Domain identification

This notebook illustrates how to identify AI domains based on specific use cases.¶

Import libraries¶

In [1]:

from risk_atlas_nexus.blocks.inference import (
    RITSInferenceEngine,
    WMLInferenceEngine,
    OllamaInferenceEngine,
    VLLMInferenceEngine,
)
from risk_atlas_nexus.blocks.inference.params import (
    InferenceEngineCredentials,
    RITSInferenceEngineParams,
    WMLInferenceEngineParams,
    OllamaInferenceEngineParams,
    VLLMInferenceEngineParams,
)
from risk_atlas_nexus.library import RiskAtlasNexus

from risk_atlas_nexus.blocks.inference import ( RITSInferenceEngine, WMLInferenceEngine, OllamaInferenceEngine, VLLMInferenceEngine, ) from risk_atlas_nexus.blocks.inference.params import ( InferenceEngineCredentials, RITSInferenceEngineParams, WMLInferenceEngineParams, OllamaInferenceEngineParams, VLLMInferenceEngineParams, ) from risk_atlas_nexus.library import RiskAtlasNexus

/Users/dhaval/Projects/Usage-Governance/risk-atlas-nexus/src/risk_atlas_nexus/toolkit/job_utils.py:2: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from tqdm.autonotebook import tqdm

RAN77876513E

Risk Atlas Nexus uses Large Language Models (LLMs) to infer risks dimensions. Therefore requires access to LLMs to inference or call the model.¶

Available Inference Engines: WML, Ollama, vLLM, RITS. Please follow the Inference APIs guide before going ahead.

Note: RITS is intended solely for internal IBM use and requires TUNNELALL VPN for access.

In [ ]:

inference_engine = OllamaInferenceEngine(
    model_name_or_path="granite3.2:8b",
    credentials=InferenceEngineCredentials(api_url="OLLAMA_API_URL"),
    parameters=OllamaInferenceEngineParams(
        num_predict=1000, temperature=0, repeat_penalty=1, num_ctx=8192
    ),
)

# inference_engine = WMLInferenceEngine(
#     model_name_or_path="ibm/granite-20b-code-instruct",
#     credentials={
#         "api_key": "WML_API_KEY",
#         "api_url": "WML_API_URL",
#         "project_id": "WML_PROJECT_ID",
#     },
#     parameters=WMLInferenceEngineParams(
#         max_new_tokens=1000, decoding_method="greedy", repetition_penalty=1
#     ),
# )

# inference_engine = VLLMInferenceEngine(
#     model_name_or_path="ibm-granite/granite-3.1-8b-instruct",
#     credentials=InferenceEngineCredentials(
#         api_url="VLLM_API_URL", api_key="VLLM_API_KEY"
#     ),
#     parameters=VLLMInferenceEngineParams(max_tokens=1000, temperature=0.7),
# )

# inference_engine = RITSInferenceEngine(
#     model_name_or_path="ibm/granite-20b-code-instruct",
#     credentials={
#         "api_key": "RITS_API_KEY",
#         "api_url": "RITS_API_URL",
#     },
#     parameters=RITSInferenceEngineParams(max_tokens=1000, temperature=0.7),
# )

inference_engine = OllamaInferenceEngine( model_name_or_path="granite3.2:8b", credentials=InferenceEngineCredentials(api_url="OLLAMA_API_URL"), parameters=OllamaInferenceEngineParams( num_predict=1000, temperature=0, repeat_penalty=1, num_ctx=8192 ), ) # inference_engine = WMLInferenceEngine( # model_name_or_path="ibm/granite-20b-code-instruct", # credentials={ # "api_key": "WML_API_KEY", # "api_url": "WML_API_URL", # "project_id": "WML_PROJECT_ID", # }, # parameters=WMLInferenceEngineParams( # max_new_tokens=1000, decoding_method="greedy", repetition_penalty=1 # ), # ) # inference_engine = VLLMInferenceEngine( # model_name_or_path="ibm-granite/granite-3.1-8b-instruct", # credentials=InferenceEngineCredentials( # api_url="VLLM_API_URL", api_key="VLLM_API_KEY" # ), # parameters=VLLMInferenceEngineParams(max_tokens=1000, temperature=0.7), # ) # inference_engine = RITSInferenceEngine( # model_name_or_path="ibm/granite-20b-code-instruct", # credentials={ # "api_key": "RITS_API_KEY", # "api_url": "RITS_API_URL", # }, # parameters=RITSInferenceEngineParams(max_tokens=1000, temperature=0.7), # )

[2025-05-25 22:40:11:817] - INFO - RiskAtlasNexus - OLLAMA inference engine will execute requests on the server at http://localhost:11434.
[2025-05-25 22:40:11:969] - INFO - RiskAtlasNexus - Created OLLAMA inference engine.

Create an instance of RiskAtlasNexus¶

Note: (Optional) You can specify your own directory in RiskAtlasNexus(base_dir=<PATH>) to utilize custom AI ontologies. If left blank, the system will use the provided AI ontologies.

In [3]:

risk_atlas_nexus = RiskAtlasNexus()

risk_atlas_nexus = RiskAtlasNexus()

[2025-05-25 22:40:12:40] - INFO - RiskAtlasNexus - Created RiskAtlasNexus instance. Base_dir: None

AI Domain Identification API¶

RiskAtlasNexus.identify_domain_from_usecases()

Params:

• usecases (List[str]): A List of strings describing AI usecases
• inference_engine (InferenceEngine): An LLM inference engine to identify AI tasks from usecases.

In [5]:

usecase = "Generate personalized, relevant responses, recommendations, and summaries of claims for customers to support agents to enhance their interactions with customers."

risks = risk_atlas_nexus.identify_domain_from_usecases(
    usecases=[usecase],
    inference_engine=inference_engine,
)

risks[0].prediction

usecase = "Generate personalized, relevant responses, recommendations, and summaries of claims for customers to support agents to enhance their interactions with customers." risks = risk_atlas_nexus.identify_domain_from_usecases( usecases=[usecase], inference_engine=inference_engine, ) risks[0].prediction

Inferring with OLLAMA: 100%|██████████| 1/1 [00:02<00:00,  2.14s/it]

Out[5]:

{'answer': 'Customer service/support',
 'explanation': 'Since the task is related to generating responses and recommendations for customer support agents, this falls under the Customer service/support domain. It involves creating personalized interactions with customers to enhance their experience and support agent efficiency.'}