agent#

Submodules#

react
- DEFAULT_REACT_AGENT_SYSTEM_PROMPT
- ReActAgent
  - ReActAgent.reset()
  - ReActAgent.call()

class ReActAgent(tools: List[Callable | Callable[[...], Awaitable[Any]] | FunctionTool] = [], max_steps: int = 10, add_llm_as_fallback: bool = True, examples: List[FunctionExpression] = [], *, model_client: ModelClient, model_kwargs: Dict = {}, template: str | None = None)[source]#

Bases: Component

ReActAgent uses generator as a planner that runs multiple and sequential functional call steps to generate the final response.

Users need to set up: - tools: a list of tools to use to complete the task. Each tool is a function or a function tool. - max_steps: the maximum number of steps the agent can take to complete the task. - use_llm_as_fallback: a boolean to decide whether to use an additional LLM model as a fallback tool to answer the query. - model_client: the model client to use to generate the response. - model_kwargs: the model kwargs to use to generate the response. - template: the template to use to generate the prompt. Default is DEFAULT_REACT_AGENT_SYSTEM_PROMPT.

For the generator, the default arguments are: (1) default prompt: DEFAULT_REACT_AGENT_SYSTEM_PROMPT (2) default output_processors: JsonParser

There are examples which is optional, a list of string examples in the prompt.

Example:

from core.openai_client import OpenAIClient
from components.agent.react import ReActAgent
from core.func_tool import FunctionTool
# define the tools
def multiply(a: int, b: int) -> int:
    '''Multiply two numbers.'''
    return a * b
def add(a: int, b: int) -> int:
    '''Add two numbers.'''
    return a + b
agent = ReActAgent(
    tools=[multiply, add],
    model_client=OpenAIClient(),
    model_kwargs={"model": "gpt-3.5-turbo"},
)

# Using examples:

call_multiply = FunctionExpression.from_function(
    thought="I want to multiply 3 and 4.",

Reference: [1] https://arxiv.org/abs/2210.03629, published in Mar, 2023.

reset()[source]#: Reset the agent to start a new query.

call(input: str, promt_kwargs: Dict | None = {}, model_kwargs: Dict | None = {}) → Any[source]#: prompt_kwargs: additional prompt kwargs to either replace or add to the preset prompt kwargs.