describe watcher window and run build only on first time

Author: staceysv

README.md

@@ -93,9 +93,9 @@ You can add different types of .env files based on your needs for testing, dev,
 
 If you want to run a local version of the app that's not publically accessible:
 
-1. Open up your terminal and navigate to the repo folder (e.g. /Desktop/tttc-light-js)
-2. Run `npm run build`.
-3. Run `npm run dev` to start the dev server. This will run three servers: the `next-client` frontend on localhost:3000, the `express-server` backend on localhost:8080, and the `pyserver` Python FastAPI server for the LLM calls on localhost:8000.
+1. Open up your terminal and navigate to the repo folder (e.g. `/Desktop/tttc-light-js`)
+2. If this is your first time, build the repo: `cd commmon && npm run build`.
+3. Run `npm run dev` to start the dev server. This will run three servers in new terminal windows: the `next-client` frontend on `localhost:3000`, the `express-server` backend on `localhost:8080`, and the `pyserver` Python FastAPI server for the LLM calls on `localhost:8000`. A fourth terminal window will show a listener for `/common` that rebuilds the JS files when changes are made.
 4. This build will be optimized for production.
 
 #### Using docker locally (not recommended)

contributing.md

@@ -9,8 +9,8 @@ First, pull the latest from `main` in this repo and start in the root directory
 
 1. In a new terminal, run `brew install redis` and `redis-server` to install Redis and start a local Redis server.
 2. From the repo root, `cd pyserver` and install the Python package dependencies in `requirements.txt` with your preferred method (e.g. `pip install -r requirements.txt`). Note: this will get more standardized soon.
-3. Add this line to ``next-client/.env`` and ``next-client/.env.local``:
-``export PIPELINE_EXPRESS_URL=http://localhost:8080``
+3. Add this line to `next-client/.env` and `next-client/.env.local`:
+   `export PIPELINE_EXPRESS_URL=http://localhost:8080`
 
 ### Launch the app
 

examples/README.md

@@ -4,11 +4,11 @@ Note: under very active construction as we separate a new Python server for LLM
 
 ## Quickstart
 
-To run the T3C pipeline with a local front-end & backend server, as well as a Python FastAPI server for LLM calls: 
+To run the T3C pipeline with a local front-end & backend server, as well as a Python FastAPI server for LLM calls:
 
-1. Client-side: update `next-client/.env` to point to the report generation endpoint (e.g. `export PIPELINE_EXPRESS_URL=http://localhost:8080/generate`) and run `npm run dev`
+1. Client-side: update `next-client/.env` to point to the report generation endpoint (e.g. `export PIPELINE_EXPRESS_URL=http://localhost:8080/`) and run `npm run dev`
 2. Server-side: update `express-server/.env` with OpenAI/Anthropic/GCS keys and run `npm i && npm run dev`.
-3. Python FastAPI LLM interface: install Python requirements via ``cd pyserver``, then ``pip install -r requirements.txt``. Install Redis and start a server: ``brew install redis``, ``redis-server``. From ``pyserver``, run ``source .venv/bin/activate && fastapi dev main.py``.
+3. Python FastAPI LLM interface: install Python requirements via `cd pyserver`, then `pip install -r requirements.txt`. Install Redis and start a server: `brew install redis`, `redis-server`. From `pyserver`, run `source .venv/bin/activate && fastapi dev main.py`.
 4. Navigate to `localhost:3000/create` and upload a CSV file from the UI. Make sure the CSV is formatted as follows
 
 ## Expected CSV format