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import os
import gradio as gr
from pinecone import Pinecone
from langchain_chroma import Chroma
from langchain_core.prompts import PromptTemplate
from langchain_pinecone import PineconeVectorStore
from langchain_community.vectorstores import FAISS
from langchain_community.vectorstores import LanceDB
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_google_genai import GoogleGenerativeAIEmbeddings, GoogleGenerativeAI
embeddings = GoogleGenerativeAIEmbeddings(model="models/text-embedding-004")
gemini = GoogleGenerativeAI(model="models/gemini-2.0-flash")
prompt_template = """
Context:\n {context}?\n
Question: \n{question}\n
Answer:
"""
prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
chain = prompt | gemini
def inference(pdf_path, chunk_size, chunk_overlap):
raw_documents = PyPDFLoader(pdf_path).load()
text_splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
documents = text_splitter.split_documents(raw_documents)
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
index_name = "langchain-test-index"
index = pc.Index(host="https://langchain-test-index-la2n80y.svc.aped-4627-b74a.pinecone.io")
index.delete(delete_all=True)
chroma_db = Chroma.from_documents(documents, embeddings, persist_directory="./chroma_db")
faiss_db = FAISS.from_documents(documents, embeddings)
faiss_db.save_local("./faiss_db")
lance_db = LanceDB.from_documents(documents, embeddings, uri="./lance_db")
pinecone_db = PineconeVectorStore.from_documents(documents, index_name=index_name,
embedding=embeddings)
return "All embeddings are stored in vector database"
title = "PDF Chat"
description = "A simple Gradio interface to query PDFs and compare vector database"
examples = [["data/amazon-10-k-2024.pdf", 1000, 100],
["data/goog-10-k-2023.pdf", 1000, 100]]
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
gr.Markdown(f"# {title}\n{description}")
with gr.Row():
with gr.Column():
pdf = gr.UploadButton(file_types=[".pdf"])
chunk_size = gr.Slider(0, 2000, 1000, 100, label="Size of Chunk")
chunk_overlap = gr.Slider(0, 1000, 100, 100, label="Size of Chunk Overlap")
with gr.Row():
clear_btn = gr.ClearButton(components=[pdf, chunk_size, chunk_overlap])
submit_btn = gr.Button("Store Embeddings", variant='primary')
with gr.Column():
message = gr.Textbox(label="Status", type="text")
submit_btn.click(inference, inputs=[pdf, chunk_size, chunk_overlap], outputs=message)
examples_obj = gr.Examples(examples=examples, inputs=[pdf, chunk_size, chunk_overlap])
demo.launch()
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