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7b64dcd 851495c 7b64dcd 2988b10 7b64dcd 851495c 7b64dcd 941bf07 7b64dcd 2988b10 7b64dcd 2988b10 851495c 7b64dcd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import pathlib
import typing
from langchain_community.chat_models.llamacpp import ChatLlamaCpp
from langchain_core.messages import SystemMessage
from langchain_core.prompts import (
ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
)
from langchain_core.runnables import RunnableWithMessageHistory
from voice_dialogue.utils.logger import logger
def create_langchain_chat_llamacpp_instance(
local_model_path: str,
model_params: dict | None = None
) -> ChatLlamaCpp:
logger.info(">>>>>>> Initializing LlamaCpp Langchain instance...")
model_path = pathlib.Path(local_model_path)
llamacpp_langchain_instance = ChatLlamaCpp(
model_path=str(model_path),
**model_params
)
return llamacpp_langchain_instance
def create_langchain_pipeline(langchain_instance, system_prompt: str, get_session_history: typing.Callable):
prompt = ChatPromptTemplate(messages=[
SystemMessage(content=system_prompt),
MessagesPlaceholder(variable_name="history"),
HumanMessagePromptTemplate.from_template("{input}")
])
langchain_pipeline = prompt | langchain_instance
if get_session_history is None:
raise NotImplementedError
chain_with_history = RunnableWithMessageHistory(langchain_pipeline, get_session_history,
history_messages_key='history')
return chain_with_history
def warmup_langchain_pipeline(pipeline):
logger.info("Warmup chat pipeline...")
user_input = 'Hello, this is warming up step, if you understand, output "Ok".'
config = {"configurable": {"session_id": 'warmup'}}
for _ in pipeline.stream(input={'input': user_input}, config=config):
pass
def preprocess_sentence_text(sentences):
sentence_text = ''.join(sentences)
if sentence_text:
sentence_mark = sentence_text[-1]
sentence_content = sentence_text[:-1].replace('!', ',').replace('?', ',').replace('.', ',')
sentence_text = f'{sentence_content}{sentence_mark}'
return sentence_text
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