Mannu
c2cabc01d3
G1 — Design System: 14 UI primitives (Button, Card, Modal, Sheet, Input, Textarea, Select, EmptyState, LoadingShimmer, ConfirmDialog, WashiTape, Badge, Avatar, Tabs), PageTransition with Framer Motion, sun/moon CSS vars, Caveat font, /dev/components visual showcase. G2 — Memories Pipeline: R2 presigned uploads, Sharp thumbnail generation, LiteLLM vision captions + pgvector embeddings, CSS masonry gallery with infinite scroll, private toggle, semantic search fallback to ILIKE. G3 — Medical: dose log + correction audit trail, IAP vaccine bulk import, emergency escalation page, pediatrician phone in settings. G4 — AI Brain: keyword guardrail → LLM classifier → structured DB tool-use (7 tools) → memory search → general parenting handler; ai_usage table; 22-case medical bypass safety test suite. DB migrations: 0011_memories, 0012_medical_doses, 0013_ai_usage. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
91 lines
3 KiB
TypeScript
91 lines
3 KiB
TypeScript
import { TOOL_DEFINITIONS, executeTool, type ToolContext } from "./db-tools";
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const LITELLM_URL = process.env.LITELLM_BASE_URL;
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const LITELLM_KEY = process.env.LITELLM_API_KEY;
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const QUERY_MODEL = process.env.QUERY_MODEL || "minimax-2.7";
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interface Message {
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role: "system" | "user" | "assistant" | "tool";
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content: string;
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tool_calls?: ToolCall[];
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tool_call_id?: string;
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name?: string;
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}
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interface ToolCall {
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id: string;
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type: "function";
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function: { name: string; arguments: string };
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}
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export async function answerStructuredQuery(query: string, ctx: ToolContext): Promise<string> {
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if (!LITELLM_URL || !LITELLM_KEY) return "AI not configured.";
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const now = new Date();
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const systemPrompt = `You are a data assistant for ${ctx.childName}'s baby tracking app. Today is ${now.toLocaleDateString("en-IN", { weekday: "long", day: "numeric", month: "long" })}. Use the available tools to fetch data and answer the parent's question with specific numbers and times. Keep responses concise and warm. Use "she/her" or "he/his" based on context if unknown use their name. Never interpret symptoms or give medical advice.`;
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const messages: Message[] = [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: query },
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];
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// Tool-use loop (max 3 iterations)
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for (let i = 0; i < 3; i++) {
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const res = await fetch(`${LITELLM_URL}/v1/chat/completions`, {
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method: "POST",
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headers: { "Content-Type": "application/json", "Authorization": `Bearer ${LITELLM_KEY}` },
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body: JSON.stringify({
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model: QUERY_MODEL,
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messages,
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tools: TOOL_DEFINITIONS,
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tool_choice: "auto",
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temperature: 0,
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max_tokens: 400,
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}),
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});
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if (!res.ok) {
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const err = await res.text();
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console.error("[structured-query] LLM error:", err);
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break;
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}
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const data = await res.json();
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const choice = data.choices?.[0];
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const msg = choice?.message;
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if (!msg) break;
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// If the model wants to call tools
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if (msg.tool_calls?.length) {
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messages.push({ role: "assistant", content: msg.content || "", tool_calls: msg.tool_calls });
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// Execute each tool call in parallel
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const toolResults = await Promise.all(
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msg.tool_calls.map(async (tc: ToolCall) => {
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let args: Record<string, unknown> = {};
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try { args = JSON.parse(tc.function.arguments); } catch { /* empty args */ }
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const result = await executeTool(tc.function.name, args, ctx).catch(e => ({ error: String(e) }));
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return {
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role: "tool" as const,
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tool_call_id: tc.id,
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name: tc.function.name,
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content: JSON.stringify(result),
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};
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})
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);
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messages.push(...toolResults);
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continue;
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}
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// Final text response
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if (choice?.finish_reason === "stop" && msg.content) {
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return msg.content;
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}
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break;
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}
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return "I couldn't retrieve that data right now. Please try again.";
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}
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