mirror of https://github.com/hwchase17/langchain
infra: import CI fix (#14562)
TIL `**` globstar doesn't work in make Makefile changes fix that. `__getattr__` changes allow import of all files, but raise error when accessing anything from the module. file deletions were corresponding libs change from #14559pull/14565/head^2
Erick Friis
6 months ago
committed by
GitHub
parent
48b7a0584d
commit
5418d8bfd6
@ -1,89 +0,0 @@
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, List, Optional
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.memory import BaseMemory
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from langchain_core.messages import SystemMessage
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from langchain_core.prompts.chat import MessagesPlaceholder
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from langchain_core.tools import BaseTool
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if TYPE_CHECKING:
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from langchain.agents.agent import AgentExecutor
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def _get_default_system_message() -> SystemMessage:
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return SystemMessage(
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content=(
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"Do your best to answer the questions. "
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"Feel free to use any tools available to look up "
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"relevant information, only if necessary"
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)
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)
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def create_conversational_retrieval_agent(
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llm: BaseLanguageModel,
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tools: List[BaseTool],
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remember_intermediate_steps: bool = True,
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memory_key: str = "chat_history",
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system_message: Optional[SystemMessage] = None,
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verbose: bool = False,
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max_token_limit: int = 2000,
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**kwargs: Any,
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) -> AgentExecutor:
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"""A convenience method for creating a conversational retrieval agent.
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Args:
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llm: The language model to use, should be ChatOpenAI
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tools: A list of tools the agent has access to
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remember_intermediate_steps: Whether the agent should remember intermediate
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steps or not. Intermediate steps refer to prior action/observation
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pairs from previous questions. The benefit of remembering these is if
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there is relevant information in there, the agent can use it to answer
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follow up questions. The downside is it will take up more tokens.
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memory_key: The name of the memory key in the prompt.
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system_message: The system message to use. By default, a basic one will
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be used.
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verbose: Whether or not the final AgentExecutor should be verbose or not,
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defaults to False.
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max_token_limit: The max number of tokens to keep around in memory.
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Defaults to 2000.
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Returns:
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An agent executor initialized appropriately
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"""
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
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AgentTokenBufferMemory,
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)
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from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
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from langchain.memory.token_buffer import ConversationTokenBufferMemory
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if remember_intermediate_steps:
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memory: BaseMemory = AgentTokenBufferMemory(
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memory_key=memory_key, llm=llm, max_token_limit=max_token_limit
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)
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else:
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memory = ConversationTokenBufferMemory(
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memory_key=memory_key,
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return_messages=True,
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output_key="output",
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llm=llm,
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max_token_limit=max_token_limit,
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)
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_system_message = system_message or _get_default_system_message()
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prompt = OpenAIFunctionsAgent.create_prompt(
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system_message=_system_message,
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extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
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)
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agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
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return AgentExecutor(
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agent=agent,
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tools=tools,
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memory=memory,
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verbose=verbose,
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return_intermediate_steps=remember_intermediate_steps,
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**kwargs,
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)
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@ -1,106 +0,0 @@
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"""VectorStore agent."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Dict, Optional
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from langchain_core.callbacks import BaseCallbackManager
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from langchain_core.language_models import BaseLanguageModel
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from langchain_community.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
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from langchain_community.agent_toolkits.vectorstore.toolkit import (
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VectorStoreRouterToolkit,
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VectorStoreToolkit,
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)
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if TYPE_CHECKING:
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from langchain.agents.agent import AgentExecutor
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def create_vectorstore_agent(
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llm: BaseLanguageModel,
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toolkit: VectorStoreToolkit,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = PREFIX,
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verbose: bool = False,
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agent_executor_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> AgentExecutor:
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"""Construct a VectorStore agent from an LLM and tools.
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Args:
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llm (BaseLanguageModel): LLM that will be used by the agent
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toolkit (VectorStoreToolkit): Set of tools for the agent
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callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ]
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prefix (str, optional): The prefix prompt for the agent. If not provided uses default PREFIX.
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verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ]
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agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ]
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**kwargs: Additional named parameters to pass to the ZeroShotAgent.
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Returns:
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AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response
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""" # noqa: E501
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.chains.llm import LLMChain
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tools = toolkit.get_tools()
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prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
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return AgentExecutor.from_agent_and_tools(
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agent=agent,
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tools=tools,
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callback_manager=callback_manager,
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verbose=verbose,
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**(agent_executor_kwargs or {}),
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)
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def create_vectorstore_router_agent(
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llm: BaseLanguageModel,
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toolkit: VectorStoreRouterToolkit,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = ROUTER_PREFIX,
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verbose: bool = False,
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agent_executor_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> AgentExecutor:
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"""Construct a VectorStore router agent from an LLM and tools.
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Args:
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llm (BaseLanguageModel): LLM that will be used by the agent
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toolkit (VectorStoreRouterToolkit): Set of tools for the agent which have routing capability with multiple vector stores
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callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ]
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prefix (str, optional): The prefix prompt for the router agent. If not provided uses default ROUTER_PREFIX.
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verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ]
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agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ]
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**kwargs: Additional named parameters to pass to the ZeroShotAgent.
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Returns:
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AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response.
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""" # noqa: E501
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.chains.llm import LLMChain
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tools = toolkit.get_tools()
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prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
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return AgentExecutor.from_agent_and_tools(
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agent=agent,
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tools=tools,
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callback_manager=callback_manager,
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verbose=verbose,
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**(agent_executor_kwargs or {}),
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)
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