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__import__('pysqlite3')
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
# DATABASES = {
# 'default': {
# 'ENGINE': 'django.db.backends.sqlite3',
# 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
# }
# }
import streamlit as st
from huggingface_hub import InferenceClient
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, PromptTemplate
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
import chromadb
from langchain.memory import ConversationBufferMemory
# Set page config
st.set_page_config(page_title="RAG Chatbot", page_icon="🤖", layout="wide")
# Set your Hugging Face token here
HF_TOKEN = st.secrets["HF_TOKEN"]
# Initialize your models, databases, and other components here
@st.cache_resource
def init_chroma():
persist_directory = "chroma_db"
chroma_client = chromadb.PersistentClient(path=persist_directory)
chroma_collection = chroma_client.get_or_create_collection("my_collection")
return chroma_client, chroma_collection
@st.cache_resource
def init_vectorstore():
persist_directory = "chroma_db"
embeddings = HuggingFaceEmbeddings()
vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings, collection_name="my_collection")
return vectorstore
# Initialize components
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3", token=HF_TOKEN)
chroma_client, chroma_collection = init_chroma()
vectorstore = init_vectorstore()
# Initialize memory buffer
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
def rag_query(query):
# Retrieve relevant documents using similarity search
retrieved_docs = vectorstore.similarity_search(query, k=3)
# Prepare context for LLaMA
if retrieved_docs:
context = "\n".join([doc.page_content for doc in retrieved_docs])
else:
context = ""
# Append new interaction to memory
memory.chat_memory.add_user_message(query)
# Retrieve past interactions for context
past_interactions = memory.load_memory_variables({})[memory.memory_key]
context_with_memory = f"{context}\n\nConversation History:\n{past_interactions}"
# Debugging: Display context and past interactions
# st.write("Debugging Info:")
# st.write("Context Sent to Model:", context_with_memory)
# st.write("Retrieved Documents:", [doc.page_content for doc in retrieved_docs])
# st.write("Past Interactions:", past_interactions)
# Generate response using LLaMA
messages = [
{"role": "user", "content": f"Context: {context_with_memory}\n\nQuestion: {query},it is not mandatory to use the context\n\nAnswer:"}
]
# Get the response from the client
response_content = client.chat_completion(messages=messages, max_tokens=500, stream=False)
# Process the response content
response = response_content.choices[0].message.content.split("Answer:")[-1].strip()
# If the response is empty or very short, or if no relevant documents were found, use the LLM's default knowledge
if not context or len(response.split()) < 35 or not retrieved_docs:
messages = [{"role": "user", "content": query}]
response_content = client.chat_completion(messages=messages, max_tokens=500, stream=False)
response = response_content.choices[0].message.content
# Append the response to memory
memory.chat_memory.add_ai_message(response)
return response
def process_feedback(query, response, feedback):
# st.write(f"Feedback received: {'👍' if feedback else '👎'} for query: {query}")
if feedback:
# If thumbs up, store the response in memory buffer
memory.chat_memory.add_ai_message(response)
else:
# If thumbs down, remove the response from memory buffer and regenerate the response
# memory.chat_memory.messages = [msg for msg in memory.chat_memory.messages if msg.get("content") != response]
new_query=f"{query}. Give better response"
new_response = rag_query(new_query)
st.markdown(new_response)
memory.chat_memory.add_ai_message(new_response)
# Streamlit interface
st.title("Welcome to our RAG-Based Chatbot")
st.markdown("***")
st.info('''
To use Our Mistral supported Chatbot, click Chat.
To push data, click on Store Document.
''')
col1, col2 = st.columns(2)
with col1:
chat = st.button("Chat")
if chat:
st.switch_page("pages/chatbot.py")
with col2:
rag = st.button("Store Document")
if rag:
st.switch_page("pages/management.py")
st.markdown("<div style='text-align:center;'></div>", unsafe_allow_html=True) |