id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
94c9c3e1d429-4 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""
Compute the string distance between the prediction and the reference.
Args:
inputs (Dict[str, Any]): The input values.
r... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
94c9c3e1d429-5 | """
Evaluate the string distance between the prediction and the reference.
Args:
prediction (str): The prediction string.
reference (Optional[str], optional): The reference string.
input (Optional[str], optional): The input string.
callbacks (Callbacks, op... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
94c9c3e1d429-6 | callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
)
return self._prepare_output(result)
[docs]class PairwiseStringDistanceEvalChain(PairwiseStringEvaluator, _RapidFuzzChainMixin):
"""Compute string edit distances between two pre... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
94c9c3e1d429-7 | Args:
inputs (Dict[str, Any]): The input values.
run_manager (AsyncCallbackManagerForChainRun , optional):
The callback manager.
Returns:
Dict[str, Any]: The evaluation results containing the score.
"""
return {
"score": self.comput... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
94c9c3e1d429-8 | callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""
Asynchronously evaluate the string distance between two predictions.
Args:
predi... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
87c8d0205432-0 | Source code for langchain.evaluation.agents.trajectory_eval_chain
"""A chain for evaluating ReAct style agents.
This chain is used to evaluate ReAct style agents by reasoning about
the sequence of actions taken and their outcomes. It uses a language model
chain (LLMChain) to generate the reasoning and scores.
"""
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-1 | """Parse the output text and extract the score and reasoning.
Args:
text (str): The output text to parse.
Returns:
TrajectoryEval: A named tuple containing the normalized score and reasoning.
Raises:
OutputParserException: If the score is not found in the outp... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-2 | from langchain.tools import tool
@tool
def geography_answers(country: str, question: str) -> str:
\"\"\"Very helpful answers to geography questions.\"\"\"
return f"{country}? IDK - We may never know {question}."
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-3 | extra = Extra.ignore
@property
def requires_reference(self) -> bool:
"""Whether this evaluator requires a reference label."""
return False
@property
def _tools_description(self) -> str:
"""Get the description of the agent tools.
Returns:
str: The description o... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-4 | return f"""
The following is the expected answer. Use this to measure correctness:
[GROUND_TRUTH]
{reference}
[END_GROUND_TRUTH]
"""
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
agent_tools: Optional[Sequence[BaseTool]] = None,
output_parser: Optional[TrajectoryO... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-5 | """
return ["question", "agent_trajectory", "answer", "reference"]
@property
def output_keys(self) -> List[str]:
"""Get the output keys for the chain.
Returns:
List[str]: The output keys.
"""
return ["score", "reasoning"]
[docs] def prep_inputs(self, inputs... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-6 | ) -> Dict[str, Any]:
"""Run the chain and generate the output.
Args:
inputs (Dict[str, str]): The input values for the chain.
run_manager (Optional[CallbackManagerForChainRun]): The callback
manager for the chain run.
Returns:
Dict[str, Any]: T... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-7 | the reasoning for reaching that.
"""
inputs = {
"question": input,
"agent_trajectory": self.get_agent_trajectory(agent_trajectory),
"answer": prediction,
"reference": reference,
}
return self.__call__(
inputs=inputs,
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
87c8d0205432-8 | inputs=inputs,
callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
return_only_outputs=True,
) | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/agents/trajectory_eval_chain.html |
f1b64d6a8f13-0 | Source code for langchain.evaluation.criteria.eval_chain
from __future__ import annotations
from enum import Enum
from typing import Any, Dict, List, Mapping, Optional, Union
from pydantic import Extra, Field
from langchain.callbacks.manager import Callbacks
from langchain.chains.constitutional_ai.models import Constit... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-1 | Criteria.COHERENCE: "Is the submission coherent, well-structured, and organized?",
Criteria.HARMFULNESS: "Is the submission harmful, offensive, or inappropriate?"
" If so, response Y. If not, respond N.",
Criteria.MALICIOUSNESS: "Is the submission malicious in any way?"
" If so, response Y. If not, resp... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-2 | Args:
text (str): The output text to parse.
Returns:
Dict: The parsed output.
"""
parsed = text.strip().rsplit("\n", maxsplit=1)
if len(parsed) == 1:
reasoning = ""
verdict = parsed[0]
else:
reasoning, verdict = parsed
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-3 | }
if isinstance(criteria, Criteria):
criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
elif isinstance(criteria, str):
criteria_ = {criteria: _SUPPORTED_CRITERIA[Criteria(criteria)]}
elif isinstance(criteria, ConstitutionalPrinciple):
criteria_ = {criteria.name: criteria.cr... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-4 | Returns
-------
CriteriaEvalChain
An instance of the `CriteriaEvalChain` class.
Examples
--------
>>> from langchain.chat_models import ChatAnthropic
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = ChatAnthropic(temperature=0)
>>> criteria = {"my-custom-... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-5 | ... reference="There are 3 apples",
... )
{
'score': 0,
'reasoning': 'The criterion for this task is the correctness of the submission. The submission states that there are 4 apples, but the reference indicates that there are actually 3 apples. Therefore, the submission is not correct, accur... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-6 | cls, prompt: Optional[BasePromptTemplate] = None
) -> BasePromptTemplate:
expected_input_vars = {"input", "output", "criteria"}
prompt_ = prompt or PROMPT
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f"Input variables should be {expect... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-7 | Parameters
----------
llm : BaseLanguageModel
The language model to use for evaluation.
criteria : CRITERIA_TYPE - default=None for "helpfulness"
The criteria to evaluate the runs against. It can be:
- a mapping of a criterion name to its description
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-8 | )
criteria_ = cls.resolve_criteria(criteria)
criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items())
prompt_ = prompt_.partial(criteria=criteria_str)
return cls(
llm=llm,
prompt=prompt_,
criterion_name="-".join(criteria_),
**kwa... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-9 | `requires_reference` is `True`.
input : Optional[str], default=None
The input text used to generate the prediction.
**kwargs : Any
Additional keyword arguments to pass to the `LLMChain` `__call__`
method.
Returns
-------
dict
The ev... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-10 | The predicted text to evaluate.
reference : Optional[str], default=None
The reference text to compare against. This is required if
`requires_reference` is `True`.
input : Optional[str], default=None
The input text used to generate the prediction.
**kwargs : An... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-11 | expected_input_vars = {"input", "output", "criteria", "reference"}
prompt_ = prompt or PROMPT_WITH_REFERENCES
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt_.inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
f1b64d6a8f13-12 | >>> llm = OpenAI()
>>> criteria = {
"hallucination": (
"Does this submission contain information"
" not present in the input or reference?"
),
}
>>> chain = LabeledCriteriaEvalChain.from_llm(
llm=llm,
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/criteria/eval_chain.html |
a3af5b45857b-0 | Source code for langchain.evaluation.embedding_distance.base
"""A chain for comparing the output of two models using embeddings."""
from enum import Enum
from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import Field, root_validator
from langchain.callbacks.manager import (
AsyncCallback... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-1 | @root_validator(pre=False)
def _validate_tiktoken_installed(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate that the TikTok library is installed.
Args:
values (Dict[str, Any]): The values to validate.
Returns:
Dict[str, Any]: The validated values.
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-2 | metrics = {
EmbeddingDistance.COSINE: self._cosine_distance,
EmbeddingDistance.EUCLIDEAN: self._euclidean_distance,
EmbeddingDistance.MANHATTAN: self._manhattan_distance,
EmbeddingDistance.CHEBYSHEV: self._chebyshev_distance,
EmbeddingDistance.HAMMING: self._h... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-3 | """
return np.sum(np.abs(a - b))
@staticmethod
def _chebyshev_distance(a: np.ndarray, b: np.ndarray) -> np.floating:
"""Compute the Chebyshev distance between two vectors.
Args:
a (np.ndarray): The first vector.
b (np.ndarray): The second vector.
Returns:
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-4 | {'score': 0.5}
"""
@property
def requires_reference(self) -> bool:
"""Return whether the chain requires a reference.
Returns:
bool: True if a reference is required, False otherwise.
"""
return True
@property
def evaluation_name(self) -> str:
return... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-5 | run_manager (AsyncCallbackManagerForChainRun, optional):
The callback manager.
Returns:
Dict[str, Any]: The computed score.
"""
embedded = await self.embeddings.aembed_documents(
[inputs["prediction"], inputs["reference"]]
)
vectors = np.ar... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-6 | callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Asynchronously evaluate the embedding distance between
a prediction and reference.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-7 | return f"pairwise_embedding_{self.distance_metric.value}_distance"
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Compute the score for two predictions.
Args:
inputs (Dict[str, Any]): ... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-8 | callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Evaluate the embedding distance between two predictions.
Args:
prediction (str): The outp... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
a3af5b45857b-9 | callbacks (Callbacks, optional): The callbacks to use.
tags (List[str], optional): Tags to apply to traces
metadata (Dict[str, Any], optional): metadata to apply to traces
**kwargs (Any): Additional keyword arguments.
Returns:
dict: A dictionary containing:
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/embedding_distance/base.html |
12affadb1e40-0 | Source code for langchain.tools.base
"""Base implementation for tools or skills."""
from __future__ import annotations
import asyncio
import warnings
from abc import abstractmethod
from functools import partial
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type,... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-1 | # specify valid annotations.
typehint_mandate = """
class ChildTool(BaseTool):
...
args_schema: Type[BaseModel] = SchemaClass
..."""
raise SchemaAnnotationError(
f"Tool definition for {name} must include valid type annotations"
f" for a... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-2 | [docs]def create_schema_from_function(
model_name: str,
func: Callable,
) -> Type[BaseModel]:
"""Create a pydantic schema from a function's signature.
Args:
model_name: Name to assign to the generated pydandic schema
func: Function to generate the schema from
Returns:
A pydan... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-3 | """The unique name of the tool that clearly communicates its purpose."""
description: str
"""Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
"""
args_schema: Optional[Type[BaseModel]] = None
"""Pydantic model class to vali... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-4 | Union[bool, str, Callable[[ToolException], str]]
] = False
"""Handle the content of the ToolException thrown."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def is_single_input(self) -> bool:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-5 | self,
tool_input: Union[str, Dict],
) -> Union[str, Dict[str, Any]]:
"""Convert tool input to pydantic model."""
input_args = self.args_schema
if isinstance(tool_input, str):
if input_args is not None:
key_ = next(iter(input_args.__fields__.keys()))
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-6 | to child implementations to enable tracing,
"""
raise NotImplementedError()
def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
# For backwards compatibility, if run_input is a string,
# pass as a positional argument.
if isinstance(tool_input, s... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-7 | tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
observation = (
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
if new_arg_supported
else self._run(*tool_args, **tool_kwargs)
)
except ToolException as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-8 | metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> Any:
"""Run the tool asynchronously."""
parsed_input = self._parse_input(tool_input)
if not self.verbose and verbose is not None:
verbose_ = verbose
else:
verbose_ = self.verbose
ca... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-9 | observation = self.handle_tool_error(e)
else:
raise ValueError(
f"Got unexpected type of `handle_tool_error`. Expected bool, str "
f"or callable. Received: {self.handle_tool_error}"
)
await run_manager.on_tool_end(
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-10 | # --- Tool ---
@property
def args(self) -> dict:
"""The tool's input arguments."""
if self.args_schema is not None:
return self.args_schema.schema()["properties"]
# For backwards compatibility, if the function signature is ambiguous,
# assume it takes a single string ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-11 | **kwargs: Any,
) -> Any:
"""Use the tool asynchronously."""
if self.coroutine:
new_argument_supported = signature(self.coroutine).parameters.get(
"callbacks"
)
return (
await self.coroutine(
*args,
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-12 | """The input arguments' schema."""
func: Callable[..., Any]
"""The function to run when the tool is called."""
coroutine: Optional[Callable[..., Awaitable[Any]]] = None
"""The asynchronous version of the function."""
# --- Runnable ---
[docs] async def ainvoke(
self,
input: Union[... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-13 | ) -> str:
"""Use the tool asynchronously."""
if self.coroutine:
new_argument_supported = signature(self.coroutine).parameters.get(
"callbacks"
)
return (
await self.coroutine(
*args,
callbacks=run... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-14 | return a + b
tool = StructuredTool.from_function(add)
tool.run(1, 2) # 3
"""
name = name or func.__name__
description = description or func.__doc__
assert (
description is not None
), "Function must have a docstring if description not p... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-15 | - Function must have a docstring
Examples:
.. code-block:: python
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
12affadb1e40-16 | elif len(args) == 0:
# if there are no arguments, then we use the function name as the tool name
# Example usage: @tool(return_direct=True)
def _partial(func: Callable[[str], str]) -> BaseTool:
return _make_with_name(func.__name__)(func)
return _partial
else:
rais... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
d32338bb0021-0 | Source code for langchain.tools.convert_to_openai
from typing import TypedDict
from langchain.tools import BaseTool, StructuredTool
[docs]class FunctionDescription(TypedDict):
"""Representation of a callable function to the OpenAI API."""
name: str
"""The name of the function."""
description: str
""... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/convert_to_openai.html |
0004f4a4901e-0 | Source code for langchain.tools.plugin
from __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base impo... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
0004f4a4901e-1 | """Tool for getting the OpenAPI spec for an AI Plugin."""
plugin: AIPlugin
api_spec: str
args_schema: Type[AIPluginToolSchema] = AIPluginToolSchema
[docs] @classmethod
def from_plugin_url(cls, url: str) -> AIPluginTool:
plugin = AIPlugin.from_url(url)
description = (
f"Cal... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
2256d942f176-0 | Source code for langchain.tools.ifttt
"""From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.
# Creating a webhook
- Go to https://ifttt.com/create
# Configuring the "If This"
- Click on the "If This" button in the IFTTT interface.
- Search for "Webhooks" in the search bar.
- Choose the first... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
2256d942f176-1 | - To get your webhook URL go to https://ifttt.com/maker_webhooks/settings
- Copy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.
"""
from typing import Optional
import requests
from langchain.callbacks.manager import CallbackManagerForToo... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
30c48af14553-0 | Source code for langchain.tools.requests.tool
# flake8: noqa
"""Tools for making requests to an API endpoint."""
import json
from typing import Any, Dict, Optional
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/requests/tool.html |
30c48af14553-1 | [docs]class RequestsPostTool(BaseRequestsTool, BaseTool):
"""Tool for making a POST request to an API endpoint."""
name = "requests_post"
description = """Use this when you want to POST to a website.
Input should be a json string with two keys: "url" and "data".
The value of "url" should be a string... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/requests/tool.html |
30c48af14553-2 | Input should be a json string with two keys: "url" and "data".
The value of "url" should be a string, and the value of "data" should be a dictionary of
key-value pairs you want to PATCH to the url.
Be careful to always use double quotes for strings in the json string
The output will be the text respons... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/requests/tool.html |
30c48af14553-3 | key-value pairs you want to PUT to the url.
Be careful to always use double quotes for strings in the json string.
The output will be the text response of the PUT request.
"""
def _run(
self, text: str, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
"""Run the tool... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/requests/tool.html |
30c48af14553-4 | async def _arun(
self,
url: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Run the tool asynchronously."""
return await self.requests_wrapper.adelete(_clean_url(url)) | https://api.python.langchain.com/en/latest/_modules/langchain/tools/requests/tool.html |
25be514711c7-0 | Source code for langchain.tools.playwright.base
from __future__ import annotations
from typing import TYPE_CHECKING, Optional, Tuple, Type
from pydantic import root_validator
from langchain.tools.base import BaseTool
if TYPE_CHECKING:
from playwright.async_api import Browser as AsyncBrowser
from playwright.sync... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/base.html |
25be514711c7-1 | raise ValueError("Either async_browser or sync_browser must be specified.")
return values
[docs] @classmethod
def from_browser(
cls,
sync_browser: Optional[SyncBrowser] = None,
async_browser: Optional[AsyncBrowser] = None,
) -> BaseBrowserTool:
"""Instantiate the tool.... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/base.html |
4e11f42244a0-0 | Source code for langchain.tools.playwright.click
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrows... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/click.html |
4e11f42244a0-1 | page = get_current_page(self.sync_browser)
# Navigate to the desired webpage before using this tool
selector_effective = self._selector_effective(selector=selector)
from playwright.sync_api import TimeoutError as PlaywrightTimeoutError
try:
page.click(
selecto... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/click.html |
ebb4b3688ce0-0 | Source code for langchain.tools.playwright.extract_hyperlinks
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Optional, Type
from pydantic import BaseModel, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToo... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_hyperlinks.html |
ebb4b3688ce0-1 | soup = BeautifulSoup(html_content, "lxml")
# Find all the anchor elements and extract their href attributes
anchors = soup.find_all("a")
if absolute_urls:
base_url = page.url
links = [urljoin(base_url, anchor.get("href", "")) for anchor in anchors]
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_hyperlinks.html |
9fdea6b8a2cd-0 | Source code for langchain.tools.playwright.extract_text
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_text.html |
9fdea6b8a2cd-1 | async def _arun(
self, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
) -> str:
"""Use the tool."""
if self.async_browser is None:
raise ValueError(f"Asynchronous browser not provided to {self.name}")
# Use Beautiful Soup since it's faster than looping throu... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/extract_text.html |
2c9884b5cf49-0 | Source code for langchain.tools.playwright.current_page
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrows... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/current_page.html |
79cc64018406-0 | Source code for langchain.tools.playwright.navigate
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBr... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate.html |
79cc64018406-1 | page = await aget_current_page(self.async_browser)
response = await page.goto(url)
status = response.status if response else "unknown"
return f"Navigating to {url} returned status code {status}" | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate.html |
70ff3e01108c-0 | Source code for langchain.tools.playwright.get_elements
from __future__ import annotations
import json
from typing import TYPE_CHECKING, List, Optional, Sequence, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
70ff3e01108c-1 | ) -> List[dict]:
"""Get elements matching the given CSS selector."""
elements = page.query_selector_all(selector)
results = []
for element in elements:
result = {}
for attribute in attributes:
if attribute == "innerText":
val: Optional[str] = element.inner_tex... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
70ff3e01108c-2 | """Use the tool."""
if self.async_browser is None:
raise ValueError(f"Asynchronous browser not provided to {self.name}")
page = await aget_current_page(self.async_browser)
# Navigate to the desired webpage before using this tool
results = await _aget_elements(page, selector, ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/get_elements.html |
e1b5d74fadb1-0 | Source code for langchain.tools.playwright.navigate_back
from __future__ import annotations
from typing import Optional, Type
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.playwright.base import BaseBrow... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate_back.html |
e1b5d74fadb1-1 | response = await page.go_back()
if response:
return (
f"Navigated back to the previous page with URL '{response.url}'."
f" Status code {response.status}"
)
else:
return "Unable to navigate back; no previous page in the history" | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/navigate_back.html |
24026ca48289-0 | Source code for langchain.tools.playwright.utils
"""Utilities for the Playwright browser tools."""
from __future__ import annotations
import asyncio
from typing import TYPE_CHECKING, Any, Coroutine, TypeVar
if TYPE_CHECKING:
from playwright.async_api import Browser as AsyncBrowser
from playwright.async_api impo... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/utils.html |
24026ca48289-1 | return context.pages[-1]
[docs]def create_async_playwright_browser(headless: bool = True) -> AsyncBrowser:
"""
Create an async playwright browser.
Args:
headless: Whether to run the browser in headless mode. Defaults to True.
Returns:
AsyncBrowser: The playwright browser.
"""
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/playwright/utils.html |
efbb12ea87a8-0 | Source code for langchain.tools.file_management.file_search
import fnmatch
import os
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/file_search.html |
efbb12ea87a8-1 | relative_path = os.path.relpath(absolute_path, dir_path_)
matches.append(relative_path)
if matches:
return "\n".join(matches)
else:
return f"No files found for pattern {pattern} in directory {dir_path}"
except Exception as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/file_search.html |
8fa76a511dc2-0 | Source code for langchain.tools.file_management.move
import shutil
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMP... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/move.html |
8fa76a511dc2-1 | shutil.move(str(source_path_), destination_path_)
return f"File moved successfully from {source_path} to {destination_path}."
except Exception as e:
return "Error: " + str(e)
# TODO: Add aiofiles method | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/move.html |
31df266bc7d7-0 | Source code for langchain.tools.file_management.delete
import os
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLA... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/delete.html |
a8e52eaa6baf-0 | Source code for langchain.tools.file_management.read
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
Base... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/read.html |
b0af2970b5d6-0 | Source code for langchain.tools.file_management.write
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
Bas... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/write.html |
b0af2970b5d6-1 | except Exception as e:
return "Error: " + str(e)
# TODO: Add aiofiles method | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/write.html |
c124d23d6106-0 | Source code for langchain.tools.file_management.copy
import shutil
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMP... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/copy.html |
c124d23d6106-1 | except Exception as e:
return "Error: " + str(e)
# TODO: Add aiofiles method | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/copy.html |
a795d9689423-0 | Source code for langchain.tools.file_management.list_dir
import os
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils import (
INVALID_PATH_TEMP... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/list_dir.html |
de07699f0bcf-0 | Source code for langchain.tools.file_management.utils
import sys
from pathlib import Path
from typing import Optional
from pydantic import BaseModel
[docs]def is_relative_to(path: Path, root: Path) -> bool:
"""Check if path is relative to root."""
if sys.version_info >= (3, 9):
# No need for a try/excep... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/utils.html |
de07699f0bcf-1 | if not is_relative_to(full_path, root):
raise FileValidationError(
f"Path {user_path} is outside of the allowed directory {root}"
)
return full_path | https://api.python.langchain.com/en/latest/_modules/langchain/tools/file_management/utils.html |
ccea5c59137d-0 | Source code for langchain.tools.youtube.search
"""
Adapted from https://github.com/venuv/langchain_yt_tools
CustomYTSearchTool searches YouTube videos related to a person
and returns a specified number of video URLs.
Input to this tool should be a comma separated list,
- the first part contains a person name
- and th... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/youtube/search.html |
ccea5c59137d-1 | else:
num_results = 2
return self._search(person, num_results) | https://api.python.langchain.com/en/latest/_modules/langchain/tools/youtube/search.html |
41344d0fe9ec-0 | Source code for langchain.tools.jira.tool
"""
This tool allows agents to interact with the atlassian-python-api library
and operate on a Jira instance. For more information on the
atlassian-python-api library, see https://atlassian-python-api.readthedocs.io/jira.html
To use this tool, you must first set as environment ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/jira/tool.html |
41344d0fe9ec-1 | """Use the Atlassian Jira API to run an operation."""
return self.api_wrapper.run(self.mode, instructions) | https://api.python.langchain.com/en/latest/_modules/langchain/tools/jira/tool.html |
3352bfa08017-0 | Source code for langchain.tools.metaphor_search.tool
"""Tool for the Metaphor search API."""
from typing import Dict, List, Optional, Union
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.me... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/metaphor_search/tool.html |
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