Prompt

semantic search API for app

Artificial Intelligence / AI Platforms1 observationLast seen Oct 8, 2026

Latest observation

Oct 8, 2026OpenAI APIWeb search: off

Could you clarify what you need by “semantic search API for app”?

I can help in a few ways:

  1. Design an API for semantic search
  2. Recommend tools/services to use
  3. Write backend code for an API endpoint
  4. Design a vector database schema
  5. Help integrate it into your app

If you want, I can give you a quick starter design like:

Example semantic search API

Endpoint: POST /search

{
  "query": "wireless noise cancelling headphones",
  "filters": {
    "category": "electronics",
    "price_max": 300
  },
  "top_k": 10
}

Response:

{
  "results": [
    {
      "id": "prod_123",
      "title": "Sony WH-1000XM5",
      "score": 0.92,
      "snippet": "Premium wireless noise cancelling headphones..."
    }
  ]
}

Typical implementation:

  • Create embeddings for your content
  • Store them in a vector DB like Pinecone, Weaviate, Milvus, pgvector
  • Embed the user query
  • Run nearest-neighbor similarity search
  • Optionally rerank results with an LLM or cross-encoder

If you tell me your stack (e.g. Node.js, Python, Firebase, PostgreSQL) and what you’re searching (products, docs, chat, images), I can build the API spec or code for you.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

Want this interpreted for your brand?

Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.