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c2a3021bb0
Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com> Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com> Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: ZhangShenao <15201440436@163.com> Co-authored-by: Friso H. Kingma <fhkingma@gmail.com> Co-authored-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Nuno Campos <nuno@langchain.dev> Co-authored-by: Morgante Pell <morgantep@google.com>
127 lines
3.9 KiB
Python
127 lines
3.9 KiB
Python
"""Test ChatDeepInfra wrapper."""
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from typing import List
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from langchain_core.messages import BaseMessage, HumanMessage
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from langchain_core.messages.ai import AIMessage
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from langchain_core.messages.tool import ToolMessage
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from langchain_core.outputs import ChatGeneration, LLMResult
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from langchain_core.runnables.base import RunnableBinding
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from pydantic import BaseModel
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from langchain_community.chat_models.deepinfra import ChatDeepInfra
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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class GenerateMovieName(BaseModel):
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"Get a movie name from a description"
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description: str
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def test_chat_deepinfra() -> None:
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"""Test valid call to DeepInfra."""
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chat = ChatDeepInfra(
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max_tokens=10,
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)
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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_chat_deepinfra_streaming() -> None:
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callback_handler = FakeCallbackHandler()
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chat = ChatDeepInfra(
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callbacks=[callback_handler],
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streaming=True,
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max_tokens=10,
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)
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response = chat.invoke([HumanMessage(content="Hello")])
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assert callback_handler.llm_streams > 0
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assert isinstance(response, BaseMessage)
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async def test_async_chat_deepinfra() -> None:
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"""Test async generation."""
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chat = ChatDeepInfra(
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max_tokens=10,
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)
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message = HumanMessage(content="Hello")
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response = await chat.agenerate([[message]])
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 1
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assert len(response.generations[0]) == 1
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generation = response.generations[0][0]
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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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async def test_async_chat_deepinfra_streaming() -> None:
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callback_handler = FakeCallbackHandler()
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chat = ChatDeepInfra(
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# model="meta-llama/Llama-2-7b-chat-hf",
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callbacks=[callback_handler],
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max_tokens=10,
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streaming=True,
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timeout=5,
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)
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message = HumanMessage(content="Hello")
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response = await chat.agenerate([[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) == 1
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assert len(response.generations[0]) == 1
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generation = response.generations[0][0]
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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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def test_chat_deepinfra_bind_tools() -> None:
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class Foo(BaseModel):
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pass
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chat = ChatDeepInfra(
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max_tokens=10,
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)
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tools = [Foo]
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chat_with_tools = chat.bind_tools(tools)
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assert isinstance(chat_with_tools, RunnableBinding)
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chat_tools = chat_with_tools.tools
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assert chat_tools
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assert chat_tools == {
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"tools": [
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{
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"function": {
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"description": "",
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"name": "Foo",
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"parameters": {"properties": {}, "type": "object"},
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},
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"type": "function",
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}
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]
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}
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def test_tool_use() -> None:
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llm = ChatDeepInfra(model="meta-llama/Meta-Llama-3-70B-Instruct", temperature=0)
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llm_with_tool = llm.bind_tools(tools=[GenerateMovieName], tool_choice=True)
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msgs: List = [
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HumanMessage(content="It should be a movie explaining humanity in 2133.")
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]
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ai_msg = llm_with_tool.invoke(msgs)
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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="Year 2133",
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tool_call_id=ai_msg.additional_kwargs["tool_calls"][0]["id"],
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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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