> For the complete documentation index, see [llms.txt](https://docs.envector.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.envector.io/1.4.x/sdk-user-guide/initialize/index-configuration/1.-basic-index-identification.md).

# 1. 📇 Name and Dimension

Index name and dimension are the two required basics for any index. Together they give the index its unique identity (name) and shape (vector dimension).

> Recommendation: Although you can set defaults via `init_index_config()`, it’s clearer and safer to pass `index_name` and `dim` explicitly to `create_index()`. This makes it easy to create multiple distinct indexes from a single client instance.

***

**Parameters (required)**

* `index_name` (str): Unique identifier for the index. Used in all subsequent operations (insert, search, drop, etc.).
* `dim` (int): Vector dimension for this index. Every vector you insert must match this value. For example, OpenAI’s `text-embedding-ada-002` uses `1536`.

**Why these two?**

* Identity: `index_name` is the stable handle you use to address the index on the server.
* Shape: `dim` locks the index to a single embedding size and protects against accidental inserts with the wrong length.

**Usage Example**

The snippet below shows the recommended way to pass these parameters to `create_index()` (after you’ve connected and optionally initialized index configuration).

```python
import pyenvector as ev

# Assume the client has already been initialized with ev.init()

# Create a new index named 'product-vectors' for 768-dimensional vectors
ev.create_index(index_name="product-vectors", dim=768)

# You can now create another, separate index with a different name and dimension
ev.create_index(index_name="image-embeddings", dim=1024)
```


---

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