Update Modal Readme (#1825)

examples/deployment/modal-example/README.md

@@ -2,17 +2,17 @@
 
 Deployment example for [modal.com](https://www.modal.com). This example demonstrates how to deploy a FastAPI webapp to Modal with an RTVI compatible `/connect` endpoint that launches a Pipecat pipeline in a separate Modal container and returns a room/token for the client to join. This example also supports providing a parameter to the `/connect` endpoint for specifying which Pipecat pipeline to launch; openai, gemini, or vllm. The vllm pipeline points to a self-hosted OpenAI compatible LLM, using a llama model (neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16), deployed to Modal.
 
+![](diagram.jpg)
+
 # Running this Example
 
-## Prerequisites
+## Install the Modal CLI
 
-Setup a Modal account and install it on your machine if you have not already, following their easy 3-steps in their [Getting Started Guide](https://modal.com/docs/guide#getting-started)
+Setup a Modal account and install it on your machine if you have not already, following their easy 3 steps in their [Getting Started Guide](https://modal.com/docs/guide#getting-started)
 
 ## Deploy a self-serve LLM
 
-1. Follow the Modal Guide and example for [Deploying an OpenAI-compatible LLM service with vLLM](https://modal.com/docs/examples/vllm_inference).
-
-    The TLDR, though, is to simply do the following from within this directory:
+1. Deploy Modal's OpenAI-compatible LLM service:
 
    ```bash
    git clone https://github.com/modal-labs/modal-examples
@@ -20,45 +20,57 @@ Setup a Modal account and install it on your machine if you have not already, fo
    modal deploy 06_gpu_and_ml/llm-serving/vllm_inference.py
    ```
 
-2. Jot down the endpoint from the previous step to use in the bot_vllm file mentioned below. It will look something like: `https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run`
+   Refer to Modal's guide and example for [Deploying an OpenAI-compatible LLM service with vLLM](https://modal.com/docs/examples/vllm_inference) for more details.
 
-**Note:** This Modal example is their [initial getting started example](https://modal.com/docs/examples/vllm_inference) with a Llama-3.1 model. By default, it will tear down the container after 15 minutes of inactivity and can take 5-10 minutes to re-start, during which time it is unusable. So for the purposes of just getting started and this example, we recommend visiting the `/docs` endpoint (`https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run/docs`) for your deployed llm in a browser to trigger the cold start. Then wait for the page to load, indicating its ready before trying to connect your client.
+2. Take note of the endpoint URL from the previous step, which will look like:
+   ```
+   https://{your-workspace}--example-vllm-openai-compatible-serve.modal.run
+   ```
+   You'll need this for the `bot_vllm.py` file in the next section.
+
+    **Note:**  The default Modal LLM example uses Llama-3.1 and will shut down after 15 minutes of inactivity. Cold starts take 5-10 minutes. To prepare the service, we recommend visiting the `/docs` endpoint (`https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run/docs`) for your deployed LLM and wait for it to fully load before connecting your client.
 
 ## Deploy FastAPI App and Pipecat pipeline to Modal 
 
 1. Setup environment variables
 
-```bash
-cd server
-cp env.example .env
-# Modify .env to provide your service API Keys
-```
+   ```bash
+   cd server
+   cp env.example .env
+   # Modify .env to provide your service API Keys
+   ```
 
-Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+   Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
 
-1. Update the `modal_url` in `server/src/bot_vllm.py` to point to the url produced from the self-serve llm deploy, mentioned above.
+2. Update the `modal_url` in `server/src/bot_vllm.py` to point to the url produced from the self-serve llm deploy, mentioned above.
 
-2. From within the `server` directory, test the app locally:
+3. From within the `server` directory, test the app locally:
 
-```bash
-modal serve app.py
-```
+   ```bash
+   modal serve app.py
+   ```
 
 4. Deploy to production
 
-```bash
-modal deploy app.py
-```
+   ```bash
+   modal deploy app.py
+   ```
 
-5. Jot down the endpoint from the previous step to use in the client's app.js file mentioned its README. It will look something like: `https://<Modal workspace>--pipecat-modal-fastapi-app.modal.run`
+5. Note the endpoint URL produced from this deployment. It will look like:
 
-## Launch and Talk to your Bots running on Modal
+   ```bash
+   https://{your-workspace}--pipecat-modal-fastapi-app.modal.run
+   ```
 
-## Option 1: Direct Link
+   You'll need this URL for the client's `app.js` configuration mentioned in its README.
+
+## Launch your bots on Modal
+
+### Option 1: Direct Link
 
 Simply click on the url displayed after running the server or deploy step to launch an agent and be redirected to a Daily room to talk with the launched bot. This will use the OpenAI pipeline.
 
-## Option 2: Connect via an RTVI Client
+### Option 2: Connect via an RTVI Client
 
 Follow the instructions provided in the [client folder's README](client/javascript/README.md) for building and running a custom client that connects to your Modal endpoint. The provided client provides a dropdown for choosing which bot pipeline to run.
 
@@ -71,13 +83,9 @@ In your [Modal dashboard](https://modal.com/apps), you should have two Apps list
     1. `fastapi_app`: This function is running the endpoints that your client will interact with and initiate starting a new pipeline (`/`, `/connect`, `/status`). Click on this function to see logs for each endpoint hit.
     2. `bot_runner`: This function handles launching and running a bot pipeline. Click on this function to get a list of all pipeline runs and access each run's logs.
 
-## Diagram of Deployment
-
-![](diagram.jpg)
-
 # Modal + Pipecat Tips
 
-- In most other Pipecat examples, we use Popen to launch the pipeline process from the /connect endpoint. In this example, we instead use a Modal function with its own Modal image defined. This change ensures that each run of the Pipeline happens in a isolated, customizable container.
-- For the FastAPI and most common Pipecat Pipeline containers, a default debian_slim CPU-only should be all that's required to run. GPU containers are needed for self-hosted services.
+- In most other Pipecat examples, we use `Popen` to launch the pipeline process from the `/connect` endpoint. In this example, we use a Modal function instead. This allows us to run the pipelines using a separately defined Modal image as well as run each pipeline in an isolated container.
+- For the FastAPI and most common Pipecat Pipeline containers, a default `debian_slim` CPU-only should be all that's required to run. GPU containers are needed for self-hosted services.
 - To minimize cold starts of the pipeline and reduce latency for users, set `min_containers=1` on the Modal Function that launches the pipeline to ensure at least one warm instance of your function is always available.
 - For next steps on running a self-hosted llm and reducing latency, check out all of [Modal's LLM examples](https://modal.com/docs/examples/vllm_inference).