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from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.prompts import MessagesPlaceholder
from langchain.chains import create_history_aware_retriever
from langchain_pinecone.vectorstores import Pinecone
from uuid import uuid4
from langchain_groq import ChatGroq
from langchain.schema.retriever import BaseRetriever
from dotenv import load_dotenv
import os
load_dotenv()
class Rag:
def __init__(self, vectorstore: Pinecone, retriever: BaseRetriever):
self.model = ChatGroq(
model="llama-3.1-70b-versatile",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=os.getenv('GROQ_API_KEY')
)
self.system_prompt = (
"""
You are "Ask me about RAG", a knowledgeable librarian specializing in RAG research papers. A user has requested assistance with research paper recommendations.
We have retrieved {num_docs} research paper(s) related to the user's query. These papers are listed below:
{context}
Please provide detailed information for EACH research paper retrieved, including:
1. The title of the research paper.
2. A concise summary of its content, highlighting key findings or topics covered.
3. Relevant details for locating or referencing the paper (e.g., a link, DOI, university, journal name, or organization).
Format your response as a numbered list, preserving the order in which the papers were retrieved.
"""
)
self.contextualize_q_system_prompt = (
"Given a chat history and the latest user question "
"which might reference context in the chat history, "
"formulate a standalone question which can be understood "
"without the chat history. Do NOT answer the question, "
"just reformulate it if needed and otherwise return it as is."
)
self.contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", self.contextualize_q_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
]
)
self.qa_prompt = ChatPromptTemplate.from_messages(
[
("system", self.system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
]
)
self.vectorstore = vectorstore
self.retriever = retriever
def storeDocumentsInVectorstore(self, documents):
uuids = [str(uuid4()) for _ in range(len(documents))]
self.vectorstore.add_documents(documents=documents, ids=uuids)
def createRagChain(self):
self.question_answer_chain = create_stuff_documents_chain(self.model, self.qa_prompt)
self.history_aware_retriever = create_history_aware_retriever(self.model, self.retriever, self.contextualize_q_prompt)
self.rag_chain = create_retrieval_chain(self.history_aware_retriever, self.question_answer_chain)
def generateResponse(self, question, chat_history):
retrieved_docs = self.vectorstore.as_retriever().get_relevant_documents(question)
num_docs = len(retrieved_docs)
ai_msg = self.rag_chain.invoke({
"num_docs": num_docs,
"input": question,
"chat_history": chat_history
})
return ai_msg
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