set_llm_context

API reference for set_llm_context

set_llm_context

set_llm_context()

Set additional context information for LLM interactions.

The LLM context allows you to provide additional background information or available options that can be tracked alongside model invocations. This context will be added to telemetry spans as ‘gen_ai.llm.context’ and can be used for debugging or analysis in Fiddler’s platform.

The context persists until explicitly changed by calling this function again with a new value. Works automatically in both synchronous and asynchronous contexts.

Parameters

Parameter	Type	Required	Default	Description
model	Model	✗	None	The Model instance to attach context to
context	str	✗	None	Context string providing additional information about available options, constraints, or background for the LLM interaction

Example

set_llm_context

set_llm_context()

Set additional context information for LLM interactions.

The LLM context allows you to provide additional background information or available options that can be tracked alongside model invocations. This context will be added to telemetry spans as ‘gen_ai.llm.context’ and can be used for debugging or analysis in Fiddler’s platform.

The context persists until explicitly changed by calling this function again with a new value. Works automatically in both synchronous and asynchronous contexts.

Parameters

Parameter	Type	Required	Default	Description
model	Model	✗	None	The Model instance to attach context to
context	str	✗	None	Context string providing additional information about available options, constraints, or background for the LLM interaction

Example

from strands.models.openai import OpenAIModel
from fiddler_strandsagents import set_llm_context

model = OpenAIModel(api_key="...", model_id="gpt-4")
set_llm_context(
    model,
'Available hotels: Hilton, Marriott, Hyatt...'
)

# Now when the model is invoked, the context will be
# included in the telemetry span
agent = Agent(model=model, system_prompt="You are a travel assistant")
response = agent("Which hotel should I book?")