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import streamlit as st
from streamlit.logger import get_logger
from langchain_core.messages import HumanMessage
from models.openai.finetuned_models import finetuned_models, get_finetuned_chain
from models.openai.role_models import get_role_chain, get_template_role_models
from models.databricks.scenario_sim_biz import get_databricks_biz_chain
from models.databricks.texter_sim_llm import get_databricks_chain
from models.ta_models.cpc_utils import cpc_predict_message
logger = get_logger(__name__)
def get_chain(issue, language, source, memory, temperature, texter_name=""):
if source in ("OA_finetuned"):
OA_engine = finetuned_models[f"{issue}-{language}"]
return get_finetuned_chain(OA_engine, memory, temperature)
elif source in ('OA_rolemodel'):
template = get_template_role_models(issue, language, texter_name=texter_name)
return get_role_chain(template, memory, temperature)
elif source in ('CTL_llama2'):
if language == "English":
language = "en"
elif language == "Spanish":
language = "es"
return get_databricks_biz_chain(source, issue, language, memory, temperature)
elif source in ('CTL_llama3'):
if language == "English":
language = "en"
elif language == "Spanish":
language = "es"
return get_databricks_chain(source, issue, language, memory, temperature, texter_name=texter_name)
def custom_chain_predict(llm_chain, input, stop):
inputs = llm_chain.prep_inputs({"input":input, "stop":stop})
llm_chain._validate_inputs(inputs)
outputs = llm_chain._call(inputs)
llm_chain._validate_outputs(outputs)
phase = cpc_predict_message(st.session_state['context'], st.session_state['last_message'])
st.session_state['last_phase'] = phase
logger.debug(phase)
llm_chain.memory.chat_memory.add_user_message(
HumanMessage(inputs['input'], response_metadata={"phase":phase})
)
for out in outputs[llm_chain.output_key]:
llm_chain.memory.chat_memory.add_ai_message(out)
return outputs[llm_chain.output_key] |