id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
1003b619db12-9 | run_manager: CallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
vectordbkwargs = inputs.get("vectordbkwargs", {})
full_kwargs = {**self.search_kwargs, **vectordbkwargs}
return self.vectorstore.similarity_search(
question, k=self.top_k_docs_for_context, **ful... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-10 | callbacks=callbacks,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
b7bf40e40fe4-0 | Source code for langchain.chains.qa_generation.base
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_ge... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
b7bf40e40fe4-1 | Returns:
a QAGenerationChain class
"""
_prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
chain = LLMChain(llm=llm, prompt=_prompt)
return cls(llm_chain=chain, **kwargs)
@property
def _chain_type(self) -> str:
raise NotImplementedError
@property
def i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
fc7df566eccd-0 | Source code for langchain.chains.router.multi_retrieval_qa
"""Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
fc7df566eccd-1 | default_retriever: Optional[BaseRetriever] = None,
default_prompt: Optional[PromptTemplate] = None,
default_chain: Optional[Chain] = None,
**kwargs: Any,
) -> MultiRetrievalQAChain:
if default_prompt and not default_retriever:
raise ValueError(
"`default_r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
fc7df566eccd-2 | prompt = PromptTemplate(
template=prompt_template, input_variables=["history", "query"]
)
_default_chain = ConversationChain(
llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
)
return cls(
router_chain=router_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
821169c82ea8-0 | Source code for langchain.chains.router.llm_router
"""Base classes for LLM-powered router chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
821169c82ea8-1 | raise ValueError
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
output = cast(... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
821169c82ea8-2 | [docs] def parse(self, text: str) -> Dict[str, Any]:
try:
expected_keys = ["destination", "next_inputs"]
parsed = parse_and_check_json_markdown(text, expected_keys)
if not isinstance(parsed["destination"], str):
raise ValueError("Expected 'destination' to b... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
32cc6faeb9a8-0 | Source code for langchain.chains.router.base
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from typing import Any, Dict, List, Mapping, NamedTuple, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
C... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
32cc6faeb9a8-1 | destination_chains: Mapping[str, Chain]
"""Chains that return final answer to inputs."""
default_chain: Chain
"""Default chain to use when none of the destination chains are suitable."""
silent_errors: bool = False
"""If True, use default_chain when an invalid destination name is provided.
Defa... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
32cc6faeb9a8-2 | else:
raise ValueError(
f"Received invalid destination chain name '{route.destination}'"
)
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = ru... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
454a77e94830-0 | Source code for langchain.chains.router.multi_prompt
"""Use a single chain to route an input to one of multiple llm chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
from langchain.chains.llm ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
454a77e94830-1 | destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
chain = LLMChain(llm=llm, prompt=prompt)
destination_chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
2908d49b3182-0 | Source code for langchain.chains.router.embedding_router
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.router.base import RouterChain
from langchain.docstore.document import ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/embedding_router.html |
2908d49b3182-1 | """Convenience constructor."""
documents = []
for name, descriptions in names_and_descriptions:
for description in descriptions:
documents.append(
Document(page_content=description, metadata={"name": name})
)
vectorstore = vectorsto... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/router/embedding_router.html |
e0954e3530ec-0 | Source code for langchain.chains.natbot.base
"""Implement an LLM driven browser."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
e0954e3530ec-1 | "Directly instantiating an NatBotChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"] is not None:
values["llm_chain"] = LLMChain(llm... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
e0954e3530ec-2 | ) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
url = inputs[self.input_url_key]
browser_content = inputs[self.input_browser_content_key]
llm_cmd = self.llm_chain.predict(
objective=self.objective,
url=url[:100],
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/base.html |
0735a79f93ff-0 | Source code for langchain.chains.natbot.crawler
# flake8: noqa
import time
from sys import platform
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Optional,
Set,
Tuple,
TypedDict,
Union,
)
if TYPE_CHECKING:
from playwright.sync_api import Browser, CDPSession, ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-1 | )
self.page: Page = self.browser.new_page()
self.page.set_viewport_size({"width": 1280, "height": 1080})
self.page_element_buffer: Dict[int, ElementInViewPort]
self.client: CDPSession
[docs] def go_to_page(self, url: str) -> None:
self.page.goto(url=url if "://" in url else "h... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-2 | else:
print("Could not find element")
[docs] def type(self, id: Union[str, int], text: str) -> None:
self.click(id)
self.page.keyboard.type(text)
[docs] def enter(self) -> None:
self.page.keyboard.press("Enter")
[docs] def crawl(self) -> List[str]:
page = self.page
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-3 | ),
}
)
tree = self.client.send(
"DOMSnapshot.captureSnapshot",
{"computedStyles": [], "includeDOMRects": True, "includePaintOrder": True},
)
strings: Dict[int, str] = tree["strings"]
document: Dict[str, Any] = tree["documents"][0]
nodes... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-4 | node_name: Optional[str], has_click_handler: Optional[bool]
) -> str:
if node_name == "a":
return "link"
if node_name == "input":
return "input"
if node_name == "img":
return "img"
if (
node_name == "... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-5 | )
is_parent_desc_anchor, anchor_id = hash_tree[parent_id_str]
# even if the anchor is nested in another anchor, we set the "root" for all descendants to be ::Self
if node_name == tag:
value: Tuple[bool, Optional[int]] = (True, node_id)
elif (
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-6 | elem_left_bound = x
elem_top_bound = y
elem_right_bound = x + width
elem_lower_bound = y + height
partially_is_in_viewport = (
elem_left_bound < win_right_bound
and elem_right_bound >= win_left_bound
and elem_top_bound < win... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-7 | if ancestor_exception and ancestor_node:
ancestor_node.append(
{
"type": "attribute",
"key": key,
"value": element_attributes[key],
}
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-8 | elements_of_interest = []
id_counter = 0
for element in elements_in_view_port:
node_index = element.get("node_index")
node_name = element.get("node_name")
element_node_value = element.get("node_value")
node_is_clickable = element.get("is_clickable")
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
0735a79f93ff-9 | if inner_text != "":
elements_of_interest.append(
f"""<{converted_node_name} id={id_counter}{meta}>{inner_text}</{converted_node_name}>"""
)
else:
elements_of_interest.append(
f"""<{converted_node_name} id={id_counter}{m... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/natbot/crawler.html |
c223385bbea1-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from lan... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
c223385bbea1-1 | output_key="revised_statement",
)
chains = [
create_draft_answer_chain,
list_assertions_chain,
check_assertions_chain,
revised_answer_chain,
]
question_to_checked_assertions_chain = SequentialChain(
chains=chains,
input_variables=["question"],
outp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
c223385bbea1-2 | arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMCheckerChain with an llm is deprecated. "
"Please instantiate with question_to_checked_a... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
c223385bbea1-3 | ) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs[self.input_key]
output = self.question_to_checked_assertions_chain(
{"question": question}, callbacks=_run_manager.get_child()
)
return {self.output_key:... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
9d8b446084d4-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManage... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
9d8b446084d4-1 | """Input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + [... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
9d8b446084d4-2 | _chain_type_kwargs = chain_type_kwargs or {}
combine_documents_chain = load_qa_chain(
llm, chain_type=chain_type, **_chain_type_kwargs
)
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
@abstractmethod
def _get_docs(
self,
question: str,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
9d8b446084d4-3 | return {self.output_key: answer, "source_documents": docs}
else:
return {self.output_key: answer}
@abstractmethod
async def _aget_docs(
self,
question: str,
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get documents to d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
9d8b446084d4-4 | else:
return {self.output_key: answer}
[docs]class RetrievalQA(BaseRetrievalQA):
"""Chain for question-answering against an index.
Example:
.. code-block:: python
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.vec... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
9d8b446084d4-5 | """Vector Database to connect to."""
k: int = 4
"""Number of documents to query for."""
search_type: str = "similarity"
"""Search type to use over vectorstore. `similarity` or `mmr`."""
search_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Extra search args."""
@root_validator()
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
9d8b446084d4-6 | self,
question: str,
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
raise NotImplementedError("VectorDBQA does not support async")
@property
def _chain_type(self) -> str:
"""Return the chain type."""
return "vecto... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
c7ce8052df32-0 | Source code for langchain.chains.llm_symbolic_math.base
"""Chain that interprets a prompt and executes python code to do symbolic math."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager impo... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
c7ce8052df32-1 | def _evaluate_expression(self, expression: str) -> str:
try:
import sympy
except ImportError as e:
raise ImportError(
"Unable to import sympy, please install it with `pip install sympy`."
) from e
try:
output = str(sympy.sympify(exp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
c7ce8052df32-2 | return {self.output_key: answer}
async def _aprocess_llm_result(
self,
llm_output: str,
run_manager: AsyncCallbackManagerForChainRun,
) -> Dict[str, str]:
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
te... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
c7ce8052df32-3 | async def _acall(
self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
await _run_manager.on_text(inputs[self.input_key])
llm_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_symbolic_math/base.html |
6064f03f5f47-0 | Source code for langchain.chains.flare.base
from __future__ import annotations
import re
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Tuple
import numpy as np
from langchain.callbacks.manager import (
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
6064f03f5f47-1 | llm: OpenAI = Field(
default_factory=lambda: OpenAI(
max_tokens=32, model_kwargs={"logprobs": 1}, temperature=0
)
)
def _extract_tokens_and_log_probs(
self, generations: List[Generation]
) -> Tuple[Sequence[str], Sequence[float]]:
tokens = []
log_probs = [... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
6064f03f5f47-2 | end = idx + num_pad_tokens + 1
if idx - low_idx[i] < min_token_gap:
spans[-1][1] = end
else:
spans.append([idx, end])
return ["".join(tokens[start:end]) for start, end in spans]
[docs]class FlareChain(Chain):
"""Chain that combines a retriever, a question generator,
a... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
6064f03f5f47-3 | self,
questions: List[str],
user_input: str,
response: str,
_run_manager: CallbackManagerForChainRun,
) -> Tuple[str, bool]:
callbacks = _run_manager.get_child()
docs = []
for question in questions:
docs.extend(self.retriever.get_relevant_documents... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
6064f03f5f47-4 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
user_input = inputs[self.input_keys[0]]
response = ""
for i in r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
6064f03f5f47-5 | ) -> FlareChain:
"""Creates a FlareChain from a language model.
Args:
llm: Language model to use.
max_generation_len: Maximum length of the generated response.
**kwargs: Additional arguments to pass to the constructor.
Returns:
FlareChain class wit... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/base.html |
981c6c9d2e58-0 | Source code for langchain.chains.flare.prompts
from typing import Tuple
from langchain.prompts import PromptTemplate
from langchain.schema import BaseOutputParser
[docs]class FinishedOutputParser(BaseOutputParser[Tuple[str, bool]]):
"""Output parser that checks if the output is finished."""
finished_value: str ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/flare/prompts.html |
8e9dc1bade62-0 | 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 |
8e9dc1bade62-1 | - 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 |
2402ccf9ee95-0 | Source code for langchain.tools.render
"""Different methods for rendering Tools to be passed to LLMs.
Depending on the LLM you are using and the prompting strategy you are using,
you may want Tools to be rendered in a different way.
This module contains various ways to render tools.
"""
from typing import List
from lan... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/render.html |
2402ccf9ee95-1 | """Format tool into the OpenAI function API."""
if tool.args_schema:
return convert_pydantic_to_openai_function(
tool.args_schema, name=tool.name, description=tool.description
)
else:
return {
"name": tool.name,
"description": tool.description,
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/render.html |
df52d741fbc3-0 | Source code for langchain.tools.base
"""Base implementation for tools or skills."""
from __future__ import annotations
import asyncio
import inspect
import warnings
from abc import abstractmethod
from functools import partial
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, List, Optiona... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-1 | class _SchemaConfig:
"""Configuration for the pydantic model."""
extra: Any = Extra.forbid
arbitrary_types_allowed: bool = True
[docs]def create_schema_from_function(
model_name: str,
func: Callable,
) -> Type[BaseModel]:
"""Create a pydantic schema from a function's signature.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-2 | """Interface LangChain tools must implement."""
def __init_subclass__(cls, **kwargs: Any) -> None:
"""Create the definition of the new tool class."""
super().__init_subclass__(**kwargs)
args_schema_type = cls.__annotations__.get("args_schema", None)
if args_schema_type is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-3 | that after the tool is called, the AgentExecutor will stop looping.
"""
verbose: bool = False
"""Whether to log the tool's progress."""
callbacks: Callbacks = Field(default=None, exclude=True)
"""Callbacks to be called during tool execution."""
callback_manager: Optional[BaseCallbackManager] = F... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-4 | def args(self) -> dict:
if self.args_schema is not None:
return self.args_schema.schema()["properties"]
else:
schema = create_schema_from_function(self.name, self._run)
return schema.schema()["properties"]
# --- Runnable ---
@property
def input_schema(self... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-5 | )
# --- Tool ---
def _parse_input(
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... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-6 | to child implementations to enable tracing,
"""
return await asyncio.get_running_loop().run_in_executor(
None,
partial(self._run, **kwargs),
*args,
)
def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
# For backwards... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-7 | {"name": self.name, "description": self.description},
tool_input if isinstance(tool_input, str) else str(tool_input),
color=start_color,
name=run_name,
**kwargs,
)
try:
tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-8 | verbose: Optional[bool] = None,
start_color: Optional[str] = "green",
color: Optional[str] = "green",
callbacks: Callbacks = None,
*,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = None,
**kwargs: Any... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-9 | raise e
elif isinstance(self.handle_tool_error, bool):
if e.args:
observation = e.args[0]
else:
observation = "Tool execution error"
elif isinstance(self.handle_tool_error, str):
observation = self.handle_too... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-10 | **kwargs: Any,
) -> Any:
if not self.coroutine:
# If the tool does not implement async, fall back to default implementation
return await asyncio.get_running_loop().run_in_executor(
None, partial(self.invoke, input, config, **kwargs)
)
return await ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-11 | return (
self.func(
*args,
callbacks=run_manager.get_child() if run_manager else None,
**kwargs,
)
if new_argument_supported
else self.func(*args, **kwargs)
)
raise NotImplemen... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-12 | description: str,
return_direct: bool = False,
args_schema: Optional[Type[BaseModel]] = None,
coroutine: Optional[
Callable[..., Awaitable[Any]]
] = None, # This is last for compatibility, but should be after func
**kwargs: Any,
) -> Tool:
"""Initialize t... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-13 | # --- Tool ---
@property
def args(self) -> dict:
"""The tool's input arguments."""
return self.args_schema.schema()["properties"]
def _run(
self,
*args: Any,
run_manager: Optional[CallbackManagerForToolRun] = None,
**kwargs: Any,
) -> Any:
"""Use t... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-14 | )
[docs] @classmethod
def from_function(
cls,
func: Optional[Callable] = None,
coroutine: Optional[Callable[..., Awaitable[Any]]] = None,
name: Optional[str] = None,
description: Optional[str] = None,
return_direct: bool = False,
args_schema: Optional[Type[... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-15 | description = description or source_function.__doc__
if description is None:
raise ValueError(
"Function must have a docstring if description not provided."
)
# Description example:
# search_api(query: str) - Searches the API for the query.
sig = s... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-16 | @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:
# Searches the API for the query.
return
"""
def _make_with... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
df52d741fbc3-17 | args_schema=schema,
infer_schema=infer_schema,
)
# If someone doesn't want a schema applied, we must treat it as
# a simple string->string function
if func.__doc__ is None:
raise ValueError(
"Function must have a... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/base.html |
bf2de3a0258f-0 | Source code for langchain.tools.plugin
from __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import BaseModel
from langchain.to... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/plugin.html |
bf2de3a0258f-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 |
e7d849325ee9-0 | Source code for langchain.tools.yahoo_finance_news
from typing import Iterable, Optional
from requests.exceptions import HTTPError, ReadTimeout
from urllib3.exceptions import ConnectionError
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.document_loaders.web_base import WebBaseLoader
f... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/yahoo_finance_news.html |
e7d849325ee9-1 | except (HTTPError, ReadTimeout, ConnectionError):
if not links:
return f"No news found for company that searched with {query} ticker."
if not links:
return f"No news found for company that searched with {query} ticker."
loader = WebBaseLoader(web_paths=links)
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/yahoo_finance_news.html |
39c30f12750f-0 | Source code for langchain.tools.multion.create_session
from typing import TYPE_CHECKING, Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.base import BaseTool
if TYPE_CHECKING:
# This is for linting and IDE typeh... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/multion/create_session.html |
39c30f12750f-1 | Always the first step to run any activities that can be done using browser.
"""
args_schema: Type[CreateSessionSchema] = CreateSessionSchema
def _run(
self,
query: str,
url: Optional[str] = "https://www.google.com/",
run_manager: Optional[CallbackManagerForToolRun] = None... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/multion/create_session.html |
fcdfb7eff4e6-0 | Source code for langchain.tools.multion.update_session
from typing import TYPE_CHECKING, Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.base import BaseTool
if TYPE_CHECKING:
# This is for linting and IDE typeh... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/multion/update_session.html |
fcdfb7eff4e6-1 | tabId: str = ""
def _run(
self,
tabId: str,
query: str,
url: Optional[str] = "https://www.google.com/",
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> dict:
try:
try:
response = multion.update_session(tabId, {"input": que... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/multion/update_session.html |
fdb825f4f3d4-0 | Source code for langchain.tools.dataforseo_api_search.tool
"""Tool for the DataForSeo SERP API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.pydantic_v1 import Field
from langchain.tools.base import BaseTool
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/dataforseo_api_search/tool.html |
fdb825f4f3d4-1 | "A comprehensive Google Search API provided by DataForSeo."
"This tool is useful for obtaining real-time data on current events "
"or popular searches."
"The input should be a search query and the output is a JSON object "
"of the query results."
)
api_wrapper: DataForSeoAPIWrapp... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/dataforseo_api_search/tool.html |
9f74245d3a7f-0 | Source code for langchain.tools.interaction.tool
"""Tools for interacting with the user."""
import warnings
from typing import Any
from langchain.tools.human.tool import HumanInputRun
[docs]def StdInInquireTool(*args: Any, **kwargs: Any) -> HumanInputRun:
"""Tool for asking the user for input."""
warnings.warn(... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/interaction/tool.html |
33f594656582-0 | Source code for langchain.tools.openapi.utils.api_models
"""Pydantic models for parsing an OpenAPI spec."""
from __future__ import annotations
import logging
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-1 | }
INVALID_LOCATION_TEMPL = (
'Unsupported APIPropertyLocation "{location}"'
" for parameter {name}. "
+ f"Valid values are {[loc.value for loc in SUPPORTED_LOCATIONS]}"
)
SCHEMA_TYPE = Union[str, Type, tuple, None, Enum]
[docs]class APIPropertyBase(BaseModel):
"""Base model for an API property."""
#... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-2 | MediaType,
Parameter,
RequestBody,
Schema,
)
class APIProperty(APIPropertyBase):
"""A model for a property in the query, path, header, or cookie params."""
location: APIPropertyLocation = Field(alias="location")
"""The path/how it's being passed to... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-3 | return schema_type
@staticmethod
def _get_schema_type(
parameter: Parameter, schema: Optional[Schema]
) -> SCHEMA_TYPE:
if schema is None:
return None
schema_type: SCHEMA_TYPE = APIProperty._cast_schema_list_type(schema)
if schema_t... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-4 | elif schema is None:
return None
elif not isinstance(schema, Schema):
raise ValueError(f"Error dereferencing schema: {schema}")
return schema
[docs] @staticmethod
def is_supported_location(location: str) -> bool:
"""Return whether the pr... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-5 | @classmethod
def _process_object_schema(
cls, schema: Schema, spec: OpenAPISpec, references_used: List[str]
) -> Tuple[Union[str, List[str], None], List["APIRequestBodyProperty"]]:
from openapi_schema_pydantic import (
Reference,
)
properti... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-6 | pass
return f"Array<{ref_name}>"
else:
pass
if isinstance(items, Schema):
array_type = cls.from_schema(
schema=items,
name=f"{name}Item",
required=True, # ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-7 | properties=properties,
references_used=references_used,
)
# class APIRequestBodyProperty(APIPropertyBase):
class APIRequestBody(BaseModel):
"""A model for a request body."""
description: Optional[str] = Field(alias="description")
"""The description of the requ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-8 | spec=spec,
)
)
else:
api_request_body_properties.append(
APIRequestBodyProperty(
name="body",
required=True,
type=schema.type,
defau... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-9 | """The HTTP method of the operation."""
properties: Sequence[APIProperty] = Field(alias="properties")
# TODO: Add parse in used components to be able to specify what type of
# referenced object it is.
# """The properties of the operation."""
# components: Dict[str, BaseModel] = F... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-10 | path: str,
method: str,
) -> "APIOperation":
"""Create an APIOperation from an OpenAPI spec."""
operation = spec.get_operation(path, method)
parameters = spec.get_parameters_for_operation(operation)
properties = cls._get_properties_from_parameters(para... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-11 | elif isinstance(type_, type) and issubclass(type_, Enum):
return " | ".join([f"'{e.value}'" for e in type_])
else:
return str(type_)
def _format_nested_properties(
self, properties: List[APIRequestBodyProperty], indent: int = 2
) -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-12 | params.append(
f"{prop_desc}\n\t\t{prop_name}{prop_required}: {prop_type},"
)
formatted_params = "\n".join(params).strip()
description_str = f"/* {self.description} */" if self.description else ""
typescript_definition = f"""
{description_str}
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
33f594656582-13 | raise NotImplementedError("Only supported for pydantic v1")
[docs] class APIOperation(BaseModel): # type: ignore[no-redef]
def __init__(self, *args: Any, **kwargs: Any) -> None:
raise NotImplementedError("Only supported for pydantic v1") | https://api.python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
d33c3dc8095b-0 | Source code for langchain.tools.google_search.tool
"""Tool for the Google search API."""
from typing import Optional
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools.base import BaseTool
from langchain.utilities.google_search import GoogleSearchAPIWrapper
[docs]class GoogleSearchRu... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/google_search/tool.html |
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