Agent Implementation

import { chat } from './agent';

const result = await chat("Do you have any copy paper?");
console.log(result.output);
// "Yes! We carry several types. Let me check what's available..."

const chat = traceable(
  async (question: string): Promise<{ messages: any[]; output: string }> => {
    // Function implementation
  },
  { name: "Emma", metadata: { thread_id: threadId } }
);

while (responseMessage.tool_calls) {
  // Execute tool calls
  for (const toolCall of responseMessage.tool_calls) {
    const functionArgs = JSON.parse(toolCall.function.arguments);
    
    if (toolCall.function.name === "query_database") {
      result = await queryDatabase(functionArgs.query, dbPath);
    } else if (toolCall.function.name === "search_knowledge_base") {
      result = await searchKnowledgeBase(functionArgs.query);
    }
  }
  
  // Get next response
  response = await client.chat.completions.create({
    model: "gpt-5-nano",
    messages,
    tools,
    tool_choice: "auto",
  });
}

function getThreadHistory(threadId: string): any[] {
  return threadStore[threadId] || [];
}

function saveThreadHistory(threadId: string, messages: any[]): void {
  threadStore[threadId] = messages;
}

import { setThreadId } from './agent';

setThreadId("custom-thread-id");

import { loadKnowledgeBase } from './agent';

await loadKnowledgeBase("./knowledge_base");
// Knowledge base loaded from cache: 15 documents

if (embeddingsAreStale(kbPath, cachePath)) {
  console.log("Knowledge base documents changed, regenerating embeddings...");
  await generateAndCacheEmbeddings(kbPath, cachePath);
} else {
  const cacheData = JSON.parse(fs.readFileSync(cachePath, "utf-8"));
  knowledgeBaseDocs = cacheData.docs;
  knowledgeBaseEmbeddings = cacheData.embeddings;
}

function cosineSimilarity(a: number[], b: number[]): number {
  let dot = 0, normA = 0, normB = 0;
  for (let i = 0; i < a.length; i++) {
    dot += a[i] * b[i];
    normA += a[i] * a[i];
    normB += b[i] * b[i];
  }
  return dot / (Math.sqrt(normA) * Math.sqrt(normB));
}

const QUERY_DATABASE_TOOL = {
  type: "function" as const,
  function: {
    name: "query_database",
    description: "SQL query to get information about our inventory for customers like products, quantities and prices.",
    parameters: {
      type: "object",
      properties: {
        query: {
          type: "string",
          description: `SQL query to execute against the inventory database.

YOU DO NOT KNOW THE SCHEMA. ALWAYS discover it first:
1. Query 'SELECT name FROM sqlite_master WHERE type="table"' to see available tables
2. Use 'PRAGMA table_info(table_name)' to inspect columns for each table
3. Only after understanding the schema, construct your search queries`,
        },
      },
      required: ["query"],
    },
  },
};

const SEARCH_KNOWLEDGE_BASE_TOOL = {
  type: "function" as const,
  function: {
    name: "search_knowledge_base",
    description: "Search company knowledge base for information about policies, procedures, company info, shipping, returns, ordering, contact information, store locations, and business hours.",
    parameters: {
      type: "object",
      properties: {
        query: {
          type: "string",
          description: "Natural language question or search query about company policies or information",
        },
      },
      required: ["query"],
    },
  },
};

import { chat, loadKnowledgeBase, setThreadId } from './agent';
import { uuid7 } from 'langsmith';

// Initialize
const threadId = uuid7();
setThreadId(threadId);
await loadKnowledgeBase('./knowledge_base');

// First interaction
const result1 = await chat("What types of paper do you have?");
console.log(result1.output);

// Follow-up (maintains conversation context)
const result2 = await chat("What's the price of the first option?");
console.log(result2.output);