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#%%
import os
from dotenv import load_dotenv
load_dotenv('../../.env')
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.runnables import RunnablePassthrough
import schemas
from prompts import (
raw_prompt,
raw_prompt_formatted,
history_prompt_formatted,
format_context,
tokenizer
)
from data_indexing import DataIndexer
# data_indexer = DataIndexer()
llm = HuggingFaceEndpoint(
repo_id="meta-llama/Meta-Llama-3-8B-Instruct",
huggingfacehub_api_token=os.environ['HF_TOKEN'],
max_new_tokens=512,
stop_sequences=[tokenizer.eos_token],
streaming=True,
)
simple_chain = (raw_prompt | llm).with_types(input_type=schemas.UserQuestion)
# %%
# data_indexer = DataIndexer()
# TODO: create formatted_chain by piping raw_prompt_formatted and the LLM endpoint.
formatted_chain = (raw_prompt_formatted | llm).with_types(input_type=schemas.UserQuestion)
# # TODO: use history_prompt_formatted and HistoryInput to create the history_chain
history_chain = (history_prompt_formatted | llm).with_types(input_type=schemas.HistoryInput)
# # TODO: Let's construct the standalone_chain by piping standalone_prompt_formatted with the LLM
# standalone_chain = None
# input_1 = RunnablePassthrough.assign(new_question=standalone_chain)
# input_2 = {
# 'context': lambda x: format_context(data_indexer.search(x['new_question'])),
# 'standalone_question': lambda x: x['new_question']
# }
# input_to_rag_chain = input_1 | input_2
# # TODO: use input_to_rag_chain, rag_prompt_formatted,
# # HistoryInput and the LLM to build the rag_chain.
# rag_chain = None
# # TODO: Implement the filtered_rag_chain. It should be the
# # same as the rag_chain but with hybrid_search = True.
# filtered_rag_chain = None
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