> ## Documentation Index > Fetch the complete documentation index at: https://thenile.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # KnowledgeAI - PDF search assistant for your organization knowledgeaisnapshot In this tutorial, you will learn how to use Nile's tenant virtualization, user management and vector embedding features to build a SaaS application that allows users to search the PDF documents in an organization. The SaaS will allow users to upload a PDF document, and then ask questions about the document. Using embeddings generated by OpebAI and stored in Nile, and a similarity search using pg\_vector, to provide GPT-3.5 context that will help answer the questions. The embeddings are stored per tenant and data and workload is isolated to a tenant. ## 1. Create a database 1. Sign up for an invite to [Nile](https://thenile.dev) if you don't have one already 2. You should see a welcome message. Click on "Lets get started" 3. Give your workspace and database names, or you can accept the default auto-generated names. ## 2. Create a table with pg\_vector extension Once you created your database, you'll land in Nile's web-based SQL editor. Great place to create the tables we need for this app. Lets start with the embeddings table. ```sql theme={null} CREATE TABLE "file_embedding" ( "id" UUID DEFAULT (gen_random_uuid()), "tenant_id" UUID NOT NULL, "file_id" UUID NOT NULL, "embedding_api_id" UUID NOT NULL, "embedding" vector(1024), "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, "pageContent" TEXT, "location" TEXT, CONSTRAINT "file_embedding_pkey" PRIMARY KEY ("id", "tenant_id"), CONSTRAINT "file_embedding_file_id_fkey" FOREIGN KEY ("file_id", "tenant_id") REFERENCES "file" ("id", "tenant_id") ); ``` This is a tenant-aware table that stores the embeddings of the PDF documents. You can see that each row belongs to a specific tenant, and that the `embedding` column is of type `vector(1024)`. Vector type is provided by the pg\_vector extension for storing embeddings. By storing embeddings in a tenant-aware table, we can use Nile's built-in tenant isolation to ensure that information about PDFs won't leak between tenants. ## 3. Create metadata tables We'll need few more tables to store information about the PDF documents, the conversations with them and the users. Go ahead and create these. ```bash theme={null} CREATE TABLE "file" ( "id" UUID DEFAULT (gen_random_uuid()), "tenant_id" UUID NOT NULL, "url" TEXT, "key" TEXT, "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, "user_id" UUID NOT NULL, "user_picture" TEXT, "user_name" TEXT, "isIndex" Boolean, "name" TEXT, "pageAmt" INTEGER, CONSTRAINT "file_pkey" PRIMARY KEY ("id", "tenant_id"), CONSTRAINT "unique_key_per_tenant" UNIQUE ("tenant_id", "key") ); CREATE TABLE "message" ( "id" UUID DEFAULT (gen_random_uuid()), "tenant_id" UUID NOT NULL, "text" TEXT, "isUserMessage" BOOLEAN, "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, "user_id" UUID NOT NULL, "user_picture" TEXT, "user_name" TEXT, "fileId" UUID, CONSTRAINT "message_pkey" PRIMARY KEY ("id", "tenant_id"), CONSTRAINT "message_fileId_fkey" FOREIGN KEY ("fileId", "tenant_id") REFERENCES "file" ("id", "tenant_id") ); CREATE TABLE "user_subscription" ( "id" UUID DEFAULT (gen_random_uuid()), "user_id" UUID NOT NULL, "tenant_id" UUID NOT NULL, "stripe_customer_id" TEXT, "stripe_subscription_id" TEXT, "stripe_price_id" TEXT, "stripe_current_period_end" TIMESTAMP, CONSTRAINT "subscription_pkey" PRIMARY KEY ("id", "tenant_id"), CONSTRAINT "user_subscription_user_id_fkey" FOREIGN KEY ("user_id", "tenant_id") REFERENCES users.tenant_users ("user_id", "tenant_id"), CONSTRAINT "unique_stripe_customer_id" UNIQUE ("stripe_customer_id", "tenant_id"), CONSTRAINT "unique_stripe_subscription_id" UNIQUE ("stripe_subscription_id", "tenant_id") ); ``` If all went well, you'll see the new tables in the panel on the left hand side of the query editor. You can also see Nile's built-in tenant table next to it. ### 3. Getting credentials In the left-hand menu, click on "Settings" and then select "Credentials". Generate credentials and keep them somewhere safe. These give you access to the database. ### 4. Setting up Google Authentication This demo uses Google authentication for signup. You will need to configure this in both Google and Nile, following the instructions [in the example](https://github.com/niledatabase/niledatabase/blob/main/examples/user_management/social_login_google/NextJS/README.md). ### 5. Setting up 3rd Party SaaS This example requires a few more 3rd party SaaS accounts. You'll need to set them up and grab API keys to configure this example: * [UploadThing](https://uploadthing.com): Used for storing PDFs * [OpenAI](https://openai.com/): Used to generate embeddings of the uploaded documents and for the chat itself * [Stripe](https://stripe.com/): This is optional: The application has support for limited free tier and a powerful paid tier. You can learn more about [integrating Nile and Stripe in the integration guide](https://www.thenile.dev/docs/platform/integrations/stripe). ### 6. Setting the environment * If you haven't cloned this project yet, now will be an excellent time to do so. Since it uses NextJS, we can use `create-next-app` for this: ```bash theme={null} npx create-next-app -e https://github.com/niledatabase/niledatabase/tree/main/examples/ai/ai_pdf nile-ai-pdf cd nile-ai-pdf ``` * Rename `.env.example` to `.env.local`, and update it with your workspace and database name. *(Your workspace and database name are displayed in the header of the Nile dashboard.)* Fill in the username and password with the credentials you picked up in the previous step. And fill in the access keys for UploadThing and OpenAI. * Install dependencies with `yarn install` or `npm install`.