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Philip Okugbe
232beda471
* feat(ai): add AI_VECTOR_DRIVER and turbopuffer configuration * feat(ai): carry workspace and target space ids on vector lifecycle events * feat(ai): add vector driver interface and turbopuffer request helpers * feat(ai): add pgvector driver behind the vector driver interface * refactor(ai): route vector reads and writes through the vector driver * feat(ai): add turbopuffer vector driver * feat(ai): rebuild turbopuffer namespaces when the embedding model changes * feat(ai): warm the vector namespace cache on session start * fix(ai): harden turbopuffer misconfiguration and reset failure paths * fix(ai): collapse blank-line runs in extracted page text * fix(ai): skip full re-embed when ai search is re-enabled within the delete grace window * sync * fix(ai): store real embedding dimensions instead of serialized vector length * fix(ai): filter search hits by the page's current space at query time * fix(ai): retry the page moved-to-space vector patch job * sync * feat(ai): pre-warm the vector namespace * fix(ai): pass AI_VECTOR_DRIVER through the client build config
92 lines
2.2 KiB
TypeScript
92 lines
2.2 KiB
TypeScript
import api from "@/lib/api-client.ts";
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import { IPageSearchParams } from "@/features/search/types/search.types.ts";
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export interface IAiSearchResponse {
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answer: string;
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sources?: Array<{
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pageId: string;
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title: string;
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slugId: string;
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spaceSlug: string;
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similarity: number;
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distance: number;
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chunkIndex: number;
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excerpt: string;
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}>;
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}
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export async function hintVectorCache(): Promise<void> {
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try {
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await api.post("/ai/vector-cache-hint");
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} catch {
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// best-effort cache hint
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}
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}
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export async function aiAnswers(
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params: IPageSearchParams,
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onChunk?: (chunk: { content?: string; sources?: any[] }) => void,
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): Promise<IAiSearchResponse> {
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const response = await fetch("/api/ai/answers", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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credentials: "include",
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body: JSON.stringify(params),
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});
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if (!response.ok) {
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throw new Error(`HTTP error! status: ${response.status}`);
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}
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const reader = response.body?.getReader();
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const decoder = new TextDecoder();
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let answer = "";
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let sources: any[] = [];
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let buffer = "";
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if (reader) {
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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buffer += decoder.decode(value, { stream: true });
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const lines = buffer.split("\n");
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// Keep the last incomplete line in the buffer
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buffer = lines.pop() || "";
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for (const line of lines) {
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if (line.startsWith("data: ")) {
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const data = line.slice(6);
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if (data === "[DONE]") break;
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try {
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const parsed = JSON.parse(data);
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if (parsed.error) {
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throw new Error(parsed.error);
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}
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if (parsed.content) {
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answer += parsed.content;
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onChunk?.({ content: parsed.content });
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}
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if (parsed.sources) {
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sources = parsed.sources;
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onChunk?.({ sources: parsed.sources });
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}
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} catch (e) {
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if (e instanceof Error) {
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throw e;
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}
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// Skip invalid JSON
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}
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}
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}
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}
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}
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return { answer, sources };
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}
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