Prompt
I'm building a document search feature and want an AI API for embeddings
Latest observation
If you’re building document search, an embeddings API is a great fit.
What embeddings do
Embeddings turn text into vectors that capture semantic meaning, so you can:
- search by meaning, not just keywords
- rank documents by relevance
- do clustering, deduplication, and recommendations
Typical search flow
- Chunk documents into passages
- Create embeddings for each chunk
- Store them in a vector database or search index
- When a user searches, embed the query
- Find the nearest vectors and return matching chunks
Good embedding API options
Common choices include:
- OpenAI Embeddings API
- Cohere Embed
- Voyage AI
- Google Vertex AI embeddings
- Azure OpenAI embeddings
What to look for
When choosing an API, compare:
- quality on your domain
- latency
- cost per token / per request
- embedding dimension size
- multilingual support
- batching support
- rate limits
OpenAI example
If you want, I can help you wire this up with:
- OpenAI embeddings + vector DB
- Python or JavaScript code
- Postgres pgvector
- Pinecone / Weaviate / FAISS
- chunking strategy for documents
If you'd like, I can give you a complete starter architecture for document search and a minimal code example.
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.