import { RLMSolveRequest, RLMSolveResult } from './rlm_types';
/**
* Mock function representing a call to the MCP tool.
* In a real scenario, this would use an MCP client to call the server.
*/
async function callRLMSolve(req: RLMSolveRequest): Promise<RLMSolveResult> {
console.log(`[Batch] Solving: ${req.query}`);
// Simulate API call delay
await new Promise(resolve => setTimeout(resolve, 500));
return {
status: "ok",
answer: `Result for ${req.query}`,
metrics: { wall_time_sec: 0.5 }
};
}
async function runBatch() {
const tasks: RLMSolveRequest[] = [
{ query: "Task 1: Audit hashing.py", globs: ["rlm_mcp_server/hashing.py"] },
{ query: "Task 2: Audit ingest.py", globs: ["rlm_mcp_server/ingest.py"] },
{ query: "Task 3: Audit server.py", globs: ["rlm_mcp_server/server.py"] }
];
console.log(`Starting batch of ${tasks.length} tasks...`);
const results = await Promise.all(tasks.map(callRLMSolve));
results.forEach((res, i) => {
const status = res.status;
if (status === "ok") {
console.log(`Task ${i + 1} Success: ${res.answer}`);
} else if (status === "partial") {
console.log(`Task ${i + 1} Partial: ${res.answer}`);
} else if (status === "error") {
console.log(`Task ${i + 1} Error`);
} else {
// Forward-compatibility guard
console.log(`Task ${i + 1} Unknown status: ${status}`);
console.log(`Task ${i + 1} Answer: ${res.answer}`);
}
});
}
runBatch().catch(console.error);
