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
• Pinecone or 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 Pinecone or 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:

1. Sign up on their website, then create a database cluster. Find it under the Database sidebar tab.
2. Create a collection by switching to Collections the tab and creating a blank collection.
3. Create an index by switching to the Atlas Search tab and clicking Create Search Index.
4. 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!

5. 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