LLMs  

Telecom Tower Assistant: Multi-Modal LLM's for Field Service Repairs

Table of Contents

  1. Introduction

  2. What Is a Multi-Modal AI Assistant?

  3. Real-World Scenario: Enterprise Field Service Assistant for Telecom Towers

  4. Architectural Components & Tool Integration

  5. Complete Implementation with Tool-Calling and Streaming

  6. Best Practices for Enterprise Deployment

  7. Conclusion

1. Introduction

As an enterprise cloud architect building GenAI systems at scale, I often field questions like: “How do you stitch vision, speech, and document understanding into a single agentic assistant that actually works in production?”

The answer isn’t just about models—it’s about orchestration, tool reliability, and secure, observable pipelines. In this article, we’ll build a multi-modal AI assistant that ingests images, text, and structured data to solve real problems—using only production-grade, open tools (FastAPI, LangChain, OpenAI-compatible APIs, and Azure Blob Storage).

2. What Is a Multi-Modal AI Assistant?

A multi-modal AI assistant processes and reasons over multiple input types—images, text, audio, PDFs—and uses external tools (APIs, databases, vision models) to act on the world. Unlike chatbots, it:

  • Chooses which tools to invoke based on user intent

  • Fuses modalities (e.g., “What’s wrong with this image of a telecom tower?”)

  • Returns structured, actionable outputs

This requires tool-aware reasoning, state management, and fail-safe execution—all essential for enterprise use.

3. Real-World Scenario: Telecom Field Service Assistant

Problem: Field engineers inspect remote cell towers. They snap photos of damaged antennas or corroded mounts and ask, “Is this urgent? What parts do I need?”

Solution: A multi-modal assistant that:

  1. Accepts an image + text query

  2. Calls a vision tool to detect component damage

  3. Queries a parts inventory API

  4. Returns repair recommendations + part SKUs

This reduces truck rolls, accelerates resolution, and integrates with existing SAP/ServiceNow workflows.

PlantUML Diagram

4. Architectural Components & Tool Integration

We’ll use:

  • FastAPI: Enterprise-ready Python server

  • LangChain + LangGraph: For agentic orchestration

  • Azure Blob Storage: Secure image ingestion

  • OpenAI-compatible vision model (e.g., GPT-4o or Azure OpenAI)

  • Mock inventory API: Simulates backend ERP

Key design principles:

  • Stateless image uploads → signed URLs

  • Tool schemas strictly typed via Pydantic

  • Streaming responses for UX responsiveness

5. Complete Implementation with Tool-Calling and Streaming

# main.py
from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
import aiohttp
import asyncio
import uuid
import os
from azure.storage.blob import BlobServiceClient
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage
import tempfile

# ----------------------------
# Config (in real prod: Azure Key Vault)
# ----------------------------
AZURE_CONN_STR = os.getenv("AZURE_STORAGE_CONNECTION_STRING")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
INVENTORY_API_URL = "https://api.enterprise-inventory.local/parts"

# ----------------------------
# Tools
# ----------------------------
@tool
async def analyze_tower_image(image_url: str) -> str:
    """Analyze telecom tower image for damage using vision model."""
    async with aiohttp.ClientSession() as session:
        payload = {
            "model": "gpt-4o",
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "Analyze this telecom tower component. List any visible damage, corrosion, or misalignment. Be concise."},
                        {"type": "image_url", "image_url": {"url": image_url}}
                    ]
                }
            ],
            "max_tokens": 300
        }
        headers = {"Authorization": f"Bearer {OPENAI_API_KEY}"}
        async with session.post("https://api.openai.com/v1/chat/completions", json=payload, headers=headers) as resp:
            if resp.status != 200:
                raise Exception(f"Vision API failed: {await resp.text()}")
            data = await resp.json()
            return data["choices"][0]["message"]["content"]

@tool
async def lookup_repair_parts(damage_description: str) -> str:
    """Look up required repair parts based on damage description."""
    async with aiohttp.ClientSession() as session:
        async with session.post(
            f"{INVENTORY_API_URL}/lookup",
            json={"damage": damage_description},
            headers={"Content-Type": "application/json"}
        ) as resp:
            if resp.status == 200:
                parts = await resp.json()
                return f"Required parts: {', '.join(parts['skus'])}"
            else:
                return "No matching parts found. Escalate to senior engineer."

# ----------------------------
# Agent Setup
# ----------------------------
tools = [analyze_tower_image, lookup_repair_parts]
llm = ChatOpenAI(model="gpt-4o", api_key=OPENAI_API_KEY, temperature=0)
agent_executor = create_react_agent(llm, tools)

# ----------------------------
# FastAPI App
# ----------------------------
app = FastAPI(title="Telecom Field Assistant", version="1.0")

blob_service = BlobServiceClient.from_connection_string(AZURE_CONN_STR)
container_name = "tower-images"

@app.post("/assist")
async def assist_engineer(
    query: str,
    image: UploadFile = File(...),
    background_tasks: BackgroundTasks = None
):
    # Upload image securely
    blob_name = f"{uuid.uuid4()}.jpg"
    blob_client = blob_service.get_blob_client(container=container_name, blob=blob_name)
    
    with tempfile.NamedTemporaryFile(delete=False) as tmp:
        tmp.write(await image.read())
        tmp_path = tmp.name

    try:
        with open(tmp_path, "rb") as data:
            blob_client.upload_blob(data)
        image_url = blob_client.url  # SAS token should be added in prod
    finally:
        os.unlink(tmp_path)

    # Stream agent response
    async def event_stream():
        inputs = {"messages": [HumanMessage(content=[
            {"type": "text", "text": query},
            {"type": "image_url", "image_url": {"url": image_url}}
        ])]}
        async for chunk in agent_executor.astream(inputs, stream_mode="values"):
            last_msg = chunk["messages"][-1]
            if hasattr(last_msg, 'content') and last_msg.content:
                yield f"data: {last_msg.content}\n\n"
            await asyncio.sleep(0.1)
        yield "data: [DONE]\n\n"

    return StreamingResponse(event_stream(), media_type="text/event-stream")
Untitled

Key Features

  • Secure image handling: Uploaded to Azure Blob with unique names

  • Async tool calling: Non-blocking I/O for high throughput

  • Streaming SSE: Real-time UX without waiting for full response

  • Modular tools: Easy to replace vision or inventory APIs

6. Best Practices for Enterprise Deployment

  1. Security

    • Always use SAS tokens with short expiry for blob URLs

    • Validate/malware-scan uploaded files

  2. Observability

    • Log tool calls with correlation IDs (e.g., Azure Monitor)

    • Track latency per modality (image vs. text)

  3. Resilience

    • Add retry + circuit breaker to tool calls

    • Fallback to human-in-the-loop on ambiguous images

  4. Cost Control

    • Cache repeated image analyses

    • Set usage quotas per engineer/team

7. Conclusion

Multi-modal assistants aren’t sci-fi—they’re solving real field service, insurance, and manufacturing problems today. But success hinges on robust tool integration, not just prompt engineering.

This architecture scales: swap Azure Blob for S3, GPT-4o for Claude 3.5 Sonnet, or add speech-to-text for voice queries. The core—agentic orchestration over reliable tools—remains unchanged.

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