TeradataAI authentication precedence is: authorization object, explicit parameters, configuration file, then environment variables.
Example 1: Instantiate the TeradataAI using model "anthropic.claude-v2"
>>> llm_aws = TeradataAI(api_type = "aws",
access_key = "<AWS bedrock access key>",
secret_key = "<AWS bedrock secret key>",
session_key = "<AWS bedrock session key>",
region = "us-west-2",
model_name = "anthropic.claude-v2")
Example 2: Instantiate the TeradataAI using model "anthropic.claude-v2” and configuring the relevant environment variables
>>> os.environ["AWS_DEFAULT_REGION"] = "us-west-2"
>>> os.environ["AWS_ACCESS_KEY_ID"] = "<Enter AWS Access Key ID>"
>>> os.environ["AWS_SECRET_ACCESS_KEY"] = "<Enter AWS Secret Key>"
>>> os.environ["AWS_SESSION_TOKEN"] = "<Enter AWS Session key>"
>>> llm_aws = TeradataAI(api_type = "aws",
model_name = "anthropic.claude-v2")
Example 3: Instantiate the TeradataAI using model "anthropic.claude-v2" and passing the sensitive information using a ‘.env’ file
>>> llm_aws = TeradataAI(api_type = "aws",
model_name = "anthropic.claude-v2",
config_file = "<path to .env file>")
-------------- env file ----------------
AWS_DEFAULT_REGION = <aws region>
AWS_ACCESS_KEY_ID = <aws access key>
AWS_SECRET_ACCESS_KEY = <aws secret key>
AWS_SESSION_TOKEN = <aws session token>
----------------------------------------
Example 4: Instantiate the TeradataAI using model "anthropic.claude-v2" and passing the authorization information to each argument of the function
>>> llm_aws = TeradataAI(api_type = "aws",
model_name = "anthropic.claude-v2",
authorization = "<authorization object>")
Example 1: Instantiate TeradataAI using model "gpt-3.5-turbo"
>>> llm_azure = TeradataAI(api_type = "azure",
api_base = "<https://****.openai.azure.com/>",
api_version = "2000-11-35",
api_key = <provide your llm API key>,
deployment_id = <provide your azure AI engine name>,
model_name = "gpt-3.5-turbo")
Example 2: Instantiate TeradataAI using model "gpt-3.5-turbo" and configuring the relevant environment variables
>>> os.environ['AZURE_OPENAI_API_KEY'] = <provide your azure AI API key>
>>> os.environ['AZURE_OPENAI_ENDPOINT'] = <provide your azure AI engine name>
>>> os.environ['AZURE_OPENAI_API_VERSION'] = <provide your azure AI version>
>>> os.environ['AZURE_OPENAI_DEPLOYMENT_ID'] = <provide your azure AI deployment id>
>>> llm_azure = TeradataAI(api_type="azure",
model_name="gpt-3.5-turbo"
)
Example 3: Instantiate TeradataAI using model "gpt-3.5-turbo" and passing the sensitive information using a ‘.env’ file
>>> llm_azure = TeradataAI(api_type = "azure",
model_name = "gpt-3.5-turbo",
config_file = "<path to .env file>")
-------------- env file ----------------
AZURE_OPENAI_API_KEY = <azure AI API key>
AZURE_OPENAI_ENDPOINT = <https://****.openai.azure.com/>
AZURE_OPENAI_API_VERSION = 2000-11-35
AZURE_OPENAI_DEPLOYMENT_ID = <azure AI engine name>
----------------------------------------
Example 4: Instantiate TeradataAI using model "gpt-3.5-turbo" and passing the authorization information to each argument of the function
>>> llm_azure = TeradataAI(api_type = "azure",
model_name = "gpt-3.5-turbo",
authorization = "<authorization object>"
Example 1: Instantiate TeradataAI using model "gemini-1.5-pro-001"
>>> llm_gcp = TeradataAI(api_type = "gcp",
project = "<GCP project name>",
model_name = "gemini-1.5-pro-001",
region = "us-central1",
enable_safety = True,
access_token = "<GCP ACCESS TOKEN>",
model_args = {"temperature": 1,
"top_p": 0.95}
)
Example 2: Instantiate TeradataAI using model "gemini-1.5-pro-001" and configuring the relevant environment variables
>>> os.environ['GOOGLE_CLOUD_PROJECT'] = <GCP project name>
>>> os.environ['GOOGLE_CLOUD_REGION'] = <GCP cloud region>
>>> os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = <GCP access token>
>>> llm_gcp = TeradataAI(api_type="gcp",
model_name="gemini-1.5-pro-001"
)
Example 3: Instantiate TeradataAI using model "gemini-1.5-pro-001" and passing the sensitive information using a ‘.env’ file
>>> llm_gcp = TeradataAI(api_type = "gcp",
model_name = "gemini-1.5-pro-001",
config_file = "<path to .env file>
-------------- env file ----------------
GOOGLE_CLOUD_PROJECT = <GCP project name>
GOOGLE_CLOUD_REGION = 'us-central1'
GOOGLE_APPLICATION_CREDENTIALS = <GCP access token>
----------------------------------------
Example 4: Instantiate TeradataAI using model "gemini-1.5-pro-001" and passing the authorization information to each argument of the function
>>> llm_gcp = TeradataAI(api_type = "gcp",
model_name = "gemini-1.5-pro-001",
authorization = "<authorization object>"
Example 1: Instantiate TeradataAI using model "sentence-transformers/all-MiniLM-L6-v2" and default environment
>>> model_name = 'sentence-transformers/all-MiniLM-L6-v2'
>>> model_args = {'transformer_class': 'AutoModelForTokenClassification', 'task' : 'token-classification'}
>>> llm_hf = TeradataAI(api_type = "hugging_face",
model_name = model_name,
model_args = model_args)
Example 2 : Instantiate TeradataAI using model "lakshyakh93/deberta_finetuned_pii" and configuring the “ues_args” to provide a custom environment
>>> model_name = 'lakshyakh93/deberta_finetuned_pii'
>>> model_args = {'transformer_class': 'AutoModelForTokenClassification', 'task' : 'token-classification'}
# Using an existing user environment 'demo'.
>>> ues_args = {'env_name': 'demo'}
>>> llm = TeradataAI(api_type = "hugging_face",
model_name = model_name,
model_args = model_args,
ues_args = ues_args)
>>> llm_nim = TeradataAI(api_type = "nim",
api_key = "<nim api key>"
api_base = "<nim base url>",
model_name = "meta/llama-3.1-8b-instruct")
Example 2: Instantiate the TeradataAI using model "meta/llama-3.1-8b-instruct" and configuring the relevant environment variables
>>> import os
>>> os.environ["NVIDIA_API_KEY"] = "<NVIDIA NIM API key>"
>>> llm_nim = TeradataAI(api_type = "nim",
api_base = "<nim base url>",
model_name = "meta/llama-3.1-8b-instruct")
Example 3: Instantiate the TeradataAI using model "meta/llama-3.1-8b-instruct" and passing the sensitive information using a ‘.env’ file
-------------- env file ----------------
"NVIDIA_API_KEY" = "<NVIDIA NIM API key>"
----------------------------------------
llm = TeradataAI(api_type = "nim",
api_base = "<nim base url>",
model_name = "meta/llama-3.1-8b-instruct")
Example 4: Instantiate the TeradataAI using model "meta/llama-3.1-8b-instruct" and passing the authorization information to each argument of the function
Set up the environment variable to work with the NVIDIA NIM model.
>>> import os
>>> os.environ["NVIDIA_API_KEY"] = "<NVIDIA NIM API key>"
>>> llm = TeradataAI(api_type = "nim",
api_base = "<nim base url>",
model_name = "meta/llama-3.1-8b-instruct")
Create an LLM endpoint for "api_type" = 'onnx' for the ONNX model "bge-small-en-v1.5" with "model_id" as 'td-bge-small' that does not yet exist in the database and is to be stored in table with "table_name" as 'onnx_models'.
>>> from teradatagenai import TeradataAI
>>> llm = TeradataAI(api_type = "onnx",
model_name = "bge-small-en-v1.5",
model_id = "td-bge-small",
model_path = "/path/to/onnx/model",
tokenizer_path = "/path/to/onnx/tokenizer,
table_name = "onnx_models")
Example 1: Create LLM endpoint for "api_type" = 'onnx' for the ONNX model "bge-small-en-v1.5" with "model_id" as 'td-bge-small' that does not yet exist in the database and is to be stored in default table
>>> from teradatagenai import TeradataAI
>>> llm = TeradataAI(api_type = "onnx",
model_name = "bge-small-en-v1.5",
model_id = "td-bge-small",
model_path = "/path/to/onnx/model",
tokenizer_path = "/path/to/onnx/tokenizer)
Example 2: Create LLM endpoint for "api_type" as 'onnx' for the ONNX model "bge-m3" with "model_id" as 'td-bge-m3' already stored in the table 'onnx_models'
>>> from teradatagenai import TeradataAI
>>> llm = TeradataAI(api_type = "onnx",
model_name = "bge-m3",
model_id = "td-bge-m3",
table_name = "onnx_models")
Example 3: Create LLM endpoint for "api_type" as 'onnx' for the ONNX model "bge-m3" with "model_id" as 'td-bge-m3' already stored in the default table
>>> from teradatagenai import TeradataAI
>>> llm = TeradataAI(api_type = "onnx",
model_name = "bge-m3",
model_id = "td-bge-m3")