Claude API Semantic Search Implementation Guide

Does the Claude API do semantic search?

Not directly. Claude's API is a text-generation endpoint — it accepts messages and returns completions, it doesn't expose an embeddings endpoint for turning text into vectors. Semantic search requires vectors, a similarity index, and a retrieval step, none of which Claude performs on its own. What Claude is genuinely good at in a search pipeline is query understanding, re-ranking retrieved candidates, and synthesizing a final answer from the results.

So a real "Claude API semantic search implementation" is a hybrid system: an embedding model generates vectors and does the nearest-neighbor retrieval, and Claude sits on top to rewrite queries, judge relevance, and write the final response. This is the same pattern used in most production RAG (retrieval-augmented generation) systems. Below is a working architecture you can implement today.

Architecture overview

A practical pipeline has five stages:

1. Chunk and embed your documents with a dedicated embeddings model (Voyage AI, OpenAI, Cohere, or an open-source model).
2. Store vectors in a vector database (pgvector, Pinecone, Weaviate, Qdrant).
3. Rewrite the user's query with Claude so it matches how your documents are phrased.
4. Retrieve top-k candidates by vector similarity, then optionally re-rank with Claude for relevance.
5. Synthesize the answer with Claude, grounded in the retrieved chunks.

Claude touches steps 3, 4 (optionally), and 5. Steps 1–2 are embedding/vector-database work and happen outside the Claude API.

Step 1: Generate embeddings for your corpus

Chunk documents into 200–500 token passages with some overlap, then embed each chunk:

// Using any embeddings provider — Claude has no embeddings endpoint
const res = await fetch("https://api.embeddings-provider.com/v1/embed", {
  method: "POST",
  headers: { "Authorization": `Bearer ${EMBED_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({ input: chunkText, model: "embed-large" })
});
const { embedding } = await res.json();

Store embedding alongside the chunk text and metadata (source, section, URL) in your vector database.

Step 2: Rewrite the query with Claude before retrieval

Raw user queries ("why is it slow after the update") rarely match how documentation is phrased. Claude can expand the query into search-friendly terms before you embed it and run similarity search:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet",
    "max_tokens": 200,
    "messages": [{
      "role": "user",
      "content": "Rewrite this search query into 3 alternate phrasings optimized for retrieving technical documentation. Query: why is it slow after the update"
    }]
  }'

Embed the original query plus the rewrites, run similarity search for each, and merge the candidate sets. This typically improves recall more than any single embedding model change.

Step 3: Re-rank candidates with Claude

Vector similarity alone often returns results that are topically close but not actually useful for the question. Claude can re-rank a shortlist (10–20 candidates) by relevance, using tool calls to force structured output:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet",
    "max_tokens": 500,
    "tools": [{
      "name": "rank_results",
      "description": "Rank search results by relevance to the query",
      "input_schema": {
        "type": "object",
        "properties": {
          "ranked_ids": { "type": "array", "items": { "type": "string" } }
        },
        "required": ["ranked_ids"]
      }
    }],
    "messages": [{
      "role": "user",
      "content": "Query: why is it slow after the update\n\nCandidates:\n[1] id:a chunk:...\n[2] id:b chunk:...\n\nRank the candidate IDs from most to least relevant using the rank_results tool."
    }]
  }'

Tool use guarantees you get back a parseable list instead of free-form prose. Details on schemas and tool call handling are in the tools docs.

Only re-rank with Claude after narrowing with vector search — running an LLM over your entire corpus per query is slow and expensive. Vector search filters millions of chunks down to a few dozen; Claude's job is picking the best few from that shortlist.

Step 4: Synthesize the answer

Once you have your top 3–5 re-ranked chunks, pass them to Claude as context and ask it to answer, citing sources:

const response = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
    "content-type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-sonnet",
    max_tokens: 800,
    messages: [{
      role: "user",
      content: `Answer the question using only the context below. Cite which source each claim comes from.\n\nContext:\n${topChunks.join("\n\n")}\n\nQuestion: ${userQuery}`
    }]
  })
});

For a chat-style search experience, stream the answer back to the UI token by token instead of waiting for the full response — see the streaming docs for the event format.

Where SubToAPI fits

SubToAPI turns your existing Claude access into a standard HTTPS API with application API keys (sub_live_...), streaming, tool use, and usage metadata per key — which is exactly what the rewriting, re-ranking, and synthesis steps above need. Instead of managing Claude access per environment, you issue a scoped key per service (search-frontend, re-ranker, ingestion worker) and track usage separately in one dashboard. Start with the quickstart, check request/response shapes in the messages docs, and see pricing — plans start at €9/month with a free trial at signup.

Practical tips

• Cache query rewrites. Common queries repeat; cache the rewrite step to cut latency and cost.
• Keep re-ranking shortlists small. 10–20 candidates is the sweet spot — fewer tokens, better focus.
• Log which chunks get cited. This tells you which parts of your corpus are actually useful and which embeddings are noise.
• Separate embedding model choice from Claude model choice. You can swap embedding providers without touching the Claude-based stages of the pipeline.

questions

Does Claude have a native embeddings API? No. Claude's API handles chat completions, streaming, and tool calls, but not vector embeddings. You need a separate embeddings provider for the vector search layer.

Can Claude replace a vector database entirely? No, not for anything beyond a small, fixed set of documents. Vector similarity search scales to millions of chunks cheaply; feeding your entire corpus into Claude's context window per query doesn't scale and costs far more.

What's the minimum viable semantic search stack with Claude? Embeddings model + vector database for retrieval, plus Claude for query rewriting and final answer synthesis. Re-ranking with Claude is optional and mainly helps when vector search alone returns noisy results.