Merge pull request #33 from defenseunicorns/better-readme-instructions

better README instructions and descriptions

README.md

@@ -2,39 +2,47 @@
 
 ## Description
 
-API to expose LLM backends via FastAPI and gRPC.
+A Python API that exposes LLM backends, via FastAPI and gRPC, in the OpenAI API specification.
+
+## Usage
+
+See [instructions](#instructions) to get the API up and running. Then, go to http://localhost:8080/docs for the Swagger documentation on API usage.
 
 ## Instructions
 
-Choose a LeapfrogAI model backend and get that running separately. Conisder:
-    * https://github.com/defenseunicorns/leapfrogai-backend-ctransformers
-    * https://github.com/defenseunicorns/leapfrogai-backend-whisper
+The instructions in this section assume the following:
+
+1. Properly installed and configured Python 3.11.x, to include its development tools
+2. The `config.yaml` is created based on the `config-example.yaml`
+3. You have chosen a LeapfrogAI model backend and have that running. Some examples of existing backends:
 
-#### Run Locally
+- https://github.com/defenseunicorns/leapfrogai-backend-ctransformers
+- https://github.com/defenseunicorns/leapfrogai-backend-whisper
 
-1. Setup `config.yaml`, see `config-example.yaml` for common examples.
+### Local Development
 
-2. Setup and run the API
+For cloning a model locally and running the development backend.
 
 ```bash
 # Setup Python Virtual Environment
-python -m venv .venv
-source .venv/bin/activate
+make create-venv
+make activate-venv
 make requirements-dev
 
 # Start Model Backend
 make dev
 ```
 
-### Docker Run
+### Docker Container
+
+#### Image Build and Run
+
+For local image building and running.
 
 ```bash
 # Build the docker image
 docker build -t ghcr.io/defenseunicorns/leapfrogai/leapfrogai-api:latest .
 
 # Run the docker container
-docker run -p 8080:8080 ghcr.io/defenseunicorns/leapfrogai/leapfrogai-api:latest
+docker run -p 8080:8080 -v ./config.yaml:/leapfrogai/config.yaml ghcr.io/defenseunicorns/leapfrogai/leapfrogai-api:latest
 ```
-
-1. Create `config.yaml`, see `config-example.yaml` for common examples.
-2. Copy `config.yaml` into the work directory of the container while it is running.