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-# Database Query Plugin
+# Database Query Extension
 
-Note: This project is experimental and is not an officially supported Google product.
+Note: This project is experimental and is not an officially supported Google
+product.
+
+
+## Introduction
+
+This project is intended to be a working demo showcasing how to extend an
+LLM-based application using Cloud Databases for [Retrieval Augmented Generation
+(RAG)][rag]. It's intended to both demonstrate how to best use advanced RAG
+patterns with Databases, as well as provide a basic outline for extending your
+own LLMs.
+
+This demo showcases an "SFO Airport Assistant": a San Francisco Airport-based
+AI assistant that has access to information about airports, flights, and
+amenities. It can help answer users questions like:
+* Are there any luxury shops?
+* Where can I get coffee near gate A6?
+* Where can I find a gift?
+* What flights are headed to NYC tomorrow? 
+
+[rag]: https://www.promptingguide.ai/techniques/rag
+
+## Table of Contents
+<!-- TOC depthfrom:2 -->
+
+- [Introduction](#introduction)
+- [Table of Contents](#table-of-contents)
+- [Architecture](#architecture)
+    - [Overview](#overview)
+    - [Extension as a service](#extension-as-a-service)
+- [Deploying](#deploying)
+    - [Setting up your Database](#setting-up-your-database)
+    - [Deploying the Extension Service](#deploying-the-extension-service)
+    - [Running the LLM-based Application](#running-the-llm-based-application)
+- [Writing your own extension](#writing-your-own-extension)
+
+<!-- /TOC -->
+
+## Architecture 
+
+### Overview
+
+![Overview](./architecture.png)
+
+This demo contains 3 key parts:
+1. **Application** -- The LLM-based app that acts as orchestrates layer for the
+   interaction with the LLM.
+1. **Extension** -- The extension service provides the application concrete,
+   discrete actions that allow the LLM to interact with the Database.
+1. **Database** -- The database containing the data the LLM can use to answer
+   questions. For this application, the database used was intentionally designed
+   to be interchangeable in order to make it easier to run this on your
+   preferred database.
+
+### Extension as a service
+
+While it's often possible to expose similar functionality directly into your
+application, running your extension service has several benefits:
+1. **Better recall** - LLMs perform better when given smaller, discrete tasks
+   they can use to accomplish larger goals. By mapping a specific action to a
+   specify, pre-determined query it significantly improves the LLMs ability to
+   leverage it successfully.
+1. **Better scalability** - Running the extension as a separate service both
+   allows multiple different LLMs to leverage it, as well as allowing it to
+   scale independently. It allows allows production best practices such as
+   connection pooling or caching.
+1. **Better security** - LLMs are susceptible to attacks such as "jailbreaking"
+   to circumvent safety measures that are put in place. Using an intermediary
+   service allows the application to handle authentication and authorization
+   through more standard and secure channels. 
+
+## Deploying
+
+Deploying this demo consists of 3 steps:
+1. Setting up your Database -- creating your database and initializing it with
+   data
+1. Deploying your Extension -- deploying your extension service and connecting
+   it to your database
+1. Running the LLM-based application -- running your application locally
+
+### Setting up your Database
+
+The extension service uses an interchangeable 'datastore' interface. Choose one
+of any of the database's listed below to set up and initialize your database:
+
+// TODO: complete this link
+* [Set up and configure AlloyDB][]
+
+### Deploying the Extension Service
+
+// TODO: instructions for deploying the extension service
+
+### Running the LLM-based Application
+
+// TODO: Instructions for running app locally
+
+## Writing your own extension
+
+This demo can also serve as a starting point for writing your own extension. The
+directory is organized into the following folders:
+
+| Directory                                    | Description                                                                           |
+|----------------------------------------------|---------------------------------------------------------------------------------------|
+| [`data`](/data)                              | Contains CSV files with the dataset for a working demo.                               |
+| [`extension-service`](/extension-service)    | Contains the service for extending an LLM with information from the database.         |
+| [`langchain_tools_demo`](/extension-service) | Contains an git statLLM-based application that that uses the extension service via LangChain. |
+
+You can copy or fork the `extension-service` folder to customize it to your
+needs. There are two main places you want to start:
+- `extension-service/app/routes.py` - contains the API endpoints that the LLM
+  will call
+- `extension-service/datastore/datastore.py` - contains the interface used to
+  abstract the database. There are specific implementations of this in the
+  `providers` folder that can be customized with logic for your specific schema. 
 
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