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Similarity Search Api Sdk

Similarity Search Api Sdk

Remote
github.com/nexus-mcp-infraSearch0Updated 8w ago
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About Similarity Search Api Sdk

An API and MCP server for ranking and scoring pre-computed vectors using cosine similarity and normalized mutual information. It is intended for AI agents and callers that supply their own vectors rather than raw text.

Key features

  1. 01Cosine and NMI score fusion
  2. 02Entropy-calibrated blend weight
  3. 03Vector search and ranking
  4. 04Batch scoring of vector pairs
  5. 05Outlier vector detection

Use cases

  1. 01Ranking a corpus against a query vector
  2. 02Inspecting entropy-based alpha calibration
  3. 03Scoring many vector pairs with a fixed alpha
  4. 04Finding outlier vectors

MCP support

Provides a streamable HTTP MCP endpoint with five tools for vector ranking, scoring, entropy profiling, outlier detection, and alpha calibration.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://similarity-search-api-production.up.railway.app/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

3
ParameterTypeRequiredDescription
top_knumberNoNumber of top-ranked results to return, ordered by descending fusion score. Capped at 1000 by the core service regardless of corpus size.
n_binsnumberNoNumber of histogram bins used to discretize continuous dimensions when estimating NMI. Must be between 3 and 50.
query_vectorarrayYesDense numeric vector representing the query item. Must have the same dimensionality as all corpus_vectors entries.
alpha_overridenumberNoFixed alpha weight for cosine component in [0.0, 1.0]. If omitted, alpha is auto-calibrated from corpus entropy. Set to 1.0 to use pure cosine; 0.0 for pure NMI.
corpus_vectorsarrayYesList of dense numeric vectors forming the corpus to rank against. Each inner array must match query_vector dimensionality. Maximum 500000 entries.

Details

From the project README

Similarity Search API

Stateless similarity search over pre-computed vectors — NMI (normalized mutual information) + cosine fusion, with an entropy-calibrated blending weight computed per request. No vector database, no index to maintain, no infrastructure to run.

Available both as a plain HTTP API and as an MCP server (5 tools) for AI agents.


Important: this operates on vectors, not raw text

This API does not embed text for you. query and corpus entries are pre-computed numeric vectors (e.g. from your own embedding model). If you need text-to-vector embedding first, run that upstream and pass the resulting vectors here.


Base URL

https://similarity-search-api-production.up.railway.app

Authentication

The 3 business endpoints (/similarity/search, /similarity/calibrate-alpha/v1, /similarity/batch-score) require no API key -- only a valid x402 payment (see "Pricing" below). The X-API-Key gate was dropped from these routes 2026-08-25; Stripe metered billing explicitly excludes them too, so passing a key does nothing on these 3 routes today. The deprecated, unpriced /similarity/calibrate-alpha (no /v1 suffix) still requires X-API-Key and always 501s regardless.

/health requires no authentication.

Pricing

Two ways to pay, same endpoints:

  • x402 (pay-per-call, USDC on Base) — currently on Base Sepolia testnet, $0.01/call, no account or API key required beyond the x402 payment flow itself. A request without payment gets 402 Payment Required with the payment details in the payment-required response header.
  • Stripe (metered billing) — for callers provisioned with an API key and a Stripe customer on the account.

Endpoints

POST /similarity/search

Rank a corpus against a query vector using the composite score.

{
  "query": { "id": "q1", "vector": [0.12, -0.4, 0.91, "..."] },
  "corpus": [
    { "id": "doc1", "vector": [0.10, -0.35, 0.88, "..."] },
    { "id": "doc2", "vector": [0.55, 0.02, -0.14, "..."] }
  ],
  "top_k": 10,
  "nmi_bins": 10,
  "alpha_override": null
}

All vectors in query and corpus must share the same dimensionality (2-4096 dims). top_k up to 1000. alpha_override (optional) pins the cosine/NMI blend weight instead of calibrating it from corpus entropy.

Response:

{
  "results": [
    { "id": "doc1", "composite_score": 0.91, "cosine_similarity": 0.89, "nmi_score": 0.94, "rank": 1 }
  ],
  "calibrated_alpha": 0.73,
  "corpus_entropy": 3.85,
  "query_id": "q1",
  "corpus_size": 2,
  "latency_ms": 43,
  "request_fingerprint": "..."
}
POST /similarity/calibrate-alpha/v1

Compute the entropy-calibrated alpha for a corpus without running a full search - useful for inspecting/debugging calibration behavior before committing to a search call.

POST /similarity/batch-score

Score up to 10,000 (vector_a, vector_b) pairs with a fixed alpha - no corpus/entropy overhead.

{
  "pairs": [[[0.1, 0.2], [0.15, 0.19]]],
  "alpha": 0.5,
  "nmi_bins": 10
}
GET /health

Liveness probe. No auth required. Not billed (excluded from both Stripe and x402).

Note: POST /similarity/calibrate-alpha (without /v1) is a deprecated alias kept for backward compatibility - use /similarity/calibrate-alpha/v1.


MCP tools

Connect an MCP-compatible client (Claude, Cursor, etc.) to the streamable HTTP endpoint at: https://similarity-search-api-production.up.railway.app/mcp

Exposes 5 tools: rank_items_by_nmi_cosine_fusion, estimate_corpus_entropy_profile, score_pair_nmi_cosine, find_outlier_vectors_by_nmi_deficit, calibrate_alpha_from_query_entropy.


The scoring method

composite_score = alpha * cosine(query, doc) + (1 - alpha) * NMI_normalized(query, doc)

alpha is calibrated per-request from the Shannon entropy of the submitted corpus (unless you pass alpha_override) - high-entropy (dispersed) corpora lean toward cosine; low-entropy (dense/narrow) corpora lean toward NMI, which captures statistical dependence that cosine's geometric angle misses.


Limits

  • Corpus size: up to 500,000 items per request
  • Vector dimensionality: 2-4,096
  • batch-score pairs: up to 10,000 per request

Comments

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