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base.py
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base.py
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"""OpenAI Agent.
Simple wrapper around AgentRunner + OpenAIAgentWorker.
For the legacy implementation see:
```python
from llama_index.legacy.agent.legacy.openai.base import OpenAIAgent
```
"""
from typing import (
Any,
List,
Optional,
Type,
)
from llama_index.legacy.agent.openai.step import OpenAIAgentWorker
from llama_index.legacy.agent.runner.base import AgentRunner
from llama_index.legacy.callbacks import (
CallbackManager,
)
from llama_index.legacy.llms.base import ChatMessage
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.llms.openai import OpenAI
from llama_index.legacy.memory.chat_memory_buffer import ChatMemoryBuffer
from llama_index.legacy.memory.types import BaseMemory
from llama_index.legacy.objects.base import ObjectRetriever
from llama_index.legacy.tools import BaseTool
DEFAULT_MODEL_NAME = "gpt-3.5-turbo-0613"
DEFAULT_MAX_FUNCTION_CALLS = 5
class OpenAIAgent(AgentRunner):
"""OpenAI agent.
Subclasses AgentRunner with a OpenAIAgentWorker.
For the legacy implementation see:
```python
from llama_index.legacy.agent.legacy.openai.base import OpenAIAgent
```
"""
def __init__(
self,
tools: List[BaseTool],
llm: OpenAI,
memory: BaseMemory,
prefix_messages: List[ChatMessage],
verbose: bool = False,
max_function_calls: int = DEFAULT_MAX_FUNCTION_CALLS,
default_tool_choice: str = "auto",
callback_manager: Optional[CallbackManager] = None,
tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
) -> None:
"""Init params."""
callback_manager = callback_manager or llm.callback_manager
step_engine = OpenAIAgentWorker.from_tools(
tools=tools,
tool_retriever=tool_retriever,
llm=llm,
verbose=verbose,
max_function_calls=max_function_calls,
callback_manager=callback_manager,
prefix_messages=prefix_messages,
)
super().__init__(
step_engine,
memory=memory,
llm=llm,
callback_manager=callback_manager,
default_tool_choice=default_tool_choice,
)
@classmethod
def from_tools(
cls,
tools: Optional[List[BaseTool]] = None,
tool_retriever: Optional[ObjectRetriever[BaseTool]] = None,
llm: Optional[LLM] = None,
chat_history: Optional[List[ChatMessage]] = None,
memory: Optional[BaseMemory] = None,
memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
verbose: bool = False,
max_function_calls: int = DEFAULT_MAX_FUNCTION_CALLS,
default_tool_choice: str = "auto",
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
prefix_messages: Optional[List[ChatMessage]] = None,
**kwargs: Any,
) -> "OpenAIAgent":
"""Create an OpenAIAgent from a list of tools.
Similar to `from_defaults` in other classes, this method will
infer defaults for a variety of parameters, including the LLM,
if they are not specified.
"""
tools = tools or []
chat_history = chat_history or []
llm = llm or OpenAI(model=DEFAULT_MODEL_NAME)
if not isinstance(llm, OpenAI):
raise ValueError("llm must be a OpenAI instance")
if callback_manager is not None:
llm.callback_manager = callback_manager
memory = memory or memory_cls.from_defaults(chat_history, llm=llm)
if not llm.metadata.is_function_calling_model:
raise ValueError(
f"Model name {llm.model} does not support function calling API. "
)
if system_prompt is not None:
if prefix_messages is not None:
raise ValueError(
"Cannot specify both system_prompt and prefix_messages"
)
prefix_messages = [ChatMessage(content=system_prompt, role="system")]
prefix_messages = prefix_messages or []
return cls(
tools=tools,
tool_retriever=tool_retriever,
llm=llm,
memory=memory,
prefix_messages=prefix_messages,
verbose=verbose,
max_function_calls=max_function_calls,
callback_manager=callback_manager,
default_tool_choice=default_tool_choice,
)