docs: setup

README.md

@@ -48,4 +48,48 @@ Study-Llama is a demo application for organizing, extracting, and searching stud
   - `static/`: Images and assets.  
   - `templates/`: HTML templates.
 - **src/**  
-  - `study_llama/`: Python backend modules for classification, extraction, search, and database logic.
+  - `study_llama/`: Python backend modules for classification, extraction, search, and database logic.
+
+## Set Up
+
+**Clone this repository:**
+
+```bash
+git clone https://github.com/run-llama/study-llama
+cd /study-llama
+```
+
+**Deploy the LlamaAgent:**
+
+In order for the classify-extract and vector search agent workflows to receive and process queries, they needs to be deployed to the cloud (or at least accessible through a public endpoint). The easiest way to do so is to use [`llamactl`](https://developers.llamaindex.ai/python/llamaagents/llamactl/getting-started/) and deploy the agent workflow as a [LlamaAgent](https://developers.llamaindex.ai/python/llamaagents/overview/):
+
+```bash
+uv tool install -U llamactl
+llamactl auth # authenticate
+llamactl deployments create # create a deployment from the current repository
+```
+
+In order for the LlamaAgent to work, you will need the following environment variables in a `.env` file (`llamactl` manages environments autonomously):
+
+- `OPENAI_API_KEY` to interact with GPT-4.1 for email generation
+- `LLAMA_CLOUD_API_KEY` and `LLAMA_CLOUD_PROJECT_ID` to get predictions from LlamaClassify and LlamaExtract
+- `POSTGRES_CONNECTION_STRING` to connect to the Postgres database with the uploaded files and the classification rules (you can use [Neon](https://neon.com), [Supabase](https://supabase.com), [Prisma](https://prisma.io) or a self-hosted Postgres instance)
+- `QDRANT_API_KEY` and `QDRANT_HOST`, to upload sumamries and question/answers to perform vector search and retrieval (you can use [Qdrant Cloud](https://qdrant.tech) or a self-hosted Qdrant instance).
+
+**Deploy the frontend**
+
+Once the agent is deployed, build the Docker image for the frontend (needed to interact with the LlamaAgents we just created), and deploy it through services like [Dokploy](https://dokploy.com) or [Coolify](https://coolify.io).
+
+```bash
+docker build . -t your-username/study-llama:prod
+# docker login ghcr.io # (uncomment if you wish to use the GitHub container registry)
+docker push your-username/study-llama:prod
+```
+
+The frontend service uses a few env variables:
+
+- `LLAMA_CLOUD_API_KEY`, `FILES_API_ENDPOINT` (which will presumably be `https://api.cloud.llamaindex.ai/deployments/study-llama/workflows/classify-and-extract/run`) and `SEARCH_API_ENDPOINT` (which will presumably be `https://api.cloud.llamaindex.ai/deployments/study-llama/workflows/search/run`), the API key and the API endpoints to interact with your deployed LlamaAgent
+- `POSTGRES_CONNECTION_STRING` to connect to the Postgres database with the uploaded files, the classification rules and the user auth (you can use [Neon](https://neon.com), [Supabase](https://supabase.com), [Prisma](https://prisma.io) or a self-hosted Postgres instance, but it has to be the **same as for the LlamaAgent**)
+- `CACHE_TABLE` and `RATE_LIMITING_TABLE`, the table names for the SQLite database taking care of caching and rate limiting.
+
+Services like Dokploy or Coolify offer you to set these environment variables through their own environment management interfaces.