feat: add a small CSV analyzer demo app using LangDB

demo/csv-analyzer/README.md

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+# CSV Analyzer with LangDB
+
+Ask natural language questions about your large CSV data! This app uses [LangDB](https://langdb.dev) to take your questions and analyze the data behind the scenes.
+
+## Features
+
+- Upload any CSV file (like LinkedIn engagement data)
+- Ask questions in plain English
+- Get smart insights
+- Powered by [LangDB](https://github.com/langdb/pylangdb)
+
+## Setup Instructions
+
+1. **Clone the repo**  
+   ```bash
+    git clone https://github.com/langdb/pylangdb.git
+    cd demo/csv-analyzer
+    ```
+
+2. **Install dependencies**
+    ```bash
+     pip install -r requirements.txt
+    ```
+
+3. **Set your LangDB API key**
+    - Create a `.env` file:
+    ```bash
+     LANGDB_API_KEY=langdb_XXXXXX
+    ```
+
+    - Never commit this file to Git. Add it in `.gitignore`
+
+4. **Add your CSV file**
+
+5. **Run the app**
+    ```bash
+     python app.py
+    ```
+

demo/csv-analyzer/app.py

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+import pandas as pd
+from pylangdb import LangDb
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+API_KEY = os.getenv("LANGDB_API_KEY")
+
+# Read CSV
+df = pd.read_csv("sample.csv")
+data = df.to_dict(orient='records')
+
+question = "Analyze the engagement data of company's  LinkedIn page for April month. What are the key insights and trends? Suggestions to improve the engagement rate?"
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": f'Analyze this CSV file data: {data} and nswer the question: {question}'},
+]
+# Call Gemini Model using LangDB
+client = LangDb(api_key=API_KEY)
+response = client.completion(
+    model="gemini-1.5-pro-latest",
+    messages=messages,
+    temperature=0.7,
+    # max_tokens=1000,
+)
+
+print(response['content'])