mirror of
https://github.com/hwchase17/langchain.git
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4bb1a11e02
- **Description:** - Add `bind_tools` method to support tool calling - Add `with_structured_output` method to support structured output
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
import os
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.tools import tool
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from langchain_community.chat_models import MiniMaxChat
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def test_chat_minimax_not_group_id() -> None:
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if "MINIMAX_GROUP_ID" in os.environ:
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del os.environ["MINIMAX_GROUP_ID"]
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chat = MiniMaxChat() # type: ignore[call-arg]
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response = chat.invoke("你好呀")
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assert isinstance(response, AIMessage)
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assert isinstance(response.content, str)
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def test_chat_minimax_with_stream() -> None:
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chat = MiniMaxChat() # type: ignore[call-arg]
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for chunk in chat.stream("你好呀"):
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assert isinstance(chunk, AIMessage)
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assert isinstance(chunk.content, str)
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@tool
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def add(a: int, b: int) -> int:
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"""Adds a and b."""
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return a + b
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@tool
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def multiply(a: int, b: int) -> int:
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"""Multiplies a and b."""
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return a * b
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def test_chat_minimax_with_tool() -> None:
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"""Test MinimaxChat with bind tools."""
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chat = MiniMaxChat() # type: ignore[call-arg]
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tools = [add, multiply]
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chat_with_tools = chat.bind_tools(tools)
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query = "What is 3 * 12?"
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messages = [HumanMessage(query)]
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ai_msg = chat_with_tools.invoke(messages)
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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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messages.append(ai_msg) # type: ignore[arg-type]
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for tool_call in ai_msg.tool_calls:
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selected_tool = {"add": add, "multiply": multiply}[tool_call["name"].lower()]
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tool_output = selected_tool.invoke(tool_call["args"]) # type: ignore[attr-defined]
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messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"])) # type: ignore[arg-type]
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response = chat_with_tools.invoke(messages)
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assert isinstance(response, AIMessage)
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class AnswerWithJustification(BaseModel):
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"""An answer to the user question along with justification for the answer."""
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answer: str
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justification: str
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def test_chat_minimax_with_structured_output() -> None:
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"""Test MiniMaxChat with structured output."""
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llm = MiniMaxChat() # type: ignore
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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response = structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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assert isinstance(response, AnswerWithJustification)
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def test_chat_tongyi_with_structured_output_include_raw() -> None:
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"""Test MiniMaxChat with structured output."""
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llm = MiniMaxChat() # type: ignore
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structured_llm = llm.with_structured_output(
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AnswerWithJustification, include_raw=True
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)
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response = structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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assert isinstance(response, dict)
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assert isinstance(response.get("raw"), AIMessage)
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assert isinstance(response.get("parsed"), AnswerWithJustification)
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