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import gradio as gr
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.prompts import PromptTemplate
from langchain_community.llms import LlamaCpp
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain import hub
# Set up callback manager and model parameters
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
n_gpu_layers = 0
n_batch = 512
llm = LlamaCpp(
model_path="./models/phi-2.Q2_K.gguf",
n_gpu_layers=n_gpu_layers, n_batch=n_batch,
n_ctx = 4096,
temperature=0.7,
max_tokens=4096,
top_p=1,
callback_manager=callback_manager,
verbose=False,
)
# Load the prompt
prompt = hub.pull("rlm/rag-prompt")
# Function to format documents
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# Main function to process the question and URL
def get_answer(question, url):
# Load data from the provided URL
loader = WebBaseLoader(url)
data = loader.load()
# Split the data into small chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
# Store the data in Vector Store
vectorstore = Chroma.from_documents(documents=all_splits, embedding=HuggingFaceEmbeddings())
retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 3})
retrieved_docs = retriever.invoke(question)
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
answer = ""
for chunk in rag_chain.stream(question):
answer += chunk
yield answer
yield answer
# Create the Gradio interface
iface = gr.Interface(
fn=get_answer,
inputs=[gr.Textbox(lines=1, placeholder="Enter your question here..."),
gr.Textbox(lines=1, placeholder="Enter the website URL here...")],
outputs="text",
title="Web-based Question Answering System",
description="Ask a question about the content of a webpage and get an answer.",
examples=[
["Which are the top 5 companies in the world with their revenue in table format?", "https://www.investopedia.com/biggest-companies-in-the-world-by-market-cap-5212784"]
]
)
# Launch the app
iface.launch(share=True)
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