# Building a Hybrid Search Query
The following describes how to build and run a Hybrid Search query. A Hybrid search performs both a Classic Search and a Vector Search. It then assigns a multiplier (weight) to the resulting matches and then retrieves the documents with the top highest scores for retrieval.
**To build a hybrid search query –**
Define the Hybrid Search query schema by specifying the following –
```
hybrid_query_schema = {
'params': data_point
}
```
## Running the Hybrid Search Query
Copy the following code snippet to run the lexical search query –
results = hyperspace_client.search(hybrid_query_schema,
size=5,
function_name='score_function',
collection_name=collection_name)
**Where**–
* lexical\_query\_schema – Specifies the document for similarity search and the multiplier of the return score, as described in Step 3, Defining the Classic Query Schema.
* size – Specifies the number of results to return.
* function\_name – Specifies the scoring function to be used in the Classic Search query as described in Step 1, [Creating the Scoring Function](https://docs.hyper-space.io/hyperspace-docs/projects/setting-up/building-and-running-queries/building-a-lexical-classic-search-query).
* collection\_name – Specifies the Collection in which to search.
## Assigning Score Weights
By default, all query components, vector and lexical, are assigned with weight = 1.0. To change that, add a key named "knn**"** that includes a key "query**"** for the lexical search and designated keys with vector field name ('vector\_field\_1' in the example) for the vector search.
```python
hybrid_query_schema = {
'params': data_point,
'knn': [{'field':'query','boost': 0.05},
{'field':'vector_field_1','boost': 0.6}]
}
```
In the above example, the vector\_field\_1 score will be multiplied by 0.6 in the overall score and the lexical query score by 0.05.
{% hint style="info" %}
All fields of type dense\_vector in data\_point will be included in the vector search. Unless specified otherwise in a relevant 'boost; key under the 'knn' key, the corresponding weight will be assigned the default value of 1.0.
{% endhint %}
## Building a Hybrid Score Function
The KNN score can be included in the lexical score function. To do that, use the function [`distance`](https://docs.hyper-space.io/hyperspace-docs/api-documentation/score-function/distance)('vector\_field\_1') or [knn\_filter](https://docs.hyper-space.io/hyperspace-docs/api-documentation/score-function/knn_filter)('vector\_field\_1', min\_score=params\['min\_score']). The distance function returns the KNN distance, while the knn\_filter function returns 1 if the KNN score is above min\_score and 0 otherwise. Both functions can only be used in the last return statement.
```python
def score_function_hybrid( params , doc ):
score0 = 1.0
boost = 1.0
if match('genres') and match('adult') and not match('title'):
if doc['rating'] > 7.0:
boost = 2.0
if match('genres'):
score0 += boost * rarity_sum('production_companies')
return score0 * knn_filter('vector_field_1', min_score=0.3)
```
In the above example, the score function will return score0 if the KNN score of 'vector\_field\_1' is above 0.3, and zero otherwise.
```python
def score_function_hybrid( params , doc ):
score0 = 1.0
boost = 1.0
if match('genres') and match('adult') and not match('title'):
if doc['rating'] > 7.0:
boost = 2.0
if match('genres'):
score0 += boost * rarity_sum('production_companies')
return score0 * knn_filter('vector_field_1', min_score=0.3)
```
---
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