seraph-agent/dist/__tests__/chat.test.js

An extremely lightweight, SRE autonomous AI agent for seamless integration with common observability tasks.

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const chat_1 = require("../chat");
const llm_1 = require("../llm");
jest.mock('../llm');
const mockCreateLLMProvider = llm_1.createLLMProvider;
describe('chat', () => {
    const config = {};
    const generateMock = jest.fn();
    beforeEach(() => {
        generateMock.mockClear();
        mockCreateLLMProvider.mockReturnValue({
            generate: generateMock,
        });
        // Ensure a default mock resolution to prevent ".match of undefined" error
        generateMock.mockResolvedValue('This is a default response.');
    });
    const getSystemPrompt = (tools) => `You are Seraph, a lightweight, autonomous SRE agent. Your primary goal is to analyze logs, detect anomalies, and provide insightful, actionable responses. You have access to a set of external tools through the Model Context Protocol (MCP) to help you accomplish this.

When you receive a request, you must follow the ReAct (Reasoning + Act) framework.

**Thinking Process:**

1.  **Analyze Request**: Understand the user's intent. Is it a question about a recent log? A request for an action? Or a command to analyze a specific log?
2.  **Determine Tool Necessity**: Decide if an external tool is required.
    -   If the request is about logs that are provided in the context, you do not need a tool. Your response should be a direct answer based on the log data.
    -   If the request is a general question (e.g., "What's the weather?"), you must use an appropriate tool.
    -   **If you need information but have no tools, you must ask the user for the information or context directly.**
3.  **Formulate a Plan**: Create a plan that outlines the steps to fulfill the user's request. This plan should be broken down into specific actions.
4.  **Act**: If a tool is needed, output the correct tool call in the specified format. The format is 
Action: tool_name[tool_input]
. Do not use any other text.
5.  **Observe**: You will be provided with the output of the tool.
6.  **Synthesize and Respond**: Combine your internal knowledge, log context, and the tool's output to formulate a concise, helpful, and clear response.

**Available Tools:**
${tools.length > 0 ? tools.map((tool) => `- ${tool.name}: ${tool.description}`).join('\n') : 'No tools available.'}
`;
    it('should call the LLM provider with the message and system prompt', async () => {
        const message = 'Hello, world!';
        const tools = [];
        const systemPrompt = getSystemPrompt(tools);
        const userRequest = `User Request: ${message}`;
        const expectedPrompt = `${systemPrompt}\n\n${userRequest}`;
        await (0, chat_1.chat)(message, config, tools);
        expect(generateMock).toHaveBeenCalledWith(expectedPrompt);
    });
    it('should call the LLM provider with a formatted prompt including logs and system prompt', async () => {
        const message = 'What is the error?';
        const logs = ['ERROR: Something went wrong', 'WARN: Something else is fishy'];
        const tools = [];
        const systemPrompt = getSystemPrompt(tools);
        const userRequest = `
      Based on the following recent logs, answer the user's question.

      Logs:
      ${logs.join('\n')}

      Question:
      ${message}
      `;
        const expectedPrompt = `${systemPrompt}\n\n${userRequest}`;
        await (0, chat_1.chat)(message, config, tools, logs);
        expect(generateMock).toHaveBeenCalledWith(expectedPrompt);
    });
    it('should return the generated response', async () => {
        const message = 'Hello, world!';
        const expectedResponse = 'This is a generated response.';
        generateMock.mockResolvedValue(expectedResponse);
        const response = await (0, chat_1.chat)(message, config, []);
        expect(response).toBe(expectedResponse);
    });
});