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Semantic Search
AI-powered vector search for content discovery
intermediate
POST /search/semanticOverview
Semantic search uses LangChain and OpenSearch vector embeddings to find content based on meaning rather than exact keyword matches. Queries are converted to vectors and compared against indexed content embeddings for similarity.
This is useful for natural language queries like "photos of sunsets" which would match content tagged with "beach evening" or "golden hour photography".
API Endpoint
POST
/search/semanticQuery Parameters:
query.query— Natural language search string
Usage Example
import { semanticSearch } from "@newgraph-signals/actions/search";
// Using the signals-based action
const results = semanticSearch({ q: "photos of nature" });
// The action returns a signal that updates with results
effect(() => {
console.log("Search results:", results.value);
});
// Raw API call
const response = await newgraphClient.api.search.semanticList({
query: { query: "photos of nature" }
});Response Schema
{
value: [
{
id: string; // Post or mood ID
contentUrl: string; // Media URL
description: string; // Content description
score: number; // Similarity score (0-1)
}
]
}Performance Notes
Latency
~500ms average (includes embedding generation)
Backend
LangChain + OpenSearch vector index