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import streamlit as st
import ollama
import torch
import os
import logging
import accelerate
from langchain import hub
from langchain_community.document_loaders.web_base import WebBaseLoader
from langchain_community.vectorstores.faiss import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from typing import List, Tuple, Dict, Any
from langchain_ollama import OllamaEmbeddings
##### Logging
# Logging configuration
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logger = logging.getLogger(__name__)
prompt = hub.pull("rlm/rag-prompt")
embeddings = OllamaEmbeddings(model='nomic-embed-text', base_url="http://localhost:11434")
@st.cache_resource(show_spinner=True)
def extract_model_names(
models_info: Dict[str, List[Dict[str, Any]]],
) -> Tuple[str, ...]:
"""
Extract model names from the provided models information.
Args:
models_info (Dict[str, List[Dict[str, Any]]]): Dictionary containing information about available models.
Returns:
Tuple[str, ...]: A tuple of model names.
"""
logger.info("Extracting model names from models_info")
model_names = tuple(model["name"] for model in models_info["models"])
logger.info(f"Extracted model names: {model_names}")
return model_names
def format_docs(docs):
return "\n\n".join([doc.page_content for doc in docs])
## RAG with URL
def rag_with_url(target_url, model, prompt):
loader = WebBaseLoader(target_url)
raw_document = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, length_function=len)
splited_document = text_splitter.split_documents(raw_document)
vector_store = FAISS.from_documents(splited_document, embeddings)
retriever = vector_store.as_retriever()
relevant_documents = retriever.get_relevant_documents(prompt)
final_prompt = prompt + " " + " ".join([doc.page_content for doc in relevant_documents])
AI_Respose = model.invoke(final_prompt)
return AI_Respose.content
def main() -> None:
st.set_page_config(layout="centered")
st.title("🧠 This is a RAG Chatbot to any URL with LLama-3.2 and Langchain !!!")
st.write("You can select which local LLM from Ollama to be used !!!")
from transformers import AutoTokenizer, AutoModelForCausalLM
token = os.environ["HF_TOKEN"]
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct",token=token)
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct", token=token)
# URL text box for user input
url_input = st.text_input("Enter a URL to be queried:", "")
with st.form("llm-form"):
text = st.text_area("Enter your question below:")
submit = st.form_submit_button("Submit")
if "chat_history" not in st.session_state:
st.session_state['chat_history'] = []
if submit and text:
with st.spinner("Generating response..."):
response = rag_with_url(url_input, model, text)
st.session_state['chat_history'].append({"user": text, "ollama": response})
st.write(response)
st.write("## Chat History")
for chat in reversed(st.session_state['chat_history']):
st.write(f"**🧑 User**: {chat['user']}")
st.write(f"**🧠 Assistant**: {chat['ollama']}")
st.write("---")
if __name__ == "__main__":
main()
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