dosubot[bot]
commented
10 months ago 🤖
Hello again, @ZinanYang1995! It's great to see you diving deeper into the world of Pinecone and LangChain. I'm here to assist you, as always. Let's take a look at your code.
Based on your code and the requirements you've mentioned, you can modify your code to check if a PDF has already been stored as embeddings in Pinecone. Here's a general approach:
-
Generate a unique identifier for each PDF. This could be a hash of the file's contents, or a UUID if the same PDF can be uploaded multiple times and should be treated as a different document each time.
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Before adding the PDF's embeddings to Pinecone, check if an embedding with the same identifier already exists in Pinecone.
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If the embedding already exists, use the existing embedding. If not, generate a new embedding and add it to Pinecone.
Here's a rough example of how you might implement this:
import hashlib
# Generate a unique identifier for the PDF
pdf_content = pdf.read()
pdf_hash = hashlib.sha256(pdf_content).hexdigest()
# Check if the PDF's embeddings already exist in Pinecone
if pdf_hash in index:
# The embeddings already exist, so use them
embeddings = index[pdf_hash]
else:
# The embeddings don't exist, so generate them and add them to Pinecone
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002").embed(pdf_content)
index[pdf_hash] = embeddingsPlease note that this is a simplified example and may not work as-is. You'll need to adapt it to fit your specific use case and handle potential errors.
Also, please note that this approach assumes that Pinecone allows you to check if an embedding with a specific identifier exists, and to retrieve an existing embedding by its identifier. You would need to consult Pinecone's documentation or API reference to confirm this and to see how to implement these operations.
As for handling PDF files in LangChain, it uses various loaders to load and process PDF documents. The specific way embeddings are handled would depend on the specific loader and the implementation of its methods. For example, you're using the UnstructuredPDFLoader in your code to load the PDF file and return a Document object, which can then be used for further processing.
I hope this helps! If you have any more questions or need further clarification, feel free to ask.
Sources
- libs/community/langchain_community/vectorstores/matching_engine.py
- libs/langchain/langchain/document_loaders/init.py
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.
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ZinanYang1995
Issue you'd like to raise.
Hi there,
I am learning how to use Pinecone properly with LangChain and OpenAI Embedding. I built an application which can allow user upload PDFs and ask questions about the PDFs. In the application I used Pinecone as the vector database and store embeddings in Pinecone. However, I want to make change to my code so that whenever an user upload a PDF, the application can check if the PDF already been store as embeddings in Pinecone, and if yes, use the old embeddings; If no, then upload new embeddings.
Here is my code:
Imports
import streamlit as st import os from apikey import apikey import pickle from PyPDF2 import PdfReader
Streamlit - user interface
from streamlit_extras.add_vertical_space import add_vertical_space
Langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS from langchain.llms import OpenAI from langchain.chains.question_answering import load_qa_chain from langchain.callbacks import get_openai_callback from langchain.chat_models.openai import ChatOpenAI from langchain.schema import (SystemMessage, HumanMessage, AIMessage) from langchain.schema import Document from langchain.document_loaders import UnstructuredPDFLoader
Pinecone
from langchain.vectorstores import Pinecone import pinecone from apikey import pinecone_api_key import uuid
os.environ['OPENAI_API_KEY'] = apikey
User Interface
Side Bar
with st.sidebar: st.title('🚀 Zi-GPT Version 2.0') st.markdown('''
About
Main Page
def main(): st.header("Zi's PDF Helper: Chat with PDF")
if name == 'main': main()
Give me some recommedations on what should I do or change.
Suggestion:
No response