add sql example

bootstrap/database/db-init-job.yaml

@@ -13,7 +13,7 @@ spec:
         command: ['sh', '-c', 'until nc -z -v -w30 $POSTGRESQL_DATABASE 5432; do echo "Waiting for database connection..."; sleep 2; done;']
         env:
         - name: POSTGRESQL_DATABASE
-          value: agenticdb.agentic-db.svc.cluster.local
+          value: agenticdb.agentic-zone.svc.cluster.local
       containers:
       - name: postgresql
         image: registry.redhat.io/rhel9/postgresql-13:latest
@@ -34,7 +34,7 @@ spec:
                 name: agenticdb
                 key: database-password
           - name: POSTGRESQL_DATABASE_HOST
-            value: agenticdb.agentic-db.svc.cluster.local
+            value: agenticdb.agentic-zone.svc.cluster.local
         command: ["/bin/bash", "-c"]
         args:
         - |

bootstrap/database/deployment.yaml

@@ -44,17 +44,17 @@ spec:
             - name: POSTGRESQL_USER
               valueFrom:
                 secretKeyRef:
-                  name: claimdb
+                  name: agenticdb
                   key: database-user
             - name: POSTGRESQL_PASSWORD
               valueFrom:
                 secretKeyRef:
-                  name: claimdb
+                  name: agenticdb
                   key: database-password
             - name: POSTGRESQL_DATABASE
               valueFrom:
                 secretKeyRef:
-                  name: claimdb
+                  name: agenticdb
                   key: database-name
           securityContext:
             capabilities: {}
@@ -66,11 +66,11 @@ spec:
           terminationMessagePath: /dev/termination-log
           terminationMessagePolicy: File
           volumeMounts:
-            - name: claimdb-data
+            - name: agenticdb-data
               mountPath: /var/lib/pgsql/data
       volumes:
-        - name: claimdb-data
+        - name: agenticdb-data
           persistentVolumeClaim:
-            claimName: claimdb
+            claimName: agenticdb
   strategy:
     type: Recreate

bootstrap/database/kustomization.yaml

@@ -13,6 +13,6 @@ resources:
 - secret.yaml
 - deployment.yaml
 - service.yaml
-# - sql-script-configmap.yaml
+- sql-script-configmap.yaml
 # wave 2
 - db-init-job.yaml

bootstrap/database/pvc.yaml

@@ -2,7 +2,7 @@
 apiVersion: v1
 kind: PersistentVolumeClaim
 metadata:
-  name: claimdb
+  name: agenticdb
   namespace: agentic-zone
   labels:
     app: agentic-db

bootstrap/database/secret.yaml

@@ -1,7 +1,7 @@
 kind: Secret
 apiVersion: v1
 metadata:
-  name: claimdb
+  name: agenticdb
   namespace: agentic-zone
   labels:
     app: agentic-db

bootstrap/database/sql-script-configmap.yaml

@@ -0,0 +1,64 @@
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: sql-script-configmap
+  annotations:
+    argocd.argoproj.io/sync-wave: "1"
+data:
+  script.sql: |
+    -- Ensure the schema exists
+    CREATE SCHEMA IF NOT EXISTS agenticdb AUTHORIZATION agenticdb;
+
+    -- Drop existing tables if they exist
+    DROP TABLE IF EXISTS agenticdb.transactions CASCADE;
+
+    -- Drop existing sequences if they exist
+    DROP SEQUENCE IF EXISTS agenticdb.transactions_id_seq;
+
+    -- Create a sequence for transaction IDs
+    CREATE SEQUENCE IF NOT EXISTS agenticdb.transactions_id_seq
+    INCREMENT 1
+    START 1
+    MINVALUE 1
+    MAXVALUE 2147483647
+    CACHE 1;
+
+    -- Create the transactions table
+    CREATE TABLE IF NOT EXISTS agenticdb.transactions
+    (
+        id INTEGER NOT NULL DEFAULT nextval('agenticdb.transactions_id_seq'::regclass),
+        transaction_id TEXT COLLATE pg_catalog."default" UNIQUE NOT NULL,
+        client_name TEXT COLLATE pg_catalog."default" NOT NULL,
+        transaction_type TEXT COLLATE pg_catalog."default" CHECK (transaction_type IN ('BUY', 'SELL', 'SHORT SELL')),
+        stock_symbol TEXT COLLATE pg_catalog."default" NOT NULL,
+        shares INTEGER NOT NULL CHECK (shares > 0),
+        price_per_share NUMERIC(10,2) NOT NULL CHECK (price_per_share > 0),
+        broker TEXT COLLATE pg_catalog."default",
+        transaction_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+        PRIMARY KEY (id)
+    )
+    WITH (
+        OIDS = FALSE
+    )
+    TABLESPACE pg_default;
+
+    -- Link the sequence to the table
+    ALTER SEQUENCE agenticdb.transactions_id_seq OWNED BY agenticdb.transactions.id;
+
+    -- Insert sample transactions
+
+    -- TRANSACTION 1: Alice Johnson buys 50 shares of Apple (AAPL)
+    INSERT INTO agenticdb.transactions (transaction_id, client_name, transaction_type, stock_symbol, shares, price_per_share, broker, transaction_time)
+    VALUES ('TXN1001', 'Alice Johnson', 'BUY', 'AAPL', 50, 185.75, 'Morgan Stanley', '2024-06-15 09:30:00');
+
+    -- TRANSACTION 2: Michael Smith sells 100 shares of Tesla (TSLA)
+    INSERT INTO agenticdb.transactions (transaction_id, client_name, transaction_type, stock_symbol, shares, price_per_share, broker, transaction_time)
+    VALUES ('TXN1002', 'Michael Smith', 'SELL', 'TSLA', 100, 245.20, 'Charles Schwab', '2024-06-15 10:15:00');
+
+    -- TRANSACTION 3: Emma Davis buys 200 shares of Microsoft (MSFT)
+    INSERT INTO agenticdb.transactions (transaction_id, client_name, transaction_type, stock_symbol, shares, price_per_share, broker, transaction_time)
+    VALUES ('TXN1003', 'Emma Davis', 'BUY', 'MSFT', 200, 340.10, 'JPMorgan Chase', '2024-06-15 11:45:00');
+
+    -- TRANSACTION 4: Robert Miller short-sells 75 shares of Nvidia (NVDA)
+    INSERT INTO agenticdb.transactions (transaction_id, client_name, transaction_type, stock_symbol, shares, price_per_share, broker, transaction_time)
+    VALUES ('TXN1004', 'Robert Miller', 'SHORT SELL', 'NVDA', 75, 462.85, 'Goldman Sachs', '2024-06-15 14:30:00');

content/modules/ROOT/pages/03-02-tools-usage.adoc

@@ -16,12 +16,18 @@ Through tool calling, LLMs can:
 - **Perform Specialized Calculations**: Use tools like code interpreters to execute complex calculations or data transformations.
 - **Format Responses for Structured Systems**: Generate responses in specific schemas required by APIs or databases, ensuring smooth communication with these systems.
 
-== Exercise: Tools Usage - Practical Example
+== Exercise: Tools Usage (Internet Search) - Practical Example
 
 Let's see the Tools Usage in Action!
 
 From the `agentic-workshop/lab-materials/02` folder, please open the notebook called `02.1-tools-calling.ipynb` and follow the instructions.
 
+== Exercise: Tools Usage (Database Query) - Practical Example
+
+Let's see the Tools Usage in Action!
+
+From the `agentic-workshop/lab-materials/02` folder, please open the notebook called `02.2-tools-calling-sql.ipynb` and follow the instructions.
+
 == Benefits of Using Tools with LLMs
 
 Incorporating tools into LLM workflows enables AI systems to handle a broader range of tasks, increasing both accuracy and flexibility. For example:

content/modules/ROOT/pages/03-03-chain-of-thought.adoc

@@ -27,4 +27,4 @@ image::04/04-02-cot.png[Chain of Thought, scaledwidth="20%"]
 
 Let's see the Chain of Thought in Action!
 
-From the `agentic-workshop/lab-materials/02` folder, please open the notebook called `02.2-chain-of-thought.ipynb` and follow the instructions.
+From the `agentic-workshop/lab-materials/02` folder, please open the notebook called `02.3-chain-of-thought.ipynb` and follow the instructions.

content/modules/ROOT/pages/03-04-react-prompting.adoc

@@ -22,4 +22,4 @@ image::04/04-01-react-basic.png[ReAct Basic]
 
 Let's see the ReAct Pattern in Action!
 
-From the `agentic-workshop/lab-materials/02` folder, please open the notebook called `2.3-react-prompting.ipynb` and follow the instructions.
+From the `agentic-workshop/lab-materials/02` folder, please open the notebook called `2.4-react-prompting.ipynb` and follow the instructions.

content/modules/ROOT/pages/03-05-rag.adoc

@@ -16,6 +16,12 @@ In a RAG setup, the model follows a two-step process:
 
 image::04/04-04-rag.jpg[RAG]
 
+== Exercise: RAG Pattern - Practical Example
+
+Let's see the RAG Pattern in Action!
+
+From the `agentic-workshop/lab-materials/03` folder, please open all the notebooks on that folder in order and follow the instructions.
+
 == Benefits of RAG
 
 - **Enhanced Accuracy**: RAG reduces reliance on outdated or incomplete training data, leading to more accurate and contextually relevant responses.