> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# cohere_rerank_simple()

> Rerank search results by semantic relevance with a simplified response

Rerank a list of documents by semantic relevance to a query. This function returns a simplified table format with just
the document index, text, and relevance score.

## Samples

### Rerank search results

Improve search relevance by reordering results:

```sql theme={"dark"}
SELECT *
FROM ai.cohere_rerank_simple(
    'rerank-english-v3.0',
    'What is a database?',
    ARRAY[
        'PostgreSQL is a relational database',
        'Python is a programming language',
        'A database stores and manages data',
        'JavaScript is used for web development'
    ]
)
ORDER BY relevance_score DESC;
```

Returns:

```text theme={"dark"}
 index |              document              | relevance_score
-------+------------------------------------+-----------------
     2 | A database stores and manages data |        0.95
     0 | PostgreSQL is a relational database|        0.89
     1 | Python is a programming language   |        0.12
     3 | JavaScript is used for web dev     |        0.08
```

### Limit results with top\_n

Return only the most relevant documents:

```sql theme={"dark"}
SELECT *
FROM ai.cohere_rerank_simple(
    'rerank-english-v3.0',
    'time-series databases',
    ARRAY[
        'TimescaleDB extends PostgreSQL for time-series data',
        'MongoDB is a document database',
        'Time-series data has temporal ordering',
        'Redis is an in-memory cache'
    ],
    top_n => 2
)
ORDER BY relevance_score DESC;
```

### Use in a search pipeline

Combine vector search with reranking:

```sql theme={"dark"}
WITH vector_results AS (
    SELECT content, embedding <=> query_embedding AS distance
    FROM documents
    ORDER BY embedding <=> query_embedding
    LIMIT 20
)
SELECT content, relevance_score
FROM vector_results
CROSS JOIN LATERAL ai.cohere_rerank_simple(
    'rerank-english-v3.0',
    'user query here',
    ARRAY_AGG(content) OVER (),
    top_n => 5
) AS reranked
WHERE content = document
ORDER BY relevance_score DESC;
```

## Arguments

| Name                 | Type      | Default | Required | Description                                              |
| -------------------- | --------- | ------- | -------- | -------------------------------------------------------- |
| `model`              | `TEXT`    | -       | ✔        | The Cohere reranking model (e.g., `rerank-english-v3.0`) |
| `query`              | `TEXT`    | -       | ✔        | The search query                                         |
| `documents`          | `TEXT[]`  | -       | ✔        | Array of documents to rerank                             |
| `api_key`            | `TEXT`    | `NULL`  | ✖        | Cohere API key. If not provided, uses configured secret  |
| `api_key_name`       | `TEXT`    | `NULL`  | ✖        | Name of the secret containing the API key                |
| `top_n`              | `INT`     | `NULL`  | ✖        | Return only the top N most relevant documents            |
| `max_tokens_per_doc` | `INT`     | `NULL`  | ✖        | Maximum tokens per document (for truncation)             |
| `verbose`            | `BOOLEAN` | `FALSE` | ✖        | Enable verbose logging for debugging                     |

## Returns

`TABLE`: A table with the following columns:

| Column            | Type     | Description                                                 |
| ----------------- | -------- | ----------------------------------------------------------- |
| `index`           | `INT`    | Original index of the document in the input array (0-based) |
| `document`        | `TEXT`   | The document text                                           |
| `relevance_score` | `FLOAT8` | Relevance score (0.0 to 1.0, higher is more relevant)       |

## Related functions

* [`cohere_rerank()`][cohere_rerank]: full API response with additional metadata
* [`cohere_embed()`][cohere_embed]: generate embeddings for vector search

[cohere_embed]: /api-reference/pgai/model-calling/cohere/cohere_embed

[cohere_rerank]: /api-reference/pgai/model-calling/cohere/cohere_rerank