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import gradio as gr
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
# Initialize the HuggingFaceInstructEmbeddings
hf = HuggingFaceInstructEmbeddings(
model_name="hkunlp/instructor-large",
embed_instruction="Represent the document for retrieval: ",
query_instruction="Represent the query for retrieval: "
)
# Load and process the PDF files
loader = PyPDFLoader('./new_papers/', glob="./*.pdf")
documents = loader.load()
# Create a Chroma vector store from the PDF documents
db = Chroma.from_documents(documents, hf, collection_name="my-collection")
class PDFRetrievalTool:
def __init__(self):
self.retriever = db.as_retriever(search_kwargs={"k": 1})
def __call__(self, query):
# Run the query through the retriever
response = self.retriever.run(query)
return response['result']
# Create the Gradio interface using the PDFRetrievalTool
tool = gr.Interface(
PDFRetrievalTool(),
inputs=gr.Textbox(),
outputs=gr.Textbox(),
live=True,
title="PDF Retrieval Tool",
description="This tool indexes PDF documents and retrieves relevant answers based on a given query.",
)
# Launch the Gradio interface
tool.launch()
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