Langchain is a powerful PDF chat app that allows you to communicate with others while viewing and editing PDF files. This handy tool is perfect for collaborating on documents, giving feedback, or simply discussing the content of a PDF file with your colleagues or classmates. In this step-by-step tutorial, I will show you how to use Langchain to chat with others about your PDF files.
Step 1: Access Langchain
To get started with Langchain, you will first need to access the website where the PDF chat app is available. Simply open your web browser and type in the URL for Langchain. Once the website loads, you should see a homepage with a login or sign-up option.
Step 2: Sign Up or Log In
If you are not already a user of Langchain, you will need to sign up for an account. Simply click on the sign-up button and provide the required information, such as your email address and a password. If you already have an account, you can log in using your credentials.
Step 3: Upload a PDF File
Once you have logged in to Langchain, you can start using the app by uploading a PDF file. To do this, click on the “Upload” button and select the PDF file from your computer that you want to chat about. The file will be uploaded to the app, and you will be able to view its contents.
Step 4: Invite Others to Chat
Now that your PDF file is uploaded, you can invite others to join the chat and discuss the content of the file. To do this, click on the “Invite” button and enter the email addresses of the people you want to invite. They will receive an email with a link to join the chat.
Step 5: Start Chatting
Once your colleagues or classmates have joined the chat, you can start discussing the PDF file. You can type messages in the chat box, share your thoughts, ask questions, or provide feedback on the content. The other participants will be able to see your messages and respond accordingly.
Step 6: View and Edit the PDF File
While chatting with others, you can also view and edit the PDF file in real-time. Langchain allows you to highlight text, draw annotations, add comments, and make other edits to the document. These changes will be visible to all participants in the chat, making it easy to collaborate on the content.
Step 7: Save and Share the Chat
Once you have finished chatting and editing the PDF file, you can save the chat session for future reference. Langchain allows you to save the chat history, including all messages, annotations, and edits made to the PDF file. You can also share the chat with others by sending them a link to access the saved session.
In conclusion, Langchain is a versatile PDF chat app that makes it easy to collaborate with others on PDF files. By following this step-by-step tutorial, you can start using Langchain to chat about your PDF files and work together with your colleagues or classmates. Give it a try today and see how it can improve your document collaboration experience!
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Thankyou buddy.
How can I make it so that the user can repeatedly ask questions on the document like ChatGPT with the AI having history of previous conversations?
Real nice explaination, Could u help me out with the open ai api key? It only works if you have a paid openai account , any other alternative?
I am having a problem. Where I always get a response saying cannot answer questions based on the context provided. How do I fix this ??
can we upload more than one pdf?
Sir I’am facing an error EOFError: Ran out of input can you please help me out to solve this error , i have followed the same approach that you have done but still I’am facing thi error please help me out
Could you please tell me how to deploy the application as well?
Is it free?
In case you're having difficulty with storing pickel files.
store_name = pdf.name[:-4]
index_folder = f'src/faiss_store/{store_name}'
if os.path.exists(index_folder):
try:
vectorstores = FAISS.load_local(index_folder, OpenAIEmbeddings(),allow_dangerous_deserialization=True)
st.write("Loaded vectorstores from local storage")
except Exception as e:
st.write(f"Failed to load local storage: {e}")
else:
try:
embeddings = OpenAIEmbeddings(model="text-embedding-3-large", dimensions=1024)
faiss_index = faiss.IndexFlatL2(embeddings.dimensions)
# Initialize the docstore
docstore = InMemoryDocstore()
# Initialize the index_to_docstore_id
index_to_docstore_id = {}
# vectorstores = FAISS(chunks,embeddings)
vectorstores = FAISS(embedding_function=embeddings, index=faiss_index, docstore=docstore, index_to_docstore_id=index_to_docstore_id)
vectorstores.save_local(index_folder)
st.write("Saved vectorstores to local storage")
except Exception as e:
st.write(f"Failed to save local storage: {e}")
Hey, thank you for this. Can it work with a lot of document? Like if I upload about 1000 documents?
im use local ollama with pdf ,but models running was slowly.
I get this error (when i load a pdf): AttributeError: module 'openai' has no attribute 'error' can you please help i have been debugging for 2 hours?
which python version you have used?
I have this use-case where there are different types of documents. I can parse documents using document loaders using langchain. But, there are images also in these documents. I want to store them as metadata and if answer generated from a context chunk it show the image also. Please help.
mannnnn i am facing a lot of problems can somebody assist me!!!!!!!
Bro if the pdf file have images how can I access 😢 them please put video for this kind of LM bro.
help, i am getting this error while uploading it AxiosError: Request failed with status code 403
can we make it with another llm model?
Link to the code is expired. Can you please update that.