> 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.md).

# Search

## Search and Scoring API Usage

You can either use the **`search` API** directly for a one-step operation, or call the following APIs separately for more control:

* **`scoring`** – compute encrypted similarity scores.
* **`decrypt_score`** – decrypt scores on the client side.
* **`get_topk_metadata_results`** – retrieve top-k metadata entries using the decrypted scores.

Since the index metric is **inner product (IP)**, vectors must be **normalized before insertion and querying**.

***

### 1. Basic Search

The simplest way is to call the `search` API directly, which performs scoring, decryption, and metadata retrieval internally.

```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
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'}]
```

***

### 2. Scoring Only

Instead of full search, you can compute similarity scores in ciphertext.

```python
search_index = ev.Index("example_index")
query = vecs[0]

# Encrypt query for secure scoring
encrypted_query = search_index.cipher.encrypt_query(query)

# Perform scoring
scores = search_index.scoring(encrypted_query)
```

***

### 3. Decrypting Scores

Scores returned from `scoring` are encrypted. Use `decrypt_score` on the client side to reveal the plaintext values safely.

```python
dec_scores = search_index.decrypt_score(scores)
print(dec_scores[:5])
# [0.7518, 0.7420, ..., 0.9999]
```

***

### 4. Retrieving Top-k Metadata

Once you have plaintext scores, you can request the top-k metadata separately.

```python
topk_result = search_index.get_topk_metadata_results(
    dec_scores, top_k=2, output_fields=["metadata"]
)

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

***

### ✅ Summary

* Use **`search()`** if you want an all-in-one API.
* Use **`scoring()` + `decrypt_score()` + `get_topk_metadata_results()`** if you need fine-grained control over each step.
* For **multi-query search**, simply provide multiple queries at once (e.g., a 2D numpy array).
  * The results will be returned as a **list**, where each element corresponds to the result set of one query.

**Example:**

```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'}, ...]
```


---

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