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(sap) add tests and docs for new SAP modules
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Vasilisa Parshikova
Sameer Kankute
parent
f6e331a436
commit
f7cfda452d
2 changed files with 369 additions and 0 deletions
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@ -506,6 +506,181 @@ response = embedding(
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print(response.data[0]["embedding"]) # Vector representation
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```
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### Additional Modules
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The SAP Gen AI Hub includes additional modules for advanced use cases:
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- [Grounding](https://help.sap.com/docs/sap-ai-core/generative-ai/grounding-035c455a5a424697b60f4a24b6d791fe?locale=en-US)
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- [Translation](https://help.sap.com/docs/sap-ai-core/generative-ai/translation?locale=en-US)
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- [Data Masking](https://help.sap.com/docs/sap-ai-core/generative-ai/data-masking-d9a54d9ca54b40beacbd24e1663ec3b4?locale=en-US)
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- [Content Filtering](https://help.sap.com/docs/sap-ai-core/generative-ai/content-filtering?locale=en-US)
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#### Grounding
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Grounding is a service designed to handle data-related tasks, such as grounding and retrieval, using vector databases. It provides specialized data retrieval through these databases, grounding the retrieval process with your own external and context-relevant data. Grounding combines generative AI capabilities with the ability to use real-time, precise data to improve decision-making and business operations for specific AI-driven business solutions.
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##### Prerequisites
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To use the Grounding module in the orchestration pipeline, you need to prepare the knowledge base in advance.
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Generative AI hub offers multiple options for users to provide data (prepare a knowledge base):
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- For Option 1: Upload the documents to a supported data repository and run the data pipeline to vectorize the documents.
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- For Option 2: Provide the chunks of document via Vector API directly.
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To use grounding, choose from one of the following options.
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Usage example:
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```python showLineNumbers title="Grounding Example"
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from litellm import completion
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grounding_config = {
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'type': 'document_grounding_service',
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'config': {
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'filters': [
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{'id': 's3-docs',
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'data_repository_type': 'vector',
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'search_config': {'max_chunk_count': 2},
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'data_repositories': ['012345-6789-0123-4567-890123456789']
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}
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],
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'placeholders': {'input': ['user_query'], 'output': 'grounding_response'},
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'metadata_params': ['source', 'webUrl', 'title', 'mimeType', 'fileSuffix']
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}
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}
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response = completion(model="sap/gpt-4o",
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messages=[
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{"content":"""Facility Solutions Company provides services to luxury residential complexes,
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apartments, individual homes, and commercial properties such as office buildings, retail
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spaces, industrial facilities, and educational institutions. Customers are encouraged to
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reach out with maintenance requests, service deficiencies, follow-ups, or any issues they
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need by email.""", "role": "system"},
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{"content":"""You are a helpful assistant for any queries for answering questions.
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Answer the request by providing relevant answers that fit to the request.
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Request: {{ ?user_query }}
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Context:{{ ?grounding_response }}""", "role": "user"}
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],
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placeholder_values={"user_query": "Is there a complaint?"},
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grounding=grounding_config
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)
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print(response.choices[0].message.content)
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```
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For more information about all available grounding configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/using-grounding-module-e1c4dd100dfb42ab890e1d95f3516187?locale=en-US).
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#### Translation
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The translation module allows you to translate LLM text prompts into a chosen target language.
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```python showLineNumbers title="Translation Example"
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from litellm import completion
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translation_config = {
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'input':
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{'type': 'sap_document_translation',
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'config':
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{'source_language': 'en-US',
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'target_language': 'de-DE'}
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},
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'output':
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{'type': 'sap_document_translation',
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'config':
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{'source_language': 'de-DE',
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'target_language': 'fr-FR'}
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}
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}
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response = completion(model="sap/gpt-4o",
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messages=[{"role": "user", "content": "Hello world!"}],
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translation=translation_config)
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print(response.choices[0].message.content)
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```
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For more information about all available translation configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/translation?locale=en-US)
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#### Data Masking
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The data masking module serves to anonymize or pseudonymize personally identifiable information from the input for selected entities.
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```python showLineNumbers title="Data Masking Example"
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from litellm import completion
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masking_config = {
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'providers':
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[
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{
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'type': 'sap_data_privacy_integration',
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'method': 'anonymization',
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'entities': [
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{'type': 'profile-address'},
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{'type': 'profile-email'},
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{'type': 'profile-phone'},
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{'type': 'profile-person'},
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{'type': 'profile-location'}
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]
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}
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]
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}
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mock_cv = "some text with personal information"
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response = completion(model="sap/gpt-4o",
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messages=[{"role": "user", "content": "Give a one sentence summary of the CV. CV: {{?cv}}?"}],
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placeholder_values={"cv": mock_cv},
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masking=masking_config)
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print(response.choices[0].message.content)
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```
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For more information about all available data masking configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/enhancing-model-consumption-with-data-masking-66ad6f469afc4c2cbaa91a27a33f7b21?locale=en-US)
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#### Content Filtering
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The content filtering module allows you to filter input and output based on content safety criteria.
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The module supports two services:
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* Azure Content Safety
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* Llama Guard 3
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```python showLineNumbers title="Content Filtering Example"
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from litellm import completion
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filtering_config_azure = {
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'input':
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{
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'filters':
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[
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{'type': 'azure_content_safety',
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'config':
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{'hate': 0,
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'sexual': 0,
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'violence': 0,
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'self_harm': 0
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}
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}
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]
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},
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'output':
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{
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'filters':
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[
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{'type': 'azure_content_safety',
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'config': {'hate': 0,
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'sexual': 0,
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'violence': 0,
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'self_harm': 0
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}
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}
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]
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}
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}
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response = completion(model="sap/gpt-4o",
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messages=[{"role": "user", "content": "Hello world!"}],
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filtering=filtering_config_azure)
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print(response.choices[0].message.content)
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# The model responds normally because the content does not violate any safety rules.
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try:
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response = completion(model="sap/gpt-4o",
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messages=[{"role": "user", "content": "I hate you"}],
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filtering=filtering_config_azure)
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except Exception as e:
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print(e)
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# The service raises an error:
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# "Input Filter: Content filtered due to safety violations. Please modify the prompt and try again."
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```
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For more information about all available content filtering configurations, see the [documentation](https://help.sap.com/docs/sap-ai-core/generative-ai/content-filtering?locale=en-US)
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## Reference
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### Supported Parameters
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194
tests/test_litellm/llms/sap/chat/test_sap_additional_modules.py
Normal file
194
tests/test_litellm/llms/sap/chat/test_sap_additional_modules.py
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@ -0,0 +1,194 @@
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from litellm.llms.sap.chat.transformation import GenAIHubOrchestrationConfig
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def test_sap_placeholder_defaults():
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config = GenAIHubOrchestrationConfig().transform_request(
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model="gpt-4o",
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messages=[
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{"role": "user", "content": "Hello. Answer {{ ?user_query }}"}
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],
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optional_params={'deployment_url': "shouldn't be in results",
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"placeholder_defaults": {"user_query": "default value"}},
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litellm_params={},
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headers={}
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)
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assert config["config"]["modules"]["prompt_templating"]["prompt"]["defaults"] == {"user_query": "default value"}
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assert config["config"]["modules"]["prompt_templating"]["model"]["params"] == {}
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def test_sap_placeholder_values():
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placeholder_values = {"user_query": "Some text"}
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config = GenAIHubOrchestrationConfig().transform_request(
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model="gpt-4o",
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messages=[
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{"role": "user", "content": "Hello. Answer {{ ?user_query }}"}
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],
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optional_params={'deployment_url': "shouldn't be in results",
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"placeholder_values": placeholder_values},
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litellm_params={},
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headers={}
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)
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assert config["placeholder_values"] == placeholder_values
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assert config["config"]["modules"]["prompt_templating"]["model"]["params"] == {}
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def test_sap_grounding():
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grounding_config = {
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'type': 'document_grounding_service',
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'config': {
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'filters': [
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{'id': 's3-docs',
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'data_repository_type': 'vector',
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'search_config': {'max_chunk_count': 2},
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'data_repositories': ['123456890-test']
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}
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],
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'placeholders': {'input': ['user_query'], 'output': 'grounding_response'},
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'metadata_params': ['source', 'webUrl', 'title', 'mimeType', 'fileSuffix']
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}
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}
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placeholder_values = {"user_query": "Some text"}
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config = GenAIHubOrchestrationConfig().transform_request(
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model="gpt-4o",
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messages=[
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{"role": "user", "content": "Hello. Answer {{ ?user_query }} using context: {{ ?grounding_response }}"}
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],
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optional_params={'deployment_url': "shouldn't be in results",
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"grounding": grounding_config,
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"placeholder_values": placeholder_values},
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litellm_params={},
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headers={}
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)
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assert config["config"]["modules"]["grounding"] == grounding_config
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assert config["placeholder_values"] == placeholder_values
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assert config["config"]["modules"]["prompt_templating"]["model"]["params"] == {}
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def test_sap_filtering():
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filtering_config_azure = {
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'input':
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{
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'filters':
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[
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{'type': 'azure_content_safety',
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'config':
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{'hate': 0,
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'sexual': 0,
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'violence': 0,
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'self_harm': 0
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}
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}
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]
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},
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'output':
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{
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'filters':
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[
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{'type': 'azure_content_safety',
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'config': {'hate': 0,
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'sexual': 0,
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'violence': 0,
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'self_harm': 0
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}
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}
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]
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}
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}
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filtering_config_llama = {
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'input':
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{
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'filters':
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[
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{
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'type': 'llama_guard_3_8b',
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'config': {'hate': True,
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"elections": True}
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}
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]
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},
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'output':
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{
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'filters':
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[
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{
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'type': 'llama_guard_3_8b',
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'config': {'hate': True, "elections": True}
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}
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]
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}
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}
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config = GenAIHubOrchestrationConfig().transform_request(
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model="gpt-4o",
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messages=[{"role": "user", "content": "Hello."}],
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optional_params={'deployment_url': "shouldn't be in results",
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"filtering": filtering_config_azure},
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litellm_params={},
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headers={}
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)
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assert config["config"]["modules"]["filtering"] == filtering_config_azure
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assert config["config"]["modules"]["prompt_templating"]["model"]["params"] == {}
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config = GenAIHubOrchestrationConfig().transform_request(
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model="gpt-4o",
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messages=[{"role": "user", "content": "Hello."}],
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optional_params={'deployment_url': "shouldn't be in results",
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"filtering": filtering_config_llama},
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litellm_params={},
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headers={}
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)
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assert config["config"]["modules"]["filtering"] == filtering_config_llama
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assert config["config"]["modules"]["prompt_templating"]["model"]["params"] == {}
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def test_sap_masking():
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masking_config = {
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'providers':
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[
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{
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'type': 'sap_data_privacy_integration',
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'method': 'anonymization',
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'entities': [
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{'type': 'profile-address'},
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{'type': 'profile-email'},
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{'type': 'profile-phone'},
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{'type': 'profile-person'},
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{'type': 'profile-location'}
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]
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}
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]
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}
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config = GenAIHubOrchestrationConfig().transform_request(
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model="gpt-4o",
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messages=[{"role": "user", "content": "Hello."}],
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optional_params={'deployment_url': "shouldn't be in results",
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"masking": masking_config},
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litellm_params={},
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headers={}
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)
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assert config["config"]["modules"]["masking"] == masking_config
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assert config["config"]["modules"]["prompt_templating"]["model"]["params"] == {}
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def test_sap_translation():
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translation_config = {
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'input':
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{'type': 'sap_document_translation',
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'config':
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{'source_language': 'en-US',
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'target_language': 'de-DE'}
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},
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'output':
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{'type': 'sap_document_translation',
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'config':
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{'source_language': 'de-DE',
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'target_language': 'fr-FR'}
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}
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}
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config = GenAIHubOrchestrationConfig().transform_request(
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model="gpt-4o",
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messages=[{"role": "user", "content": "Hello."}],
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optional_params={'deployment_url': "shouldn't be in results",
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"translation": translation_config},
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litellm_params={},
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headers={}
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)
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assert config["config"]["modules"]["translation"] == translation_config
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assert config["config"]["modules"]["prompt_templating"]["model"]["params"] == {}
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