AI Tasks identification

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

Import libraries¶

In [ ]:

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

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 = 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 = OllamaInferenceEngine(
#     model_name_or_path="hf.co/ibm-granite/granite-20b-code-instruct-8k-GGUF",
#     credentials=InferenceEngineCredentials(api_url="OLLAMA_API_URL"),
#     parameters=OllamaInferenceEngineParams(
#         num_predict=1000, num_ctx=8192, temperature=0.7, repeat_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 = 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 = OllamaInferenceEngine( # model_name_or_path="hf.co/ibm-granite/granite-20b-code-instruct-8k-GGUF", # credentials=InferenceEngineCredentials(api_url="OLLAMA_API_URL"), # parameters=OllamaInferenceEngineParams( # num_predict=1000, num_ctx=8192, temperature=0.7, repeat_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), # )

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 [ ]:

risk_atlas_nexus = RiskAtlasNexus()

risk_atlas_nexus = RiskAtlasNexus()

AI Tasks Identification API¶

RiskAtlasNexus.identify_ai_tasks_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 [ ]:

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_ai_tasks_from_usecases(
    usecases=[usecase],
    inference_engine=inference_engine,
)

risks

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_ai_tasks_from_usecases( usecases=[usecase], inference_engine=inference_engine, ) risks