Planned for deprecation. The Rerank model will be removed in a future release. For code search, use WarpGrep instead. WarpGrep is a search agent that handles retrieval, ranking, and file reading in one call, replacing the need to manage embeddings, vector databases, and reranking pipelines yourself.

Overview

The Rerank API improves search quality by reordering candidate results based on their relevance to a query. Our latest morph-rerank-v3 model achieves state-of-the-art performance across all coding benchmarks for accuracy:speed ratio - no rerank model comes close. It’s designed specifically for code-related content and goes beyond traditional keyword matching to understand semantic intent.

Custom API Endpoint

Unlike our Apply and Embedding models that use OpenAI-compatible APIs, the Rerank model uses a custom endpoint designed specifically for reranking tasks. It is Cohere client compatible.

Endpoint Reference

import requests

def rerank_results(query: str, documents: list[str], top_n: int = 5):
    response = requests.post(
        "https://api.morphllm.com/v1/rerank",
        headers={
            "Authorization": f"Bearer your-morph-api-key",
            "Content-Type": "application/json"
        },
        json={
            "model": "morph-rerank-v3",
            "query": query,
            "documents": documents,
            "top_n": top_n
        }
    )

    return response.json()

# Example usage
query = "How to implement authentication in Express.js"
documents = [
    "This Express.js middleware provides authentication using JWT tokens and protects routes.",
    "Express.js is a popular web framework for Node.js applications.",
    "Authentication is the process of verifying a user's identity.",
    "This example shows how to build a RESTful API with Express.js.",
    "Learn how to implement OAuth2 authentication in your Express.js application.",
    "Implementing user authentication with Passport.js in Express applications."
]

results = rerank_results(query, documents)
print(results)

async function rerankResults(query, documents, topN = 5) {
  const response = await fetch("https://api.morphllm.com/v1/rerank", {
    method: "POST",
    headers: {
      Authorization: "Bearer your-morph-api-key",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "morph-rerank-v3",
      query: query,
      documents: documents,
      top_n: topN,
    }),
  });

  return await response.json();
}

// Example usage
const query = "How to implement authentication in Express.js";
const documents = [
  "This Express.js middleware provides authentication using JWT tokens and protects routes.",
  "Express.js is a popular web framework for Node.js applications.",
  "Authentication is the process of verifying a user's identity.",
  "This example shows how to build a RESTful API with Express.js.",
  "Learn how to implement OAuth2 authentication in your Express.js application.",
  "Implementing user authentication with Passport.js in Express applications.",
];

rerankResults(query, documents).then((results) => console.log(results));

curl --request POST \
  --url https://api.morphllm.com/v1/rerank \
  --header 'Authorization: Bearer your-morph-api-key' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "morph-rerank-v3",
    "query": "How to implement authentication in Express.js",
    "documents": [
      "This Express.js middleware provides authentication using JWT tokens and protects routes.",
      "Express.js is a popular web framework for Node.js applications.",
      "Authentication is the process of verifying a user'\''s identity.",
      "This example shows how to build a RESTful API with Express.js.",
      "Learn how to implement OAuth2 authentication in your Express.js application.",
      "Implementing user authentication with Passport.js in Express applications."
    ],
    "top_n": 3
  }'

Parameters

Parameter	Type	Required	Description
`model`	string	Yes	The model ID to use for reranking. Use `morph-rerank-v3` (latest) or `morph-rerank-v3`.
`query`	string	Yes	The search query to compare documents against.
`documents`	array	No*	An array of document strings to be reranked. Required if `embedding_ids` is not provided.
`embedding_ids`	array	No*	An array of embedding IDs to rerank. Required if `documents` is not provided. Remote content storage must be enabled.
`top_n`	integer	No	Number of top results to return. Default is all documents.

* Either documents or embedding_ids must be provided.

Using Embedding IDs

When you have previously generated embeddings with Morph’s embedding model, you can use the embedding IDs for reranking:

async function rerankWithEmbeddingIds(query, embeddingIds, topN = 5) {
  const response = await fetch("https://api.morphllm.com/v1/rerank", {
    method: "POST",
    headers: {
      Authorization: "Bearer your-morph-api-key",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "morph-rerank-v3",
      query: query,
      embedding_ids: embeddingIds,
      top_n: topN,
    }),
  });

  return await response.json();
}

// Example with embedding IDs
const query = "React state management patterns";
const embeddingIds = [
  "emb_123456789",
  "emb_987654321",
  "emb_456789123",
  "emb_789123456",
  "emb_321654987",
];

rerankWithEmbeddingIds(query, embeddingIds, 3).then((results) =>
  console.log(results)
);

Remote Content Storage

To use embedding IDs for reranking, you must enable remote content storage in your account settings. This allows Morph to retrieve the content associated with each embedding ID for reranking purposes. Benefits of using embedding IDs:

Reduced payload size: Avoid sending large document content in each request
Better integration: Seamlessly works with content that was previously embedded
Security: Content is securely stored within your account’s storage
Convenience: No need to maintain document content separately from embeddings

Response Format

{
  "model": "morph-rerank-v3",
  "results": [
    {
      "index": 0,
      "document": "This Express.js middleware provides authentication using JWT tokens and protects routes.",
      "relevance_score": 0.92
    },
    {
      "index": 5,
      "document": "Implementing user authentication with Passport.js in Express applications.",
      "relevance_score": 0.87
    },
    {
      "index": 4,
      "document": "Learn how to implement OAuth2 authentication in your Express.js application.",
      "relevance_score": 0.79
    }
  ]
}

Features

morph-rerank-v3 (Latest)

State-of-the-Art Performance: Achieves SoTA results across all coding benchmarks for accuracy:speed ratio - no rerank model comes close
Unmatched Speed: Fastest reranking inference in the market while delivering superior accuracy
Enhanced Context Understanding: Improved semantic understanding of code relationships and intent

Core Features (All Models)

Code-Aware: Specifically optimized for ranking code-related content
Context Understanding: Considers the full context of both query and documents
Relevance Scoring: Provides numerical scores indicating relevance
Efficient Processing: Optimized for quick reranking of large result sets
Language Agnostic: Works with all major programming languages
Embedding ID Support: Integrates with previously generated embeddings
Remote Content Storage: Option to use securely stored content with embedding IDs

Integration with Search Systems

The Rerank model is typically used as a second-pass ranking system after an initial retrieval step:

Initial Retrieval: Use embeddings or keyword search to retrieve an initial set of candidates
Reranking: Apply the Rerank model to sort the candidates by relevance to the query
Presentation: Display the reranked results to the user

This two-stage approach combines the efficiency of initial retrieval methods with the accuracy of deep neural reranking models.

For best code search performance, we recommend using WarpGrep — our intelligent code search tool that combines fast retrieval with automatic reranking. WarpGrep handles the entire search pipeline for you, delivering 20x faster results than stock grepping.

Get Started

Models

API Reference

Integrations

Rerank Model

Overview

Custom API Endpoint

Endpoint Reference

Parameters

Using Embedding IDs

Remote Content Storage

Response Format

Features

morph-rerank-v3 (Latest)

Core Features (All Models)

Integration with Search Systems

​Overview

​Custom API Endpoint

​Endpoint Reference

​Parameters

​Using Embedding IDs

​Remote Content Storage

​Response Format

​Features

​morph-rerank-v3 (Latest)

​Core Features (All Models)

​Integration with Search Systems

Overview

Custom API Endpoint

Endpoint Reference

Parameters

Using Embedding IDs

Remote Content Storage

Response Format

Features

morph-rerank-v3 (Latest)

Core Features (All Models)

Integration with Search Systems