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import gradio as gr
import bs4
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import OllamaEmbeddings
import ollama
# Function to load, split, and retrieve documents
def load_and_retrieve_docs(url):
loader = WebBaseLoader(
web_paths=(url,),
bs_kwargs=dict()
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
embeddings = OllamaEmbeddings(model="mistral")
vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
return vectorstore.as_retriever()
# Function to format documents
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# Function that defines the RAG chain
def rag_chain(url, question):
retriever = load_and_retrieve_docs(url)
retrieved_docs = retriever.invoke(question)
formatted_context = format_docs(retrieved_docs)
formatted_prompt = f"Question: {question}\n\nContext: {formatted_context}"
response = ollama.chat(model='mistral', messages=[{'role': 'user', 'content': formatted_prompt}])
return response['message']['content']
# Gradio interface
iface = gr.Interface(
fn=rag_chain,
inputs=["text", "text"],
outputs="text",
title="RAG Chain Question Answering",
description="Enter a URL and a query to get answers from the RAG chain."
)
# Launch the app
iface.launch()
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