rowboat/apps/rowboat_agents/tests/sample_requests/tmp4.json

{
  "messages": [
    {
      "role": "system",
      "content": ""
    },
    {
      "version": "v1",
      "chatId": "",
      "createdAt": "2025-05-08T07:06:48.280Z",
      "role": "assistant",
      "content": "How can I help you today?",
      "agenticSender": "Example Agent",
      "agenticResponseType": "external"
    },
    {
      "role": "user",
      "content": "Tell me about article 1",
      "version": "v1",
      "chatId": "",
      "createdAt": "2025-05-08T07:06:53.518Z"
    },
    {
      "version": "v1",
      "chatId": "",
      "createdAt": "2025-05-08T07:06:55.690Z",
      "role": "assistant",
      "tool_calls": [
        {
          "id": "call_uzRd4Y1CeBioJ9h26XeGhCvH",
          "function": {
            "name": "rag_search",
            "arguments": "{\"query\":\"article 1\"}"
          },
          "type": "function"
        }
      ],
      "agenticSender": "Example Agent",
      "agenticResponseType": "internal"
    },
    {
      "version": "v1",
      "chatId": "",
      "createdAt": "2025-05-08T07:06:55.690Z",
      "role": "tool",
      "content": "{\n  \"Information\": [\n    {\n      \"title\": \"text\",\n      \"name\": \"text\",\n      \"content\": \"This is article 1.\",\n      \"docId\": \"681c50f17e7a18621c7215cd\",\n      \"sourceId\": \"681c50f17e7a18621c7215cc\"\n    },\n    {\n      \"title\": \"text\",\n      \"name\": \"text\",\n      \"content\": \"This is article 2.\",\n      \"docId\": \"681c51067e7a18621c7215cf\",\n      \"sourceId\": \"681c51067e7a18621c7215ce\"\n    },\n    {\n      \"title\": \"text\",\n      \"name\": \"text\",\n      \"content\": \"This is article 3.\",\n      \"docId\": \"681c51137e7a18621c7215d1\",\n      \"sourceId\": \"681c51137e7a18621c7215d0\"\n    }\n  ]\n}",
      "tool_call_id": "call_uzRd4Y1CeBioJ9h26XeGhCvH",
      "tool_name": ""
    },
    {
      "version": "v1",
      "chatId": "",
      "createdAt": "2025-05-08T07:06:57.096Z",
      "role": "assistant",
      "content": "Article 1 states: \"This is article 1.\"\n\nIf you need more details or information about another article, please let me know!",
      "agenticSender": "Example Agent",
      "agenticResponseType": "external"
    }
  ],
  "lastRequest": {
    "projectId": "8e59c4b6-91d2-42e9-8990-41583f4105f1",
    "messages": [
      {
        "content": "",
        "role": "system",
        "sender": null,
        "tool_calls": null,
        "tool_call_id": null,
        "tool_name": null
      },
      {
        "content": "How can I help you today?",
        "role": "assistant",
        "sender": "Example Agent",
        "tool_calls": null,
        "tool_call_id": null,
        "tool_name": null,
        "response_type": "external"
      },
      {
        "content": "Tell me about article 1",
        "role": "user",
        "sender": null,
        "tool_calls": null,
        "tool_call_id": null,
        "tool_name": null
      }
    ],
    "state": {
      "last_agent_name": "Example Agent",
      "tokens": {
        "total": 0,
        "prompt": 0,
        "completion": 0
      }
    },
    "agents": [
      {
        "name": "Example Agent",
        "type": "conversation",
        "description": "An example agent that uses the rag_search tool to fetch information for customer support queries.",
        "instructions": "## 🧑‍💼 Role:\nYou are a helpful customer support assistant who fetches information using the rag_search tool.\n\n---\n## ⚙️ Steps to Follow:\n1. Ask the user what they would like help with.\n2. If the user's query requires information or an answer, use the [@tool:rag_search] tool with the user's question as the query.\n3. Provide the user with an answer based on the information retrieved from the tool.\n4. If the user's issue is about follow-up or requires human support, ask for their email address and let them know someone will contact them soon.\n5. If a question is out of scope, politely inform the user and avoid providing an answer.\n\n---\n## 🎯 Scope:\n✅ In Scope:\n- Answering customer support questions using information from articles.\n- Asking the user their issue and getting their email if needed.\n\n❌ Out of Scope:\n- Questions unrelated to customer support.\n- Providing answers without using the rag_search tool when information is needed.\n\n---\n## 📋 Guidelines:\n✔️ Dos:\n- Use [@tool:rag_search](#mention) to fetch accurate information for user queries.\n- Provide clear and concise answers based on the tool's results.\n- Ask for the user's email if the issue requires follow-up.\n\n🚫 Don'ts:\n- Do not provide answers without fetching information when required.\n- Do not ask for details other than the user's email.\n\n# Examples\n- **User** : What is your refund policy?\n - **Agent actions**: Call [@tool:rag_search](#mention)\n - **Agent response**: Our refund policy is as follows: <summary from rag_search results>\n\n- **User** : How can I change my password?\n - **Agent actions**: Call [@tool:rag_search](#mention)\n - **Agent response**: To change your password, follow these steps: <steps from rag_search results>\n\n- **User** : I need help with my order.\n - **Agent actions**: Call [@tool:rag_search](#mention)\n - **Agent response**: Can you please provide more details about your order issue? <additional info from rag_search results if relevant>\n\n- **User** : I want someone to contact me about a billing issue.\n - **Agent response**: Sure, could you please provide your email address so someone from our team can contact you soon?\n\n- **User** : Can you tell me about your subscription plans?\n - **Agent actions**: Call [@tool:rag_search](#mention)\n - **Agent response**: Here are our subscription plans: <details from rag_search results>",
        "model": "gpt-4.1",
        "controlType": "retain",
        "ragDataSources": [
          "681c50f17e7a18621c7215cc",
          "681c51067e7a18621c7215ce",
          "681c51137e7a18621c7215d0"
        ],
        "ragK": 1,
        "ragReturnType": "chunks",
        "tools": [
          "rag_search"
        ],
        "prompts": [],
        "connectedAgents": []
      }
    ],
    "tools": [
      {
        "name": "web_search",
        "description": "Fetch information from the web based on chat context",
        "parameters": {
          "type": "object",
          "properties": {}
        },
        "isLibrary": true
      },
      {
        "name": "rag_search",
        "description": "Fetch articles with knowledge relevant to the query",
        "parameters": {
          "type": "object",
          "properties": {
            "query": {
              "type": "string",
              "description": "The query to retrieve articles for"
            }
          },
          "required": [
            "query"
          ]
        },
        "isLibrary": true
      }
    ],
    "prompts": [],
    "startAgent": "Example Agent",
    "mcpServers": [],
    "toolWebhookUrl": ""
  },
  "lastResponse": {
    "state": {
      "last_agent_name": "Example Agent",
      "tokens": {
        "total": 1581,
        "prompt": 1533,
        "completion": 48
      }
    },
    "messages": [
      {
        "version": "v1",
        "chatId": "",
        "createdAt": "2025-05-08T07:06:55.690Z",
        "role": "assistant",
        "tool_calls": [
          {
            "id": "call_uzRd4Y1CeBioJ9h26XeGhCvH",
            "function": {
              "name": "rag_search",
              "arguments": "{\"query\":\"article 1\"}"
            },
            "type": "function"
          }
        ],
        "agenticSender": "Example Agent",
        "agenticResponseType": "internal"
      },
      {
        "version": "v1",
        "chatId": "",
        "createdAt": "2025-05-08T07:06:55.690Z",
        "role": "tool",
        "content": "{\n  \"Information\": [\n    {\n      \"title\": \"text\",\n      \"name\": \"text\",\n      \"content\": \"This is article 1.\",\n      \"docId\": \"681c50f17e7a18621c7215cd\",\n      \"sourceId\": \"681c50f17e7a18621c7215cc\"\n    },\n    {\n      \"title\": \"text\",\n      \"name\": \"text\",\n      \"content\": \"This is article 2.\",\n      \"docId\": \"681c51067e7a18621c7215cf\",\n      \"sourceId\": \"681c51067e7a18621c7215ce\"\n    },\n    {\n      \"title\": \"text\",\n      \"name\": \"text\",\n      \"content\": \"This is article 3.\",\n      \"docId\": \"681c51137e7a18621c7215d1\",\n      \"sourceId\": \"681c51137e7a18621c7215d0\"\n    }\n  ]\n}",
        "tool_call_id": "call_uzRd4Y1CeBioJ9h26XeGhCvH",
        "tool_name": ""
      },
      {
        "version": "v1",
        "chatId": "",
        "createdAt": "2025-05-08T07:06:57.096Z",
        "role": "assistant",
        "content": "Article 1 states: \"This is article 1.\"\n\nIf you need more details or information about another article, please let me know!",
        "agenticSender": "Example Agent",
        "agenticResponseType": "external"
      }
    ]
  }
}