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import os
import re
import logging
from models.custom_parsers import CustomStringOutputParser
from utils.app_utils import get_random_name
from app_config import ENDPOINT_NAMES, SOURCES
from models.databricks.custom_databricks_llm import CustomDatabricksLLM
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate
from typing import Any, List, Mapping, Optional, Dict
_DATABRICKS_TEMPLATE_ = """{history}
helper: {input}
texter:"""
def get_databricks_chain(source, issue, language, memory, temperature=0.8, texter_name="Kit"):
endpoint_name = ENDPOINT_NAMES.get(source, "texter_simulator")
PROMPT = PromptTemplate(
input_variables=['history', 'input'],
template=_DATABRICKS_TEMPLATE_
)
llm = CustomDatabricksLLM(
# endpoint_url="https://dbc-6dca8e8f-4084.cloud.databricks.com/serving-endpoints/databricks-meta-llama-3-1-70b-instruct/invocations",
endpoint_url=os.environ['DATABRICKS_URL'].format(endpoint_name=endpoint_name),
bearer_token=os.environ["DATABRICKS_TOKEN"],
texter_name=texter_name,
issue=issue,
language=language,
temperature=temperature,
max_tokens=256,
)
llm_chain = ConversationChain(
llm=llm,
prompt=PROMPT,
memory=memory,
output_parser=CustomStringOutputParser(),
verbose=True,
)
logging.debug(f"loaded Databricks model")
return llm_chain, None |