I have an opensearch schema in which one of the field is called products. products contains a list of products the document is relevant about.

I have a user query parser which identifies which products the user is talking about. I want to do semantic search or hybrid search on this schema, but with a filter on the products field.
user input products: [‘prod-1’, ‘prod-2’]

value of products in a document: [‘prod-1’, ‘prod-3’] – This document must be searched because product 1 is there.

Schema:

{
    "settings": {
      "index": {
        "number_of_shards": 1,
        "number_of_replicas": 1,
        "knn": true,
        "knn.space_type": "cosinesimil"  
      }
    },
    "mappings": {
        "properties": {
"page_content": {
            "type": "text"
        },
        "products": {
            "type": "keyword"
        }
}}

Is this the right and ideal way to create schema as products are a list?

def format_query(text, query_vector, text_fields, vector_field, alpha, method, limit, filter_products=[]):
    # Define the query
    if method == 'semantic':
        # Semantic search using only the vector similarity
        base_query = {
                "knn": {
            vector_field: {
                    "vector": query_vector,  # Replace with your actual query vector components
                        "k": limit  # Adjust the number of nearest neighbors you want to retrieve
                        }
                      }
                    }
    elif method == 'hybrid':
        # Hybrid search combining text search scores and vector similarity
        base_query = {
            "function_score": {
                "query": {
                    "multi_match": {
                        "query": text,
                        "fields": text_fields
                    }
                },
                "functions": [
                    {
                        "script_score": {
                            "script": {
                                "source": f"cosineSimilarity(params.query_vector, doc['{vector_field}']) * params.alpha + (1.0 - params.alpha)",
                                "params": {"query_vector": query_vector, "alpha": alpha}
                            }
                        }
                    }
                ],
                "boost_mode": "replace"
            }
        }
    else:
        raise ValueError("Unsupported search method")
    # Define the filter for products if products is provided and not empty
    
    filter_query = []
    if filter_products:
        filter_query.append({
            "terms": {
                "products": filter_products
            }
        })
    
    # Combine the base query and filter query
    if filter_query:
        query = {
            "bool": {
                "must": [base_query],
                "filter": filter_query
            }
        }
        #query = base_query
    else:
        query = base_query
    
    print(query)
    return query

This query returns nothing if filter_products are not empty

I have tried various methods, validated the uploaded data and schema. It would be great if someone could please help me solve this.