File size: 1,670 Bytes
cae3cb9 018fb30 f7493dd 037c950 d44faea 037c950 008f20f 037c950 018fb30 92b65f4 cbed288 c7297e1 f514bc9 c7297e1 18cb8f3 68b31c9 59caffa 403222a 88751a5 037c950 403222a 018fb30 05727e9 68b31c9 018fb30 68b31c9 037c950 68b31c9 018fb30 037c950 018fb30 037c950 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import os
import gradio as gr
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
# Use Hugging Face Inference API embeddings
inference_api_key = os.environ['HF']
api_hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
api_key=inference_api_key,
model_name="sentence-transformers/all-MiniLM-l6-v2"
)
# Load and process the PDF files
loader = PyPDFLoader("./new_papers/ReACT.pdf")
documents = loader.load()
print("-----------")
print(documents)
print("-----------")
# Load the document, split it into chunks, embed each chunk, and load it into the vector store.
text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=50)
vdocuments = text_splitter.split_documents(documents)
# Create Chroma vector store for API embeddings
api_db = Chroma.from_documents(vdocuments, api_hf_embeddings, collection_name="api-collection")
print(api_db.similarity_search("What is react"))
# Define the PDF retrieval function
def pdf_retrieval(query):
# Run the query through the retriever
response = api_db.similarity_search(query)
return response
# Create Gradio interface for the API retriever
# Create Gradio interface for the API retriever
api_tool = gr.Interface(
fn=pdf_retrieval,
inputs=[gr.Textbox()],
outputs=gr.Textbox(),
live=True,
title="API PDF Retrieval Tool",
description="This tool indexes PDF documents and retrieves relevant answers based on a given query (HF Inference API Embeddings).",
)
# Launch the Gradio interface
api_tool.launch()
|