semantic search embeddings API

If you want to build semantic search using embeddings, the basic API flow is:

1. Embed your documents
2. Store vectors in a vector DB or index
3. Embed the user query
4. Compare query vector to document vectors using cosine similarity / dot product
5. Return top matches

Typical API shape

You might expose endpoints like:

• POST /embed

  • input: text or batch of texts
  • output: embedding vectors

• POST /search

  • input: query text, top_k
  • output: ranked matching documents

Example request

POST /search
{
  "query": "best way to learn machine learning",
  "top_k": 5
}

Example response

{
  "results": [
    {"id": "doc_12", "score": 0.92, "text": "An introduction to machine learning..."},
    {"id": "doc_87", "score": 0.89, "text": "How to study ML effectively..."}
  ]
}

If you mean OpenAI-style embeddings API

A common pattern is:

from openai import OpenAI
client = OpenAI()

resp = client.embeddings.create(
    model="text-embedding-3-small",
    input=["your document text", "another text"]
)
vectors = [item.embedding for item in resp.data]

Then for search:

query_vec = client.embeddings.create(
    model="text-embedding-3-small",
    input="best way to learn machine learning"
).data[0].embedding

Important notes

• Use the same embedding model for documents and queries.
• For retrieval, cosine similarity is common.
• For scale, store embeddings in:
  • FAISS
  • Pinecone
  • Weaviate
  • Qdrant
  • Milvus
  • or a DB with vector support

If you want, I can give you:

1. a full semantic search API design
2. a Python/FastAPI example
3. a Node.js example
4. a DB schema + vector search implementation