Val Town Code SearchReturn to Val Town

77    }
78    
79    /* Hide scrollbar but keep functionality */
80    .hide-scrollbar {
81      -ms-overflow-style: none;

9const MISTRAL_API_URL = 'https://api.mistral.ai/v1/chat/completions';
10
11export async function generateMistralResponse(
12  userMessage: string,
13  context: VectorSearchResult[] = [],
57}
58
59function createSystemPrompt(context: VectorSearchResult[]): string {
60  const basePrompt = `You are Nelson-GPT, a specialized medical AI assistant powered by the Nelson Textbook of Pediatrics. You provide evidence-based answers to healthcare professionals and medical students focusing on pediatric medicine.
61
89}
90
91export async function* streamMistralResponse(response: Response): AsyncGenerator<string, void, unknown> {
92  const reader = response.body?.getReader();
93  if (!reader) {
132}
133
134export async function generateNonStreamingResponse(
135  userMessage: string,
136  context: VectorSearchResult[] = []

4const EMBEDDING_MODEL = 'sentence-transformers/all-MiniLM-L6-v2';
5
6export async function generateEmbedding(text: string): Promise<number[]> {
7  try {
8    const response = await fetch(
44}
45
46export async function generateEmbeddings(texts: string[]): Promise<number[][]> {
47  try {
48    const embeddings = await Promise.all(
56}
57
58// Utility function to normalize embeddings (optional, for better similarity search)
59export function normalizeEmbedding(embedding: number[]): number[] {
60  const magnitude = Math.sqrt(embedding.reduce((sum, val) => sum + val * val, 0));
61  return embedding.map(val => val / magnitude);
63
64// Calculate cosine similarity between two embeddings
65export function cosineSimilarity(a: number[], b: number[]): number {
66  if (a.length !== b.length) {
67    throw new Error('Embeddings must have the same length');

7export const supabase = createClient(supabaseUrl, supabaseServiceKey);
8
9export async function searchSimilarContent(
10  embedding: number[],
11  limit: number = 5,
35}
36
37export async function insertNelsonContent(
38  content: string,
39  embedding: number[],
64}
65
66export async function createNelsonContentTable(): Promise<void> {
67  try {
68    // Create the table if it doesn't exist
74    }
75
76    // Create the vector similarity search function
77    const { error: functionError } = await supabase.rpc('create_match_function');
78    
79    if (functionError && !functionError.message.includes('already exists')) {
80      console.error('Error creating function:', functionError);
81      throw new Error(`Failed to create function: ${functionError.message}`);
82    }
83  } catch (error) {
88
89// Initialize database schema
90export async function initializeDatabase(): Promise<void> {
91  try {
92    // Check if the table exists

6
7- 🏥 Medical AI assistant with evidence-based responses
8- 📱 Progressive Web App (PWA) with offline functionality
9- 💬 ChatGPT-like interface with real-time streaming responses
10- 🔍 RAG (Retrieval-Augmented Generation) workflow
46│   │   └── MessageBubble.tsx
47│   ├── hooks/
48│   │   └── useChat.ts     # Chat functionality hook
49│   ├── services/
50│   │   ├── api.ts         # API client

1/** @jsxImportSource https://esm.sh/react@18.2.0?dev */
2
3export function ValTownLogo () {
4  return (
5    <svg

3const USER_ENDPOINT = "/api/user";
4
5export function useUser() {
6  const [data, setData] = useState<any>(null);
7  const [loading, setLoading] = useState(true);

1import { useEffect } from "react";
2
3export function useUsageStats(messages: any[], usages: any[]) {
4  useEffect(() => {
5    if (!messages?.length) return;

7 *
8 * @param {Array} dependencies - Array of dependencies that trigger scrolling when changed
9 * @returns {Object} An object containing containerRef and scrollToBottom function
10 */
11export function useScrollToBottomContainer(dependencies = []) {
12  const containerRef = useRef(null);
13
28
29// body scroll version
30export function useScrollToBottom(dependencies = []) {
31  const bottomRef = useRef(null);
32

6 * This includes a fallback to inference calls data when usage data is missing
7 */
8export async function getUserSummary() {
9  // First, get the standard grouped data from the usage table
10  const groupedUsageData = await sqlite.execute(`