add openai integration

get-started/overview.mdx

@@ -31,6 +31,14 @@ Chainlit is an open-source Python package to build production ready Conversation
 Chainlit is compatible with all Python programs and libraries. That being said, it comes with a set of integrations with popular libraries and frameworks.
 
 <CardGroup cols={2}>
+ <Card
+ title="OpenAI"
+ icon="circle"
+ color="#dc2626"
+ href="/integrations/openai">
+ Learn how to explore your OpenAI calls in Chainlit.
+ </Card>
+
  <Card
  title="LangChain"
  icon="circle"

integrations/openai.mdx

@@ -0,0 +1,82 @@
+---
+title: OpenAI
+---
+
+<Note>
+ We support the OpenAI python library starting at version 1.0.0.
+</Note>
+
+The benefits of this integration is that you can see the OpenAI API calls in a step in the UI, and you can explore them in the prompt playground.
+
+You will also get the full generation details (prompt, completion, tokens per second...) in your Literal dashboard if your project is using Literal.
+
+You need to add `cl.instrument_openai()` after creating your OpenAI client.
+
+<Warning>
+ You shouldn't configure this integration if you're already using another integration like Embedchain, Haystack, Langchain or LlamaIndex. Both integrations would record the same generation and create duplicate steps in the UI.
+</Warning>
+
+## Prerequisites
+
+Before getting started, make sure you have the following:
+
+- A working installation of Chainlit
+- The OpenAI package installed
+- An OpenAI API key
+- Basic understanding of Python programming
+
+## Step 1: Create a Python file
+
+Create a new Python file named `app.py` in your project directory. This file will contain the main logic for your LLM application.
+
+## Step 2: Write the Application Logic
+
+In `app.py`, import the necessary packages and define one function to handle messages incoming from the UI.
+
+```python
+from openai import AsyncOpenAI
+
+import chainlit as cl
+
+client = AsyncOpenAI()
+
+# Instrument the OpenAI client
+cl.instrument_openai()
+
+settings = {
+ "model": "gpt-3.5-turbo",
+ "temperature": 0,
+ # ... more settings
+}
+
+@cl.on_message
+async def on_message(message: cl.Message):
+ response = await client.chat.completions.create(
+ messages=[
+ {
+ "content": "You are a helpful bot, you always reply in Spanish",
+ "role": "system"
+ },
+ {
+ "content": input,
+ "role": "user"
+ }
+ ],
+ **settings
+ )
+ await cl.Message(content=response.choices[0].message.content).send()
+```
+
+## Step 3: Fill the environment variables
+
+Create a file named `.env` in the same folder as your `app.py` file. Add your OpenAI API key in the `OPENAI_API_KEY` variable. You can optionally add your Literal API key in the `LITERAL_API_KEY`.
+
+## Step 4: Run the Application
+
+To start your app, open a terminal and navigate to the directory containing `app.py`. Then run the following command:
+
+```bash
+chainlit run app.py -w
+```
+
+The `-w` flag tells Chainlit to enable auto-reloading, so you don't need to restart the server every time you make changes to your application. Your chatbot UI should now be accessible at http://localhost:8000.

mint.json

@@ -54,6 +54,7 @@
  {
  "group": "Integrations",
  "pages": [
+ "integrations/openai",
  "integrations/langchain",
  "integrations/llama-index",
  "integrations/haystack",