> For the complete documentation index, see [llms.txt](https://docs.tonic.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tonic.ai/textual/textual-integrations/snowflake-native-app-and-spcs/textual-spcs.md). # Using Textual with Snowpark Container Services directly Snowpark Container Services (SPCS) allow developers to run containerized workloads directly within Snowflake. Because Tonic Textual is distributed using a private Docker repository, you can use these images in SPCS to run Textual workloads. It is quicker to use the [Snowflake Native App](/textual/textual-integrations/snowflake-native-app-and-spcs/about-the-snowflake-native-app.md), but SPCS allows for more customization. ## Add images to the repository To use the Textual images, you must add them to Snowflake. The Snowflake [documentation](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-registry-repository) and [tutorial](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/tutorial-1) walks through the process in great detail, but the basic steps are as follows: 1. [Set up an image repository in Snowflake](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/common-setup#create-snowflake-objects). 2. To pull down the required images, you must have access to our private Docker image repository on [Quay.io](http://quay.io/). You should have been provided credentials during onboarding.\ \ If you require new credentials, or you experience issues accessing the repository, contact .\ \ Once you have access, pull down the following images: * `textual-snowflake` * `textual-roberta-gpu` 3. [Use the Docker CLI to upload the images to the image repository.](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/tutorial-1#build-an-image-and-upload) The images are now available in Snowflake. ## Create the Textual service The Textual service exposes the functions that are used to redact sensitive values in Snowflake. You create the Textual service in `textual_api_pool`. ```sql DROP SERVICE IF EXISTS textual; CREATE SERVICE textual IN COMPUTE POOL textual_api_pool FROM SPECIFICATION $$ spec: containers: - name: textual image: your_image_repository_url/textual-snowflake:latest env: SOLAR_SECRET: your_encryption_string SOLAR_ROBERTA_URL: https://textual-roberta-service:8801 ENVIRONMENT_NAME: your_environment_name endpoints: - name: textual port: 9002 $$ QUERY_WAREHOUSE='TEXTUAL' MIN_INSTANCES=1 MAX_INSTANCES=1; ``` ## Create the Roberta service Next, you create the Roberta service, which recognizes personally identifiable information (PII) and other sensitive values in text. You create the Roberta service in `textual_ml_pool`. ### Without model-based custom entity types If you do not plan to use the service to detect model-based custom entity types, then: ```sql DROP SERVICE IF EXISTS textual_roberta_service; CREATE SERVICE textual_roberta_service IN COMPUTE POOL textual_ml_pool FROM SPECIFICATION $$ spec: containers: - name: textualml image: your_image_repository_url/textual-roberta-gpu:latest env: ENVIRONMENT_NAME: your_environment_name $$ MIN_INSTANCES=1 MAX_INSTANCES=1; ``` ### With model-based custom entity types If you do plan to detect model-based custom entity types, then when you create the service, you must create a volume mount to store the models. #### Obtain the volume UID and GID The configuration includes the volume UID and GID. First, create the version of the service without the custom entity types, with an added command in the `containers` section: 
DROP SERVICE IF EXISTS textual_roberta_service;
CREATE SERVICE textual_roberta_service
  IN COMPUTE POOL textual_ml_pool
  FROM SPECIFICATION $$
    spec:
      containers:
      - name: textualml
        image: your_image_repository_url/textual-roberta-gpu:latest
        resources:
          requests:
            nvidia.com/gpu: 1
          limits:
            nvidia.com/gpu: 1
        env:
          ENVIRONMENT_NAME: your_environment_name
          command:
          - /bin/sh
          - -c
          - "id && sleep 600"
     $$
   MIN_INSTANCES=1
   MAX_INSTANCES=1;
When the service starts, it returns the values of the UID and GID. #### Creating the service with the volume mount After you obtain the UID and GID, to create the service with the volume mount. {% code overflow="wrap" %} ```sql DROP SERVICE IF EXISTS textual_roberta_service; CREATE SERVICE textual_roberta_service IN COMPUTE POOL textual_ml_pool FROM SPECIFICATION $$ spec: containers: - name: textualml image: your_image_repository_url/textual-roberta-gpu:latest env: ENVIRONMENT_NAME: your_environment_name volumeMounts: - name: stage-volume # Must match the volume name below mountPath: /usr/bin/textual/custom_models # Path in the container file system endpoints: - name: textualmlendpoint port: 8801 protocol: TCP volumes: - name: stage-volume source: "@textual_db.public.textual_models" uid: gid: $$ MIN_INSTANCES=1 MAX_INSTANCES=1; ``` {% endcode %} ## Create functions You can create custom SQL functions that use your API and ML services. These functions are accessible from directly within Snowflake. {% code overflow="wrap" %} ```sql CREATE OR REPLACE FUNCTION public.textual_redact(TEXT VARCHAR, REDACT_OPTIONS VARIANT) RETURNS varchar SERVICE = CONTEXT_HEADERS = (current_user, current_account) ENDPOINT = textual AS '/api/redact'; CREATE OR REPLACE FUNCTION public.textual_redact(TEXT VARCHAR) RETURNS varchar SERVICE = CONTEXT_HEADERS = (current_user) ENDPOINT = textual AS '/api/redact'; CREATE OR REPLACE FUNCTION public.textual_parse(PATH VARCHAR, STAGE_NAME VARCHAR, md5sum VARCHAR) returns varchar SERVICE=core.textual_service CONTEXT_HEADERS = (current_user) endpoint=textual MAX_BATCH_ROWS=10 as '/api/parse/start'; ``` {% endcode %} ## Example usage It can take a couple of minutes for the containers to start. After the containers are started, you can use the functions that you created in Snowflake. To test the functions, use an existing table. You can also create this simple test table: ```sql CREATE TABLE Messages ( Message TEXT ); INSERT INTO Messages (Message) VALUES ('Hi my name is John Smith and I drive a Honda'); INSERT INTO Messages (Message) VALUES ('Hi John, mine is Jane Doe'); ``` You use the function in the same way as any other user-defined function. You can pass in additional configuration to determine how to process specific built-in and model-based custom entity types. For example: {% code overflow="wrap" %} ```sql SELECT public.textual_redact('Hi, my name is John Smith and I drive a Honda.', PARSE_JSON('{"generatorConfig":{"CUSTOM_CAR_MAKE":"Redaction","NAME_GIVEN":"Synthesis"},"customPiiEntityIds":["CUSTOM_CAR_MAKE|org1|entity1|model2"]}')); ``` {% endcode %} Note that to specify custom entity types: * You must first [mount the entity types to a Snowflake stage](/textual/textual-integrations/snowflake-native-app-and-spcs/snowflake-app-setup.md#mounting-model-based-custom-entity-types). * You provide the identifier in both `generatorConfig` and `customPiiEntityIds`. * In `customPiiEntityIds`, the model path must match the mounted folder structure. By default, the function redacts the entity values. In other words, it replaces the values with a placeholder that includes the type. `Synthesis` indicates to replace the value with a realistic replacement value. `Off` indicates to leave the value as is. The `textual_redact` function works identically to the [`textual_redact` function in the Snowflake Native App](/textual/textual-integrations/snowflake-native-app-and-spcs/snowflake-app-use.md#snowflake-app-textual-redact). Here are response for the text examples above, showing redacted and synthesized values for the entity types:
MessageRedactedSynthesized
Hi my name is John Smith and I drive a HondaHi my name is [NAME_GIVEN_Kx0Y7] [NAME_FAMILY_s9TTP0] and I drive a [CUSTOM_CAR_MAKE_pY3MN]Hi my name is Lamar Jones and I drive a Subaru.
Hi John, mine is Jane DoeHi [NAME_GIVEN_Kx0Y7], mine is [NAME_GIVEN_veAy9] [NAME_FAMILY_6eC2]Hi Lamar, mine is Doris Perez.
The `textual_parse` function works identically to the [`textual_parse` function in the Snowflake Native App](/textual/textual-integrations/snowflake-native-app-and-spcs/snowflake-app-use.md#snowflake-app-textual-parse). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.tonic.ai/textual/textual-integrations/snowflake-native-app-and-spcs/textual-spcs.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.