User (Intranet)
      ↓
AI Agent (Local LLM - Qwen/LLaMA)
      ↓
Intent Detection + Query Understanding
      ↓
Common Database (SQLite / DuckDB)
      ↓
Power BI (Connected to same DB)
      ↓
Dashboard
      ↓
Embedded in AI Agent UI

AI Agent + Power BI Integration (Intranet Setup)

🔷 Overview

This system connects an AI Agent (Python + Docker + DuckDB + Parquet + SQLite) with a dashboard created in Power BI.

👉 Core Idea:

  • AI Agent processes & stores data
  • Power BI reads same data
  • Dashboard is embedded back into AI Agent UI

🔷 Architecture

User Query
↓
AI Agent (Local Model – Qwen/LLaMA)
↓
Intent Detection + Data Processing
↓
Common Database (DuckDB / Parquet / SQLite)
↓
Power BI (Connected to same data)
↓
Dashboard
↓
Embedded in AI Agent UI

🔷 Technologies Used

  • AI Model: /
  • Backend: Python
  • Storage: DuckDB, SQLite, Parquet
  • Visualization:
  • Embedding:
  • API (optional):
  • Containerization: Docker

🔷 Step-by-Step Implementation

✅ Step 1: Setup AI Agent (Already Done)

  • Accept user query
  • Extract data from Excel/PDF
  • Convert into structured format

✅ Step 2: Data Processing

data = extract_data(file)

✅ Step 3: Store Data in Common Database

import duckdb

con = duckdb.connect("data.db")
con.execute("CREATE TABLE IF NOT EXISTS sales AS SELECT * FROM data")

data.to_parquet("data/sales.parquet")

✅ Step 4: Connect Power BI to Data

Option A: Parquet

  • Power BI → Get Data → Parquet

Option B: DuckDB (ODBC)

  • Install DuckDB ODBC
  • Power BI → Get Data → ODBC

✅ Step 5: Create Dashboard (One-Time)

  • Create charts
  • Save report
  • Publish (optional)

✅ Step 6: Handle User Query

if "dashboard" in query:
    intent = "dashboard"

✅ Step 7: Update Data

save_to_duckdb(data)
save_to_parquet(data)

✅ Step 8: Refresh Power BI

  • Auto refresh (recommended)
    OR
trigger_refresh(dataset_id)

✅ Step 9: Get Embed URL

embed_url = "https://powerbi/report/..."

✅ Step 10: Show Dashboard in UI

<iframe src="embed_url" width="100%" height="600"></iframe>

🔷 🔥 PSEUDO CODE (IMPORTANT)

🔹 Main Flow

function handle_user_query(query):

    # Step 1: Detect intent using local model
    intent = detect_intent(query)

    if intent == "dashboard":

        # Step 2: Fetch or extract data
        data = get_uploaded_data()

        # Step 3: Process data
        cleaned_data = process_data(data)

        # Step 4: Store in database
        store_in_duckdb(cleaned_data)
        store_in_parquet(cleaned_data)

        # Step 5: Refresh Power BI dataset
        refresh_powerbi()

        # Step 6: Return dashboard लिंक
        return {
            "type": "dashboard",
            "url": EMBED_URL
        }

    else:
        return normal_ai_response(query)

🔹 Intent Detection (LLM)

function detect_intent(query):

    if "dashboard" in query:
        return "dashboard"

    elif "report" in query:
        return "dashboard"

    else:
        return "normal"

🔹 Data Storage

function store_in_duckdb(data):

    connect to duckdb
    create table if not exists
    insert data

function store_in_parquet(data):

    save file as parquet

🔹 Power BI Refresh

function refresh_powerbi():

    # Option 1: Do nothing (auto refresh enabled)
    return True

    # Option 2: API call
    call Power BI API to refresh dataset

🔹 UI Handling

function render_response(response):

    if response.type == "dashboard":
        show iframe(response.url)
    else:
        show text(response.message)

🔷 Full Logic Flow

User Query
↓
AI Agent (Intent Detection using local model)
↓
Process Data
↓
Store in DuckDB / Parquet
↓
Power BI reads same data
↓
Dashboard refresh
↓
Return embed URL
↓
Display in UI

🔷 Docker Setup

volumes:
  - ./data:/data

🔷 Installation Requirements

1. Python Libraries

pip install duckdb pandas pyarrow

2. Power BI

  • Install Power BI Desktop

3. DuckDB ODBC (Optional)

  • Install DuckDB ODBC driver

4. Docker

  • Install Docker

5. Local LLM (Ollama)

ollama run qwen

🔷 Key Design Points

  • No direct AI → Power BI data transfer
  • Use common database
  • Power BI reads same data
  • Use iframe for embedding
  • Works fully in intranet





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