Fix

Author: aamini7

scenarios/PostgresRAGLLM/app.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/chat.py

@@ -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/postgres-rag-llm.md

@@ -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