Embedding Model

Create semantic embeddings for code with our OpenAI-compatible API

Planned for deprecation. The Embedding 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 Embedding API converts code and text into high-dimensional vectors that capture semantic meaning. Our latest morph-embedding-v3 model delivers state-of-the-art performance on code retrieval tasks, enabling powerful search, clustering, and similarity operations for code-related applications.

Endpoint Reference

Python

from openai import OpenAI

# Initialize the OpenAI client with Morph's API endpoint
client = OpenAI(
    api_key="your-morph-api-key",
    base_url="https://api.morphllm.com/v1"
)

def get_embeddings(text: str) -> list[float]:
    response = client.embeddings.create(
        model="morph-embedding-v3",
        input=text
    )
    return response.data[0].embedding

# Example: Get embeddings for code chunks
def embed_code_chunks(code_chunks: list[str]) -> list[dict]:
    results = []

    for chunk in code_chunks:
        embedding = get_embeddings(chunk)
        results.append({
            "text": chunk,
            "embedding": embedding
        })

    return results

# Store these embeddings in a vector database of your choice

Parameters

Parameter	Type	Required	Description
model	string	Yes	The model ID to use for embedding generation. Use morph-embedding-v3 (latest) or morph-embedding-v3.
input	string or array	Yes	The text to generate embeddings for. Can be a string or an array of strings.
encoding_format	string	No	The format in which the embeddings are returned. Options are float and base64. Default is float.

Response Format

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [0.0023064255, -0.009327292, ...],
      "index": 0
    }
  ],
  "model": "morph-embedding-v3",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Features

morph-embedding-v3 (Latest)

• State-of-the-Art Performance: Achieves SoTA results across all coding benchmarks for accuracy:speed ratio - no embedding model comes close
• 1024 Dimensions: Optimal dimensionality for rich semantic representation while maintaining efficiency
• Unmatched Speed: Fastest inference in the market while delivering superior accuracy on code retrieval tasks
• Enhanced Code Understanding: Improved semantic understanding of code structure and intent
• Better Cross-Language Support: Superior understanding of relationships between different programming languages
• Improved Context Handling: Better performance on longer code snippets and complex codebases

Core Features (All Models)

• Code Optimized: Specially trained to understand programming languages and code semantics
• High Dimensionality: Creates rich embeddings that capture nuanced relationships between code concepts
• Language Support: Works with all major programming languages including Python, JavaScript, Java, Go, and more
• Contextual Understanding: Captures semantic meanings rather than just syntactic similarities
• Batch Processing: Efficiently processes multiple inputs in a single API call

Common Use Cases

• Semantic Code Search: Create powerful code search systems that understand intent
• Similar Code Detection: Find similar implementations or potential code duplications
• Code Clustering: Group related code snippets for organization or analysis
• Relevance Ranking: Rank code snippets by relevance to a query
• Concept Tagging: Automatically tag code with relevant concepts or categories