id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
e6d9fca7cb29-10 | )
next_step_output = self._execute_next_step(self.run_manager)
output = self._process_next_step_output(next_step_output, self.run_manager)
self.update_iterations()
return output
async def _acall_next(self) -> dict[str, Any]:
"""
Perform a single iteration of the async... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
544866b1933d-0 | Source code for langchain.agents.schema
from typing import Any, Dict, List, Tuple
from langchain.prompts.chat import ChatPromptTemplate
from langchain.schema import AgentAction
[docs]class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
"""Chat prompt template for the agent scratchpad."""
def _construct_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/schema.html |
8891f0a6cd33-0 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import (
Any,
Callable,
Dict,
Li... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-1 | """Return values of the agent."""
return ["output"]
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return None
[docs] @abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
)... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-2 | """Return response when agent has been stopped due to max iterations."""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-3 | save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
agent_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w"... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-4 | [docs] @abstractmethod
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: St... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-5 | _dict["_type"] = str(self._agent_type)
return _dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the agent.
Args:
file_path: Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-6 | """Runnable to call to get agent action."""
_input_keys: List[str] = []
"""Input keys."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
retur... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-7 | **kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
output = await self.runnable.ainvoke(inputs, config={"callbacks": callbacks})
return output
[docs]class LLMSingleActionAgent(BaseS... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-8 | intermediate_steps=intermediate_steps,
stop=self.stop,
callbacks=callbacks,
**kwargs,
)
return self.output_parser.parse(output)
[docs] async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-9 | """Return dictionary representation of agent."""
_dict = super().dict()
del _dict["output_parser"]
return _dict
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return self.allowed_tools
@property
def return_values(self) -> List[str]:
return ["output"]
de... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-10 | """
full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
return self.output_parser.parse(full_output)
[docs] async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
c... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-11 | @root_validator()
def validate_prompt(cls, values: Dict) -> Dict:
"""Validate that prompt matches format."""
prompt = values["llm_chain"].prompt
if "agent_scratchpad" not in prompt.input_variables:
logger.warning(
"`agent_scratchpad` should be a variable in prompt... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-12 | llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-13 | thoughts += (
"\n\nI now need to return a final answer based on the previous steps:"
)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
full_output = self.llm_chain.predict(**full_inputs)
# ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-14 | ) -> str:
return query
[docs]class AgentExecutor(Chain):
"""Agent that is using tools."""
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
"""The agent to run for creating a plan and determining actions
to take at each step of the execution loop."""
tools: Sequence[BaseTool]
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-15 | If a string, the string itself will be sent to the LLM as an observation.
If a callable function, the function will be called with the exception
as an argument, and the result of that function will be passed to the agent
as an observation.
"""
trim_intermediate_steps: Union[
int, Callable... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-16 | tools = values["tools"]
if isinstance(agent, BaseMultiActionAgent):
for tool in tools:
if tool.return_direct:
raise ValueError(
"Tools that have `return_direct=True` are not allowed "
"in multi-action agents"
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-17 | """Return the input keys.
:meta private:
"""
return self.agent.input_keys
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
if self.return_intermediate_steps:
return self.agent.return_values + [... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-18 | output, color="green", verbose=self.verbose
)
final_output = output.return_values
if self.return_intermediate_steps:
final_output["intermediate_steps"] = intermediate_steps
return final_output
def _take_next_step(
self,
name_to_tool_map: Dict[str, Base... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-19 | observation = self.handle_parsing_errors(e)
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
if run_manager:
run_manager.on_agent_action(output, color="green")
to... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-20 | )
else:
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = InvalidTool().run(
{
"requested_tool_name": agent_action.tool,
"available_tool_names": list(name_to_tool_map.keys()),
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-21 | if e.send_to_llm:
observation = str(e.observation)
text = str(e.llm_output)
else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_err... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-22 | if return_direct:
tool_run_kwargs["llm_prefix"] = ""
# We then call the tool on the tool input to get an observation
observation = await tool.arun(
agent_action.tool_input,
verbose=self.verbose,
color=color,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-23 | )
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
while self._should_cont... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-24 | # Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name for tool in self.tools], excluded_colors=["green"... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
8891f0a6cd33-25 | self.early_stopping_method, intermediate_steps, **inputs
)
return await self._areturn(
output, intermediate_steps, run_manager=run_manager
)
except TimeoutError:
# stop early when interrupted by the async timeout
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
b329de8b13af-0 | Source code for langchain.agents.agent_types
from enum import Enum
[docs]class AgentType(str, Enum):
"""Enumerator with the Agent types."""
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
REACT_DOCSTORE = "react-docstore"
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
CONVERSATIONAL_REACT... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html |
15152b18f43a-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.callbacks.base import BaseCallbackMa... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
15152b18f43a-1 | agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION
if agent is not None and agent_path is not None:
raise ValueError(
"Both `agent` and `agent_path` are specified, "
"but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
6a8689243cb0-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import BaseMultiActionAgent, BaseSingleActionAgent
from langchain.agents.tools import Tool
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
6a8689243cb0-1 | tools: List of tools this agent has access to.
**kwargs: Additional key word arguments passed to the agent executor.
Returns:
An agent executor.
"""
if "_type" not in config:
raise ValueError("Must specify an agent Type in config")
load_from_tools = config.pop("load_from_llm_and_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
6a8689243cb0-2 | del config["output_parser"]
combined_config = {**config, **kwargs}
return agent_cls(**combined_config) # type: ignore
[docs]def load_agent(
path: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Unified method for loading an agent from LangChainHub or local fs... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
6a8689243cb0-3 | # Load the agent from the config now.
return load_agent_from_config(config, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
9a56d3f01e31-0 | Source code for langchain.agents.output_parsers.json
from __future__ import annotations
import logging
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.output_parsers.json import parse_json_markdown
from langchain.schema import AgentAction, AgentFinish, OutputParserException
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/json.html |
9a56d3f01e31-1 | )
except Exception as e:
raise OutputParserException(f"Could not parse LLM output: {text}") from e
@property
def _type(self) -> str:
return "json-agent" | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/json.html |
66cfea25df6c-0 | Source code for langchain.agents.output_parsers.xml
from typing import Union
from langchain.agents import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish
[docs]class XMLAgentOutputParser(AgentOutputParser):
"""Parses tool invocations and final answers in XML format.
Expects output to be ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/xml.html |
66cfea25df6c-1 | else:
raise ValueError
[docs] def get_format_instructions(self) -> str:
raise NotImplementedError
@property
def _type(self) -> str:
return "xml-agent" | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/xml.html |
903b9a64ef4f-0 | Source code for langchain.agents.output_parsers.react_json_single_input
import json
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
FINAL_ANSW... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_json_single_input.html |
903b9a64ef4f-1 | if not found:
# Fast fail to parse Final Answer.
raise ValueError("action not found")
action = found.group(1)
response = json.loads(action.strip())
includes_action = "action" in response
if includes_answer and includes_action:
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_json_single_input.html |
ffc113b29e7a-0 | Source code for langchain.agents.output_parsers.openai_functions
import asyncio
import json
from json import JSONDecodeError
from typing import List, Union
from langchain.agents.agent import AgentOutputParser
from langchain.schema import (
AgentAction,
AgentFinish,
OutputParserException,
)
from langchain.sc... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_functions.html |
ffc113b29e7a-1 | f"the `arguments` is not valid JSON."
)
# HACK HACK HACK:
# The code that encodes tool input into Open AI uses a special variable
# name called `__arg1` to handle old style tools that do not expose a
# schema and expect a single string argument as an input... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_functions.html |
ffc113b29e7a-2 | None, self.parse_result, result
)
[docs] def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
raise ValueError("Can only parse messages") | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_functions.html |
43562eacb773-0 | Source code for langchain.agents.output_parsers.react_single_input
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
FINAL_ANSWER_ACTION = "Fina... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_single_input.html |
43562eacb773-1 | includes_answer = FINAL_ANSWER_ACTION in text
regex = (
r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
)
action_match = re.search(regex, text, re.DOTALL)
if action_match:
if includes_answer:
raise OutputParserException(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_single_input.html |
43562eacb773-2 | send_to_llm=True,
)
else:
raise OutputParserException(f"Could not parse LLM output: `{text}`")
@property
def _type(self) -> str:
return "react-single-input" | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_single_input.html |
4f682cfca2b2-0 | Source code for langchain.agents.output_parsers.self_ask
from typing import Sequence, Union
from langchain.agents.agent import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
[docs]class SelfAskOutputParser(AgentOutputParser):
"""Parses self-ask style LLM calls.
Ex... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/self_ask.html |
ff77b53ce201-0 | Source code for langchain.agents.agent_toolkits.azure_cognitive_services
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,
AzureCogsImageAnalysisTo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/azure_cognitive_services.html |
e3de1bb782d8-0 | Source code for langchain.agents.agent_toolkits.base
"""Toolkits for agents."""
from abc import ABC, abstractmethod
from typing import List
from langchain.pydantic_v1 import BaseModel
from langchain.tools import BaseTool
[docs]class BaseToolkit(BaseModel, ABC):
"""Base Toolkit representing a collection of related t... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/base.html |
36adc3c5fe52-0 | Source code for langchain.agents.agent_toolkits.multion.toolkit
"""MultiOn agent."""
from __future__ import annotations
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.multion.create_session import MultionCreateSession
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/multion/toolkit.html |
8e6c9839761f-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/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
8e6c9839761f-1 | name="json_explorer",
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: B... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
913afdc94eea-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/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
913afdc94eea-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/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
c61a26783835-0 | Source code for langchain.agents.agent_toolkits.openapi.planner
"""Agent that interacts with OpenAPI APIs via a hierarchical planning approach."""
import json
import re
from functools import partial
from typing import Any, Callable, Dict, List, Optional
import yaml
from langchain.agents.agent import AgentExecutor
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-1 | #
# Requests tools with LLM-instructed extraction of truncated responses.
#
# Of course, truncating so bluntly may lose a lot of valuable
# information in the response.
# However, the goal for now is to have only a single inference step.
MAX_RESPONSE_LENGTH = 5000
"""Maximum length of the response to be returned."""
de... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-2 | response = response[: self.response_length]
return self.llm_chain.predict(
response=response, instructions=data["output_instructions"]
).strip()
async def _arun(self, text: str) -> str:
raise NotImplementedError()
[docs]class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-3 | """Maximum length of the response to be returned."""
llm_chain: LLMChain = Field(
default_factory=_get_default_llm_chain_factory(PARSING_PATCH_PROMPT)
)
"""LLMChain used to extract the response."""
def _run(self, text: str) -> str:
try:
data = json.loads(text)
except ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-4 | response=response, instructions=data["output_instructions"]
).strip()
async def _arun(self, text: str) -> str:
raise NotImplementedError()
[docs]class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
"""A tool that sends a DELETE request and parses the response."""
name: str = "req... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-5 | partial_variables={"endpoints": "- " + "- ".join(endpoint_descriptions)},
)
chain = LLMChain(llm=llm, prompt=prompt)
tool = Tool(
name=API_PLANNER_TOOL_NAME,
description=API_PLANNER_TOOL_DESCRIPTION,
func=chain.run,
)
return tool
def _create_api_controller_agent(
api_url:... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-6 | allowed_tools=[tool.name for tool in tools],
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
def _create_api_controller_tool(
api_spec: ReducedOpenAPISpec,
requests_wrapper: RequestsWrapper,
llm: BaseLanguageModel,
) -> Tool:
"""Expose controller as a tool.
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-7 | )
[docs]def create_openapi_agent(
api_spec: ReducedOpenAPISpec,
requests_wrapper: RequestsWrapper,
llm: BaseLanguageModel,
shared_memory: Optional[ReadOnlySharedMemory] = None,
callback_manager: Optional[BaseCallbackManager] = None,
verbose: bool = True,
agent_executor_kwargs: Optional[Dict[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
c61a26783835-8 | tools=tools,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
) | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/planner.html |
3c9c816216b2-0 | Source code for langchain.agents.agent_toolkits.openapi.spec
"""Quick and dirty representation for OpenAPI specs."""
from dataclasses import dataclass
from typing import List, Tuple
from langchain.utils.json_schema import dereference_refs
[docs]@dataclass(frozen=True)
class ReducedOpenAPISpec:
"""A reduced OpenAPI ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
3c9c816216b2-1 | if dereference:
endpoints = [
(name, description, dereference_refs(docs, full_schema=spec))
for name, description, docs in endpoints
]
# 3. Strip docs down to required request args + happy path response.
def reduce_endpoint_docs(docs: dict) -> dict:
out = {}
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/spec.html |
6572ccb237a8-0 | Source code for langchain.agents.agent_toolkits.gitlab.toolkit
"""GitHub Toolkit."""
from typing import Dict, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.gitlab.prompt import (
COMMENT_ON_ISSUE_PROMPT,
CREATE_FILE_PROMPT,
CREATE... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/gitlab/toolkit.html |
6572ccb237a8-1 | "description": CREATE_FILE_PROMPT,
},
{
"mode": "read_file",
"name": "Read File",
"description": READ_FILE_PROMPT,
},
{
"mode": "update_file",
"name": "Update File",
"descripti... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/gitlab/toolkit.html |
18e7fe6b343e-0 | Source code for langchain.agents.agent_toolkits.powerbi.toolkit
"""Toolkit for interacting with a Power BI dataset."""
from typing import List, Optional, Union
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
18e7fe6b343e-1 | return [
QueryPowerBITool(
llm_chain=self._get_chain(),
powerbi=self.powerbi,
examples=self.examples,
max_iterations=self.max_iterations,
output_token_limit=self.output_token_limit,
tiktoken_model_name=self.tikto... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/toolkit.html |
a92d8f4f46c6-0 | 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/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
a92d8f4f46c6-1 | tools = toolkit.get_tools()
tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
agent = ZeroShotAgent(
llm_chain=LLMChain(
llm=llm,
prompt=ZeroShotAgent.create_prompt(
tools,
prefix=prefix.format(top_k=top_k).format(tables=tabl... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/base.html |
fefd5c32e647-0 | 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/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
fefd5c32e647-1 | """
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()
tables = powerbi.table_names if powerbi else toolkit.powerbi.table_na... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/powerbi/chat_base.html |
3feed96ffea2-0 | Source code for langchain.agents.agent_toolkits.github.toolkit
"""GitHub Toolkit."""
from typing import Dict, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.github.prompt import (
COMMENT_ON_ISSUE_PROMPT,
CREATE_FILE_PROMPT,
CREATE... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/github/toolkit.html |
3feed96ffea2-1 | "description": CREATE_FILE_PROMPT,
},
{
"mode": "read_file",
"name": "Read File",
"description": READ_FILE_PROMPT,
},
{
"mode": "update_file",
"name": "Update File",
"descripti... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/github/toolkit.html |
e7af71177a6a-0 | Source code for langchain.agents.agent_toolkits.jira.toolkit
from typing import Dict, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.tools import BaseTool
from langchain.tools.jira.prompt import (
JIRA_CATCH_ALL_PROMPT,
JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
JIRA_GET_ALL_PROJE... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
e7af71177a6a-1 | },
]
tools = [
JiraAction(
name=action["name"],
description=action["description"],
mode=action["mode"],
api_wrapper=jira_api_wrapper,
)
for action in operations
]
return cls(tools=tools)
[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/jira/toolkit.html |
b172cc9d6909-0 | 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/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
b172cc9d6909-1 | 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/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
6d3780e83fcb-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/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
6d3780e83fcb-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 ImportError("Spark is not installed. run `pip install pyspark`.")
if input_variables is None:
input_variables = ["df", "input", "agent_scra... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
046495bdb863-0 | Source code for langchain.agents.agent_toolkits.file_management.toolkit
from __future__ import annotations
from typing import List, Optional
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import root_validator
from langchain.tools import BaseTool
from langchain.tools.file_manage... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
046495bdb863-1 | raise ValueError(
f"File Tool of name {tool_name} not supported."
f" Permitted tools: {list(_FILE_TOOLS)}"
)
return values
[docs] def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
allowed_tools = self.selected_to... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/file_management/toolkit.html |
b22ec3c58e63-0 | Source code for langchain.agents.agent_toolkits.sql.toolkit
"""Toolkit for interacting with an SQL database."""
from typing import List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import Field
from langchain.schema.language_model import BaseLanguageModel
from langchain.tools ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
b22ec3c58e63-1 | )
query_sql_database_tool_description = (
"Input to this tool is a detailed and correct SQL query, output is a "
"result from the database. If the query is not correct, an error message "
"will be returned. If an error is returned, rewrite the query, check the "
"... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/toolkit.html |
254c5a5159db-0 | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, Dict, List, Optional, Sequence
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
254c5a5159db-1 | agent_executor_kwargs: Optional[Dict[str, Any]] = None,
extra_tools: Sequence[BaseTool] = (),
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct an SQL agent from an LLM and tools."""
tools = toolkit.get_tools() + list(extra_tools)
prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
254c5a5159db-2 | raise ValueError(f"Agent type {agent_type} not supported at the moment.")
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
effe5f0c4b29-0 | Source code for langchain.agents.agent_toolkits.amadeus.toolkit
from __future__ import annotations
from typing import TYPE_CHECKING, List
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.pydantic_v1 import Field
from langchain.tools import BaseTool
from langchain.tools.amadeus.closest_airport... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/amadeus/toolkit.html |
8be310d73601-0 | Source code for langchain.agents.agent_toolkits.nla.toolkit
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.nla.tool import NLATool
from langchain.pydantic_v1 import Field
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
8be310d73601-1 | 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,
) -> NLAToolkit:
"""Instantiate the toolkit by creating too... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
8be310d73601-2 | # 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
def from_llm_and_ai_plugin_url(
cls,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
549dbafd4de7-0 | Source code for langchain.agents.agent_toolkits.nla.tool
"""Tool for interacting with a single API with natural language definition."""
from typing import Any, Optional
from langchain.agents.tools import Tool
from langchain.chains.api.openapi.chain import OpenAPIEndpointChain
from langchain.schema.language_model import... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/tool.html |
549dbafd4de7-1 | api_operation = APIOperation.from_openapi_spec(spec, path, method)
chain = OpenAPIEndpointChain.from_api_operation(
api_operation,
llm,
requests=requests,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
**kwargs,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/tool.html |
89803e6bdd65-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional, Sequence, Tuple
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.pandas.prompt import (
FUNCTIONS_WITH_DF... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
89803e6bdd65-1 | include_dfs_head = True
else:
suffix_to_use = SUFFIX_NO_DF
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 = MULT... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
89803e6bdd65-2 | elif include_df_in_prompt:
suffix_to_use = SUFFIX_WITH_DF
include_df_head = True
else:
suffix_to_use = SUFFIX_NO_DF
include_df_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
if include_df_head:
input_variables ... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
89803e6bdd65-3 | if isinstance(df, list):
for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_multi_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variab... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
89803e6bdd65-4 | tools = [PythonAstREPLTool(locals={"df": df})]
system_message = SystemMessage(content=prefix + suffix_to_use)
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
return prompt, tools
def _get_functions_multi_prompt(
dfs: Any,
prefix: Optional[str] = None,
suffix: Optional[... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.