> 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/insert.md).

# Insert

## Inserting Data into Index

There are multiple ways to insert data into an index:

* Plaintext vectors (with automatic index encryption)
* Pre-encrypted vectors (CipherBlock)
* Vectors with metadata

⚠️ **Note:** Since the index metric is *inner product (IP)*, vectors must be normalized before insertion. Normalization ensures that similarity scores reflect cosine similarity rather than raw magnitude differences.

***

## 1. Inserting Plaintext (with Index Encryption)

You can insert plaintext vectors in bulk using `list[list[float]]`, `list[np.ndarray]`, or a 2D `numpy.ndarray`.\
If `index_encryption` is enabled in the index configuration, the vectors will be automatically encrypted before insertion.\
For detailed configuration, refer to *Index Configuration*.

Example

```python
import numpy as np
import pyenvector as ev

ev.init(address="localhost:50050", key_path="keys", key_id="example")

index = ev.create_index("example_index", dim=512)

vecs = np.random.rand(100, 512)
# vecs = [[0.001, 0.02, ..., 0.127]] # List[List[float]]
# vecs = [np.random.rand(512)] * 100 # List[np.ndarray]

# Normalize vectors before insertion (required for inner product metric)
vecs = vecs / np.linalg.norm(vecs, axis=1, keepdims=True)

index.insert(vecs)
# Output: 
# Index(
#    IndexConfig(
#        index_name="example_index",
#        dim=512,
#        key_path='keys',
#        key_id='example',
#    ),
#  num_entities=100,
#  cipher=<pyenvector.crypto.cipher.Cipher object at 0x7f1776199d60>
#)
        
```

## 2. Inserting with Metadata

You can insert vectors together with metadata.\
Metadata should be provided as a list of strings. If `metadata_encryption=True` is enabled in the index configuration, the metadata will be encrypted before being stored.

Example

<pre class="language-python"><code class="lang-python">import numpy as np
import pyenvector as ev

ev.init(address="localhost:50050", key_path="keys", key_id="example")

index = ev.create_index("example_index", dim=512)

vecs = np.random.rand(100, 512)
# Normalize vectors for inner product metric
vecs = vecs / np.linalg.norm(vecs, axis=1, keepdims=True)

metadatas = [f"metadata_{i}" for i in range(100)]

index.insert(vecs, metadata=metadatas)
# Output: 
# Index(
#    IndexConfig(
<strong>#        index_name="example_index",
</strong>#        dim=512,
#        key_path='keys',
#        key_id='example',
#    ),
#  num_entities=100,
#  cipher=&#x3C;pyenvector.crypto.cipher.Cipher object at 0x7f1776199d60>
#)
</code></pre>

***

## When Does Inserted Data Become Searchable?

By default, `Index.insert()` submits both the **ingest** and **indexing** stages and loads the result, so the data becomes searchable soon after the call returns. For high-throughput pipelines, you can defer indexing with `execute_until="flush"` and trigger it later via `Index.indexing()` — see [Index Operation Lifecycle](/1.4.x/sdk-user-guide/advanced-user-guide/index-operation-lifecycle.md) for the full lifecycle and `await_completion` options.


---

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