mirror of
https://github.com/hwchase17/langchain
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4cf523949a
- **Description:** Tongyi uses different client for chat model and vision model. This PR chooses proper client based on model name to support both chat model and vision model. Reference [tongyi document](https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api?spm=a2c4g.11186623.0.0.27404c9a7upm11) for details. ``` from langchain_core.messages import HumanMessage from langchain_community.chat_models import ChatTongyi llm = ChatTongyi(model_name='qwen-vl-max') image_message = { "image": "https://lilianweng.github.io/posts/2023-06-23-agent/agent-overview.png" } text_message = { "text": "summarize this picture", } message = HumanMessage(content=[text_message, image_message]) llm.invoke([message]) ``` - **Issue:** None - **Dependencies:** None - **Twitter handle:** None
238 lines
7.9 KiB
Python
238 lines
7.9 KiB
Python
"""Test Alibaba Tongyi Chat Model."""
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from typing import Any, List, cast
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from langchain_core.callbacks import CallbackManager
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
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from langchain_core.messages.ai import AIMessageChunk
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from langchain_core.messages.tool import ToolCall, ToolMessage
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from langchain_core.outputs import ChatGeneration, LLMResult
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from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, SecretStr
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from pytest import CaptureFixture
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from langchain_community.chat_models.tongyi import ChatTongyi
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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_FUNCTIONS: Any = [
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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_initialization() -> None:
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"""Test chat model initialization."""
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for model in [
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ChatTongyi(model_name="qwen-turbo", api_key="xyz"), # type: ignore[arg-type, call-arg]
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ChatTongyi(model="qwen-turbo", dashscope_api_key="xyz"), # type: ignore[call-arg]
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]:
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assert model.model_name == "qwen-turbo"
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assert cast(SecretStr, model.dashscope_api_key).get_secret_value() == "xyz"
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def test_api_key_is_string() -> None:
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llm = ChatTongyi(dashscope_api_key="secret-api-key") # type: ignore[call-arg]
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assert isinstance(llm.dashscope_api_key, SecretStr)
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def test_api_key_masked_when_passed_via_constructor(
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capsys: CaptureFixture,
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) -> None:
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llm = ChatTongyi(dashscope_api_key="secret-api-key") # type: ignore[call-arg]
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print(llm.dashscope_api_key, end="") # noqa: T201
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captured = capsys.readouterr()
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assert captured.out == "**********"
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def test_default_call() -> None:
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"""Test default model call."""
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chat = ChatTongyi() # type: ignore[call-arg]
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response = chat.invoke([HumanMessage(content="Hello")])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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def test_model() -> None:
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"""Test model kwarg works."""
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chat = ChatTongyi(model="qwen-plus") # type: ignore[call-arg]
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response = chat.invoke([HumanMessage(content="Hello")])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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def test_vision_model() -> None:
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"""Test model kwarg works."""
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chat = ChatTongyi(model="qwen-vl-max") # type: ignore[call-arg]
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response = chat.invoke(
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[
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HumanMessage(
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content=[
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{
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"image": "https://python.langchain.com/v0.1/assets/images/run_details-806f6581cd382d4887a5bc3e8ac62569.png"
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},
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{"text": "Summarize the image"},
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]
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)
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]
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)
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, list)
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def test_functions_call_thoughts() -> None:
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chat = ChatTongyi(model="qwen-plus") # type: ignore[call-arg]
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prompt_tmpl = "Use the given functions to answer following question: {input}"
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prompt_msgs = [
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HumanMessagePromptTemplate.from_template(prompt_tmpl),
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]
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prompt = ChatPromptTemplate(messages=prompt_msgs) # type: ignore[arg-type, call-arg]
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chain = prompt | chat.bind(functions=_FUNCTIONS)
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message = HumanMessage(content="What's the weather like in Shanghai today?")
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response = chain.batch([{"input": message}])
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assert isinstance(response[0], AIMessage)
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assert "tool_calls" in response[0].additional_kwargs
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def test_multiple_history() -> None:
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"""Tests multiple history works."""
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chat = ChatTongyi() # type: ignore[call-arg]
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response = chat.invoke(
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[
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HumanMessage(content="Hello."),
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AIMessage(content="Hello!"),
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HumanMessage(content="How are you doing?"),
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]
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)
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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def test_stream() -> None:
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"""Test that stream works."""
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chat = ChatTongyi(streaming=True) # type: ignore[call-arg]
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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response = chat.invoke(
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[
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HumanMessage(content="Hello."),
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AIMessage(content="Hello!"),
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HumanMessage(content="Who are you?"),
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],
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stream=True,
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config={"callbacks": callback_manager},
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)
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assert callback_handler.llm_streams > 0
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assert isinstance(response.content, str)
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def test_multiple_messages() -> None:
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"""Tests multiple messages works."""
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chat = ChatTongyi() # type: ignore[call-arg]
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message = HumanMessage(content="Hi, how are you.")
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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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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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class GenerateUsername(BaseModel):
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"Get a username based on someone's name and hair color."
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name: str
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hair_color: str
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def test_tool_use() -> None:
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llm = ChatTongyi(model="qwen-turbo", temperature=0) # type: ignore
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llm_with_tool = llm.bind_tools(tools=[GenerateUsername])
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msgs: List = [
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HumanMessage(content="Sally has green hair, what would her username be?")
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]
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ai_msg = llm_with_tool.invoke(msgs)
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# assert ai_msg is None
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# ai_msg.content = " "
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assert isinstance(ai_msg, AIMessage)
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assert isinstance(ai_msg.tool_calls, list)
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assert len(ai_msg.tool_calls) == 1
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tool_call = ai_msg.tool_calls[0]
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assert "args" in tool_call
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tool_msg = ToolMessage(
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content="sally_green_hair",
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tool_call_id=ai_msg.tool_calls[0]["id"], # type: ignore
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name=ai_msg.tool_calls[0]["name"],
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)
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msgs.extend([ai_msg, tool_msg])
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llm_with_tool.invoke(msgs)
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# Test streaming
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ai_messages = llm_with_tool.stream(msgs)
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first = True
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for message in ai_messages:
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if first:
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gathered = message
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first = False
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else:
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gathered = gathered + message # type: ignore
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assert isinstance(gathered, AIMessageChunk)
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streaming_tool_msg = ToolMessage(
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content="sally_green_hair",
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name=tool_call["name"],
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tool_call_id=tool_call["id"] if tool_call["id"] else " ",
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)
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msgs.extend([gathered, streaming_tool_msg])
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llm_with_tool.invoke(msgs)
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def test_manual_tool_call_msg() -> None:
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"""Test passing in manually construct tool call message."""
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llm = ChatTongyi(model="qwen-turbo", temperature=0) # type: ignore
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llm_with_tool = llm.bind_tools(tools=[GenerateUsername])
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msgs: List = [
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HumanMessage(content="Sally has green hair, what would her username be?"),
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AIMessage(
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content=" ",
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tool_calls=[
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ToolCall(
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name="GenerateUsername",
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args={"name": "Sally", "hair_color": "green"},
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id="foo",
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)
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],
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),
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ToolMessage(content="sally_green_hair", tool_call_id="foo"),
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]
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output: AIMessage = cast(AIMessage, llm_with_tool.invoke(msgs))
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assert output.content
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# Should not have called the tool again.
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assert not output.tool_calls and not output.invalid_tool_calls
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