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Prerequisites

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1. Prepare Your Inputs

Decide what code snippets or text you want to embed. You can embed multiple strings in a single request. 
inputs = [
    "def add(a, b): return a + b",
    "class User: pass",
    # ... more code or text
]

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2. Call the Embeddings API

Send your inputs to the embeddings endpoint to get vector representations. 
import requests

url = "https://embeddings.endpoint.relace.run/v1/code/embed"
api_key = "[YOUR_API_KEY]"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

data = {
    "model": "relace-embed-v1",
    "input": inputs,
    # Optional: "output_dtype": "float"  # or "binary"
}

response = requests.post(url, headers=headers, json=data)
embeddings = response.json()

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3. Parse Embedding Results

The API returns an array of embeddings, one for each input string, and usage info. 
{
  "results": [
    { "index": 0, "embedding": [0.123, 0.456, 0.789] },
    { "index": 1, "embedding": [0.234, 0.567, 0.89] }
  ],
  "usage": {
    "total_tokens": 8
  }
}
For more details and options, see the API reference.

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4. Store and Search with a Vector Database

Once you have your embeddings, you can store them in a vector database such as Turbopuffer or Pinecone. These databases are designed for efficient similarity search over large collections of vectors.

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Example: Storing Embeddings

Most vector databases let you upsert (insert or update) vectors with an ID and metadata: 
import pinecone

# Initialize Pinecone (example)
pinecone.init(api_key="[PINECONE_API_KEY]", environment="us-west1-gcp")
index = pinecone.Index("my-embeddings-index")

# Prepare vectors for upsert
vectors = [
    (f"code-{r['index']}", r["embedding"], {"source": inputs[r["index"]]})
    for r in embeddings["results"]
]

# Upsert to Pinecone
index.upsert(vectors)
Turbopuffer and other vector databases have similar APIs for inserting vectors.

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Example: Semantic Search with a User Query

To search, embed the user query using the same API, then query the vector database for the most similar vectors (using cosine similarity or the database’s default metric): 
# Embed the user query
query = "How do I add two numbers?"
query_data = {
    "model": "relace-embed-v1",
    "input": [query]
}
query_response = requests.post(url, headers=headers, json=query_data)
query_embedding = query_response.json()["results"][0]["embedding"]

# Query Pinecone for similar code
results = index.query(vector=query_embedding, top_k=5, include_metadata=True)
for match in results["matches"]:
    print(f"Score: {match['score']}, Source: {match['metadata']['source']}")
This will return the most relevant code snippets or documents from your database, ranked by similarity to the user query. For more details, see the Turbopuffer docs or Pinecone docs.