Discover Connected Crimes with Web Search (TypeScript/LangGraph)

Overview

In Exercise 05, you learned to search internal documents using the Grounding Service. Your Evidence Analyst can now retrieve factual evidence from security logs, bank records, and termination letters without hallucination.

But investigations don't happen in a vacuum. What if similar crimes happened elsewhere? What if your suspects have public criminal records? What if this heist is part of a larger criminal network?

In this exercise, you'll add web search capabilities using Perplexity's sonar-pro model to gather external intelligence and discover patterns across the internet.

Understand Web Search with Sonar-Pro

Why Do We Need Web Search?

Your current investigation system is powerful but limited to internal data:

What You Can Do Now	What You Can't Do Yet
✅ Search museum's internal evidence documents	❌ Search public criminal databases
✅ Retrieve bank records, security logs	❌ Find similar crimes in other cities
✅ Analyze suspects based on internal evidence	❌ Check if suspects have public criminal records
✅ Ground responses in factual documents	❌ Discover criminal network connections
✅ Avoid hallucination for internal data	❌ Monitor online art markets for stolen items

The Problem: Real investigations combine internal evidence (what happened here) with external intelligence (what's happening elsewhere). Your agents need both!

Document Grounding vs. Web Search

Aspect	Document Grounding (Exercise 05)	Web Search (This Exercise)
Data Source	Internal documents (pre-uploaded)	Real-time web information
Coverage	Organization-specific evidence	Global public information
Freshness	Historical documents (static)	Current information (updated constantly)
Use Cases	Internal logs, private records, policies	News, criminal records, pattern analysis
Search Method	Vector similarity (semantic)	Web search + LLM synthesis
Tool Function	`callGroundingServiceTool()`	`callSonarProSearchTool()`
When to Use	"What does our evidence say?"	"What do public records show?"

🎯 Key Insight: These are not alternatives — they work together!

How Sonar-Pro Integrates with SAP Cloud SDK for AI

Sonar-Pro is called through the OrchestrationClient using the same promptTemplating API you already use for GPT-4o and Claude:

import { OrchestrationClient } from "@sap-ai-sdk/orchestration";

const webSearchClient = new OrchestrationClient(
  {
    promptTemplating: {
      model: {
        name: "sonar-pro",  // Perplexity's web search model
        params: {
          temperature: 0.2,  // Lower for factual results
        },
      },
      prompt: {
        template: [
          {
            role: "system",
            content: "Search for factual information with citations",
          },
          { role: "user", content: "{{?search_query}}" },
        ],
      },
    },
  },
  { resourceGroup: process.env.RESOURCE_GROUP },
);

Key Difference:

model.name: "gpt-4o" → Regular reasoning LLM
model.name: "sonar-pro" → Web search-enabled LLM
Both use the same OrchestrationClient API!

⚠️ Before continuing: Verify that sonar-pro is available in your SAP AI Core resource group. Go to SAP AI Launchpad → Generative AI Hub → Models and confirm you can see a Perplexity sonar-pro deployment. If it is not listed, ask your workshop instructor to enable it.

Add The Web Search Tool

Step 1: Create the Sonar-Pro Search Tool

👉 Open /project/JavaScript/starter-project/src/tools.ts

👉 Add this tool after the callGroundingServiceTool function:

const webSearchClient = new OrchestrationClient(
  {
    promptTemplating: {
      model: {
        name: "sonar-pro",
        params: {
          temperature: 0.2,
        },
      },
      prompt: {
        template: [
          {
            role: "system",
            content:
              "You are a web search assistant specializing in criminal intelligence. Search for accurate, recent information and always provide source citations with URLs and dates.",
          },
          { role: "user", content: "{{?search_query}}" },
        ],
      },
    },
  },
  { resourceGroup: process.env.RESOURCE_GROUP },
);

export async function callSonarProSearchTool(search_query: string): Promise<string> {
  try {
    const response = await webSearchClient.chatCompletion({
      placeholderValues: { search_query },
    });
    return response.getContent() ?? "No search results found";
  } catch (error) {
    const errorMessage = error instanceof Error ? error.message : String(error);
    console.error("❌ Sonar-pro web search failed:", errorMessage);
    if (error instanceof Error && error.stack) console.error(error.stack);
    return `Error calling sonar-pro web search: ${errorMessage}`;
  }
}

💡 Understanding the Web Search Tool:

model.name: "sonar-pro" — Perplexity's web search-enabled model, called through SAP AI Core

temperature: 0.2 — Lower temperature for factual, consistent results

{{?search_query}} — Template placeholder receives the search query

Returns synthesized answers with web source citations

Add The Intelligence Researcher Agent

Step 1: Update Type Definitions

👉 Open /project/JavaScript/starter-project/src/types.ts

👉 Add the intelligence_report field to the AgentState annotation after evidence_analysis and before final_conclusion:

  intelligence_report: Annotation<string | undefined>({
    reducer: (_, update) => update,
    default: () => undefined,
  }),

Step 2: Add Agent Configuration

👉 Open /project/JavaScript/starter-project/src/agentConfigs.ts

👉 Add this configuration after evidenceAnalyst and before leadDetective:

  intelligenceResearcher: {
    systemPrompt: (suspectNames: string) => `You are an Open-Source Intelligence (OSINT) Researcher.
    You are an OSINT specialist who excels at finding patterns across multiple crime scenes.
    You search public databases, news archives, and criminal records to connect seemingly isolated incidents.
    Your expertise has uncovered several international art theft rings, and you know how to distinguish professional criminals from amateurs.

    Your goal: Search the web for similar art thefts, criminal patterns, and suspect backgrounds to determine if this heist is part of a larger criminal network

    Analyze the suspects: ${suspectNames}

    Search for:
    1. Public criminal records or prior convictions for each suspect
    2. Similar art theft incidents with the same modus operandi (insider job, no forced entry)
    3. Connections to known art theft rings or criminal networks
    4. News reports or public information about any of the suspects
    5. Recent museum heists in Europe with similar patterns`,
  },

👉 Also update the leadDetective system prompt to include intelligenceReport as a third parameter before suspectNames:

  leadDetective: {
    systemPrompt: (
      appraisalResult: string,
      evidenceAnalysis: string,
      intelligenceReport: string,
      suspectNames: string,
    ) =>
      `You are the lead detective on this high-profile art theft case...

      1. INSURANCE APPRAISAL: ${appraisalResult}
      2. EVIDENCE ANALYSIS (Internal Documents): ${evidenceAnalysis}
      3. INTELLIGENCE REPORT (Web Search): ${intelligenceReport}
      4. SUSPECTS: ${suspectNames}
      ...`
  }

(Use the full prompt text from the solution — don't truncate it in the actual file.)

Step 3: Add the Intelligence Researcher Node

👉 Open /project/JavaScript/starter-project/src/investigationWorkflow.ts

👉 First, update the import to include the new tool:

import { callRPT1Tool, callGroundingServiceTool, callSonarProSearchTool } from "./tools.js";

👉 Second, add the Intelligence Researcher node after evidenceAnalystNode and before leadDetectiveNode:

  private async intelligenceResearcherNode(state: AgentStateType): Promise<Partial<AgentStateType>> {
    console.log("\n🔍 Intelligence Researcher starting web search...");

    try {
      const suspects = state.suspect_names.split(",").map((s) => s.trim());
      const intelligenceResults: string[] = [];

      for (const suspect of suspects) {
        console.log(`  Searching public records for: ${suspect}`);
        const query = `${suspect} criminal record art theft security technician Europe background check`;
        const result = await callSonarProSearchTool(query);
        intelligenceResults.push(`Background check for ${suspect}:\n${result}`);
      }

      console.log("  Searching for similar art theft incidents...");
      const patternQuery =
        "museum art theft insider job no forced entry Europe similar incidents criminal network";
      const patternResult = await callSonarProSearchTool(patternQuery);
      intelligenceResults.push(`Similar Art Theft Patterns:\n${patternResult}`);

      const intelligenceReport = `Intelligence Research Complete: ${intelligenceResults.join("\n\n")}
      Summary: Conducted OSINT research on all suspects and identified similar crime patterns`;

      console.log("✅ Intelligence research complete");

      return {
        intelligence_report: intelligenceReport,
        messages: [...state.messages, { role: "assistant", content: intelligenceReport }],
      };
    } catch (error) {
      const errorMsg = error instanceof Error ? error.message : String(error);
      console.error("❌ Intelligence research failed:", errorMsg);
      if (error instanceof Error && error.stack) {
        console.error(error.stack);
      }
      return {
        intelligence_report: `Error during intelligence research: ${errorMsg}`,
        messages: [
          ...state.messages,
          { role: "assistant", content: `Error during intelligence research: ${errorMsg}` },
        ],
      };
    }
  }

👉 Third, update leadDetectiveNode to pass intelligence_report to the system prompt. Find the AGENT_CONFIGS.leadDetective.systemPrompt(...) call and update it to:

          content: AGENT_CONFIGS.leadDetective.systemPrompt(
            state.appraisal_result || "No appraisal result available",
            state.evidence_analysis || "No evidence analysis available",
            state.intelligence_report || "No intelligence report available",
            state.suspect_names,
          ),

👉 Fourth, update buildGraph() to include the intelligence researcher node and rewire edges:

    workflow
      .addNode("appraiser", this.appraiserNode.bind(this))
      .addNode("evidence_analyst", this.evidenceAnalystNode.bind(this))
      .addNode("intelligence_researcher", this.intelligenceResearcherNode.bind(this))
      .addNode("lead_detective", this.leadDetectiveNode.bind(this))
      .addEdge(START, "appraiser")
      .addEdge("appraiser", "evidence_analyst")
      .addEdge("evidence_analyst", "intelligence_researcher")
      .addEdge("intelligence_researcher", "lead_detective")
      .addEdge("lead_detective", END);

👉 Fifth, update the kickoff method's logging to include the intelligence report:

    console.log("\n--- Intelligence Report ---");
    console.log(result.intelligence_report ?? "(not set)");

(Add after the evidence analysis log line)

Step 4: Verify TypeScript compiles

Run from project/JavaScript/solution/:

cd project/JavaScript/solution
npm run build

If it compiles without errors, all type changes are consistent.

Run Your Enhanced Investigation

cd project/JavaScript/starter-project
npm install
npm run dev

⏱️ This may take 3-6 minutes as your agents run through all four steps.

Understanding the Enhanced Graph

flowchart TD
    A[Appraiser Node] --> B[Evidence Analyst Node]
    B --> C[Intelligence Researcher Node]
    C --> D[Lead Detective Node]
    D --> E[Final Conclusion]

In LangGraph, each node receives the full AgentState and returns a partial state update. The intelligence_report field flows from the Intelligence Researcher node to the Lead Detective node through the state.

Key Takeaways

Sonar-Pro provides real-time web search through the same OrchestrationClient API you already use
LangGraph state carries intelligence_report forward to the Lead Detective automatically
Deterministic orchestration: the node calls the tool directly (no LLM tool-calling loop) — predictable and efficient
Multi-source intelligence: grounding (internal) + web search (external) → detective synthesis

Next Steps

You now have a complete intelligence gathering system with:

✅ Structured data predictions (RPT-1)
✅ Internal document search (Grounding Service)
✅ External web intelligence (Sonar-Pro)

In the next exercise, you'll add the Lead Detective Agent who will synthesize all three sources to solve the museum art theft mystery.

Troubleshooting

Issue: Error: Model sonar-pro not found or 404 from OrchestrationClient

Solution: Verify that sonar-pro is deployed in your SAP AI Core resource group. Check SAP AI Launchpad → Generative AI Hub → Models.

Issue: callSonarProSearchTool is not exported

Solution: Ensure you exported the function: export async function callSonarProSearchTool(...)

Issue: TypeScript error on state.intelligence_report

Solution: Ensure you added the intelligence_report field to AgentState in types.ts before the Annotation.Root closing brace.

Issue: Web search takes too long

Solution: Sonar-pro queries can take 10-30 seconds per search; this is normal for real-time web crawling.

Resources

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Discover Connected Crimes with Web Search (TypeScript/LangGraph)

Overview

Understand Web Search with Sonar-Pro

Why Do We Need Web Search?

Document Grounding vs. Web Search

How Sonar-Pro Integrates with SAP Cloud SDK for AI

Add The Web Search Tool

Step 1: Create the Sonar-Pro Search Tool

Add The Intelligence Researcher Agent

Step 1: Update Type Definitions

Step 2: Add Agent Configuration

Step 3: Add the Intelligence Researcher Node

Step 4: Verify TypeScript compiles

Run Your Enhanced Investigation

Understanding the Enhanced Graph

Key Takeaways

Next Steps

Troubleshooting

Resources

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Discover Connected Crimes with Web Search (TypeScript/LangGraph)

Overview

Understand Web Search with Sonar-Pro

Why Do We Need Web Search?

Document Grounding vs. Web Search

How Sonar-Pro Integrates with SAP Cloud SDK for AI

Add The Web Search Tool

Step 1: Create the Sonar-Pro Search Tool

Add The Intelligence Researcher Agent

Step 1: Update Type Definitions

Step 2: Add Agent Configuration

Step 3: Add the Intelligence Researcher Node

Step 4: Verify TypeScript compiles

Run Your Enhanced Investigation

Understanding the Enhanced Graph

Key Takeaways

Next Steps

Troubleshooting

Resources