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

import { OpenAI } from "openai";

const client = new OpenAI({
  apiKey: "your-morph-api-key",
  baseURL: "https://api.morphllm.com/v1",
});

async function getEmbeddings(text) {
  const response = await client.embeddings.create({
    model: "morph-embedding-v3",
    input: text,
  });

  return response.data[0].embedding;
}

// Example: Get embeddings for code chunks
async function embedCodeChunks(codeChunks) {
  const results = [];

  for (const chunk of codeChunks) {
    const embedding = await getEmbeddings(chunk);
    results.push({
      text: chunk,
      embedding: embedding,
    });
  }

  return results;
}

// Example usage
const codeChunks = [
  "function calculateSum(a, b) { return a + b; }",
  "class UserRepository { constructor(database) { this.db = database; } }",
];

embedCodeChunks(codeChunks).then((results) => console.log(results));

curl --request POST \
  --url https://api.morphllm.com/v1/embeddings \
  --header 'Authorization: Bearer your-morph-api-key' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "morph-embedding-v3",
    "input": "Your code or text to embed"
  }'