
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message = HumanMessage(content="Hello")
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response = chat([message])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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def test_chat_openai_model() -> None:
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"""Test ChatOpenAI wrapper handles model_name."""
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chat = ChatOpenAI(model="foo")
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assert chat.model_name == "foo"
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chat = ChatOpenAI(model_name="bar")
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assert chat.model_name == "bar"
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def test_chat_openai_system_message() -> None:
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"""Test ChatOpenAI wrapper with system message."""
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chat = ChatOpenAI(max_tokens=10)
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system_message = SystemMessage(content="You are to chat with the user.")
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human_message = HumanMessage(content="Hello")
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response = chat([system_message, human_message])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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@pytest.mark.scheduled
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def test_chat_openai_generate() -> None:
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"""Test ChatOpenAI wrapper with generate."""
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chat = ChatOpenAI(max_tokens=10, n=2)
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message = HumanMessage(content="Hello")
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response = chat.generate([[message], [message]])
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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assert response.llm_output
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for generations in response.generations:
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assert len(generations) == 2
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@pytest.mark.scheduled
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def test_chat_openai_multiple_completions() -> None:
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"""Test ChatOpenAI wrapper with multiple completions."""
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chat = ChatOpenAI(max_tokens=10, n=5)
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message = HumanMessage(content="Hello")
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response = chat._generate([message])
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assert isinstance(response, ChatResult)
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assert len(response.generations) == 5
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for generation in response.generations:
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assert isinstance(generation.message, BaseMessage)
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assert isinstance(generation.message.content, str)
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@pytest.mark.scheduled
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def test_chat_openai_streaming() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = ChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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message = HumanMessage(content="Hello")
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response = chat([message])
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assert callback_handler.llm_streams > 0
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assert isinstance(response, BaseMessage)
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@pytest.mark.scheduled
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def test_chat_openai_streaming_generation_info() -> None:
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"""Test that generation info is preserved when streaming."""
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class _FakeCallback(FakeCallbackHandler):
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saved_things: dict = {}
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def on_llm_end(
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self,
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*args: Any,
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**kwargs: Any,
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) -> Any:
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# Save the generation
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self.saved_things["generation"] = args[0]
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callback = _FakeCallback()
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callback_manager = CallbackManager([callback])
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chat = ChatOpenAI(
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max_tokens=2,
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temperature=0,
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callback_manager=callback_manager,
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)
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list(chat.stream("hi"))
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generation = callback.saved_things["generation"]
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# `Hello!` is two tokens, assert that that is what is returned
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assert generation.generations[0][0].text == "Hello!"
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def test_chat_openai_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatOpenAI(max_tokens=10)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model_name
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def test_chat_openai_streaming_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatOpenAI(max_tokens=10, streaming=True)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model_name
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def test_chat_openai_invalid_streaming_params() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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with pytest.raises(ValueError):
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ChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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n=5,
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)
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@pytest.mark.scheduled
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async def test_async_chat_openai() -> None:
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"""Test async generation."""
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chat = ChatOpenAI(max_tokens=10, n=2)
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message = HumanMessage(content="Hello")
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response = await chat.agenerate([[message], [message]])
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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assert response.llm_output
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for generations in response.generations:
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assert len(generations) == 2
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@pytest.mark.scheduled
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async def test_async_chat_openai_streaming() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = ChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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message = HumanMessage(content="Hello")
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response = await chat.agenerate([[message], [message]])
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assert callback_handler.llm_streams > 0
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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for generations in response.generations:
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assert len(generations) == 1
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@pytest.mark.scheduled
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async def test_async_chat_openai_bind_functions() -> None:
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"""Test ChatOpenAI wrapper with multiple completions."""
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class Person(BaseModel):
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"""Identifying information about a person."""
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name: str = Field(..., title="Name", description="The person's name")
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age: int = Field(..., title="Age", description="The person's age")
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fav_food: Optional[str] = Field(
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default=None, title="Fav Food", description="The person's favorite food"
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)
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chat = ChatOpenAI(
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max_tokens=30,
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n=1,
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streaming=True,
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).bind_functions(functions=[Person], function_call="Person")
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "Use the provided Person function"),
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("user", "{input}"),
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]
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)
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chain = prompt | chat
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message = HumanMessage(content="Sally is 13 years old")
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response = await chain.abatch([{"input": message}])
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assert isinstance(response, list)
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assert len(response) == 1
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for generation in response:
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assert isinstance(generation, AIMessage)
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def test_chat_openai_extra_kwargs() -> None:
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"""Test extra kwargs to chat openai."""
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# Check that foo is saved in extra_kwargs.
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llm = ChatOpenAI(foo=3, max_tokens=10)
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assert llm.max_tokens == 10
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assert llm.model_kwargs == {"foo": 3}
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# Test that if extra_kwargs are provided, they are added to it.
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llm = ChatOpenAI(foo=3, model_kwargs={"bar": 2})
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assert llm.model_kwargs == {"foo": 3, "bar": 2}
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# Test that if provided twice it errors
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with pytest.raises(ValueError):
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ChatOpenAI(foo=3, model_kwargs={"foo": 2})
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# Test that if explicit param is specified in kwargs it errors
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with pytest.raises(ValueError):
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ChatOpenAI(model_kwargs={"temperature": 0.2})
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# Test that "model" cannot be specified in kwargs
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with pytest.raises(ValueError):
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ChatOpenAI(model_kwargs={"model": "gpt-3.5-turbo-instruct"})
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@pytest.mark.scheduled
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def test_openai_streaming() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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for token in llm.stream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_astream() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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async for token in llm.astream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_abatch() -> None:
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"""Test streaming tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_abatch_tags() -> None:
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"""Test batch tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = await llm.abatch(
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["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
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)
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for token in result:
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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def test_openai_batch() -> None:
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"""Test batch tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_ainvoke() -> None:
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"""Test invoke tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
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assert isinstance(result.content, str)
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@pytest.mark.scheduled
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def test_openai_invoke() -> None:
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"""Test invoke tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
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assert isinstance(result.content, str)
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