id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
0e790c0dcca7-14 | run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManagerForToolRun(RunManager, ToolManagerMixin):
"""Callback manager for tool run."""
def get_child(self, tag: Optional[str] = None) -> CallbackManager:
"""Get a child callback manager.
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-15 | run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):
"""Async callback manager for tool run."""
def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
"""Get a child cal... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-16 | run_id=self.run_id,
parent_run_id=self.parent_run_id,
**kwargs,
)
class CallbackManager(BaseCallbackManager):
"""Callback manager that can be used to handle callbacks from langchain."""
def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str]... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-17 | messages: List[List[BaseMessage]],
**kwargs: Any,
) -> List[CallbackManagerForLLMRun]:
"""Run when LLM starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
messages (List[List[BaseMessage]]): The list of messages.
run_id (UUID, optional):... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-18 | inputs (Dict[str, Any]): The inputs to the chain.
run_id (UUID, optional): The ID of the run. Defaults to None.
Returns:
CallbackManagerForChainRun: The callback manager for the chain run.
"""
if run_id is None:
run_id = uuid4()
_handle_event(
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-19 | run_id = uuid4()
_handle_event(
self.handlers,
"on_tool_start",
"ignore_agent",
serialized,
input_str,
run_id=run_id,
parent_run_id=self.parent_run_id,
tags=self.tags,
**kwargs,
)
return C... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-20 | local_tags,
)
class AsyncCallbackManager(BaseCallbackManager):
"""Async callback manager that can be used to handle callbacks from LangChain."""
@property
def is_async(self) -> bool:
"""Return whether the handler is async."""
return True
async def on_llm_start(
self,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-21 | )
)
await asyncio.gather(*tasks)
return managers
async def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
**kwargs: Any,
) -> Any:
"""Run when LLM starts running.
Args:
serialized (... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-22 | serialized: Dict[str, Any],
inputs: Dict[str, Any],
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> AsyncCallbackManagerForChainRun:
"""Run when chain starts running.
Args:
serialized (Dict[str, Any]): The serialized chain.
inputs (Dict[str, Any]): ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-23 | input_str (str): The input to the tool.
run_id (UUID, optional): The ID of the run. Defaults to None.
parent_run_id (UUID, optional): The ID of the parent run.
Defaults to None.
Returns:
AsyncCallbackManagerForToolRun: The async callback manager
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-24 | Defaults to None.
local_tags (Optional[List[str]], optional): The local tags.
Defaults to None.
Returns:
AsyncCallbackManager: The configured async callback manager.
"""
return _configure(
cls,
inheritable_callbacks,
loc... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-25 | Defaults to None.
local_tags (Optional[List[str]], optional): The local tags. Defaults to None.
Returns:
T: The configured callback manager.
"""
callback_manager = callback_manager_cls(handlers=[])
if inheritable_callbacks or local_callbacks:
if isinstance(inheritable_callbacks, ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-26 | )
tracer_v2 = tracing_v2_callback_var.get()
tracing_v2_enabled_ = (
env_var_is_set("LANGCHAIN_TRACING_V2") or tracer_v2 is not None
)
tracer_project = os.environ.get(
"LANGCHAIN_PROJECT", os.environ.get("LANGCHAIN_SESSION", "default")
)
debug = _get_debug()
if (
verbo... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
0e790c0dcca7-27 | if tracing_v2_enabled_ and not any(
isinstance(handler, LangChainTracer)
for handler in callback_manager.handlers
):
if tracer_v2:
callback_manager.add_handler(tracer_v2, True)
else:
try:
handler = LangChainTrace... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/manager.html |
6238e2093594-0 | Source code for langchain.callbacks.argilla_callback
import os
import warnings
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
[docs]class ArgillaCallbackHandler(BaseCallbackHandler):
"""Cal... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-1 | >>> argilla_callback = ArgillaCallbackHandler(
... dataset_name="my-dataset",
... workspace_name="my-workspace",
... api_url="http://localhost:6900",
... api_key="argilla.apikey",
... )
>>> llm = OpenAI(
... temperature=0,
... callb... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-2 | `FeedbackDataset` lives in. Defaults to `None`, which means that either
`ARGILLA_API_URL` environment variable or the default
http://localhost:6900 will be used.
api_key: API Key to connect to the Argilla Server. Defaults to `None`, which
means that either `AR... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-3 | " set, it will default to `argilla.apikey`."
),
)
# Connect to Argilla with the provided credentials, if applicable
try:
rg.init(
api_key=api_key,
api_url=api_url,
)
except Exception as e:
raise Conne... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-4 | " If the problem persists please report it to"
" https://github.com/argilla-io/argilla/issues with the label"
" `langchain`."
) from e
supported_fields = ["prompt", "response"]
if supported_fields != [field.name for field in self.dataset.fields]:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-5 | [docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Do nothing when a new token is generated."""
pass
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Log records to Argilla when an LLM ends."""
# Do nothing if there's a parent_run_id... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-6 | we don't log the same input prompt twice, once when the LLM starts and once
when the chain starts.
"""
if "input" in inputs:
self.prompts.update(
{
str(kwargs["parent_run_id"] or kwargs["run_id"]): (
inputs["input"]
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-7 | self.dataset.add_records(
records=[
{
"fields": {
"prompt": " ".join(prompts), # type: ignore
"response": chain_output_val.strip(),
},
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
6238e2093594-8 | ) -> None:
"""Do nothing when tool outputs an error."""
pass
[docs] def on_text(self, text: str, **kwargs: Any) -> None:
"""Do nothing"""
pass
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Do nothing"""
pass | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/argilla_callback.html |
297f804adc8a-0 | Source code for langchain.callbacks.aim_callback
from copy import deepcopy
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
def import_aim() -> Any:
"""Import the aim python package and raise... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-1 | llm_streams (int): The number of times the text method has been called.
tool_starts (int): The number of times the tool start method has been called.
tool_ends (int): The number of times the tool end method has been called.
agent_ends (int): The number of times the agent end method has been call... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-2 | "step": self.step,
"starts": self.starts,
"ends": self.ends,
"errors": self.errors,
"text_ctr": self.text_ctr,
"chain_starts": self.chain_starts,
"chain_ends": self.chain_ends,
"llm_starts": self.llm_starts,
"llm_ends": self... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-3 | 'default' if not specified. Can be used later to query runs/sequences.
system_tracking_interval (:obj:`int`, optional): Sets the tracking interval
in seconds for system usage metrics (CPU, Memory, etc.). Set to `None`
to disable system metrics tracking.
log_system_params (:obj:`... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-4 | repo=self.repo,
system_tracking_interval=self.system_tracking_interval,
)
else:
self._run = aim.Run(
repo=self.repo,
experiment=self.experiment_name,
system_tracking_interval=self.system_tracking_... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-5 | for generation in generations
]
self._run.track(
generated,
name="on_llm_end",
context=resp,
)
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Run when LLM generates a new token."""
self.step += 1
self.llm_st... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-6 | outputs_res = deepcopy(outputs)
self._run.track(
aim.Text(outputs_res["output"]), name="on_chain_end", context=resp
)
[docs] def on_chain_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Run when chain errors."""
self.step +=... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-7 | """
Run when agent is ending.
"""
self.step += 1
self.text_ctr += 1
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Run when agent ends running."""
aim = import_aim()
self.step += 1
self.agent_ends += 1
self.ends... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-8 | log_system_params: bool = True,
langchain_asset: Any = None,
reset: bool = True,
finish: bool = False,
) -> None:
"""Flush the tracker and reset the session.
Args:
repo (:obj:`str`, optional): Aim repository path or Repo object to which
Run object ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
297f804adc8a-9 | log_system_params=log_system_params
if log_system_params
else self.log_system_params,
) | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/aim_callback.html |
ecd4b49889d8-0 | Source code for langchain.callbacks.streamlit.streamlit_callback_handler
"""Callback Handler that prints to streamlit."""
from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-1 | """Return the markdown label for a new LLMThought that doesn't have
an associated tool yet.
"""
return f"{THINKING_EMOJI} **Thinking...**"
[docs] def get_tool_label(self, tool: ToolRecord, is_complete: bool) -> str:
"""Return the label for an LLMThought that has an associated
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-2 | """
return f"{CHECKMARK_EMOJI} **Complete!**"
class LLMThought:
def __init__(
self,
parent_container: DeltaGenerator,
labeler: LLMThoughtLabeler,
expanded: bool,
collapse_on_complete: bool,
):
self._container = MutableExpander(
parent_container... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-3 | self._llm_token_writer_idx = self._container.markdown(
self._llm_token_stream, index=self._llm_token_writer_idx
)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
# `response` is the concatenation of all the tokens received by the LLM.
# If we're receiving stream... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-4 | def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._container.markdown("**Tool encountered an error...**")
self._container.exception(error)
def on_agent_action(
self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-5 | *,
max_thought_containers: int = 4,
expand_new_thoughts: bool = True,
collapse_completed_thoughts: bool = True,
thought_labeler: Optional[LLMThoughtLabeler] = None,
):
"""Create a StreamlitCallbackHandler instance.
Parameters
----------
parent_containe... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-6 | self._collapse_completed_thoughts = collapse_completed_thoughts
self._thought_labeler = thought_labeler or LLMThoughtLabeler()
def _require_current_thought(self) -> LLMThought:
"""Return our current LLMThought. Raise an error if we have no current
thought.
"""
if self._curren... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-7 | self._current_thought = None
def _prune_old_thought_containers(self) -> None:
"""If we have too many thoughts onscreen, move older thoughts to the
'history container.'
"""
while (
self._num_thought_containers > self._max_thought_containers
and len(self._comple... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-8 | )
self._current_thought.on_llm_start(serialized, prompts)
# We don't prune_old_thought_containers here, because our container won't
# be visible until it has a child.
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
self._require_current_thought().on_llm_new_token(token... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ecd4b49889d8-9 | )
self._complete_current_thought()
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._require_current_thought().on_tool_error(error, **kwargs)
self._prune_old_thought_containers()
def on_text(
self,
text: str,
... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
9414d7ad0b7b-0 | Source code for langchain.output_parsers.list
from __future__ import annotations
from abc import abstractmethod
from typing import List
from langchain.schema import BaseOutputParser
[docs]class ListOutputParser(BaseOutputParser):
"""Class to parse the output of an LLM call to a list."""
@property
def _type(... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
4d821269602f-0 | Source code for langchain.output_parsers.fix
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
from langchain.prompts.base import BasePromptTemplate
f... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
4497c1e1628a-0 | Source code for langchain.output_parsers.combining
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.schema import BaseOutputParser
[docs]class CombiningOutputParser(BaseOutputParser):
"""Class to combine multiple output parsers into one."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/combining.html |
4497c1e1628a-1 | texts = text.split("\n\n")
output = dict()
for txt, parser in zip(texts, self.parsers):
output.update(parser.parse(txt.strip()))
return output | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/combining.html |
ca3beae93447-0 | Source code for langchain.output_parsers.boolean
from langchain.schema import BaseOutputParser
[docs]class BooleanOutputParser(BaseOutputParser[bool]):
true_val: str = "YES"
false_val: str = "NO"
[docs] def parse(self, text: str) -> bool:
"""Parse the output of an LLM call to a boolean.
Args:... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/boolean.html |
aac88d3ab5b9-0 | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Callable, Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
guard: Any
api: Optional[Callable]
args: Any
kwargs: Any
@prope... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
aac88d3ab5b9-1 | )
return cls(
guard=Guard.from_rail_string(rail_str, num_reasks=num_reasks),
api=api,
args=args,
kwargs=kwargs,
)
[docs] @classmethod
def from_pydantic(
cls,
output_class: Any,
num_reasks: int = 1,
api: Optional[Calla... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
c9908752d7df-0 | Source code for langchain.output_parsers.enum
from enum import Enum
from typing import Any, Dict, List, Type
from pydantic import root_validator
from langchain.schema import BaseOutputParser, OutputParserException
[docs]class EnumOutputParser(BaseOutputParser):
enum: Type[Enum]
@root_validator()
def raise_d... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/enum.html |
1190bdccc8b5-0 | Source code for langchain.output_parsers.regex
from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex: str
output_keys: List[str]
... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html |
a366d92e841f-0 | Source code for langchain.output_parsers.retry
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
a366d92e841f-1 | chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException:
new_completio... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
a366d92e841f-2 | ) -> RetryWithErrorOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except Outp... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
a3a6535c3226-0 | Source code for langchain.output_parsers.pydantic
import json
import re
from typing import Type, TypeVar
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar(... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
a3a6535c3226-1 | @property
def _type(self) -> str:
return "pydantic" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
f02a7d5f4e38-0 | Source code for langchain.output_parsers.datetime
import random
from datetime import datetime, timedelta
from typing import List
from langchain.schema import BaseOutputParser, OutputParserException
from langchain.utils import comma_list
def _generate_random_datetime_strings(
pattern: str,
n: int = 3,
start_... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
f02a7d5f4e38-1 | ) from e
@property
def _type(self) -> str:
return "datetime" | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
4d110103d1bd-0 | Source code for langchain.output_parsers.regex_dict
from __future__ import annotations
import re
from typing import Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexDictParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex_pattern: str = r"{}:\s?([^.'\n'... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
7e1a163b42a3-0 | Source code for langchain.output_parsers.structured
from __future__ import annotations
from typing import Any, List
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import STRUCTURED_FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchai... | https://api.python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
2ac7daf52a3d-0 | Source code for langchain.utilities.searx_search
"""Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
`multiple search engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and databases and
supports the `OpenSearch
<https:/... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-1 | :class:`SearxResults` is a convenience wrapper around the raw json result.
Example usage of the ``run`` method to make a search:
.. code-block:: python
s.run(query="what is the best search engine?")
Engine Parameters
-----------------
You can pass any `accepted searx search API
<https://docs.searxng.org/dev... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-2 | .. code-block:: python
# select the github engine and pass the search suffix
s = SearchWrapper("langchain library", query_suffix="!gh")
s = SearchWrapper("langchain library")
# select github the conventional google search syntax
s.run("large language models", query_suffix="site:g... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-3 | def _get_default_params() -> dict:
return {"language": "en", "format": "json"}
class SearxResults(dict):
"""Dict like wrapper around search api results."""
_data = ""
def __init__(self, data: str):
"""Take a raw result from Searx and make it into a dict like object."""
json_data = json.l... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-4 | .. code-block:: python
from langchain.utilities import SearxSearchWrapper
# note the unsecure parameter is not needed if you pass the url scheme as
# http
searx = SearxSearchWrapper(searx_host="http://localhost:8888",
un... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-5 | if categories:
values["params"]["categories"] = ",".join(categories)
searx_host = get_from_dict_or_env(values, "searx_host", "SEARX_HOST")
if not searx_host.startswith("http"):
print(
f"Warning: missing the url scheme on host \
! assuming secure ht... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-6 | ) as response:
if not response.ok:
raise ValueError("Searx API returned an error: ", response.text)
result = SearxResults(await response.text())
self._result = result
else:
async with self.aiosession.get(
... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-7 | searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved using the `!` syntax of searx
# to select the engine using `query_suffix`
searx.run("what is the weather in France ?", query_suffix="!qwant")
"""
_params = {
... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-8 | ) -> str:
"""Asynchronously version of `run`."""
_params = {
"q": query,
}
params = {**self.params, **_params, **kwargs}
if self.query_suffix and len(self.query_suffix) > 0:
params["q"] += " " + self.query_suffix
if isinstance(query_suffix, str) an... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-9 | engines: List of engines to use for the query.
categories: List of categories to use for the query.
**kwargs: extra parameters to pass to the searx API.
Returns:
Dict with the following keys:
{
snippet: The description of the result.
... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
2ac7daf52a3d-10 | ]
[docs] async def aresults(
self,
query: str,
num_results: int,
engines: Optional[List[str]] = None,
query_suffix: Optional[str] = "",
**kwargs: Any,
) -> List[Dict]:
"""Asynchronously query with json results.
Uses aiohttp. See `results` for more i... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/searx_search.html |
385c36df6a49-0 | Source code for langchain.utilities.duckduckgo_search
"""Util that calls DuckDuckGo Search.
No setup required. Free.
https://pypi.org/project/duckduckgo-search/
"""
from typing import Dict, List, Optional
from pydantic import BaseModel, Extra
from pydantic.class_validators import root_validator
[docs]class DuckDuckGoSe... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/duckduckgo_search.html |
385c36df6a49-1 | timelimit=self.time,
)
if results is None:
return ["No good DuckDuckGo Search Result was found"]
snippets = []
for i, res in enumerate(results, 1):
if res is not None:
snippets.append(res["body"])
if len(... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/duckduckgo_search.html |
385c36df6a49-2 | if res is not None:
formatted_results.append(to_metadata(res))
if len(formatted_results) == num_results:
break
return formatted_results | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/duckduckgo_search.html |
13d5f36d4e84-0 | Source code for langchain.utilities.serpapi
"""Chain that calls SerpAPI.
Heavily borrowed from https://github.com/ofirpress/self-ask
"""
import os
import sys
from typing import Any, Dict, Optional, Tuple
import aiohttp
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.utils import get_from_dic... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
13d5f36d4e84-1 | aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python packag... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
13d5f36d4e84-2 | """Use aiohttp to run query through SerpAPI and return the results async."""
def construct_url_and_params() -> Tuple[str, Dict[str, str]]:
params = self.get_params(query)
params["source"] = "python"
if self.serpapi_api_key:
params["serp_api_key"] = self.serpap... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
13d5f36d4e84-3 | toret = res["answer_box"]["answer"]
elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
toret = res["answer_box"]["snippet"]
elif (
"answer_box" in res.keys()
and "snippet_highlighted_words" in res["answer_box"].keys()
):
tor... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/serpapi.html |
69f411563474-0 | Source code for langchain.utilities.twilio
"""Util that calls Twilio."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class TwilioAPIWrapper(BaseModel):
"""Messaging Client using Twilio.
To use, you should hav... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/twilio.html |
69f411563474-1 | that is enabled for the type of message you want to send. Phone numbers or
[short codes](https://www.twilio.com/docs/sms/api/short-code) purchased from
Twilio also work here. You cannot, for example, spoof messages from a private
cell phone number. If you are using `messaging_service_sid`, th... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/twilio.html |
69f411563474-2 | characters in length.
to: The destination phone number in
[E.164](https://www.twilio.com/docs/glossary/what-e164) format for
SMS/MMS or
[Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
for other 3rd-party chann... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/twilio.html |
8a414feefe5e-0 | Source code for langchain.utilities.metaphor_search
"""Util that calls Metaphor Search API.
In order to set this up, follow instructions at:
"""
import json
from typing import Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_d... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html |
8a414feefe5e-1 | # type: ignore
f"{METAPHOR_API_URL}/search",
headers=headers,
json=params,
)
response.raise_for_status()
search_results = response.json()
print(search_results)
return search_results["results"]
@root_validator(pre=True)
def validate_envi... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html |
8a414feefe5e-2 | query,
num_results=num_results,
include_domains=include_domains,
exclude_domains=exclude_domains,
start_crawl_date=start_crawl_date,
end_crawl_date=end_crawl_date,
start_published_date=start_published_date,
end_published_date=end_publis... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html |
8a414feefe5e-3 | data = await res.text()
return data
else:
raise Exception(f"Error {res.status}: {res.reason}")
results_json_str = await fetch()
results_json = json.loads(results_json_str)
return self._clean_results(results_json["results"])
... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/metaphor_search.html |
ec20f4389916-0 | Source code for langchain.utilities.pupmed
import json
import logging
import time
import urllib.error
import urllib.request
from typing import List
from pydantic import BaseModel, Extra
from langchain.schema import Document
logger = logging.getLogger(__name__)
[docs]class PubMedAPIWrapper(BaseModel):
"""
Wrappe... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/pupmed.html |
ec20f4389916-1 | [docs] def run(self, query: str) -> str:
"""
Run PubMed search and get the article meta information.
See https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch
It uses only the most informative fields of article meta information.
"""
try:
# Retrieve ... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/pupmed.html |
ec20f4389916-2 | article = self.retrieve_article(uid, webenv)
articles.append(article)
# Convert the list of articles to a JSON string
return articles
def _transform_doc(self, doc: dict) -> Document:
summary = doc.pop("summary")
return Document(page_content=summary, metadata=doc)
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/pupmed.html |
ec20f4389916-3 | end_tag = "</ArticleTitle>"
title = xml_text[
xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)
]
# Get abstract
abstract = ""
if "<AbstractText>" in xml_text and "</AbstractText>" in xml_text:
start_tag = "<AbstractText>"
... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/pupmed.html |
c00435765c10-0 | Source code for langchain.utilities.spark_sql
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Iterable, List, Optional
if TYPE_CHECKING:
from pyspark.sql import DataFrame, Row, SparkSession
[docs]class SparkSQL:
def __init__(
self,
spark_session: Optional[SparkSession] ... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/spark_sql.html |
c00435765c10-1 | f"ignore_tables {missing_tables} not found in database"
)
usable_tables = self.get_usable_table_names()
self._usable_tables = set(usable_tables) if usable_tables else self._all_tables
if not isinstance(sample_rows_in_table_info, int):
raise TypeError("sample_rows_in_t... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/spark_sql.html |
c00435765c10-2 | )
# Ignore the data source provider and options to reduce the number of tokens.
using_clause_index = statement.find("USING")
return statement[:using_clause_index] + ";"
[docs] def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
all_table_names = self.get_usable_t... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/spark_sql.html |
c00435765c10-3 | f"{columns_str}\n"
f"{sample_rows_str}"
)
def _convert_row_as_tuple(self, row: Row) -> tuple:
return tuple(map(str, row.asDict().values()))
def _get_dataframe_results(self, df: DataFrame) -> list:
return list(map(self._convert_row_as_tuple, df.collect()))
[docs] def run(se... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/spark_sql.html |
c00435765c10-4 | """
try:
from pyspark.errors import PySparkException
except ImportError:
raise ValueError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
try:
return self.run(command, fetch)
except PySparkExcepti... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/spark_sql.html |
48641aa1ce96-0 | Source code for langchain.utilities.google_places_api
"""Chain that calls Google Places API.
"""
import logging
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GooglePlacesAPIWrapper(BaseModel):
"""Wrapper arou... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
48641aa1ce96-1 | except ImportError:
raise ImportError(
"Could not import googlemaps python package. "
"Please install it with `pip install googlemaps`."
)
return values
[docs] def run(self, query: str) -> str:
"""Run Places search and get k number of places tha... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
48641aa1ce96-2 | "formatted_address", "Unknown"
)
phone_number = place_details.get("result", {}).get(
"formatted_phone_number", "Unknown"
)
website = place_details.get("result", {}).get("website", "Unknown")
formatted_details = (
f"{name}\nAddre... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/google_places_api.html |
478b3334d3d1-0 | Source code for langchain.utilities.scenexplain
"""Util that calls SceneXplain.
In order to set this up, you need API key for the SceneXplain API.
You can obtain a key by following the steps below.
- Sign up for a free account at https://scenex.jina.ai/.
- Navigate to the API Access page (https://scenex.jina.ai/api) an... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/scenexplain.html |
478b3334d3d1-1 | "languages": ["en"],
}
]
}
response = requests.post(self.scenex_api_url, headers=headers, json=payload)
response.raise_for_status()
result = response.json().get("result", [])
img = result[0] if result else {}
return img.get("text", "")
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/utilities/scenexplain.html |
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