Cohere, LangChain & Chroma - Create a ChatGPT Chatbot for Your PDF Files

Introduction

Use the new Cohere API to build a chatbot for multiple Large PDF files.

This repository used LangChain, Chroma, Typescript, and Next.js. LangChain is a framework that makes it easier to build scalable AI/LLM apps and chatbots. Chroma is a vectorstore for storing embeddings and your PDF in text to later retrieve similar docs.

This is an application of a RAG PDF chatbot using the powerful Cohere API. With an emphasis on reproducibility, this project offers a detailed narrative on the entire process, starting with the initial setup and concluding with a deployed chatbot.

The application build a RAG PDF Chatbot from your PDF Files. It will create chunck and store them in the Chroma Vector Database.

Pre Requisite

• What all you need

Working

Below is the flowchart using LangChains and a bird's-eye view of the process, which involves preparing the training data and integrating it into a application or service. It then moves into the core of the process by covering the selection of an appropriate programming language and installing the necessary Cohere SDK, accompanied by clear instructions and potential pitfalls to watch out for.

The project uses Chroma vector database, Cohere Command-r Model, Embed Model and Chat endpoint.

1. Install Docker Desktop for your platform.

2. Fork the repo to your Github account, so that you can change the code and add/finetune the features.

https://github.com/Sangwan70/cohere-chatbot-pdf-chroma.git

3. Clone the forked repo or download the ZIP to machine

git clone https://github.com/Sangwan70/cohere-chatbot-pdf-chroma.git

4. Change to cloned repository and Install packages

cd cohere-chatbot-pdf-chroma

npm install yarn -g

to install yarn globally.

Then run:

yarn install

After installation, you should see a node_modules folder.

5. Set up your .env file. Copy .env.example into .env Your .env file should look like this:

COHERE_API_KEY=
COLLECTION_NAME=skillpedia

If you run into errors, please review the troubleshooting section further down this page.

Prelude: Please make sure you have already downloaded node on your system and the version is 18 or greater.

Development

• Choose a collection name where you'd like to store your embeddings in Chroma. This collection will later be used for queries and retrieval.
• Chroma details

6. In utils/makechain.ts chain change the QA_PROMPT for your own usecase.

7. In a new terminal window, run Chroma in the Docker container:

docker run -d -p 8000:8000 chromadb/chroma:0.4.15

Convert your PDF files to embeddings

This repo can load multiple PDF files

8. Inside docs folder, add your pdf files or folders that contain pdf files.

9. Run the script

   npm run ingest
   

   to 'ingest' and embed your docs. If you run into errors troubleshoot below.

Run the app

Once you've verified that the embeddings and content have been successfully added to Chroma db, you can run the app

npm run dev

to launch the local dev environment, and then type a question in the chat interface.

Troubleshooting

In general, keep an eye out in the issues and discussions section of this repo for solutions.

General errors

• Make sure you're running the latest Node version. Run node -v
• Try a different PDF or convert your PDF to text first. It's possible your PDF is corrupted, scanned, or requires OCR to convert to text.
• Console.log the env variables and make sure they are exposed.
• Check that you've created an .env file that contains your valid (and working) API keys, environment and index name.
• If you change modelName in ChatCohere, make sure you have access to the api for the appropriate model.
• Make sure you have enough Cohere API credits and a valid card on your billings account.
• Check that you don't have multiple Cohere API keys in your global environment. If you do, the local env file from the project will be overwritten by systems env variable.
• Try to hard code your API keys into the process.env variables if there are still issues.

To launch the local dev environment, and then type a question in the chat interface.

Launch the browser and open http://localhost:3000