Jun 25, 2024 · Jun 25, 2024 · Jun 25, 2024 · Jun 25, 2024
diff --git a/pgml-cms/docs/api/client-sdk/search.md b/pgml-cms/docs/api/client-sdk/search.md
 {% endtabs %}

 The above query would filter out all documents that do not have a key `special` with a value `True` or (have a key `user_id` equal to 1 and a key `user_score` less than 100).

 ## **Re-ranking**

 Vector search results can be reranked in the same query they are retrieved in. To enable this, provide the `rerank` key.

 {% tabs %}
 {% tab title="JavaScript" %}
 ```javascript
 const results = await collection.vector_search(
  {
    query: {
      fields: {
        body: {
          query: "What is the best database?", parameters: {
          prompt:
              "Represent this sentence for searching relevant passages: ",
          }
        },
      },
    },
    rerank: {
        model: "mixedbread-ai/mxbai-rerank-base-v1",
        query: "What is the best database?",
        num_documents_to_rerank: 100,
    },
    limit: 5,
  },
  pipeline,
 );
 ```
 {% endtab %}

 {% tab title="Python" %}
 ```python
 results = await collection.vector_search(
    {
        "query": {
            "fields": {
                "body": {
                    "query": "What is the best database?",
                    "parameters": {
                        "prompt": "Represent this sentence for searching relevant passages: ",
                    },
                },
            },
        },
        "rerank": {
            "model": "mixedbread-ai/mxbai-rerank-base-v1",
            "query": "What is the best database",
            "num_documents_to_rerank": 100,
        },
        "limit": 5,
    },
    pipeline,
 )
 ```
 {% endtab %}

 {% tab title="Rust" %}
 ```rust
 let results = collection
    .vector_search(
        serde_json::json!({
            "query": {
                "fields": {
                    "body": {
                        "query": "What is the best database?",
                        "parameters": {
                            "prompt": "Represent this sentence for searching relevant passages: ",
                        },
                    },
                },
            },
            "rerank": {
                "model": "mixedbread-ai/mxbai-rerank-base-v1",
                "query": "What is the best database",
                "num_documents_to_rerank": 100,
            },
            "limit": 5,
        })
        .into(),
        &mut pipeline,
    )
    .await?;
 ```
 {% endtab %}

 {% tab title="C" %}
 ```cpp
 r_size = 0;
 char **results = pgml_collectionc_vector_search(collection, "{\
  \"query\": {\
    \"fields\": {\
      \"body\": {\
        \"query\": \"What is the best database?\",\
        \"parameters\": {\
          \"prompt\": \"Represent this sentence for searching relevant passages: \"\
        }\
      }\
    }\
  },\
  \"rerank\": {\
    \"model\": \"mixedbread-ai/mxbai-rerank-base-v1\",\
    \"query\": \"What is the best database\",\
    \"num_documents_to_rerank\": 100\
  },\
  \"limit\": 5\
 }",
 pipeline, &r_size);
 ```
 {% endtab %}
 {% endtabs %}

 This query will first get the top 100 documents from the initial vector search and then rerank them using the `mixedbread-ai/mxbai-rerank-base-v1` cross-encoder.

 You can specify the number of documents to rerank with the `num_documents_to_rerank` parameter. The query returns the top `limit` results after re-ranking.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -641,3 +641,119 @@ char **results = pgml_collectionc_vector_search(collection, "{\
		{% endtabs %}

		The above query would filter out all documents that do not have a key `special` with a value `True` or (have a key `user_id` equal to 1 and a key `user_score` less than 100).

		## Re-ranking

		Vector search results can be reranked in the same query they are retrieved in. To enable this, provide the `rerank` key.

		{% tabs %}
		{% tab title="JavaScript" %}
		```javascript
		const results = await collection.vector_search(
		{
		query: {
		fields: {
		body: {
		query: "What is the best database?", parameters: {
		prompt:
		"Represent this sentence for searching relevant passages: ",
		}
		},
		},
		},
		rerank: {
		model: "mixedbread-ai/mxbai-rerank-base-v1",
		query: "What is the best database?",
		num_documents_to_rerank: 100,
		},
		limit: 5,
		},
		pipeline,
		);
		```
		{% endtab %}

		{% tab title="Python" %}
		```python
		results = await collection.vector_search(
		{
		"query": {
		"fields": {
		"body": {
		"query": "What is the best database?",
		"parameters": {
		"prompt": "Represent this sentence for searching relevant passages: ",
		},
		},
		},
		},
		"rerank": {
		"model": "mixedbread-ai/mxbai-rerank-base-v1",
		"query": "What is the best database",
		"num_documents_to_rerank": 100,
		},
		"limit": 5,
		},
		pipeline,
		)
		```
		{% endtab %}

		{% tab title="Rust" %}
		```rust
		let results = collection
		.vector_search(
		serde_json::json!({
		"query": {
		"fields": {
		"body": {
		"query": "What is the best database?",
		"parameters": {
		"prompt": "Represent this sentence for searching relevant passages: ",
		},
		},
		},
		},
		"rerank": {
		"model": "mixedbread-ai/mxbai-rerank-base-v1",
		"query": "What is the best database",
		"num_documents_to_rerank": 100,
		},
		"limit": 5,
		})
		.into(),
		&mut pipeline,
		)
		.await?;
		```
		{% endtab %}

		{% tab title="C" %}
		```cpp
		r_size = 0;
		char **results = pgml_collectionc_vector_search(collection, "{\
		\"query\": {\
		\"fields\": {\
		\"body\": {\
		\"query\": \"What is the best database?\",\
		\"parameters\": {\
		\"prompt\": \"Represent this sentence for searching relevant passages: \"\
		}\
		}\
		}\
		},\
		\"rerank\": {\
		\"model\": \"mixedbread-ai/mxbai-rerank-base-v1\",\
		\"query\": \"What is the best database\",\
		\"num_documents_to_rerank\": 100\
		},\
		\"limit\": 5\
		}",
		pipeline, &r_size);
		```
		{% endtab %}
		{% endtabs %}

		This query will first get the top 100 documents from the initial vector search and then rerank them using the `mixedbread-ai/mxbai-rerank-base-v1` cross-encoder.

		You can specify the number of documents to rerank with the `num_documents_to_rerank` parameter. The query returns the top `limit` results after re-ranking.