> 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/scoring.md).

# Scoring

A **full search** consists of three steps:

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

The `scoring` API corresponds to the **first step** in this process. Instead of performing the entire full search pipeline, you can call `scoring()` directly to obtain the encrypted similarity scores only.

The query can be:

* A **plaintext query** (if `query_encryption="plain"` in the index configuration), or
* A **ciphertext query** (manually encrypted, or automatically if `query_encryption="cipher"`). Not currently supported.

***

#### Scoring with Plaintext Query

**Example**

```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 with plaintext query option
ev.init(
    address="localhost:50050",
    key_path="keys",
    key_id="example",
)

# Create index and insert vectors
index = ev.create_index("example_index", dim=512)
metadata = [f"metadata_{i}" for i in range(100)]
index.insert(vecs, metadata)

# Perform scoring with a plaintext query
search_index = ev.Index("example_index")
query = vecs[0]
scores = search_index.scoring(query)[0]

print(scores)
# Output:
# <pyenvector.crypto.block.CipherBlock object at 0x7fac8052d6d0>
```

***

📌 **Summary**

* `scoring()` returns an **encrypted vector of similarity scores** (one encrypted score for each vector stored in the index).
* With `query_encryption="plain"`, you can pass a **plaintext query**.


---

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