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from dataclasses import dataclass
from enum import Enum
from typing import Dict, List
import streamlit as st
from dotenv import load_dotenv
from langchain_community.document_loaders.blob_loaders import Blob
from loguru import logger as log
from rag.generator import MODELS, get_generator
from rag.generator.prompt import Prompt
from rag.retriever.retriever import Retriever
from rag.retriever.vector import Document
class Cohere(Enum):
USER = "USER"
BOT = "CHATBOT"
class Ollama(Enum):
USER = "user"
BOT = "assistant"
@dataclass
class Message:
role: str
message: str
def as_dict(self, client: str) -> Dict[str, str]:
if client == "cohere":
return {"role": self.role, "message": self.message}
else:
return {"role": self.role, "content": self.message}
def set_chat_users():
log.debug("Setting user and bot value")
ss = st.session_state
if ss.generator == "cohere":
ss.user = Cohere.USER.value
ss.bot = Cohere.BOT.value
else:
ss.user = Ollama.USER.value
ss.bot = Ollama.BOT.value
def clear_generator_messages():
log.debug("Clearing generator chat history")
st.session_state.generator_messages = []
@st.cache_resource
def load_retriever():
log.debug("Loading retriever model")
st.session_state.retriever = Retriever()
@st.cache_resource
def load_generator(client: str):
log.debug("Loading generator model")
st.session_state.generator = get_generator(client)
set_chat_users()
clear_generator_messages()
@st.cache_data(show_spinner=False)
def upload(files):
retriever = st.session_state.retriever
with st.spinner("Uploading documents..."):
for file in files:
source = file.name
blob = Blob.from_data(file.read())
retriever.add_pdf(blob=blob, source=source)
def display_context(documents: List[Document]):
with st.popover("See Context"):
for i, doc in enumerate(documents):
st.markdown(f"### Document {i}")
st.markdown(f"**Title: {doc.title}**")
st.markdown(doc.text)
st.markdown("---")
def display_chat():
ss = st.session_state
for msg in ss.chat:
if isinstance(msg, list):
display_context(msg)
else:
st.chat_message(msg.role).write(msg.message)
def generate_chat(query: str):
ss = st.session_state
with st.chat_message(ss.user):
st.write(query)
retriever = ss.retriever
generator = ss.generator
documents = retriever.retrieve(query, limit=5)
prompt = Prompt(query, documents)
with st.chat_message(ss.bot):
history = [m.as_dict(ss.client) for m in ss.generator_messages]
response = st.write_stream(generator.chat(prompt, history))
display_context(documents)
store_chat(query, response, documents)
def store_chat(query: str, response: str, documents: List[Document]):
log.debug("Storing chat")
ss = st.session_state
query = Message(role=ss.user, message=query)
response = Message(role=ss.bot, message=response)
ss.chat.append(query)
ss.chat.append(response)
ss.generator_messages.append(response)
ss.chat.append(documents)
def sidebar():
with st.sidebar:
st.header("Grouding")
st.markdown(
(
"These files will be uploaded to the knowledge base and used "
"as groudning if they are relevant to the question."
)
)
files = st.file_uploader(
"Choose pdfs to add to the knowledge base",
type="pdf",
accept_multiple_files=True,
)
upload(files)
st.header("Generative Model")
st.markdown("Select the model that will be used for generating the answer.")
st.selectbox("Generative Model", key="client", options=MODELS)
load_generator(st.session_state.client)
def page():
ss = st.session_state
if "generator_messages" not in st.session_state:
ss.generator_messages = []
if "chat" not in st.session_state:
ss.chat = []
display_chat()
query = st.chat_input("Enter query here")
if query:
generate_chat(query)
if __name__ == "__main__":
load_dotenv()
st.title("Retrieval Augmented Generation")
load_retriever()
sidebar()
page()
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