GPT & LangChain - Create a ChatGPT Chatbot for Twitter's The Algorithm ML Repo

Use the new GPT-3.5 api to build a chatGPT chatbot The Algorithm ML Repo.

Tech stack used includes LangChain, Pinecone, Typescript, Openai, and Next.js. LangChain is a framework that makes it easier to build scalable AI/LLM apps and chatbots. Pinecone is a vectorstore for storing embeddings and your repo in text to later retrieve similar docs. Autodoc used to create markdown files and then embedded using this repo. Original autodoc conversion done by Sam Hogan at this link. Template of this repo is from link by Mayo.

Get in touch via twitter if you have questions

The visual guide of this repo and tutorial is in the visual guide folder.

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

Development

1. Clone the repo

git clone [github https url]

2. Install packages

pnpm install

3. Set up your .env file

• Copy .env.example into .env Your .env file should look like this:

OPENAI_API_KEY=

PINECONE_API_KEY=
PINECONE_ENVIRONMENT=

PINECONE_INDEX_NAME=

• Visit openai to retrieve API keys and insert into your .env file.
• Visit pinecone to create and retrieve your API keys, and also retrieve your environment and index name from the dashboard.

4. In the config folder, replace the PINECONE_NAME_SPACE with a namespace where you'd like to store your embeddings on Pinecone when you run pnpm run ingest. This namespace will later be used for queries and retrieval.

5. In utils/makechain.ts chain change the QA_PROMPT for your own usecase. Change modelName in new OpenAIChat to gpt-3.5-turbo, if you don't have access to gpt-4. Please verify outside this repo that you have access to gpt-4, otherwise the application will not work with it.

Convert your repo to embeddings

This repo can load repo with multiple folders

1. Run Autodoc on your original repo and create markdown files for the entire repo. Save the results of autodoc as a separate repo, and provide the link in the script/ingest-data.ts file.

2. Run the script npm run ingest to 'ingest' and embed your docs. If you run into errors troubleshoot below.

3. Check Pinecone dashboard to verify your namespace and vectors have been added.

Run the app

Once you've verified that the embeddings and content have been successfully added to your Pinecone, you can run the app pnpm 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
• Ensure you have markdown files for each code file either generated using autodoc or similar tools.
• Console.log the env variables and make sure they are exposed.
• Make sure you're using the same versions of LangChain and Pinecone as this repo.
• 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 OpenAIChat note that the correct name of the alternative model is gpt-3.5-turbo
• Make sure you have access to gpt-4 if you decide to use. Test your openAI keys outside the repo and make sure it works and that you have enough API credits.
• Check that you don't have multiple OPENAPI 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.

Pinecone errors

• Make sure your pinecone dashboard environment and index matches the one in the pinecone.ts and .env files.
• Check that you've set the vector dimensions to 1536.
• Make sure your pinecone namespace is in lowercase.
• Pinecone indexes of users on the Starter(free) plan are deleted after 7 days of inactivity. To prevent this, send an API request to Pinecone to reset the counter before 7 days.
• Retry from scratch with a new Pinecone project, index, and cloned repo.

Credit

Frontend of this repo is inspired by langchain-chat-nextjs