Test styles

scenarios/ConfigurePythonContainer/configure-python-container.md

@@ -99,7 +99,7 @@ Results:
 }
 ```
 
-## Step 2: Show the current Python version
+## Show the current Python version
 
 The following command retrieves the Python runtime version currently used by your Azure App Service.
 
@@ -115,7 +115,7 @@ Results:
 "PYTHON|3.10"
 ```
 
-## Step 3: Set the desired Python version
+## Set the desired Python version
 
 Update your Azure App Service instance to use a specific Python version. Replace the desired Python version (e.g., "PYTHON|3.11") as needed.
 
@@ -124,6 +124,7 @@ export DESIRED_PYTHON_VERSION="PYTHON|3.11"
 az webapp config set --resource-group $RESOURCE_GROUP --name $APP_NAME --linux-fx-version $DESIRED_PYTHON_VERSION
 ```
 
+## Verify Version
 Verify the updated Python version:
 
 ```bash
@@ -138,7 +139,7 @@ Results:
 "PYTHON|3.11"
 ```
 
-## Step 4: List all supported Python runtime versions
+## List all supported Python runtime versions
 
 Use the following command to view all Python versions supported by Azure App Service on Linux.
 

scenarios/CreateAKSWebApp/create-aks-webapp.md

@@ -486,18 +486,18 @@ Cert-manager provides Helm charts as a first-class method of installation on Kub
 
    ```bash
    helm repo add jetstack https://charts.jetstack.io
+   helm repo update
+   helm install cert-manager jetstack/cert-manager --namespace cert-manager --version v1.7.0
    ```
 
 2. Update local Helm Chart repository cache
 
    ```bash
-   helm repo update
    ```
 
 3. Install Cert-Manager addon via helm by running the following:
 
    ```bash
-   helm install cert-manager jetstack/cert-manager --namespace cert-manager --version v1.7.0
    ```
 
 4. Apply Certificate Issuer YAML File
@@ -538,9 +538,6 @@ Cert-manager provides Helm charts as a first-class method of installation on Kub
                 nodeSelector:
                   "kubernetes.io/os": linux
     EOF
-    ```
-
-    ```bash
     cluster_issuer_variables=$(<cluster-issuer-prod.yml)
     ```
 

scenarios/ObtainPerformanceMetricsLinuxSustem/obtain-performance-metrics-linux-system.md

@@ -573,7 +573,7 @@ The metrics collected are:
 * Look for single processes with high read/write rates per second. This information is a guidance for processes with I/O more than identifying issues.
 Note: the `--human` option can be used to display numbers in human readable format (that is, `Kb`, `Mb`, `GB`).
 
-### `ps`
+### Top CPU processes
 
 Lastly `ps` command displays system processes, and can be either sorted by CPU or Memory.
 
@@ -599,6 +599,7 @@ root        2186 42.0  0.0  73524  5836 pts/1    R+   16:55   0:06 stress-ng --c
 root        2191 41.2  0.0  73524  5592 pts/1    R+   16:55   0:06 stress-ng --cpu 12 --vm 2 --vm-bytes 120% --iomix 4 --timeout 240
 ```
 
+## Top memory processes
 To sort by `MEM%` and obtain the top 10 processes:
 
 ```azurecli-interactive
@@ -634,13 +635,4 @@ echo "$extracted"
 
 To run, you can create a file with the above contents, add execute permissions by running `chmod +x gather.sh`, and run with `sudo ./gather.sh`.
 
-This script saves the output of the commands in a file located in the same directory where the script was invoked.
-
-Additionally, all the commands in the bash block codes covered in this document, can be run through `az-cli` using the run-command extension, and parsing the output through `jq` to obtain a similar output to running the commands locally: `
-
-```azurecli-interactive
-output=$(az vm run-command invoke -g $MY_RESOURCE_GROUP_NAME --name $MY_VM_NAME --command-id RunShellScript --scripts "ls -l /dev/disk/azure")
-value=$(echo "$output" | jq -r '.value[0].message')
-extracted=$(echo "$value" | awk '/\[stdout\]/,/\[stderr\]/' | sed '/\[stdout\]/d' | sed '/\[stderr\]/d')
-echo "$extracted" 
-```
+This script saves the output of the commands in a file located in the same directory where the script was invoked.

scenarios/PostgresRAGLLM/chat.py

@@ -8,7 +8,7 @@
 from db import VectorDatabase
 
 # Configure logging
-logging.basicConfig(level=logging.DEBUG)
+logging.basicConfig(level=logging.INFO)
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--api-key', dest='api_key', type=str)
@@ -18,13 +18,11 @@
 parser.add_argument('--pgpassword', dest='pgpassword', type=str)
 parser.add_argument('--pgdatabase', dest='pgdatabase', type=str)
 parser.add_argument('--populate', dest='populate', action="store_true")
-parser.add_argument('--question', dest='question', type=str, help="Question to ask the chatbot")
 args = parser.parse_args()
 
 
 class ChatBot:
     def __init__(self):
-        logging.debug("Initializing ChatBot")
         self.db = VectorDatabase(pguser=args.pguser, pghost=args.phhost, pgpassword=args.pgpassword, pgdatabase=args.pgdatabase)
         self.api = AzureOpenAI(
             azure_endpoint=args.endpoint,
@@ -39,21 +37,17 @@ def __init__(self):
         )
 
     def load_file(self, text_file: str):
-        logging.debug(f"Loading file: {text_file}")
+        logging.info(f"Loading file: {text_file}")
         with open(text_file, encoding="UTF-8") as f:
             data = f.read()
             chunks = self.text_splitter.create_documents([data])
             for i, chunk in enumerate(chunks):
                 text = chunk.page_content
                 embedding = self.__create_embedding(text)
                 self.db.save_embedding(i, text, embedding)
-
-    def __create_embedding(self, text: str):
-        logging.debug(f"Creating embedding for text: {text[:30]}...")
-        return self.api.embeddings.create(model="text-embedding-ada-002", input=text).data[0].embedding
+        logging.info("Done loading data.")
 
     def get_answer(self, question: str):
-        logging.debug(f"Getting answer for question: {question}")
         question_embedding = self.__create_embedding(question)
         context = self.db.search_documents(question_embedding)
 
@@ -80,26 +74,21 @@ def get_answer(self, question: str):
         )
         return response.choices[0].message.content
 
+    def __create_embedding(self, text: str):
+        return self.api.embeddings.create(model="text-embedding-ada-002", input=text).data[0].embedding
+
 
 def main():
     chat_bot = ChatBot()
 
     if args.populate:
-        logging.debug("Loading embedding data into database...")
         chat_bot.load_file("knowledge.txt")
-        logging.debug("Done loading data.")
-        return
-
-    if args.question:
-        logging.debug(f"Question provided: {args.question}")
-        print(chat_bot.get_answer(args.question))
-        return
-
-    while True:
-        q = input("Ask a question (q to exit): ")
-        if q == "q":
-            break
-        print(chat_bot.get_answer(q))
+    else:
+        while True:
+            q = input("Ask a question (q to exit): ")
+            if q == "q":
+                break
+            print(chat_bot.get_answer(q))
 
 
 if __name__ == "__main__":

scenarios/PostgresRAGLLM/postgres-rag-llm.md

@@ -136,101 +136,12 @@ pip install -r requirements.txt
 python chat.py --populate --api-key $API_KEY --endpoint $ENDPOINT --pguser $PGUSER --phhost $PGHOST --pgpassword $PGPASSWORD --pgdatabase $PGDATABASE
 ```
 
-## Set up Web Interface
+## Run Chat bot
 
-Create a simple web interface for the chatbot using Flask.
-
-1. **Install Flask**
-
-    ```bash
-    pip install Flask
-    ```
-
-2. **Create `app.py`**
-
-    Create a file named `app.py` in the `scenarios/PostgresRagLlmDemo` directory with the following content:
-
-    ```python
-    from flask import Flask, request, render_template
-    import subprocess
-    import os
-
-    app = Flask(__name__)
-
-    @app.route('/', methods=['GET'])
-    def home():
-        return render_template('index.html', response='')
-
-    @app.route('/ask', methods=['POST'])
-    def ask():
-        question = request.form['question']
-        result = subprocess.run([
-            'python', 'chat.py',
-            '--api-key', os.getenv('API_KEY'),
-            '--endpoint', os.getenv('ENDPOINT'),
-            '--pguser', os.getenv('PGUSER'),
-            '--phhost', os.getenv('PGHOST'),
-            '--pgpassword', os.getenv('PGPASSWORD'),
-            '--pgdatabase', os.getenv('PGDATABASE'),
-            '--question', question
-        ], capture_output=True, text=True)
-        response = result.stdout
-        return render_template('index.html', response=response)
-
-    if __name__ == '__main__':
-        app.run(host='0.0.0.0', port=5000)
-    ```
-
-3. **Create `index.html`**
-
-    Create a `templates` directory inside `scenarios/PostgresRagLlmDemo` and add an `index.html` file with the following content:
-
-    ```html
-    <!doctype html>
-    <html lang="en">
-      <head>
-        <title>Chatbot Interface</title>
-      </head>
-      <body>
-        <h1>Ask about Zytonium</h1>
-        <form action="/ask" method="post">
-          <input type="text" name="question" required>
-          <button type="submit">Ask</button>
-        </form>
-        <pre>{{ response }}</pre>
-      </body>
-    </html>
-    ```
-
-4. **Run the Web Server**
-
-    Ensure that all environment variables are exported and then run the Flask application:
-
-    ```bash
-    export API_KEY="$API_KEY"
-    export ENDPOINT="$ENDPOINT"
-    export PGUSER="$PGUSER"
-    export PGHOST="$PGHOST"
-    export PGPASSWORD="$PGPASSWORD"
-    export PGDATABASE="$PGDATABASE"
-
-    python app.py
-    ```
-
-    The web interface will be accessible at `http://localhost:5000`. You can ask questions about Zytonium through the browser.
-
-## Next Steps
-
-- Explore more features of [Azure Cognitive Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search).
-- Learn how to [use Azure OpenAI with your data](https://learn.microsoft.com/azure/cognitive-services/openai/use-your-data).
-<!-- ## Run Chat bot
-
-This final step initializes the chatbot in your terminal. You can ask it questions about Zytonium and it will use the embeddings in the postgres database to augment your query with relevant context before sending it to the LLM model.
-
-```bash
-echo "Ask the chatbot a question about Zytonium!"
-```
+To run the chatbot, paste this following command to the terminal: `cd ~/scenarios/PostgresRagLlmDemo && python chat.py --api-key $API_KEY --endpoint $ENDPOINT --pguser $PGUSER --phhost $PGHOST --pgpassword $PGPASSWORD --pgdatabase $PGDATABASE`
 
 ```bash
-python chat.py --api-key $API_KEY --endpoint $ENDPOINT --pguser $PGUSER --phhost $PGHOST --pgpassword $PGPASSWORD --pgdatabase $PGDATABASE
-``` -->
+echo "
+To run the chatbot, see the last step for more info.
+"
+```

scenarios/PostgresRAGLLM/requirements.txt

@@ -1,5 +1,4 @@
 azure-identity==1.17.1
 openai==1.55.3
 psycopg2==2.9.9
-langchain-text-splitters==0.2.2
-Flask==2.3.2
+langchain-text-splitters==0.2.2