pdftochat

Chat with your PDFs with AI

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PDFToChat is a tool that allows users to chat with their PDF documents in seconds. It is powered by Together AI and Pinecone, utilizing a tech stack including Next.js, Mixtral, M2 Bert, LangChain.js, MongoDB Atlas, Bytescale, Vercel, Clerk, and Tailwind CSS. Users can deploy the tool to Vercel or any other host by setting up Together.ai, MongoDB Atlas database, Bytescale, Clerk, and Vercel. The tool enables users to interact with PDFs through chat, with future tasks including adding features like trash icon for deleting PDFs, exploring different embedding models, implementing auto scrolling, improving replies, benchmarking accuracy, researching chunking and retrieval best practices, adding demo video, upgrading to Next.js 14, adding analytics, customizing tailwind prose, saving chats in postgres DB, compressing large PDFs, implementing custom uploader, session tracking, error handling, and support for images in PDFs.

README:

PDFToChat

Chat with your PDFs in seconds. Powered by Together AI and Pinecone.

Tech Stack · Deploy Your Own · Common Errors · Credits · Future Tasks

Tech Stack

Next.js App Router for the framework
Mixtral through Together AI inference for the LLM
M2 Bert 80M through Together AI for embeddings
LangChain.js for the RAG code
MongoDB Atlas for the vector database
Bytescale for the PDF storage
Vercel for hosting and for the postgres DB
Clerk for user authentication
Tailwind CSS for styling

Deploy Your Own

You can deploy this template to Vercel or any other host. Note that you'll need to:

Set up Together.ai
Set up a MongoDB Atlas Atlas database with 768 dimensions
- See instructions below for MongoDB
Set up Bytescale
Set up Clerk
Set up Vercel
(Optional) Set up LangSmith for tracing.

See the .example.env for a list of all the required environment variables.

You will also need to prepare your database schema by running npx prisma db push.

MongoDB Atlas

To set up a MongoDB Atlas database as the backing vectorstore, you will need to perform the following steps:

Sign up on their website, then create a database cluster. Find it under the Database sidebar tab.
Create a collection by switching to Collections the tab and creating a blank collection.
Create an index by switching to the Atlas Search tab and clicking Create Search Index.
Make sure you select Atlas Vector Search - JSON Editor, select the appropriate database and collection, and paste the following into the textbox:

{
  "fields": [
    {
      "numDimensions": 768,
      "path": "embedding",
      "similarity": "euclidean",
      "type": "vector"
    },
    {
      "path": "docstore_document_id",
      "type": "filter"
    }
  ]
}

Note that the numDimensions is 768 dimensions to match the embeddings model we're using, and that we have another index on docstore_document_id. This allows us to filter later.

You may call the index whatever you wish, just make a note of it!

Finally, retrieve and set the following environment variables:

NEXT_PUBLIC_VECTORSTORE=mongodb # Set MongoDB Atlas as your vectorstore

MONGODB_ATLAS_URI= # Connection string for your database.
MONGODB_ATLAS_DB_NAME= # The name of your database.
MONGODB_ATLAS_COLLECTION_NAME= # The name of your collection.
MONGODB_ATLAS_INDEX_NAME= # The name of the index you just created.

Common errors

Check that you've created an .env file that contains your valid (and working) API keys, environment and index name.
Check that you've set the vector dimensions to 768 and that index matched your specified field in the .env variable.
Check that you've added a credit card on Together AI if you're hitting rate limiting issues due to the free tier

Credits

Youssef for the design of the app
Mayo for the original RAG repo and inspiration
Jacob for the LangChain help
Together AI, Bytescale, Pinecone, and Clerk for sponsoring

Future tasks

These are some future tasks that I have planned. Contributions are welcome!

[ ] Add a trash icon for folks to delete PDFs from the dashboard and implement delete functionality
[ ] Try different embedding models like UAE-large-v1 to see if it improves accuracy
[ ] Explore best practices for auto scrolling based on other chat apps like chatGPT
[ ] Do some prompt engineering for Mixtral to make replies as good as possible
[ ] Protect API routes by making sure users are signed in before executing chats
[ ] Run an initial benchmark on how accurate chunking / retrieval are
[ ] Research best practices for chunking and retrieval and play around with them – ideally run benchmarks
[ ] Try out Langsmith for more observability into how the RAG app runs
[ ] Add demo video to the homepage to demonstrate functionality more easily
[ ] Upgrade to Next.js 14 and fix any issues with that
[ ] Implement sources like perplexity to be clickable with more info
[ ] Add analytics to track the number of chats & errors
[ ] Make some changes to the default tailwind prose to decrease padding
[ ] Add an initial message with sample questions or just add them as bubbles on the page
[ ] Add an option to get answers as markdown or in regular paragraphs
[ ] Implement something like SWR to automatically revalidate data
[ ] Save chats for each user to get back to later in the postgres DB
[ ] Bring up a message to direct folks to compress PDFs if they're beyond 10MB
[ ] Use a self-designed custom uploader
[ ] Use a session tracking tool to better understand how folks are using the site
[ ] Add better error handling overall with appropriate toasts when actions fail
[ ] Add support for images in PDFs with something like Nougat

For Tasks:

Click tags to check more tools for each tasks

chat with pdfs delete pdfs explore embedding models implement auto scrolling benchmark accuracy

For Jobs:

software developer data analyst researcher content creator academic

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