info.txt•6.58 kB
Certainly! Here's the **info.txt** file that summarizes everything you want to do for your **AI-powered customer support bot** using **Cursor AI** and deploying it on **Glama.ai**:
---
### **info.txt**
#### **Project Overview:**
**Project Name:** AI-Powered Customer Support Bot
**Goal:** Build a customer support bot using an **MCP (Model Context Protocol)** server that fetches real-time context from Glama.ai and delivers dynamic, context-aware responses for businesses.
---
#### **1. Project Requirements:**
- **AI Model:** Use **Cursor AI** to process natural language queries.
- **Backend:** Flask or FastAPI to serve as the backend API for querying and retrieving responses.
- **Frontend (optional):** A simple chat interface where users can send queries (could be integrated with the business's website).
- **Deployment:** Host the server and integrate with Glama.ai's API for real-time context fetching.
---
#### **2. Backend Setup:**
- **Tech Stack:**
- **Programming Language:** Python
- **Web Framework:** Flask or FastAPI
- **API Requests:** Use **requests** library to fetch responses from Cursor AI and Glama.ai API.
- **Database:** A relational database (PostgreSQL/MySQL) to store chat logs and user context. This could be connected via SQLAlchemy or similar ORM libraries.
- **Hosting:** AWS EC2 or **Glama hosting** (if available) for deployment.
---
#### **3. Integration with Glama.ai:**
- **Glama.ai API:** Set up a connection with Glama.ai’s API to fetch context dynamically.
- **Use Case:** When a customer asks a query, the backend server will send the request to **Glama.ai’s API** for contextual information (like previous interactions, user orders, etc.). The server will then send this context to **Cursor AI** to process and return an appropriate answer.
---
#### **4. MCP Server Development:**
- **MCP Server Role:** The server will listen for incoming queries, fetch relevant context using Glama.ai, and forward it to the AI model (Cursor AI) to generate a response.
- **Sample Request Flow:**
- **User Query:** A user sends a message to the bot (e.g., “What’s the status of my order?”).
- **Server Action:** The backend fetches relevant context from Glama.ai (e.g., user’s order history).
- **AI Processing:** The context is sent to **Cursor AI**, which generates a response.
- **Response to User:** The server returns the generated response to the user.
---
#### **5. Example Backend Code (Flask/FastAPI):**
```python
from flask import Flask, request, jsonify
import requests
app = Flask(__name__)
# Glama AI API Info
GLAMA_API_URL = "https://api.glama.ai/endpoint"
GLAMA_API_KEY = "your-glama-api-key"
# Cursor AI Endpoint
CURSOR_API_URL = "https://api.cursor.ai/endpoint"
CURSOR_API_KEY = "your-cursor-ai-api-key"
# Function to fetch data from Glama.ai
def fetch_from_glama(query, user_context):
headers = {'Authorization': f'Bearer {GLAMA_API_KEY}', 'Content-Type': 'application/json'}
payload = {"query": query, "context": user_context}
response = requests.post(GLAMA_API_URL, json=payload, headers=headers)
return response.json()
# Function to send query to Cursor AI and get response
def query_cursor_ai(query):
headers = {'Authorization': f'Bearer {CURSOR_API_KEY}', 'Content-Type': 'application/json'}
payload = {"query": query}
response = requests.post(CURSOR_API_URL, json=payload, headers=headers)
return response.json()
@app.route('/ask', methods=['POST'])
def ask_bot():
user_query = request.json.get('query')
user_context = request.json.get('context') # Context from previous interaction or database
# Fetch data from Glama.ai
glama_data = fetch_from_glama(user_query, user_context)
# Query Cursor AI with context and user query
cursor_response = query_cursor_ai(user_query + " " + glama_data['context'])
return jsonify(cursor_response)
if __name__ == '__main__':
app.run(debug=True)
```
---
#### **6. Contextual Data Storage:**
- **Database:** Use **PostgreSQL** or **MySQL** to store user data, chat logs, and context. For example:
- **Table 1:** Users (store user ID, name, contact info, etc.)
- **Table 2:** Chats (store chat history, timestamps, user queries, bot responses)
- **Database Integration Example (SQLAlchemy):**
```python
from flask_sqlalchemy import SQLAlchemy
db = SQLAlchemy()
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(80))
email = db.Column(db.String(120), unique=True)
class Chat(db.Model):
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey('user.id'))
query = db.Column(db.String(500))
response = db.Column(db.String(500))
db.create_all()
```
---
#### **7. Deployment Options:**
- **Platform:**
- **Glama.ai** (If it supports server deployment for integration)
- **AWS EC2** (For a more customized environment)
- **Heroku/Firebase** (For a simpler serverless option)
---
#### **8. Monetization Strategy:**
- **Subscription Model:** Charge businesses a monthly fee for using the service.
- **Basic Plan:** $29/month for up to 500 queries per month.
- **Enterprise Plan:** $199/month for unlimited queries, premium features, and customizations.
- **Pay-Per-Use Model:** Charge based on usage, e.g., $0.10 per query.
- **Custom Solutions:** Offer customized chatbot solutions and charge a one-time setup fee (starting at $500+).
---
#### **9. Testing & Iteration:**
- **Testing:** Test the bot with a few sample businesses. Ensure the model is fetching correct context from Glama.ai and Cursor AI is providing relevant responses.
- **Feedback Loop:** Adjust the logic and enhance the context to improve user satisfaction.
---
#### **10. Next Steps:**
- Set up **Glama.ai** and **Cursor AI** accounts.
- Develop the backend API (Flask or FastAPI) and integrate both AI models.
- Set up a database for storing chat logs and context.
- Deploy the API server and integrate it with a business's website or CRM.
- Start selling your service using the **subscription or pay-per-use model**.
---
This **info.txt** file outlines the core structure and steps to build and deploy your customer support bot with **MCP**. Feel free to add more specific details as your project progresses.
Let me know if you'd like to add anything else! 🚀
