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tan-talk's Introduction

Streaming Chat Completions

An example of streaming ChatGPT via the OpenAI v4.0 node SDK. See this Stack post for more information.

To see a version that does the streaming over HTTP and only writes to the database at the end, see the http-streaming branch, or this repo.

Diagram of browsers talking to Convex, which talks to OpenAI

Overview:

This app makes up a name for your user using Faker.js and allows you to chat with other users: open up multiple tabs to try it out!

If your message includes "@gpt" it will kick off a request to OpenAI's chat completion API and stream the response, updating the message as data comes back from OpenAI.

  • The frontend logic is all in App.tsx.
  • The server logic that stores and updates messages in the database is in messages.ts.
  • The asynchronous server function that makes the streaming request to OpenAI is in openai.ts.
  • The initial messages that are scheduled to be sent are in init.ts.

Running the App

npm install
npm run dev

This will configure a Convex project if you don't already have one. It requires an OpenAI API key. Set the environment variable: OPEN_API_KEY (should start with sk-) in your Convex backend via the dashboard once your backend has been configured. You can also get there via:

npx convex dashboard

Once npm run dev successfully syncs, if the database is empty, it will schedule some messages to be sent so you can see it working in action.

It will then start two processes in one terminal: vite for the frontend, and npx convex dev for syncing changes to Convex server functions.

Check it out in the scripts section of package.json.

What is Convex?

Convex is a hosted backend platform with a built-in database that lets you write your database schema and server functions in TypeScript. Server-side database queries automatically cache and subscribe to data, powering a realtime useQuery hook in our React client. There are also Python, Rust, ReactNative, and Node clients, as well as a straightforward HTTP API.

The database support NoSQL-style documents with relationships and custom indexes (including on fields in nested objects).

The query and mutation server functions have transactional, low latency access to the database and leverage our v8 runtime with determinism guardrails to provide the strongest ACID guarantees on the market: immediate consistency, serializable isolation, and automatic conflict resolution via optimistic multi-version concurrency control (OCC / MVCC).

The action server functions have access to external APIs and enable other side-effects and non-determinism in either our optimized v8 runtime or a more flexible node runtime.

Functions can run in the background via scheduling and cron jobs.

Development is cloud-first, with hot reloads for server function editing via the CLI. There is a dashbord UI to browse and edit data, edit environment variables, view logs, run server functions, and more.

There are built-in features for reactive pagination, file storage, reactive search, https endpoints (for webhooks), streaming import/export, and runtime data validation for function arguments and database data.

Everything scales automatically, and it’s free to start.

tan-talk's People

Contributors

ianmacartney avatar tangentleman avatar nicolapps avatar

tan-talk's Issues

Large token count in prior conversation can surpass max context length

To reproduce:

  • Create chat messages of 4K+ tokens
  • Call @GPT which by default uses the past 10 messages for context

An LLM with an insufficient context window will fail here.

There's a few ways I can go about tackling this:

  1. Add character count restrictions on individual message.body values (This is a solid start)
  2. Add character count maximum when passing messages to internal.openai.chat

The present issue with the second is that the logic would reasonably depend on the context length of the model. Currently, I'm not storing any information about the LLM config accessibly, so my limits would have to be arbitrary. This is still better than potential 128K token spams that could get expensive!

I propose a reasonable ~3.5K token cutoff unless the user includes a "32k" substring in their message, which would be an easy way to extend the command for use cases using longer context models.

Add interactive buttons to App.tsx

I'd like individual icons for each chat message, like an edit/delete/copy to clipboard, as well as a "delete last exchange" button. Under the hood, these would just be mutations that patch/delete the current message, or call messages:removeLastN.

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