Earn 45,000 ($450.00)

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Problem Description
The goal is to build an internal, intelligent chatbot system integrated with Slack, powered by an advanced Retrieval-Augmented Generation (RAG) architecture. This system will connect seamlessly with the company’s Databricks-backed data infrastructure (stored in S3) and leverage OpenAI or other LLM tools for real-time querying and analytics.

Key Objectives:

Enable natural language querying of business and transactional data stored in Databricks via Slack.
Provide insights on user behavior, predictions, and visualizations, such as churn rates, user engagement, and financial forecasts.
Support self-learning capabilities for continuous improvement by incorporating feedback loops from Slack (e.g., thumbs-up or thumbs-down reactions for results).
Ensure multi-region scalability and adaptability to handle growing datasets and diverse query patterns.
Automate backend workflows and offer recommendations for decision-making in real-time.
Acceptance Criteria
The project will be considered successful when the following criteria are met:

Slack Integration:

A fully functional Slack app that interacts with the chatbot via natural language queries.
Supports event subscriptions for handling user interactions.
Query and Data Retrieval:

The bot can process user queries and fetch relevant data from the Databricks instance using data in S3.
Supports both structured (SQL-like) and unstructured (natural language) queries.
AI-Powered Insights:

Generates actionable insights based on user queries (e.g., churn predictions, engagement trends, user segmentation).
Provides visual outputs (charts/graphs) for complex queries directly in Slack.
Self-Learning and Feedback Mechanism:

Integrates feedback loops to improve the accuracy and quality of responses (e.g., Slack thumbs-up/thumbs-down reactions).
Logs user feedback for future analysis and fine-tuning of models.
RAG Architecture:

Efficiently combines existing knowledge with external retrieval from the database for more accurate and contextual responses.
Scalability and Performance:

Handles high query volumes with low latency.
Ensures seamless performance across multiple teams or channels.
Secure Data Handling:

Implements proper authentication and role-based access controls for database queries.
Ensures data is encrypted both in transit and at rest.
Technical Details
Frontend:

Slack App:
Event Subscriptions: Handle events such as /slash commands or interactions from buttons, dropdowns, etc.
OAuth 2.0 Authentication for secure connections to Slack APIs.
Backend:

API Gateway:
Serves as an entry point for Slack queries routed to AWS Lambda.
Handles CORS and ensures scalable API interactions.
AWS Lambda:
Processes natural language inputs and queries the database.
Integrates OpenAI or other LLMs for generating responses.
Implements Slack verification for events (e.g., challenge verification).
Databricks:
Fetches and processes data stored in S3.
Aggregates insights (e.g., user data, transactional history, game stats).
Model Integration:

LLM:
Generates answers to queries using GPT-based models.
Enhanced with RAG (Retrieval-Augmented Generation) for domain-specific accuracy.
Self-Learning:
Uses Slack feedback (thumbs-up/thumbs-down) to retrain the model iteratively.
Logs are stored in Databricks for pattern recognition and tuning.
Workflow:

User asks a query in Slack → Query is routed to Lambda → RAG system fetches relevant data from Databricks → Response is generated by the LLM → Sent back to Slack with optional visualizations.
Feedback is logged and used to retrain the system.
Security:

Slack Signing Secret: Verifies authenticity of incoming Slack events.
Encrypted communication between Slack, AWS Lambda, and Databricks.
Fine-grained access control for database queries.
Visualization:

Slack bot integrates with tools like Matplotlib/Plotly to generate visuals for complex queries.
Supports embedding of graphs or charts directly into Slack threads.