The weaviate-filter package provides a convenient way to build GraphQL filters for Weaviate. The main class, GraphQLFilter, allows you to create complex filters by adding conditions and operands, and then retrieve the final filter object.

To install the weaviate-filter package, you can use pip:

pip install weaviate-filter

To use the weaviate-filter package, you first need to import the GraphQLFilter class and create an instance of it:

from weaviate_filter import GraphQLFilter

filter = GraphQLFilter()

You can add a condition to the filter by using the add_condition method:

filter.add_condition(path="name", operator="Like", value_type="valueString", value="John")

• path: The path of the property (e.g., "name").
• operator: The operator to use for the condition (e.g., "Like").
• value_type: The type of the value (e.g., "valueString").
• value: The value for the condition (e.g., "John").

You can add operands to the filter by using the add_operands method:

filter.add_operands(operator="And", operand1, operand2)

• operator: The operator to use for the operands (e.g., "And").
• operands: The operands to add.

You can add a list of conditions as operands to the filter by using the add_list_conditions_as_operands method:

filter.add_list_conditions_as_operands(path="name", operator="Like", value_type="valueString", values_list=["John", "Doe"])

• path: The path of the property (e.g., "name").
• operator: The operator to use for the conditions (e.g., "Like").
• value_type: The type of the values (e.g., "valueString").
• values_list: The list of values for the conditions (e.g., ["John", "Doe"]).
• condition_operator (optional): The operator to use for the operands (e.g., "Or"). Defaults to "Or".

You can add conditions for multiple paths as operands to the filter by using the add_conditions_for_multiple_paths method:

filter.add_conditions_for_multiple_paths(paths=["name", "age"], operator="Equal", value_type="valueString", value="John")

• paths: The paths of the properties (e.g., ["name", "age"]).
• operator: The operator to use for the conditions (e.g., "Equal").
• value_type: The type of the value (e.g., "valueString").
• value: The value for the conditions (e.g., "John").
• condition_operator (optional): The operator to use for the operands (e.g., "Or"). Defaults to "Or".

Once you have added all the conditions and operands, you can get the final filter object by using the get_filter method:

final_filter = filter.get_filter(operator="And")

• operator (optional): The operator for the filter (e.g., "And"). Defaults to "And".

The get_filter method will return a dictionary with the final filter object.

Here is a complete example of using the weaviate-filter package:

from weaviate_filter import GraphQLFilter

# Create a filter
filter = GraphQLFilter()

# Add a condition
filter.add_condition(path="name", operator="Like", value_type="valueString", value="John")

# Add operands
operand1 = {"path": "age", "operator": "GreaterThan", "valueInt": 30}
operand2 = {"path": "age", "operator": "LessThan", "valueInt": 50}
filter.add_operands(operator="And", operand1, operand2)

# Add list conditions as operands
filter.add_list_conditions_as_operands(path="name", operator="Like", value_type="valueString", values_list=["John", "Doe"])

# Add conditions for multiple paths
filter.add_conditions_for_multiple_paths(paths=["name", "age"], operator="Equal", value_type="valueString", value="John")

# Get the final filter object
final_filter = filter.get_filter(operator="And")

print(final_filter)

Output:

{'operator': 'And', 'operands': [{'operator': 'And', 'operands': [{'path': 'age', 'operator': 'GreaterThan', 'valueInt': 30}, {'path': 'age', 'operator': 'LessThan', 'valueInt': 50}]}, {'operator': 'Or', 'operands': [{'path': 'name', 'operator': 'Like', 'valueString': 'John'}, {'path': 'name', 'operator': 'Like', 'valueString': 'Doe'}]}, {'operator': 'Or', 'operands': [{'path': ['name'], 'operator': 'Equal', 'valueString': 'John'}, {'path': ['age'], 'operator': 'Equal', 'valueString': 'John'}]}]}

1. Import Required Modules:

   import weaviate
   from sentence_transformers import SentenceTransformer
   from weaviate_filter import GraphQLFilter

   Import the required modules. This includes the Weaviate client, the Sentence Transformers library for encoding the query, and the GraphQLFilter class for constructing the query filter.

2. Initialize SentenceTransformer Model:

   model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

   Initialize the SentenceTransformer model. This model is used to convert the text query into a vector.

3. Setup Weaviate Client:

   WEAVIATE_URL = ""
   WEAVIATE_API_KEY = ""
   client = weaviate.Client(
       url=WEAVIATE_URL,  # Replace w/ your endpoint
       auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),  # Replace w/ your Weaviate instance API key
   )

   Set up the Weaviate client. You need to replace WEAVIATE_URL and WEAVIATE_API_KEY with your own Weaviate instance's URL and API key.

4. Define Filters:

   genders = ["M", "F"]
   countries = ["Germany"]
   labels = ["dlf", "building"]

   Define the filters you want to apply. In this example, we are filtering for entries with gender "M" or "F", birth_country "Germany", and labels "dlf" or "building" with a value greater than or equal to 1.

5. Create Filter:

   filter = GraphQLFilter()
   filter.add_list_conditions_as_operands(["gender"], "Equal", "valueText", genders, "Or")
   filter.add_list_conditions_as_operands(["birth_country"], "Equal", "valueText", countries, "Or")
   filter.add_conditions_for_multiple_paths(labels, "GreaterThanEqual", "valueNumber", 1, "Or")
   where_filter = filter.get_filter(operator="Or")

   Create the filter using the GraphQLFilter class. The add_list_conditions_as_operands method is used to add a list of conditions as operands with a specified operator ("And" or "Or"). The add_conditions_for_multiple_paths method is used to add conditions for multiple paths with a specified operator. Finally, the get_filter method is used to get the final filter with a specified operator.

6. Define and Encode Query:

   query = "We were hungry"
   query_vector = model.encode(query)

   Define the query and encode it into a vector using the SentenceTransformer model.

7. Create nearVector Dictionary:

   nearVector = {
   "vector": query_vector
           }

   Create the nearVector dictionary with the encoded query vector.

8. Define Field Options:

   field_options = ["gender", "window_text", "birth_country", "dlf", "building"]

   Define the fields you want to retrieve in the query.

9. Perform Query:

   result = client.query.get(
           "Window", field_options
       ).with_near_vector(
           nearVector
       ).with_limit(2).with_additional(['certainty']
       ).with_where(where_filter).do()

   Perform the query using the Weaviate client. The with_near_vector method is used to add the nearVector to the query. The with_limit method is used to limit the number of results. The with_additional method is used to add additional fields to the query. The with_where method is used to add the where filter to the query.

10. Print Results:

    for r in result["data"]["Get"]["Window"]:
        print(r)

    Print the results of the query.