LLM Model¶

::: agenticai_core.designtime.models.llm_model.LlmModel options: show_root_heading: true show_source: false members_order: source

LlmModelConfig¶

::: agenticai_core.designtime.models.llm_model.LlmModelConfig options: show_root_heading: true show_source: false members_order: source

LlmModelBuilder¶

::: agenticai_core.designtime.models.llm_model.LlmModelBuilder options: show_root_heading: true show_source: false members_order: source show_if_no_docstring: true show_signature: true show_signature_annotations: true filters: - "!^_" # Hide private methods but show public ones

LlmModelConfigBuilder¶

::: agenticai_core.designtime.models.llm_model.LlmModelConfigBuilder options: show_root_heading: true show_source: false members_order: source show_if_no_docstring: true show_signature: true show_signature_annotations: true filters: - "!^_" # Hide private methods but show public ones

Usage Examples¶

Creating LLM Configuration¶

from agenticai_core.designtime.models.llm_model import LlmModel, LlmModelConfig

# Create model configuration
config = LlmModelConfig(
    temperature=0.7,
    max_tokens=1600,
    top_p=1.0,
    frequency_penalty=0.0,
    presence_penalty=0.0
)

# Create LLM model
llm = LlmModel(
    model="gpt-4o",
    provider="Open AI",
    connection_name="Default Connection",
    max_timeout="60 Secs",
    max_iterations="25",
    modelConfig=config
)

Using Builder Pattern¶

from agenticai_core.designtime.models.llm_model import (
    LlmModelBuilder, LlmModelConfigBuilder, LlmModelConfig
)

# Build config
config_dict = LlmModelConfigBuilder() \
    .set_temperature(0.7) \
    .set_max_tokens(1600) \
    .set_top_p(0.9) \
    .build()

config = LlmModelConfig(**config_dict)

# Build model
llm_dict = LlmModelBuilder() \
    .set_model("gpt-4o") \
    .set_provider("Open AI") \
    .set_connection_name("Default Connection") \
    .set_max_timeout("60 Secs") \
    .set_max_iterations("25") \
    .set_model_config(config) \
    .build()

llm = LlmModel(**llm_dict)

Provider-Specific Examples¶

OpenAI¶

llm = LlmModel(
    model="gpt-4o",
    provider="Open AI",
    connection_name="OpenAI Connection",
    modelConfig=LlmModelConfig(
        temperature=0.7,
        max_tokens=1600,
        frequency_penalty=0.0,
        presence_penalty=0.0,
        top_p=1.0
    )
)

Anthropic (Claude)¶

llm = LlmModel(
    model="claude-3-5-sonnet-20240620",
    provider="Anthropic",
    connection_name="Anthropic Connection",
    modelConfig=LlmModelConfig(
        temperature=1.0,
        max_tokens=1024,
        top_p=0.7,
        top_k=5  # Anthropic-specific parameter
    )
)

Azure OpenAI¶

llm = LlmModel(
    model="gpt-4",
    provider="Azure OpenAI",
    connection_name="Azure Connection",
    modelConfig=LlmModelConfig(
        temperature=0.8,
        max_tokens=2048
    )
)

Parameter Guidelines¶

Temperature (0.0 - 2.0)¶

0.0 - 0.3: Deterministic, focused responses (good for factual tasks)
0.4 - 0.7: Balanced creativity and consistency
0.8 - 1.5: Creative, diverse responses
1.6 - 2.0: Highly random (experimental)

Top P (0.0 - 1.0)¶

0.1 - 0.5: Very focused sampling
0.6 - 0.9: Balanced diversity
0.95 - 1.0: Maximum diversity

Max Tokens¶

Set based on expected response length
Consider context window limits of the model

Agent - Agents use LLM models for decision-making
App - Applications can have default LLM configurations

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