How do I build semantic search into my product with an API?

To build semantic search into your product via an API, you usually combine:

1. Embeddings to turn text into vectors
2. A vector database / index to store and search those vectors
3. An API layer in your app to ingest content and query results
4. Optionally, hybrid search (semantic + keyword) for better relevance

1) High-level architecture

Ingestion flow

• User uploads/creates content
• You split content into searchable chunks
• Generate embeddings for each chunk
• Store:
  • the chunk text
  • metadata (id, title, tags, permissions, source)
  • the embedding vector

Search flow

• User enters a query
• Generate an embedding for the query
• Search the vector index for nearest matches
• Return top results, possibly re-ranked and filtered by metadata

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2) Basic implementation steps

Step A: Choose an embedding model

Use an embedding API from a provider or self-hosted model.

Typical choices:

• Managed APIs: OpenAI, Cohere, Voyage, etc.
• Self-hosted: sentence-transformers, Hugging Face models

You want an embedding model that:

• works well on your language/domain
• is cost-effective
• supports enough throughput

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Step B: Chunk your content

Don’t embed huge documents as one blob.

Example:

• 300–1,000 tokens per chunk
• overlap 10–20% if needed

Store each chunk independently so search can return the exact passage.

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Step C: Store vectors in a vector DB

Common options:

• Pinecone
• Weaviate
• Qdrant
• Milvus
• Postgres + pgvector
• Elasticsearch/OpenSearch with vector support

Pick based on:

• scale
• latency
• filtering needs
• operational overhead
• cost

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Step D: Build an ingestion API

Example endpoints:

• POST /documents — create/update content
• POST /documents/{id}/index — embed and index it
• DELETE /documents/{id} — remove vectors on deletion

Pseudo-flow:

1. receive content
2. split into chunks
3. call embeddings API for each chunk
4. upsert vectors into DB with metadata

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Step E: Build a search API

Example:

• GET /search?q=...

Flow:

1. embed the query
2. do vector similarity search
3. optionally filter by metadata like workspace, user, product area, date
4. return ranked results

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3) Example search pipeline

Query-time

User asks:

“How do I reset my password?”

Your system:

• embeds the query
• searches nearest chunks such as:
  • “Resetting your account password”
  • “Forgot password steps”
  • “Password recovery email instructions”

Then returns:

• result text
• source document
• confidence/score
• snippet

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4) Hybrid search is often best

Pure semantic search can miss exact terms. Add keyword search for:

• product names
• error codes
• IDs
• rare proper nouns

A strong pattern:

• retrieve with vector search
• retrieve with keyword search
• merge results
• optionally rerank with an LLM or reranker model

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5) Example API design

Ingest

POST /documents
{
  "id": "doc_123",
  "title": "Password reset guide",
  "content": "..."
}

Search

GET /search?q=reset%20password&workspace_id=acme

Response

{
  "results": [
    {
      "document_id": "doc_123",
      "chunk_id": "chunk_7",
      "title": "Password reset guide",
      "snippet": "To reset your password, go to Settings > Security...",
      "score": 0.87
    }
  ]
}

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6) Things to get right

Permissions and filtering

Always enforce access control at search time:

• workspace_id
• org_id
• role-based restrictions
• document-level permissions

Freshness

If content changes:

• re-embed updated chunks
• delete stale vectors
• keep versioning if needed

Evaluation

Measure quality with:

• top-k recall
• MRR/NDCG
• human evaluation on real queries

Latency

To keep search fast:

• precompute embeddings
• use ANN indexes
• cache frequent queries
• limit chunk count returned

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7) Minimal tech stack example

A common production setup:

• Backend: Node.js / Python API
• Embeddings: managed embedding API
• Vector DB: pgvector, Qdrant, or Pinecone
• Reranking: optional cross-encoder or LLM
• Auth: your normal user auth + document filters

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8) If you want a simple MVP

You can build an MVP quickly with:

• OpenAI embeddings or similar
• PostgreSQL + pgvector
• A single /search endpoint
• Basic chunking and metadata filters

This is often enough to prove value before moving to a dedicated vector DB.

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9) Recommended next step

If you want, I can give you one of these:

1. A complete architecture diagram
2. A working example in Python or Node.js
3. A pgvector-based implementation
4. A Pinecone/Qdrant implementation
5. A design for hybrid semantic + keyword search

If you tell me your stack, I’ll tailor the API design and code sample.