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import os
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.runnables import RunnablePassthrough
import app.schemas as schemas
from app.prompts import (
raw_prompt,
raw_prompt_formatted,
history_prompt_formatted,
standalone_prompt_formatted,
format_context,
rag_prompt_formatted,
tokenizer
)
from app.data_indexing import DataIndexer
data_indexer = DataIndexer()
llm = HuggingFaceEndpoint( # type: ignore
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) # type: ignore
# 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 = (standalone_prompt_formatted | llm).with_types(input_type=schemas.HistoryInput)
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 = (input_to_rag_chain | rag_prompt_formatted | llm).with_types(input_type=schemas.HistoryInput)
# TODO: Implement the filtered_rag_chain. It should be the
# same as the rag_chain but with hybrid_search = True.
input_1 = RunnablePassthrough.assign(new_question=standalone_chain)
input_2 = {
'context': lambda x: format_context(data_indexer.search(x['new_question'], hybrid_search=True)),
'standalone_question': lambda x: x['new_question']
}
filtered_input_to_rag_chain = input_1 | input_2
filtered_rag_chain = (filtered_input_to_rag_chain | rag_prompt_formatted | llm).with_types(input_type=schemas.HistoryInput)
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