llama-index

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"""ReAct agent.
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Simple wrapper around AgentRunner + ReActAgentWorker.
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For the legacy implementation see:
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```python
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from llama_index.legacy.agent.legacy.react.base import ReActAgent
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```
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"""
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from typing import (
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    Any,
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    List,
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    Optional,
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    Sequence,
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    Type,
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)
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from llama_index.legacy.agent.react.formatter import ReActChatFormatter
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from llama_index.legacy.agent.react.output_parser import ReActOutputParser
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from llama_index.legacy.agent.react.step import ReActAgentWorker
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from llama_index.legacy.agent.runner.base import AgentRunner
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from llama_index.legacy.callbacks import (
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    CallbackManager,
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)
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from llama_index.legacy.core.llms.types import ChatMessage
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from llama_index.legacy.llms.llm import LLM
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from llama_index.legacy.llms.openai import OpenAI
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from llama_index.legacy.memory.chat_memory_buffer import ChatMemoryBuffer
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from llama_index.legacy.memory.types import BaseMemory
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from llama_index.legacy.objects.base import ObjectRetriever
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from llama_index.legacy.prompts.mixin import PromptMixinType
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from llama_index.legacy.tools import BaseTool
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DEFAULT_MODEL_NAME = "gpt-3.5-turbo-0613"
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class ReActAgent(AgentRunner):
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    """ReAct agent.
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    Subclasses AgentRunner with a ReActAgentWorker.
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    For the legacy implementation see:
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    ```python
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    from llama_index.legacy.agent.legacy.react.base import ReActAgent
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    ```
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    """
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    def __init__(
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        self,
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        tools: Sequence[BaseTool],
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        llm: LLM,
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        memory: BaseMemory,
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        max_iterations: int = 10,
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        react_chat_formatter: Optional[ReActChatFormatter] = None,
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        output_parser: Optional[ReActOutputParser] = None,
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        callback_manager: Optional[CallbackManager] = None,
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        verbose: bool = False,
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        tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
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        context: Optional[str] = None,
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    ) -> None:
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        """Init params."""
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        callback_manager = callback_manager or llm.callback_manager
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        if context and react_chat_formatter:
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            raise ValueError("Cannot provide both context and react_chat_formatter")
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        if context:
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            react_chat_formatter = ReActChatFormatter.from_context(context)
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        step_engine = ReActAgentWorker.from_tools(
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            tools=tools,
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            tool_retriever=tool_retriever,
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            llm=llm,
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            max_iterations=max_iterations,
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            react_chat_formatter=react_chat_formatter,
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            output_parser=output_parser,
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            callback_manager=callback_manager,
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            verbose=verbose,
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        )
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        super().__init__(
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            step_engine,
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            memory=memory,
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            llm=llm,
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            callback_manager=callback_manager,
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        )
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    @classmethod
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    def from_tools(
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        cls,
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        tools: Optional[List[BaseTool]] = None,
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        tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
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        llm: Optional[LLM] = None,
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        chat_history: Optional[List[ChatMessage]] = None,
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        memory: Optional[BaseMemory] = None,
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        memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
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        max_iterations: int = 10,
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        react_chat_formatter: Optional[ReActChatFormatter] = None,
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        output_parser: Optional[ReActOutputParser] = None,
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        callback_manager: Optional[CallbackManager] = None,
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        verbose: bool = False,
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        context: Optional[str] = None,
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        **kwargs: Any,
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    ) -> "ReActAgent":
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        """Convenience constructor method from set of of BaseTools (Optional).
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        NOTE: kwargs should have been exhausted by this point. In other words
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        the various upstream components such as BaseSynthesizer (response synthesizer)
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        or BaseRetriever should have picked up off their respective kwargs in their
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        constructions.
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        Returns:
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            ReActAgent
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        """
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        llm = llm or OpenAI(model=DEFAULT_MODEL_NAME)
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        if callback_manager is not None:
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            llm.callback_manager = callback_manager
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        memory = memory or memory_cls.from_defaults(
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            chat_history=chat_history or [], llm=llm
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        )
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        return cls(
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            tools=tools or [],
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            tool_retriever=tool_retriever,
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            llm=llm,
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            memory=memory,
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            max_iterations=max_iterations,
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            react_chat_formatter=react_chat_formatter,
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            output_parser=output_parser,
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            callback_manager=callback_manager,
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            verbose=verbose,
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            context=context,
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        )
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    def _get_prompt_modules(self) -> PromptMixinType:
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        """Get prompt modules."""
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        return {"agent_worker": self.agent_worker}
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