Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications

1. 1
   Assess NL2SQL Limitations

   Identify where traditional NL2SQL systems fail in your context, specifically around iterative queries, multi-source data, and multi-modal information.
2. 2
   Design Streaming Data Ingestion

   Architect a system to ingest and process data streams from various sources (e.g., Kafka, Kinesis) to handle dynamic and real-time information flow.
3. 3
   Integrate Multi-Source & Multi-Modal Data

   Develop connectors and data models to combine structured databases, unstructured documents, images, and other data types into a unified intelligence layer.
4. 4
   Implement AI Agents for Interpretation

   Design and deploy AI agents (e.g., LLMs, specialized models) to interpret, integrate, and generate responses based on the diverse data streams.
5. 5
   Enable Iterative & Conversational Interactions

   Build a user interface and backend logic that supports back-and-forth dialogue, allowing users to refine queries and explore data iteratively.
6. 6
   Develop Adaptive Query Generation

   Move beyond direct SQL translation; enable agents to dynamically formulate complex queries across multiple data sources and formats based on user intent.

import json

class BlueAgent:
    def __init__(self, agent_id):
        self.agent_id = agent_id
        self.data_sources = {}

    def register_data_source(self, name, connection_details):
        """Registers a conceptual data source for the agent."""
        self.data_sources[name] = connection_details
        print(f"Agent {self.agent_id}: Registered data source '{name}'.")

    def process_stream_data(self, data_packet):
        """Simulates processing a packet of streaming data."""
        print(f"Agent {self.agent_id}: Processing stream data: {json.dumps(data_packet)}")
        # In a real scenario, this would involve parsing, enrichment, and storage
        return {"status": "processed", "agent": self.agent_id, "original_data": data_packet}

    def interpret_query(self, natural_language_query):
        """Conceptual interpretation of a natural language query."""
        print(f"Agent {self.agent_id}: Interpreting query: '{natural_language_query}'")
        # This would involve LLM calls, data source mapping, etc.
        return {"query_intent": "retrieve_sales_data", "filters": {"region": "west"}}

# Example Usage:
if __name__ == "__main__":
    sales_agent = BlueAgent("SalesAnalyticsAgent")
    sales_agent.register_data_source("CRM", {"type": "database", "host": "crm.db"})
    sales_agent.register_data_source("WebsiteLogs", {"type": "stream", "topic": "web_events"})

    # Simulate incoming streaming data
    sales_agent.process_stream_data({"event": "page_view", "user_id": "abc", "timestamp": "..."})
    sales_agent.process_stream_data({"event": "purchase", "item": "widget", "amount": 100})

    # Simulate a user query
    interpreted_query = sales_agent.interpret_query("Show me recent sales trends in the Western region.")
    print(f"Interpreted Query: {interpreted_query}")

• Paperarxiv.org