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from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langfuse.callback import CallbackHandler
import os
openai_api_key = os.environ.get("OPENAI_API_KEY")
langfuse_public_key = os.environ.get("LANGFUSE_PUBLIC_KEY")
langfuse_secret_key = os.environ.get("LANGFUSE_SECRET_KEY")
class Conversation_RAG:
def __init__(self, model_name="gpt-3.5-turbo"):
self.model_name = model_name
def create_vectordb(self):
vectordb = FAISS.load_local("./db/faiss_index", OpenAIEmbeddings())
return vectordb
def create_model(self, max_new_tokens=512, temperature=0.1):
llm = ChatOpenAI(
openai_api_key=openai_api_key,
model_name=self.model_name,
temperature=temperature,
max_tokens=max_new_tokens,
)
return llm
def create_conversation(self, model, vectordb, k_context=5, instruction="Use the following pieces of context to answer the question at the end by. Generate the answer based on the given context only. If you do not find any information related to the question in the given context, just say that you don't know, don't try to make up an answer. Keep your answer expressive."):
template = instruction + """
context:\n
{context}\n
data: {question}\n
"""
handler = CallbackHandler(langfuse_public_key, langfuse_secret_key)
QCA_PROMPT = PromptTemplate(input_variables=["instruction", "context", "question"], template=template)
qa = ConversationalRetrievalChain.from_llm(
llm=model,
chain_type='stuff',
retriever=vectordb.as_retriever(search_kwargs={"k": k_context}),
combine_docs_chain_kwargs={"prompt": QCA_PROMPT},
get_chat_history=lambda h: h,
verbose=True,
callbacks=[handler]
)
return qa |