> ## Documentation Index
> Fetch the complete documentation index at: https://docs.relace.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Create codebase vector embeddings for semantic search

## Prerequisites

* [Sign up](https://app.relace.ai) for a Relace account.
* Create an [API key](https://app.relace.ai/settings/api-keys).

<Steps>
  <Step title="Prepare Your Inputs">
    Decide what code snippets or text you want to embed. You can embed multiple strings in a single request.

    ```typescript theme={null}
    const inputs = [
      "def add(a, b): return a + b",
      "class User: pass",
      // ... more code or text
    ];
    ```
  </Step>

  <Step title="Call the Embeddings API">
    Send your inputs to the embeddings endpoint to get vector representations.

    ```typescript theme={null}
    const url = "https://embeddings.endpoint.relace.run/v1/code/embed";
    const apiKey = "[YOUR_API_KEY]";

    const headers = {
      "Authorization": `Bearer ${apiKey}`,
      "Content-Type": "application/json"
    };

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

    const response = await fetch(url, {
      method: "POST",
      headers: headers,
      body: JSON.stringify(data)
    });

    const embeddings = await response.json();
    ```
  </Step>

  <Step title="Parse Embedding Results">
    The API returns an array of embeddings, one for each input string, and usage info.

    ```json theme={null}
    {
      "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](/api-reference/embed-code/embed).
  </Step>

  <Step title="Store and Search with a Vector Database">
    Once you have your embeddings, you can store them in a vector database such as [Turbopuffer](https://turbopuffer.com/) or [Pinecone](https://www.pinecone.io/). These databases are designed for efficient similarity search over large collections of vectors.

    ### Example: Storing Embeddings

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

    ```typescript theme={null}
    import { Pinecone } from '@pinecone-database/pinecone';

    // Initialize Pinecone (example)
    const pinecone = new Pinecone({ apiKey: "[PINECONE_API_KEY]" });
    const index = pinecone.index("my-embeddings-index");

    // Prepare vectors for upsert
    const vectors = embeddings.results.map(r => ({
      id: `code-${r.index}`,
      values: r.embedding,
      metadata: { source: inputs[r.index] }
    }));

    // Upsert to Pinecone
    await index.upsert(vectors);
    ```

    Turbopuffer and other vector databases have similar APIs for inserting vectors.

    ### 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):

    ```typescript theme={null}
    // Embed the user query
    const query = "How do I add two numbers?";
    const queryData = {
      model: "relace-embed-v1",
      input: [query]
    };

    const queryResponse = await fetch(url, {
      method: "POST",
      headers: headers,
      body: JSON.stringify(queryData)
    });

    const queryResult = await queryResponse.json();
    const queryEmbedding = queryResult.results[0].embedding;

    // Query Pinecone for similar code
    const results = await index.query({
      vector: queryEmbedding,
      topK: 5,
      includeMetadata: true
    });

    for (const match of results.matches) {
      console.log(`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](https://turbopuffer.com/docs) or [Pinecone docs](https://docs.pinecone.io/).
  </Step>
</Steps>