278a2fed56
Is this a service or processor? How to deal with conversation history? LC has sophisticated means of this, but might get in the way of `LLMResponseAggregator`
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
import pytest
|
|
from langchain.prompts import ChatPromptTemplate
|
|
from langchain_core.language_models import FakeStreamingListLLM
|
|
|
|
from pipecat.frames.frames import (StopTaskFrame, TranscriptionFrame,
|
|
UserStartedSpeakingFrame,
|
|
UserStoppedSpeakingFrame)
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineTask
|
|
from pipecat.processors.aggregators.llm_response import (
|
|
LLMAssistantResponseAggregator, LLMUserResponseAggregator)
|
|
from pipecat.processors.logger import FrameLogger
|
|
from pipecat.services.langchain import LangchainProcessor
|
|
|
|
|
|
@pytest.fixture
|
|
def fake_llm():
|
|
responses = ["Hello dear human"]
|
|
return FakeStreamingListLLM(responses=responses)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_langchain(fake_llm: FakeStreamingListLLM):
|
|
fl_in = FrameLogger("Inner")
|
|
fl_out = FrameLogger("Outer")
|
|
|
|
messages = [("system", "Say hello to {name}"), ("human", "{input}")]
|
|
prompt = ChatPromptTemplate.from_messages(messages).partial(name="Thomas")
|
|
chain = prompt | fake_llm
|
|
proc = LangchainProcessor(chain=chain)
|
|
|
|
tma_in = LLMUserResponseAggregator(messages)
|
|
tma_out = LLMAssistantResponseAggregator(messages)
|
|
|
|
pipeline = Pipeline(
|
|
[
|
|
fl_in,
|
|
tma_in,
|
|
proc,
|
|
tma_out,
|
|
fl_out,
|
|
]
|
|
)
|
|
|
|
task = PipelineTask(pipeline)
|
|
await task.queue_frames(
|
|
[
|
|
UserStartedSpeakingFrame(),
|
|
TranscriptionFrame(text="Hi World", user_id="user", timestamp="now"),
|
|
UserStoppedSpeakingFrame(),
|
|
StopTaskFrame(),
|
|
]
|
|
)
|
|
|
|
runner = PipelineRunner()
|
|
await runner.run(task)
|