id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
9e3e8c0b73e3-5 | Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path="path/agent.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-6 | return _dict
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has take... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-7 | }
[docs]class Agent(BaseSingleActionAgent):
"""Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called "agent_scratchpad" where the agent can put its
intermediary work.
"""
llm_chain: LLMCh... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-8 | return thoughts
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has t... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-9 | """Create the full inputs for the LLMChain from intermediate steps."""
thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
return full_inputs
@property
def input_keys(self... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-10 | """Create a prompt for this class."""
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in."""
pass
@classmethod
@abstractmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
"""G... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-11 | # `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
elif early_stopping_method == "generate":
# Generate does one final forward pass
thoughts = ""
for acti... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-12 | }
class ExceptionTool(BaseTool):
name = "_Exception"
description = "Exception tool"
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
return query
async def _arun(
self,
query: str,
run_manager: Opti... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-13 | `"generate"` calls the agent's LLM Chain one final time to generate
a final answer based on the previous steps.
"""
handle_parsing_errors: Union[
bool, str, Callable[[OutputParserException], str]
] = False
"""How to handle errors raised by the agent's output parser.
Defaults to `Fals... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-14 | f"provided tools ({[tool.name for tool in tools]})"
)
return values
@root_validator()
def validate_return_direct_tool(cls, values: Dict) -> Dict:
"""Validate that tools are compatible with agent."""
agent = values["agent"]
tools = values["tools"]
if isinst... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-15 | return {tool.name: tool for tool in self.tools}[name]
def _should_continue(self, iterations: int, time_elapsed: float) -> bool:
if self.max_iterations is not None and iterations >= self.max_iterations:
return False
if (
self.max_execution_time is not None
and time... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-16 | run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
# Call the LL... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-17 | **tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-18 | color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
"""Take a single step in the thought-action-observation loo... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-19 | output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-20 | **tool_run_kwargs,
)
return agent_action, observation
# Use asyncio.gather to run multiple tool.arun() calls concurrently
result = await asyncio.gather(
*[_aperform_agent_action(agent_action) for agent_action in actions]
)
return list(result)
d... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-21 | next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-22 | color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return await self._areturn(
next_step_ou... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
9e3e8c0b73e3-23 | if agent_action.tool in name_to_tool_map:
if name_to_tool_map[agent_action.tool].return_direct:
return AgentFinish(
{self.agent.return_values[0]: observation},
"",
)
return None | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent.html |
957176a2a20f-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base im... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-1 | from langchain.tools.shell.tool import ShellTool
from langchain.tools.sleep.tool import SleepTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.tools.openweathermap.tool import OpenWeatherMapQueryRun
from langchain.utiliti... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-2 | def _get_tools_requests_delete() -> BaseTool:
return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
return ShellTool()
def _get_sleep() -> BaseTool:
return SleepTool()
_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
"python_repl": _get_python_repl,
"re... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-3 | return Tool(
name="Calculator",
description="Useful for when you need to answer questions about math.",
func=LLMMathChain.from_llm(llm=llm).run,
coroutine=LLMMathChain.from_llm(llm=llm).arun,
)
def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
chain = APIChain.from_llm... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-4 | func=chain.run,
)
def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
tmdb_bearer_token = kwargs["tmdb_bearer_token"]
chain = APIChain.from_llm_and_api_docs(
llm,
tmdb_docs.TMDB_DOCS,
headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
)
return Tool(
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-5 | def _get_google_search(**kwargs: Any) -> BaseTool:
return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_wikipedia(**kwargs: Any) -> BaseTool:
return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
def _get_arxiv(**kwargs: Any) -> BaseTool:
return ArxivQueryRun(api_wrapp... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-6 | )
def _get_searx_search(**kwargs: Any) -> BaseTool:
return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))
def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapp... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-7 | ] = {
"news-api": (_get_news_api, ["news_api_key"]),
"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
"podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
"wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-8 | "searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
"wikipedia": (_get_wikipedia, ["top_k_results", "lang"]),
"arxiv": (
_get_arxiv,
["top_k_results", "load_max_docs", "load_all_available_meta"],
),
"pupmed": (
_get_pupmed,
["top_k_results", "loa... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-9 | **kwargs: Any,
) -> BaseTool:
"""Loads a tool from the HuggingFace Hub.
Args:
task_or_repo_id: Task or model repo id.
model_repo_id: Optional model repo id.
token: Optional token.
remote: Optional remote. Defaults to False.
**kwargs:
Returns:
A tool.
"""
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-10 | Args:
tool_names: name of tools to load.
llm: Optional language model, may be needed to initialize certain tools.
callbacks: Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
Returns:
List of tools.
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
957176a2a20f-11 | f"provided: {missing_keys}"
)
sub_kwargs = {k: kwargs[k] for k in extra_keys}
tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
tools.append(tool)
elif name in _EXTRA_OPTIONAL_TOOLS:
_get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/load_tools.html |
c502485a233c-0 | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/conversational/base.html |
c502485a233c-1 | [docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/conversational/base.html |
c502485a233c-2 | validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: s... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/conversational/base.html |
b354eac541df-0 | Source code for langchain.agents.conversational_chat.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from lang... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/conversational_chat/base.html |
b354eac541df-1 | return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, too... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/conversational_chat/base.html |
b354eac541df-2 | ) -> List[BaseMessage]:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts: List[BaseMessage] = []
for action, observation in intermediate_steps:
thoughts.append(AIMessage(content=action.log))
human_message = HumanMessage(
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/conversational_chat/base.html |
40b039db3cdf-0 | Source code for langchain.agents.structured_chat.base
import re
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.structured_chat.output_parser import (
StructuredChatOutputParserWithRetries,
)
from la... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
40b039db3cdf-1 | return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _validate_tools(cls, tools: Sequence[Ba... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
40b039db3cdf-2 | template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
_memory_prompts = memory_prompts or []
messages = [
SystemMessagePromptTemplate.from_template(template),
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
40b039db3cdf-3 | )
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser(llm=llm)
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@property
de... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/structured_chat/base.html |
f31f4e77a4c5-0 | Source code for langchain.agents.openai_functions_agent.base
"""Module implements an agent that uses OpenAI's APIs function enabled API."""
import json
from dataclasses import dataclass
from json import JSONDecodeError
from typing import Any, List, Optional, Sequence, Tuple, Union
from pydantic import root_validator
fr... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/openai_functions_agent/base.html |
f31f4e77a4c5-1 | ]
else:
return [AIMessage(content=agent_action.log)]
def _create_function_message(
agent_action: AgentAction, observation: str
) -> FunctionMessage:
"""Convert agent action and observation into a function message.
Args:
agent_action: the tool invocation request from the agent
obs... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/openai_functions_agent/base.html |
f31f4e77a4c5-2 | function_name = function_call["name"]
try:
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
raise OutputParserException(
f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/openai_functions_agent/base.html |
f31f4e77a4c5-3 | of the variables. For an easy way to construct this prompt, use
`OpenAIFunctionsAgent.create_prompt(...)`
"""
llm: BaseLanguageModel
tools: Sequence[BaseTool]
prompt: BasePromptTemplate
[docs] def get_allowed_tools(self) -> List[str]:
"""Get allowed tools."""
return list([... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/openai_functions_agent/base.html |
f31f4e77a4c5-4 | **kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
selected_inputs = {
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
}
full_inputs... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/openai_functions_agent/base.html |
f31f4e77a4c5-5 | )
agent_decision = _parse_ai_message(predicted_message)
return agent_decision
[docs] @classmethod
def create_prompt(
cls,
system_message: Optional[SystemMessage] = SystemMessage(
content="You are a helpful AI assistant."
),
extra_prompt_messages: Option... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/openai_functions_agent/base.html |
f31f4e77a4c5-6 | """Construct an agent from an LLM and tools."""
if not isinstance(llm, ChatOpenAI):
raise ValueError("Only supported with ChatOpenAI models.")
prompt = cls.create_prompt(
extra_prompt_messages=extra_prompt_messages,
system_message=system_message,
)
ret... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/openai_functions_agent/base.html |
3aad8c5ebea2-0 | Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types impo... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
3aad8c5ebea2-1 | super()._validate_tools(tools)
if len(tools) != 2:
raise ValueError(f"Exactly two tools must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Lookup", "Search"}:
raise ValueError(
f"Tool names should be Lookup a... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
3aad8c5ebea2-2 | if term.lower() != self.lookup_str:
self.lookup_str = term.lower()
self.lookup_index = 0
else:
self.lookup_index += 1
lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
if len(lookups) == 0:
return "No Results"
elif sel... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
3aad8c5ebea2-3 | raise ValueError(f"Tool name should be Play, got {tool_names}")
[docs]class ReActChain(AgentExecutor):
"""Chain that implements the ReAct paper.
Example:
.. code-block:: python
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
"""
def __init__(self, llm... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/react/base.html |
9d87e1709257-0 | Source code for langchain.agents.mrkl.base
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, A... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
9d87e1709257-1 | @property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
9d87e1709257-2 | llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variable... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
9d87e1709257-3 | f"a description must always be provided."
)
super()._validate_tools(tools)
[docs]class MRKLChain(AgentExecutor):
"""Chain that implements the MRKL system.
Example:
.. code-block:: python
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.ba... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
9d87e1709257-4 | action_description="useful for searching"
),
ChainConfig(
action_name="Calculator",
action=llm_math_chain.run,
action_description="useful for doing math"
)
]
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/mrkl/base.html |
8b1bd31f5d5c-0 | Source code for langchain.agents.agent_toolkits.playwright.toolkit
"""Playwright web browser toolkit."""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Type, cast
from pydantic import Extra, root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
8b1bd31f5d5c-1 | """Check that the arguments are valid."""
lazy_import_playwright_browsers()
if values.get("async_browser") is None and values.get("sync_browser") is None:
raise ValueError("Either async_browser or sync_browser must be specified.")
return values
[docs] def get_tools(self) -> List[B... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
e32a6e52baee-0 | Source code for langchain.agents.agent_toolkits.openapi.toolkit
"""Requests toolkit."""
from __future__ import annotations
from typing import Any, List
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.json.base import crea... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
e32a6e52baee-1 | func=self.json_agent.run,
description=DESCRIPTION,
)
request_toolkit = RequestsToolkit(requests_wrapper=self.requests_wrapper)
return [*request_toolkit.get_tools(), json_agent_tool]
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
json_spe... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
119336d6d0f6-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/base.html |
119336d6d0f6-1 | input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/openapi/base.html |
c5561aedd9ea-0 | Source code for langchain.agents.agent_toolkits.python.base
"""Python agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.python.prompt import PREFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langch... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/python/base.html |
c5561aedd9ea-1 | elif agent_type == AgentType.OPENAI_FUNCTIONS:
system_message = SystemMessage(content=prefix)
_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_m... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/python/base.html |
b03d88c101f5-0 | Source code for langchain.agents.agent_toolkits.spark.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAge... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark/base.html |
b03d88c101f5-1 | ) -> AgentExecutor:
"""Construct a spark agent from an LLM and dataframe."""
if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
raise ValueError("Spark is not installed. run `pip install pyspark`.")
if input_variables is None:
input_variables = ["df", "input", "agent_scrat... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark/base.html |
604be8ad511d-0 | Source code for langchain.agents.agent_toolkits.nla.toolkit
"""Toolkit for interacting with API's using natural language."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.a... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
604be8ad511d-1 | )
http_operation_tools.append(endpoint_tool)
return http_operation_tools
[docs] @classmethod
def from_llm_and_spec(
cls,
llm: BaseLanguageModel,
spec: OpenAPISpec,
requests: Optional[Requests] = None,
verbose: bool = False,
**kwargs: Any,
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
604be8ad511d-2 | spec = OpenAPISpec.from_url(ai_plugin.api.url)
# TODO: Merge optional Auth information with the `requests` argument
return cls.from_llm_and_spec(
llm=llm,
spec=spec,
requests=requests,
verbose=verbose,
**kwargs,
)
[docs] @classmethod... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
2d7e28de58b8-0 | Source code for langchain.agents.agent_toolkits.powerbi.toolkit
"""Toolkit for interacting with a Power BI dataset."""
from typing import List, Optional
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
2d7e28de58b8-1 | prompt=PromptTemplate(
template=QUESTION_TO_QUERY,
input_variables=["tool_input", "tables", "schemas", "examples"],
),
)
return [
QueryPowerBITool(
llm_chain=chain,
powerbi=self.powerbi,
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
9dd2fc47c15c-0 | Source code for langchain.agents.agent_toolkits.powerbi.chat_base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent import AgentOutputParser
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
P... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
9dd2fc47c15c-1 | """
if toolkit is None:
if powerbi is None:
raise ValueError("Must provide either a toolkit or powerbi dataset")
toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
agent = ConversationalChatAgent.from_llm_and_tools(
llm=llm,
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
a9007248f28d-0 | Source code for langchain.agents.agent_toolkits.powerbi.base
"""Power BI agent."""
from typing import Any, Dict, List, Optional
from langchain.agents import AgentExecutor
from langchain.agents.agent_toolkits.powerbi.prompt import (
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
from langchain.agents.agent_toolkits.powerbi.t... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
a9007248f28d-1 | tools = toolkit.get_tools()
agent = ZeroShotAgent(
llm_chain=LLMChain(
llm=llm,
prompt=ZeroShotAgent.create_prompt(
tools,
prefix=prefix.format(top_k=top_k),
suffix=suffix,
format_instructions=format_instructions,
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
f755b53369db-0 | Source code for langchain.agents.agent_toolkits.spark_sql.toolkit
"""Toolkit for interacting with Spark SQL."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.tools import BaseTool
from ... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark_sql/toolkit.html |
8f52e2e1fff8-0 | Source code for langchain.agents.agent_toolkits.spark_sql.base
"""Spark SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark_sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.spark_sql.toolkit i... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
8f52e2e1fff8-1 | llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
too... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/spark_sql/base.html |
b4d0f381eac8-0 | Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, List, Optional, Union
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.base_language import BaseLanguag... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/csv/base.html |
935363887fff-0 | Source code for langchain.agents.agent_toolkits.azure_cognitive_services.toolkit
from __future__ import annotations
import sys
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools.azure_cognitive_services import (
AzureCogsFormRecognizerTool,
AzureCogsImageAn... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/azure_cognitive_services/toolkit.html |
c2b006caf35c-0 | Source code for langchain.agents.agent_toolkits.jira.toolkit
"""Jira Toolkit."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.jira.tool import JiraAction
from langchain.utilities.jira import JiraAPIWrapper
[docs]class Jira... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
1a7e6feffe57-0 | Source code for langchain.agents.agent_toolkits.vectorstore.toolkit
"""Toolkit for interacting with a vector store."""
from typing import List
from pydantic import BaseModel, Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.llms.open... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
1a7e6feffe57-1 | self.vectorstore_info.name, self.vectorstore_info.description
)
qa_with_sources_tool = VectorStoreQAWithSourcesTool(
name=f"{self.vectorstore_info.name}_with_sources",
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/toolkit.html |
aa90ee00e458-0 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit ... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
aa90ee00e458-1 | )
[docs]def create_vectorstore_router_agent(
llm: BaseLanguageModel,
toolkit: VectorStoreRouterToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = ROUTER_PREFIX,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any]... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
54d325e3ea74-0 | Source code for langchain.agents.agent_toolkits.sql.toolkit
"""Toolkit for interacting with a SQL database."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.sql_database import SQLDatab... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
54d325e3ea74-1 | "schema and sample rows for those tables. "
"Be sure that the tables actually exist by calling list_tables_sql_db "
"first! Example Input: 'table1, table2, table3'"
)
return [
QuerySQLDataBaseTool(
db=self.db, description=query_sql_database_tool_descri... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
cfe5cc7b0817-0 | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
from langcha... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/base.html |
cfe5cc7b0817-1 | **kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct a sql agent from an LLM and tools."""
tools = toolkit.get_tools()
prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
agent: BaseSingleActionAgent
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt = ZeroShotAgen... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/base.html |
cfe5cc7b0817-2 | tools=tools,
callback_manager=callback_manager,
verbose=verbose,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/sql/base.html |
e420b0bf5aa3-0 | Source code for langchain.agents.agent_toolkits.gmail.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.gmail.create_draft import GmailCreateD... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/gmail/toolkit.html |
960e10ca8716-0 | Source code for langchain.agents.agent_toolkits.file_management.toolkit
"""Toolkit for interacting with the local filesystem."""
from __future__ import annotations
from typing import List, Optional
from pydantic import root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools impo... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
960e10ca8716-1 | )
return values
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
allowed_tools = self.selected_tools or _FILE_TOOLS.keys()
tools: List[BaseTool] = []
for tool in allowed_tools:
tool_cls = _FILE_TOOLS[tool]
tools.append(t... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
bb0e366cb94c-0 | Source code for langchain.agents.agent_toolkits.zapier.toolkit
"""Zapier Toolkit."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.zapier.tool import ZapierNLARunAction
from langchain.utilities.zapier import ZapierNLAWrappe... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
bb0e366cb94c-1 | for action in actions
]
return cls(tools=tools)
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return self.tools | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/zapier/toolkit.html |
f50ec90d5116-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional, Tuple
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.pandas.prompt import (
FUNCTIONS_WITH_DF,
FUNC... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
f50ec90d5116-1 | include_dfs_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad", "num_dfs"]
if include_dfs_head:
input_variables += ["dfs_head"]
if prefix is None:
prefix = MULTI_DF_PREFIX
df_locals = {}
for i, dataframe in enumerate(dfs):
... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
f50ec90d5116-2 | include_df_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
if include_df_head:
input_variables += ["df_head"]
if prefix is None:
prefix = PREFIX
tools = [PythonAstREPLTool(locals={"df": df})]
prompt = ZeroShotAgent.create_promp... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
f50ec90d5116-3 | include_df_in_prompt=include_df_in_prompt,
)
else:
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_single_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_v... | https://api.python.langchain.com/en/stable/_modules/langchain/agents/agent_toolkits/pandas/base.html |
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