> 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/search/basic-search.md).

# Basic Search

The simplest way is to call the `search` API directly, which performs **all three steps of a full search** in a single call:

1. **Scoring** – compute similarity scores in ciphertext
2. **Decryption** – decrypt the score vector locally on the client
3. **Metadata retrieval** – request the top-k metadata entries mapped to the scores

> **Note**: Decryption is always performed on the client side with the secret key.\
> This ensures true **end-to-end encryption**, meaning sensitive data is never exposed to the server.

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

# Prepare normalized data
vecs = np.random.rand(100, 512)
vecs = vecs / np.linalg.norm(vecs, axis=1, keepdims=True)

# Initialize and insert
ev.init(address="localhost:50050", key_path="keys", key_id="example")
index = ev.create_index("example_index", dim=512)
metadata = [f"metadata_{i}" for i in range(100)]
index.insert(vecs, metadata)

# Perform search with a single query
search_index = ev.Index("example_index")
query = vecs[0]
result = search_index.search(query, top_k=2, output_fields=["metadata"])[0]

print(result)
# [{'id': 1, 'score': 0.9999, 'metadata': 'metadata_0'},
#  {'id': 59, 'score': 0.7888, 'metadata': 'metadata_58'}]
```

***

### Multi-query Search

You can also search with multiple queries at once by providing a **2D numpy array**.\
The results will be returned as a **list**, where each element corresponds to the result set of one query.

```python
# Multi-query example
queries = vecs[:3]   # Take 3 queries
results = search_index.search(queries, top_k=2, output_fields=["metadata"])

for i, r in enumerate(results):
    print(f"Query {i} results:", r)

# Output:
# Query 0 results: [{'id': 1, 'score': 0.9999, 'metadata': 'metadata_0'}, ...]
# Query 1 results: [{'id': 42, 'score': 0.8123, 'metadata': 'metadata_41'}, ...]
# Query 2 results: [{'id': 59, 'score': 0.7888, 'metadata': 'metadata_58'}, ...]
```


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.envector.io/1.4.x/sdk-user-guide/search/basic-search.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.