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# Implement Classification
import os
from langchain.prompts.chat import ChatPromptTemplate
from langchain.memory import ConversationBufferMemory
from generator import load_llm
from langchain.prompts import PromptTemplate
from retrieverV2 import process_pdf_document, create_vectorstore, rag_retriever
from langchain.schema import format_document
from langchain_core.messages import AIMessage, HumanMessage, get_buffer_string
from langchain_core.runnables import RunnableParallel
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from operator import itemgetter
from langchain_text_splitters import RecursiveCharacterTextSplitter
class ModelPipeLine:
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
def __init__(self):
self.curr_dir = os.path.dirname(__file__)
self.knowledge_dir = os.path.join(os.path.dirname(os.path.dirname(self.curr_dir)))
self.prompt_dir = os.path.join(os.path.dirname(os.path.dirname(self.curr_dir)),'prompts')
self.child_splitter = RecursiveCharacterTextSplitter(chunk_size=200)
self.parent_splitter = RecursiveCharacterTextSplitter(chunk_size=500)
self.documents = process_pdf_document([os.path.join(self.knowledge_dir, 'depression_1.pdf'), os.path.join(self.knowledge_dir, 'depression_2.pdf')])
self.vectorstore, self.store = create_vectorstore()
self.retriever = rag_retriever(self.vectorstore, self.store, self.documents, self.parent_splitter, self.child_splitter) # Create the retriever
self.llm = load_llm() # Load the LLM model
self.memory = ConversationBufferMemory(return_messages=True,
output_key="answer",
input_key="question") # Instantiate ConversationBufferMemory
def get_prompts(self, system_file_path='system_prompt_template.txt',
condense_file_path='condense_question_prompt_template.txt'):
with open(os.path.join(self.prompt_dir, system_file_path), 'r') as f:
system_prompt_template = f.read()
with open(os.path.join(self.prompt_dir, condense_file_path), 'r') as f:
condense_question_prompt = f.read()
# create message templates
ANSWER_PROMPT = ChatPromptTemplate.from_template(system_prompt_template)
# create message templates
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(condense_question_prompt)
return ANSWER_PROMPT, CONDENSE_QUESTION_PROMPT
def _combine_documents(self,docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"):
doc_strings = [format_document(doc, document_prompt) for doc in docs]
return document_separator.join(doc_strings)
def create_final_chain(self):
answer_prompt, condense_question_prompt = self.get_prompts()
# This adds a "memory" key to the input object
loaded_memory = RunnablePassthrough.assign(
chat_history=RunnableLambda(self.memory.load_memory_variables) | itemgetter("history"),
)
# Now we calculate the standalone question
standalone_question = {
"standalone_question": {
"question": lambda x: x["question"],
"chat_history": lambda x: get_buffer_string(x["chat_history"]),
}
| condense_question_prompt
| self.llm,
}
# Now we retrieve the documents
retrieved_documents = {
"docs": itemgetter("standalone_question") | self.retriever,
"question": lambda x: x["standalone_question"],
}
# Now we construct the inputs for the final prompt
final_inputs = {
"context": lambda x: self._combine_documents(x["docs"]),
"question": itemgetter("question"),
}
# And finally, we do the part that returns the answers
answer = {
"answer": final_inputs | answer_prompt | self.llm,
"docs": itemgetter("docs"),
}
# And now we put it all together!
final_chain = loaded_memory | standalone_question | retrieved_documents | answer
return final_chain
def call_conversational_rag(self,question, chain):
"""
Calls a conversational RAG (Retrieval-Augmented Generation) model to generate an answer to a given question.
This function sends a question to the RAG model, retrieves the answer, and stores the question-answer pair in memory
for context in future interactions.
Parameters:
question (str): The question to be answered by the RAG model.
chain (LangChain object): An instance of LangChain which encapsulates the RAG model and its functionality.
memory (Memory object): An object used for storing the context of the conversation.
Returns:
dict: A dictionary containing the generated answer from the RAG model.
"""
# Prepare the input for the RAG model
inputs = {"question": question}
# Invoke the RAG model to get an answer
result = chain.invoke(inputs)
# Save the current question and its answer to memory for future context
self.memory.save_context(inputs, {"answer": result["answer"]})
# Return the result
return result
ml_pipeline = ModelPipeLine()
final_chain = ml_pipeline.create_final_chain()
question = "i am feeling sad"
res = ml_pipeline.call_conversational_rag(question,final_chain)
print(res['answer'])
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