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e66c2fb8642c-1 | param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param search_wrapper: BraveSearchWrapper [Required]¶
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
classmethod from_api_key(api_key: str, search_kwargs: Optional[dict] = None, **kwargs: Any) → BraveSearch[source]¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.brave_search.tool.BraveSearch.html |
f1ea81d2f614-0 | langchain.tools.json.tool.JsonGetValueTool¶
class langchain.tools.json.tool.JsonGetValueTool(*, name: str = 'json_spec_get_value', description: str = '\n Can be used to see value in string format at a given path.\n Before calling this you should be SURE that the path to this exists.\n The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).\n ', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, spec: JsonSpec)[source]¶
Bases: BaseTool
Tool for getting a value in a JSON spec.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = '\n Can be used to see value in string format at a given path.\n Before calling this you should be SURE that the path to this exists.\n The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).\n '¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.json.tool.JsonGetValueTool.html |
f1ea81d2f614-1 | Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'json_spec_get_value'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param spec: JsonSpec [Required]¶
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object. | [
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f1ea81d2f614-2 | model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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0096ec0242bb-0 | langchain.tools.powerbi.tool.ListPowerBITool¶
class langchain.tools.powerbi.tool.ListPowerBITool(*, name: str = 'list_tables_powerbi', description: str = 'Input is an empty string, output is a comma separated list of tables in the database.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, powerbi: PowerBIDataset)[source]¶
Bases: BaseTool
Tool for getting tables names.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Input is an empty string, output is a comma separated list of tables in the database.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'list_tables_powerbi'¶
The unique name of the tool that clearly communicates its purpose. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.powerbi.tool.ListPowerBITool.html |
0096ec0242bb-1 | The unique name of the tool that clearly communicates its purpose.
param powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]¶
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.powerbi.tool.ListPowerBITool.html |
ef84afb5c7e2-0 | langchain.tools.bing_search.tool.BingSearchRun¶
class langchain.tools.bing_search.tool.BingSearchRun(*, name: str = 'bing_search', description: str = 'A wrapper around Bing Search. Useful for when you need to answer questions about current events. Input should be a search query.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: BingSearchAPIWrapper)[source]¶
Bases: BaseTool
Tool that adds the capability to query the Bing search API.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_wrapper: langchain.utilities.bing_search.BingSearchAPIWrapper [Required]¶
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Bing Search. Useful for when you need to answer questions about current events. Input should be a search query.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.bing_search.tool.BingSearchRun.html |
ef84afb5c7e2-1 | Handle the content of the ToolException thrown.
param name: str = 'bing_search'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.bing_search.tool.BingSearchRun.html |
42e1218da0ca-0 | langchain.tools.steamship_image_generation.tool.ModelName¶
class langchain.tools.steamship_image_generation.tool.ModelName(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: str, Enum
Supported Image Models for generation.
Methods
__init__(*args, **kwds)
capitalize()
Return a capitalized version of the string.
casefold()
Return a version of the string suitable for caseless comparisons.
center(width[, fillchar])
Return a centered string of length width.
count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring sub in string S[start:end].
encode([encoding, errors])
Encode the string using the codec registered for encoding.
endswith(suffix[, start[, end]])
Return True if S ends with the specified suffix, False otherwise.
expandtabs([tabsize])
Return a copy where all tab characters are expanded using spaces.
find(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
format(*args, **kwargs)
Return a formatted version of S, using substitutions from args and kwargs.
format_map(mapping)
Return a formatted version of S, using substitutions from mapping.
index(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
isalnum()
Return True if the string is an alpha-numeric string, False otherwise.
isalpha()
Return True if the string is an alphabetic string, False otherwise.
isascii()
Return True if all characters in the string are ASCII, False otherwise.
isdecimal() | [
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42e1218da0ca-1 | Return True if all characters in the string are ASCII, False otherwise.
isdecimal()
Return True if the string is a decimal string, False otherwise.
isdigit()
Return True if the string is a digit string, False otherwise.
isidentifier()
Return True if the string is a valid Python identifier, False otherwise.
islower()
Return True if the string is a lowercase string, False otherwise.
isnumeric()
Return True if the string is a numeric string, False otherwise.
isprintable()
Return True if the string is printable, False otherwise.
isspace()
Return True if the string is a whitespace string, False otherwise.
istitle()
Return True if the string is a title-cased string, False otherwise.
isupper()
Return True if the string is an uppercase string, False otherwise.
join(iterable, /)
Concatenate any number of strings.
ljust(width[, fillchar])
Return a left-justified string of length width.
lower()
Return a copy of the string converted to lowercase.
lstrip([chars])
Return a copy of the string with leading whitespace removed.
maketrans
Return a translation table usable for str.translate().
partition(sep, /)
Partition the string into three parts using the given separator.
removeprefix(prefix, /)
Return a str with the given prefix string removed if present.
removesuffix(suffix, /)
Return a str with the given suffix string removed if present.
replace(old, new[, count])
Return a copy with all occurrences of substring old replaced by new.
rfind(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rindex(sub[, start[, end]]) | [
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42e1218da0ca-2 | rindex(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rjust(width[, fillchar])
Return a right-justified string of length width.
rpartition(sep, /)
Partition the string into three parts using the given separator.
rsplit([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
rstrip([chars])
Return a copy of the string with trailing whitespace removed.
split([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
splitlines([keepends])
Return a list of the lines in the string, breaking at line boundaries.
startswith(prefix[, start[, end]])
Return True if S starts with the specified prefix, False otherwise.
strip([chars])
Return a copy of the string with leading and trailing whitespace removed.
swapcase()
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()
Return a version of the string where each word is titlecased.
translate(table, /)
Replace each character in the string using the given translation table.
upper()
Return a copy of the string converted to uppercase.
zfill(width, /)
Pad a numeric string with zeros on the left, to fill a field of the given width.
Attributes
DALL_E
STABLE_DIFFUSION
capitalize()¶
Return a capitalized version of the string.
More specifically, make the first character have upper case and the rest lower
case.
casefold()¶
Return a version of the string suitable for caseless comparisons.
center(width, fillchar=' ', /)¶
Return a centered string of length width. | [
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42e1218da0ca-3 | center(width, fillchar=' ', /)¶
Return a centered string of length width.
Padding is done using the specified fill character (default is a space).
count(sub[, start[, end]]) → int¶
Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are
interpreted as in slice notation.
encode(encoding='utf-8', errors='strict')¶
Encode the string using the codec registered for encoding.
encodingThe encoding in which to encode the string.
errorsThe error handling scheme to use for encoding errors.
The default is ‘strict’ meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and
‘xmlcharrefreplace’ as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
endswith(suffix[, start[, end]]) → bool¶
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
expandtabs(tabsize=8)¶
Return a copy where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
find(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
format(*args, **kwargs) → str¶
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces (‘{’ and ‘}’).
format_map(mapping) → str¶ | [
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42e1218da0ca-4 | format_map(mapping) → str¶
Return a formatted version of S, using substitutions from mapping.
The substitutions are identified by braces (‘{’ and ‘}’).
index(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
isalnum()¶
Return True if the string is an alpha-numeric string, False otherwise.
A string is alpha-numeric if all characters in the string are alpha-numeric and
there is at least one character in the string.
isalpha()¶
Return True if the string is an alphabetic string, False otherwise.
A string is alphabetic if all characters in the string are alphabetic and there
is at least one character in the string.
isascii()¶
Return True if all characters in the string are ASCII, False otherwise.
ASCII characters have code points in the range U+0000-U+007F.
Empty string is ASCII too.
isdecimal()¶
Return True if the string is a decimal string, False otherwise.
A string is a decimal string if all characters in the string are decimal and
there is at least one character in the string.
isdigit()¶
Return True if the string is a digit string, False otherwise.
A string is a digit string if all characters in the string are digits and there
is at least one character in the string.
isidentifier()¶
Return True if the string is a valid Python identifier, False otherwise.
Call keyword.iskeyword(s) to test whether string s is a reserved identifier,
such as “def” or “class”.
islower()¶
Return True if the string is a lowercase string, False otherwise. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.steamship_image_generation.tool.ModelName.html |
42e1218da0ca-5 | islower()¶
Return True if the string is a lowercase string, False otherwise.
A string is lowercase if all cased characters in the string are lowercase and
there is at least one cased character in the string.
isnumeric()¶
Return True if the string is a numeric string, False otherwise.
A string is numeric if all characters in the string are numeric and there is at
least one character in the string.
isprintable()¶
Return True if the string is printable, False otherwise.
A string is printable if all of its characters are considered printable in
repr() or if it is empty.
isspace()¶
Return True if the string is a whitespace string, False otherwise.
A string is whitespace if all characters in the string are whitespace and there
is at least one character in the string.
istitle()¶
Return True if the string is a title-cased string, False otherwise.
In a title-cased string, upper- and title-case characters may only
follow uncased characters and lowercase characters only cased ones.
isupper()¶
Return True if the string is an uppercase string, False otherwise.
A string is uppercase if all cased characters in the string are uppercase and
there is at least one cased character in the string.
join(iterable, /)¶
Concatenate any number of strings.
The string whose method is called is inserted in between each given string.
The result is returned as a new string.
Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’
ljust(width, fillchar=' ', /)¶
Return a left-justified string of length width.
Padding is done using the specified fill character (default is a space).
lower()¶
Return a copy of the string converted to lowercase. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.steamship_image_generation.tool.ModelName.html |
42e1218da0ca-6 | lower()¶
Return a copy of the string converted to lowercase.
lstrip(chars=None, /)¶
Return a copy of the string with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
static maketrans()¶
Return a translation table usable for str.translate().
If there is only one argument, it must be a dictionary mapping Unicode
ordinals (integers) or characters to Unicode ordinals, strings or None.
Character keys will be then converted to ordinals.
If there are two arguments, they must be strings of equal length, and
in the resulting dictionary, each character in x will be mapped to the
character at the same position in y. If there is a third argument, it
must be a string, whose characters will be mapped to None in the result.
partition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string. If the separator is found,
returns a 3-tuple containing the part before the separator, the separator
itself, and the part after it.
If the separator is not found, returns a 3-tuple containing the original string
and two empty strings.
removeprefix(prefix, /)¶
Return a str with the given prefix string removed if present.
If the string starts with the prefix string, return string[len(prefix):].
Otherwise, return a copy of the original string.
removesuffix(suffix, /)¶
Return a str with the given suffix string removed if present.
If the string ends with the suffix string and that suffix is not empty,
return string[:-len(suffix)]. Otherwise, return a copy of the original
string.
replace(old, new, count=- 1, /)¶
Return a copy with all occurrences of substring old replaced by new. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.steamship_image_generation.tool.ModelName.html |
42e1218da0ca-7 | Return a copy with all occurrences of substring old replaced by new.
countMaximum number of occurrences to replace.
-1 (the default value) means replace all occurrences.
If the optional argument count is given, only the first count occurrences are
replaced.
rfind(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
rindex(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
rjust(width, fillchar=' ', /)¶
Return a right-justified string of length width.
Padding is done using the specified fill character (default is a space).
rpartition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string, starting at the end. If
the separator is found, returns a 3-tuple containing the part before the
separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing two empty strings
and the original string.
rsplit(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left). | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.steamship_image_generation.tool.ModelName.html |
42e1218da0ca-8 | empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Splitting starts at the end of the string and works to the front.
rstrip(chars=None, /)¶
Return a copy of the string with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
split(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Note, str.split() is mainly useful for data that has been intentionally
delimited. With natural text that includes punctuation, consider using
the regular expression module.
splitlines(keepends=False)¶
Return a list of the lines in the string, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends is given and
true.
startswith(prefix[, start[, end]]) → bool¶
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
strip(chars=None, /)¶
Return a copy of the string with leading and trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
swapcase()¶
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.steamship_image_generation.tool.ModelName.html |
42e1218da0ca-9 | Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()¶
Return a version of the string where each word is titlecased.
More specifically, words start with uppercased characters and all remaining
cased characters have lower case.
translate(table, /)¶
Replace each character in the string using the given translation table.
tableTranslation table, which must be a mapping of Unicode ordinals to
Unicode ordinals, strings, or None.
The table must implement lookup/indexing via __getitem__, for instance a
dictionary or list. If this operation raises LookupError, the character is
left untouched. Characters mapped to None are deleted.
upper()¶
Return a copy of the string converted to uppercase.
zfill(width, /)¶
Pad a numeric string with zeros on the left, to fill a field of the given width.
The string is never truncated.
DALL_E = 'dall-e'¶
STABLE_DIFFUSION = 'stable-diffusion'¶ | [
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8ea4c7fb5e68-0 | langchain.tools.file_management.write.WriteFileInput¶
class langchain.tools.file_management.write.WriteFileInput(*, file_path: str, text: str, append: bool = False)[source]¶
Bases: BaseModel
Input for WriteFileTool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param append: bool = False¶
Whether to append to an existing file.
param file_path: str [Required]¶
name of file
param text: str [Required]¶
text to write to file | [
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e3da84d834ca-0 | langchain.tools.file_management.list_dir.DirectoryListingInput¶
class langchain.tools.file_management.list_dir.DirectoryListingInput(*, dir_path: str = '.')[source]¶
Bases: BaseModel
Input for ListDirectoryTool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param dir_path: str = '.'¶
Subdirectory to list. | [
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9c601415691a-0 | langchain.tools.playwright.utils.create_sync_playwright_browser¶
langchain.tools.playwright.utils.create_sync_playwright_browser(headless: bool = True) → SyncBrowser[source]¶
Create a playwright browser.
Parameters
headless – Whether to run the browser in headless mode. Defaults to True.
Returns
The playwright browser.
Return type
SyncBrowser | [
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c5d86e016049-0 | langchain.tools.file_management.move.MoveFileTool¶
class langchain.tools.file_management.move.MoveFileTool(*, name: str = 'move_file', description: str = 'Move or rename a file from one location to another', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.file_management.move.FileMoveInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, root_dir: ~typing.Optional[str] = None)[source]¶
Bases: BaseFileToolMixin, BaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.move.FileMoveInput'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Move or rename a file from one location to another'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶ | [
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c5d86e016049-1 | Handle the content of the ToolException thrown.
param name: str = 'move_file'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param root_dir: Optional[str] = None¶
The final path will be chosen relative to root_dir if specified.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
get_relative_path(file_path: str) → Path¶
Get the relative path, returning an error if unsupported.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.file_management.move.MoveFileTool.html |
156a2beaa686-0 | langchain.tools.spark_sql.tool.BaseSparkSQLTool¶
class langchain.tools.spark_sql.tool.BaseSparkSQLTool(*, db: SparkSQL)[source]¶
Bases: BaseModel
Base tool for interacting with Spark SQL.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param db: langchain.utilities.spark_sql.SparkSQL [Required]¶
model Config[source]¶
Bases: Config
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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7a84bfcadf8b-0 | langchain.tools.file_management.copy.CopyFileTool¶
class langchain.tools.file_management.copy.CopyFileTool(*, name: str = 'copy_file', description: str = 'Create a copy of a file in a specified location', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.file_management.copy.FileCopyInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, root_dir: ~typing.Optional[str] = None)[source]¶
Bases: BaseFileToolMixin, BaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.copy.FileCopyInput'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Create a copy of a file in a specified location'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶ | [
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7a84bfcadf8b-1 | Handle the content of the ToolException thrown.
param name: str = 'copy_file'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param root_dir: Optional[str] = None¶
The final path will be chosen relative to root_dir if specified.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
get_relative_path(file_path: str) → Path¶
Get the relative path, returning an error if unsupported.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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4b67d59f9f58-0 | langchain.tools.requests.tool.RequestsPutTool¶
class langchain.tools.requests.tool.RequestsPutTool(*, name: str = 'requests_put', description: str = 'Use this when you want to PUT to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PUT to the url.\n Be careful to always use double quotes for strings in the json string.\n The output will be the text response of the PUT request.\n ', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, requests_wrapper: TextRequestsWrapper)[source]¶
Bases: BaseRequestsTool, BaseTool
Tool for making a PUT request to an API endpoint.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.requests.tool.RequestsPutTool.html |
4b67d59f9f58-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this when you want to PUT to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PUT to the url.\n Be careful to always use double quotes for strings in the json string.\n The output will be the text response of the PUT request.\n '¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'requests_put'¶
The unique name of the tool that clearly communicates its purpose.
param requests_wrapper: langchain.requests.TextRequestsWrapper [Required]¶
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.requests.tool.RequestsPutTool.html |
4b67d59f9f58-2 | Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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504d5bd26e97-0 | langchain.tools.file_management.file_search.FileSearchInput¶
class langchain.tools.file_management.file_search.FileSearchInput(*, dir_path: str = '.', pattern: str)[source]¶
Bases: BaseModel
Input for FileSearchTool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param dir_path: str = '.'¶
Subdirectory to search in.
param pattern: str [Required]¶
Unix shell regex, where * matches everything. | [
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9e1799390656-0 | langchain.tools.gmail.utils.import_installed_app_flow¶
langchain.tools.gmail.utils.import_installed_app_flow() → InstalledAppFlow[source]¶
Import InstalledAppFlow class.
Returns
InstalledAppFlow class.
Return type
InstalledAppFlow | [
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b018ced715b7-0 | langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksTool¶
class langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksTool(*, name: str = 'extract_hyperlinks', description: str = 'Extract all hyperlinks on the current webpage', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, sync_browser: Optional['SyncBrowser'] = None, async_browser: Optional['AsyncBrowser'] = None)[source]¶
Bases: BaseBrowserTool
Extract all hyperlinks on the page.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param async_browser: Optional['AsyncBrowser'] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Extract all hyperlinks on the current webpage'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksTool.html |
b018ced715b7-1 | param description: str = 'Extract all hyperlinks on the current webpage'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'extract_hyperlinks'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param sync_browser: Optional['SyncBrowser'] = None¶
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator check_bs_import » all fields[source]¶
Check that the arguments are valid.
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) → BaseBrowserTool¶
Instantiate the tool.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksTool.html |
b018ced715b7-2 | Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
static scrape_page(page: Any, html_content: str, absolute_urls: bool) → str[source]¶
validator validate_browser_provided » all fields¶
Check that the arguments are valid.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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e521300bbde9-0 | langchain.tools.ddg_search.tool.DuckDuckGoSearchResults¶
class langchain.tools.ddg_search.tool.DuckDuckGoSearchResults(*, name: str = 'DuckDuckGo Results JSON', description: str = 'A wrapper around Duck Duck Go Search. Useful for when you need to answer questions about current events. Input should be a search query. Output is a JSON array of the query results', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, num_results: int = 4, api_wrapper: DuckDuckGoSearchAPIWrapper = None)[source]¶
Bases: BaseTool
Tool that queries the Duck Duck Go Search API and get back json.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [Optional]¶
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Duck Duck Go Search. Useful for when you need to answer questions about current events. Input should be a search query. Output is a JSON array of the query results'¶ | [
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e521300bbde9-1 | Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'DuckDuckGo Results JSON'¶
The unique name of the tool that clearly communicates its purpose.
param num_results: int = 4¶
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.ddg_search.tool.DuckDuckGoSearchResults.html |
e521300bbde9-2 | model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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cd3bf7082976-0 | langchain.tools.gmail.get_thread.GmailGetThread¶
class langchain.tools.gmail.get_thread.GmailGetThread(*, name: str = 'get_gmail_thread', description: str = 'Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.', args_schema: ~typing.Type[~langchain.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, api_resource: Resource = None)[source]¶
Bases: GmailBaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_resource: Resource [Optional]¶
param args_schema: Type[langchain.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain.tools.gmail.get_thread.GetThreadSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.'¶ | [
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cd3bf7082976-1 | Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'get_gmail_thread'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
classmethod from_api_resource(api_resource: Resource) → GmailBaseTool¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.get_thread.GmailGetThread.html |
cd3bf7082976-2 | Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.get_thread.GmailGetThread.html |
8d5c11b075ef-0 | langchain.tools.searx_search.tool.SearxSearchRun¶
class langchain.tools.searx_search.tool.SearxSearchRun(*, name: str = 'searx_search', description: str = 'A meta search engine.Useful for when you need to answer questions about current events.Input should be a search query.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, wrapper: SearxSearchWrapper, kwargs: dict = None)[source]¶
Bases: BaseTool
Tool that adds the capability to query a Searx instance.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A meta search engine.Useful for when you need to answer questions about current events.Input should be a search query.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param kwargs: dict [Optional]¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchRun.html |
8d5c11b075ef-1 | Handle the content of the ToolException thrown.
param kwargs: dict [Optional]¶
param name: str = 'searx_search'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
param wrapper: langchain.utilities.searx_search.SearxSearchWrapper [Required]¶
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchRun.html |
45b626041273-0 | langchain.tools.base.ToolMetaclass¶
class langchain.tools.base.ToolMetaclass(name: str, bases: Tuple[Type, ...], dct: dict)[source]¶
Bases: ModelMetaclass
Metaclass for BaseTool to ensure the provided args_schema
doesn’t silently ignored.
Create the definition of the new tool class.
Methods
__init__(*args, **kwargs)
mro()
Return a type's method resolution order.
register(subclass)
Register a virtual subclass of an ABC.
__call__(*args, **kwargs)¶
Call self as a function.
mro()¶
Return a type’s method resolution order.
register(subclass)¶
Register a virtual subclass of an ABC.
Returns the subclass, to allow usage as a class decorator. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.base.ToolMetaclass.html |
e5d33e26bb7e-0 | langchain.tools.sql_database.tool.BaseSQLDatabaseTool¶
class langchain.tools.sql_database.tool.BaseSQLDatabaseTool(*, db: SQLDatabase)[source]¶
Bases: BaseModel
Base tool for interacting with a SQL database.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param db: langchain.sql_database.SQLDatabase [Required]¶
model Config[source]¶
Bases: Config
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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590624ea0fbb-0 | langchain.tools.playwright.extract_text.ExtractTextTool¶
class langchain.tools.playwright.extract_text.ExtractTextTool(*, name: str = 'extract_text', description: str = 'Extract all the text on the current webpage', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'pydantic.main.BaseModel'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, sync_browser: Optional['SyncBrowser'] = None, async_browser: Optional['AsyncBrowser'] = None)[source]¶
Bases: BaseBrowserTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param async_browser: Optional['AsyncBrowser'] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Extract all the text on the current webpage'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.playwright.extract_text.ExtractTextTool.html |
590624ea0fbb-1 | You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'extract_text'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param sync_browser: Optional['SyncBrowser'] = None¶
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator check_acheck_bs_importrgs » all fields[source]¶
Check that the arguments are valid.
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) → BaseBrowserTool¶
Instantiate the tool.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.playwright.extract_text.ExtractTextTool.html |
590624ea0fbb-2 | Run the tool.
validator validate_browser_provided » all fields¶
Check that the arguments are valid.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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d762927c21fb-0 | langchain.tools.sql_database.tool.ListSQLDatabaseTool¶
class langchain.tools.sql_database.tool.ListSQLDatabaseTool(*, name: str = 'sql_db_list_tables', description: str = 'Input is an empty string, output is a comma separated list of tables in the database.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, db: SQLDatabase)[source]¶
Bases: BaseSQLDatabaseTool, BaseTool
Tool for getting tables names.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param db: langchain.sql_database.SQLDatabase [Required]¶
param description: str = 'Input is an empty string, output is a comma separated list of tables in the database.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'sql_db_list_tables'¶
The unique name of the tool that clearly communicates its purpose. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.sql_database.tool.ListSQLDatabaseTool.html |
d762927c21fb-1 | The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: Config
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.sql_database.tool.ListSQLDatabaseTool.html |
5328bdb8bca8-0 | langchain.tools.playwright.current_page.CurrentWebPageTool¶
class langchain.tools.playwright.current_page.CurrentWebPageTool(*, name: str = 'current_webpage', description: str = 'Returns the URL of the current page', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'pydantic.main.BaseModel'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, sync_browser: Optional['SyncBrowser'] = None, async_browser: Optional['AsyncBrowser'] = None)[source]¶
Bases: BaseBrowserTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param async_browser: Optional['AsyncBrowser'] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Returns the URL of the current page'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.playwright.current_page.CurrentWebPageTool.html |
5328bdb8bca8-1 | You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'current_webpage'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param sync_browser: Optional['SyncBrowser'] = None¶
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) → BaseBrowserTool¶
Instantiate the tool.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
validator validate_browser_provided » all fields¶
Check that the arguments are valid. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.playwright.current_page.CurrentWebPageTool.html |
5328bdb8bca8-2 | validator validate_browser_provided » all fields¶
Check that the arguments are valid.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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137bc613bc54-0 | langchain.tools.plugin.AIPlugin¶
class langchain.tools.plugin.AIPlugin(*, schema_version: str, name_for_model: str, name_for_human: str, description_for_model: str, description_for_human: str, auth: Optional[dict] = None, api: ApiConfig, logo_url: Optional[str] = None, contact_email: Optional[str] = None, legal_info_url: Optional[str] = None)[source]¶
Bases: BaseModel
AI Plugin Definition.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api: langchain.tools.plugin.ApiConfig [Required]¶
param auth: Optional[dict] = None¶
param contact_email: Optional[str] = None¶
param description_for_human: str [Required]¶
param description_for_model: str [Required]¶
param legal_info_url: Optional[str] = None¶
param logo_url: Optional[str] = None¶
param name_for_human: str [Required]¶
param name_for_model: str [Required]¶
param schema_version: str [Required]¶
classmethod from_url(url: str) → AIPlugin[source]¶
Instantiate AIPlugin from a URL. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.plugin.AIPlugin.html |
cda6fb6d7158-0 | langchain.tools.gmail.search.GmailSearch¶
class langchain.tools.gmail.search.GmailSearch(*, name: str = 'search_gmail', description: str = 'Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.', args_schema: ~typing.Type[~langchain.tools.gmail.search.SearchArgsSchema] = <class 'langchain.tools.gmail.search.SearchArgsSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, api_resource: Resource = None)[source]¶
Bases: GmailBaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_resource: Resource [Optional]¶
param args_schema: Type[langchain.tools.gmail.search.SearchArgsSchema] = <class 'langchain.tools.gmail.search.SearchArgsSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.'¶ | [
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cda6fb6d7158-1 | Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'search_gmail'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
classmethod from_api_resource(api_resource: Resource) → GmailBaseTool¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object | [
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cda6fb6d7158-2 | Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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ba22607dcf64-0 | langchain.tools.file_management.copy.FileCopyInput¶
class langchain.tools.file_management.copy.FileCopyInput(*, source_path: str, destination_path: str)[source]¶
Bases: BaseModel
Input for CopyFileTool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param destination_path: str [Required]¶
Path to save the copied file
param source_path: str [Required]¶
Path of the file to copy | [
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6412f9d31d12-0 | langchain.tools.file_management.delete.DeleteFileTool¶
class langchain.tools.file_management.delete.DeleteFileTool(*, name: str = 'file_delete', description: str = 'Delete a file', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.file_management.delete.FileDeleteInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, root_dir: ~typing.Optional[str] = None)[source]¶
Bases: BaseFileToolMixin, BaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.delete.FileDeleteInput'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Delete a file'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.file_management.delete.DeleteFileTool.html |
6412f9d31d12-1 | Handle the content of the ToolException thrown.
param name: str = 'file_delete'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param root_dir: Optional[str] = None¶
The final path will be chosen relative to root_dir if specified.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
get_relative_path(file_path: str) → Path¶
Get the relative path, returning an error if unsupported.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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ddfe26b2d0c9-0 | langchain.tools.gmail.create_draft.GmailCreateDraft¶
class langchain.tools.gmail.create_draft.GmailCreateDraft(*, name: str = 'create_gmail_draft', description: str = 'Use this tool to create a draft email with the provided message fields.', args_schema: ~typing.Type[~langchain.tools.gmail.create_draft.CreateDraftSchema] = <class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, api_resource: Resource = None)[source]¶
Bases: GmailBaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_resource: Resource [Optional]¶
param args_schema: Type[langchain.tools.gmail.create_draft.CreateDraftSchema] = <class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to create a draft email with the provided message fields.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.create_draft.GmailCreateDraft.html |
ddfe26b2d0c9-1 | You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'create_gmail_draft'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
classmethod from_api_resource(api_resource: Resource) → GmailBaseTool¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.create_draft.GmailCreateDraft.html |
ddfe26b2d0c9-2 | Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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7e15a734b061-0 | langchain.tools.vectorstore.tool.VectorStoreQATool¶
class langchain.tools.vectorstore.tool.VectorStoreQATool(*, name: str, description: str, args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, vectorstore: VectorStore, llm: BaseLanguageModel = None)[source]¶
Bases: BaseVectorStoreTool, BaseTool
Tool for the VectorDBQA chain. To be initialized with name and chain.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str [Required]¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param llm: langchain.base_language.BaseLanguageModel [Optional]¶
param name: str [Required]¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.vectorstore.tool.VectorStoreQATool.html |
7e15a734b061-1 | param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param vectorstore: langchain.vectorstores.base.VectorStore [Required]¶
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
static get_description(name: str, description: str) → str[source]¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: Config
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.vectorstore.tool.VectorStoreQATool.html |
3dc4e6769f09-0 | langchain.tools.google_serper.tool.GoogleSerperRun¶
class langchain.tools.google_serper.tool.GoogleSerperRun(*, name: str = 'google_serper', description: str = 'A low-cost Google Search API.Useful for when you need to answer questions about current events.Input should be a search query.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: GoogleSerperAPIWrapper)[source]¶
Bases: BaseTool
Tool that adds the capability to query the Serper.dev Google search API.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_wrapper: langchain.utilities.google_serper.GoogleSerperAPIWrapper [Required]¶
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A low-cost Google Search API.Useful for when you need to answer questions about current events.Input should be a search query.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.google_serper.tool.GoogleSerperRun.html |
3dc4e6769f09-1 | Handle the content of the ToolException thrown.
param name: str = 'google_serper'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.google_serper.tool.GoogleSerperRun.html |
2f0884acdafd-0 | langchain.tools.gmail.search.Resource¶
class langchain.tools.gmail.search.Resource(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: str, Enum
Enumerator of Resources to search.
Methods
__init__(*args, **kwds)
capitalize()
Return a capitalized version of the string.
casefold()
Return a version of the string suitable for caseless comparisons.
center(width[, fillchar])
Return a centered string of length width.
count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring sub in string S[start:end].
encode([encoding, errors])
Encode the string using the codec registered for encoding.
endswith(suffix[, start[, end]])
Return True if S ends with the specified suffix, False otherwise.
expandtabs([tabsize])
Return a copy where all tab characters are expanded using spaces.
find(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
format(*args, **kwargs)
Return a formatted version of S, using substitutions from args and kwargs.
format_map(mapping)
Return a formatted version of S, using substitutions from mapping.
index(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
isalnum()
Return True if the string is an alpha-numeric string, False otherwise.
isalpha()
Return True if the string is an alphabetic string, False otherwise.
isascii()
Return True if all characters in the string are ASCII, False otherwise.
isdecimal()
Return True if the string is a decimal string, False otherwise.
isdigit() | [
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2f0884acdafd-1 | Return True if the string is a decimal string, False otherwise.
isdigit()
Return True if the string is a digit string, False otherwise.
isidentifier()
Return True if the string is a valid Python identifier, False otherwise.
islower()
Return True if the string is a lowercase string, False otherwise.
isnumeric()
Return True if the string is a numeric string, False otherwise.
isprintable()
Return True if the string is printable, False otherwise.
isspace()
Return True if the string is a whitespace string, False otherwise.
istitle()
Return True if the string is a title-cased string, False otherwise.
isupper()
Return True if the string is an uppercase string, False otherwise.
join(iterable, /)
Concatenate any number of strings.
ljust(width[, fillchar])
Return a left-justified string of length width.
lower()
Return a copy of the string converted to lowercase.
lstrip([chars])
Return a copy of the string with leading whitespace removed.
maketrans
Return a translation table usable for str.translate().
partition(sep, /)
Partition the string into three parts using the given separator.
removeprefix(prefix, /)
Return a str with the given prefix string removed if present.
removesuffix(suffix, /)
Return a str with the given suffix string removed if present.
replace(old, new[, count])
Return a copy with all occurrences of substring old replaced by new.
rfind(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rindex(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. | [
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2f0884acdafd-2 | rjust(width[, fillchar])
Return a right-justified string of length width.
rpartition(sep, /)
Partition the string into three parts using the given separator.
rsplit([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
rstrip([chars])
Return a copy of the string with trailing whitespace removed.
split([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
splitlines([keepends])
Return a list of the lines in the string, breaking at line boundaries.
startswith(prefix[, start[, end]])
Return True if S starts with the specified prefix, False otherwise.
strip([chars])
Return a copy of the string with leading and trailing whitespace removed.
swapcase()
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()
Return a version of the string where each word is titlecased.
translate(table, /)
Replace each character in the string using the given translation table.
upper()
Return a copy of the string converted to uppercase.
zfill(width, /)
Pad a numeric string with zeros on the left, to fill a field of the given width.
Attributes
THREADS
MESSAGES
capitalize()¶
Return a capitalized version of the string.
More specifically, make the first character have upper case and the rest lower
case.
casefold()¶
Return a version of the string suitable for caseless comparisons.
center(width, fillchar=' ', /)¶
Return a centered string of length width.
Padding is done using the specified fill character (default is a space).
count(sub[, start[, end]]) → int¶
Return the number of non-overlapping occurrences of substring sub in | [
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2f0884acdafd-3 | Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are
interpreted as in slice notation.
encode(encoding='utf-8', errors='strict')¶
Encode the string using the codec registered for encoding.
encodingThe encoding in which to encode the string.
errorsThe error handling scheme to use for encoding errors.
The default is ‘strict’ meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and
‘xmlcharrefreplace’ as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
endswith(suffix[, start[, end]]) → bool¶
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
expandtabs(tabsize=8)¶
Return a copy where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
find(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
format(*args, **kwargs) → str¶
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces (‘{’ and ‘}’).
format_map(mapping) → str¶
Return a formatted version of S, using substitutions from mapping.
The substitutions are identified by braces (‘{’ and ‘}’).
index(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found, | [
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2f0884acdafd-4 | Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
isalnum()¶
Return True if the string is an alpha-numeric string, False otherwise.
A string is alpha-numeric if all characters in the string are alpha-numeric and
there is at least one character in the string.
isalpha()¶
Return True if the string is an alphabetic string, False otherwise.
A string is alphabetic if all characters in the string are alphabetic and there
is at least one character in the string.
isascii()¶
Return True if all characters in the string are ASCII, False otherwise.
ASCII characters have code points in the range U+0000-U+007F.
Empty string is ASCII too.
isdecimal()¶
Return True if the string is a decimal string, False otherwise.
A string is a decimal string if all characters in the string are decimal and
there is at least one character in the string.
isdigit()¶
Return True if the string is a digit string, False otherwise.
A string is a digit string if all characters in the string are digits and there
is at least one character in the string.
isidentifier()¶
Return True if the string is a valid Python identifier, False otherwise.
Call keyword.iskeyword(s) to test whether string s is a reserved identifier,
such as “def” or “class”.
islower()¶
Return True if the string is a lowercase string, False otherwise.
A string is lowercase if all cased characters in the string are lowercase and
there is at least one cased character in the string.
isnumeric()¶
Return True if the string is a numeric string, False otherwise. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.search.Resource.html |
2f0884acdafd-5 | isnumeric()¶
Return True if the string is a numeric string, False otherwise.
A string is numeric if all characters in the string are numeric and there is at
least one character in the string.
isprintable()¶
Return True if the string is printable, False otherwise.
A string is printable if all of its characters are considered printable in
repr() or if it is empty.
isspace()¶
Return True if the string is a whitespace string, False otherwise.
A string is whitespace if all characters in the string are whitespace and there
is at least one character in the string.
istitle()¶
Return True if the string is a title-cased string, False otherwise.
In a title-cased string, upper- and title-case characters may only
follow uncased characters and lowercase characters only cased ones.
isupper()¶
Return True if the string is an uppercase string, False otherwise.
A string is uppercase if all cased characters in the string are uppercase and
there is at least one cased character in the string.
join(iterable, /)¶
Concatenate any number of strings.
The string whose method is called is inserted in between each given string.
The result is returned as a new string.
Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’
ljust(width, fillchar=' ', /)¶
Return a left-justified string of length width.
Padding is done using the specified fill character (default is a space).
lower()¶
Return a copy of the string converted to lowercase.
lstrip(chars=None, /)¶
Return a copy of the string with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
static maketrans()¶
Return a translation table usable for str.translate(). | [
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2f0884acdafd-6 | static maketrans()¶
Return a translation table usable for str.translate().
If there is only one argument, it must be a dictionary mapping Unicode
ordinals (integers) or characters to Unicode ordinals, strings or None.
Character keys will be then converted to ordinals.
If there are two arguments, they must be strings of equal length, and
in the resulting dictionary, each character in x will be mapped to the
character at the same position in y. If there is a third argument, it
must be a string, whose characters will be mapped to None in the result.
partition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string. If the separator is found,
returns a 3-tuple containing the part before the separator, the separator
itself, and the part after it.
If the separator is not found, returns a 3-tuple containing the original string
and two empty strings.
removeprefix(prefix, /)¶
Return a str with the given prefix string removed if present.
If the string starts with the prefix string, return string[len(prefix):].
Otherwise, return a copy of the original string.
removesuffix(suffix, /)¶
Return a str with the given suffix string removed if present.
If the string ends with the suffix string and that suffix is not empty,
return string[:-len(suffix)]. Otherwise, return a copy of the original
string.
replace(old, new, count=- 1, /)¶
Return a copy with all occurrences of substring old replaced by new.
countMaximum number of occurrences to replace.
-1 (the default value) means replace all occurrences.
If the optional argument count is given, only the first count occurrences are
replaced.
rfind(sub[, start[, end]]) → int¶ | [
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2f0884acdafd-7 | replaced.
rfind(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
rindex(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
rjust(width, fillchar=' ', /)¶
Return a right-justified string of length width.
Padding is done using the specified fill character (default is a space).
rpartition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string, starting at the end. If
the separator is found, returns a 3-tuple containing the part before the
separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing two empty strings
and the original string.
rsplit(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Splitting starts at the end of the string and works to the front.
rstrip(chars=None, /)¶
Return a copy of the string with trailing whitespace removed. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.search.Resource.html |
2f0884acdafd-8 | rstrip(chars=None, /)¶
Return a copy of the string with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
split(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Note, str.split() is mainly useful for data that has been intentionally
delimited. With natural text that includes punctuation, consider using
the regular expression module.
splitlines(keepends=False)¶
Return a list of the lines in the string, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends is given and
true.
startswith(prefix[, start[, end]]) → bool¶
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
strip(chars=None, /)¶
Return a copy of the string with leading and trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
swapcase()¶
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()¶
Return a version of the string where each word is titlecased.
More specifically, words start with uppercased characters and all remaining
cased characters have lower case.
translate(table, /)¶
Replace each character in the string using the given translation table. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.search.Resource.html |
2f0884acdafd-9 | translate(table, /)¶
Replace each character in the string using the given translation table.
tableTranslation table, which must be a mapping of Unicode ordinals to
Unicode ordinals, strings, or None.
The table must implement lookup/indexing via __getitem__, for instance a
dictionary or list. If this operation raises LookupError, the character is
left untouched. Characters mapped to None are deleted.
upper()¶
Return a copy of the string converted to uppercase.
zfill(width, /)¶
Pad a numeric string with zeros on the left, to fill a field of the given width.
The string is never truncated.
MESSAGES = 'messages'¶
THREADS = 'threads'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.gmail.search.Resource.html |
2bb29464dff5-0 | langchain.tools.ddg_search.tool.DuckDuckGoSearchTool¶
langchain.tools.ddg_search.tool.DuckDuckGoSearchTool(*args: Any, **kwargs: Any) → DuckDuckGoSearchRun[source]¶
Deprecated. Use DuckDuckGoSearchRun instead.
Parameters
*args –
**kwargs –
Returns
DuckDuckGoSearchRun | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.ddg_search.tool.DuckDuckGoSearchTool.html |
e3479a4d12cb-0 | langchain.tools.office365.events_search.SearchEventsInput¶
class langchain.tools.office365.events_search.SearchEventsInput(*, start_datetime: str, end_datetime: str, max_results: int = 10, truncate: bool = True)[source]¶
Bases: BaseModel
Input for SearchEmails Tool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param end_datetime: str [Required]¶
The end datetime for the search query in the following format: YYYY-MM-DDTHH:MM:SS±hh:mm, where “T” separates the date and time components, and the time zone offset is specified as ±hh:mm. For example: “2023-06-09T10:30:00+03:00” represents June 9th, 2023, at 10:30 AM in a time zone with a positive offset of 3 hours from Coordinated Universal Time (UTC).
param max_results: int = 10¶
The maximum number of results to return.
param start_datetime: str [Required]¶
The start datetime for the search query in the following format: YYYY-MM-DDTHH:MM:SS±hh:mm, where “T” separates the date and time components, and the time zone offset is specified as ±hh:mm. For example: “2023-06-09T10:30:00+03:00” represents June 9th, 2023, at 10:30 AM in a time zone with a positive offset of 3 hours from Coordinated Universal Time (UTC).
param truncate: bool = True¶ | [
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e3479a4d12cb-1 | param truncate: bool = True¶
Whether the event’s body is trucated to meet token number limits. Set to False for searches that will retrieve very few results, otherwise, set to True. | [
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3d7255ef8192-0 | langchain.tools.gmail.utils.import_googleapiclient_resource_builder¶
langchain.tools.gmail.utils.import_googleapiclient_resource_builder() → build_resource[source]¶
Import googleapiclient.discovery.build function.
Returns
googleapiclient.discovery.build function.
Return type
build_resource | [
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a3443a83d53c-0 | langchain.tools.file_management.delete.FileDeleteInput¶
class langchain.tools.file_management.delete.FileDeleteInput(*, file_path: str)[source]¶
Bases: BaseModel
Input for DeleteFileTool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param file_path: str [Required]¶
Path of the file to delete | [
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024fd9f68c76-0 | langchain.tools.sleep.tool.SleepInput¶
class langchain.tools.sleep.tool.SleepInput(*, sleep_time: int)[source]¶
Bases: BaseModel
Input for CopyFileTool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param sleep_time: int [Required]¶
Time to sleep in seconds | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.sleep.tool.SleepInput.html |
eb2d0cab7993-0 | langchain.tools.gmail.utils.clean_email_body¶
langchain.tools.gmail.utils.clean_email_body(body: str) → str[source]¶
Clean email body. | [
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581ccc818e11-0 | langchain.tools.wolfram_alpha.tool.WolframAlphaQueryRun¶
class langchain.tools.wolfram_alpha.tool.WolframAlphaQueryRun(*, name: str = 'wolfram_alpha', description: str = 'A wrapper around Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: WolframAlphaAPIWrapper)[source]¶
Bases: BaseTool
Tool that adds the capability to query using the Wolfram Alpha SDK.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_wrapper: langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper [Required]¶
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | [
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581ccc818e11-1 | You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'wolfram_alpha'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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c4a03399d52a-0 | langchain.tools.office365.utils.clean_body¶
langchain.tools.office365.utils.clean_body(body: str) → str[source]¶
Clean body of a message or event. | [
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1e2b619e9a97-0 | langchain.tools.playwright.utils.run_async¶
langchain.tools.playwright.utils.run_async(coro: Coroutine[Any, Any, T]) → T[source]¶
Parameters
coro – The coroutine to run. Coroutine[Any, Any, T]
Returns
The result of the coroutine.
Return type
T | [
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30ce849e9934-0 | langchain.tools.gmail.send_message.SendMessageSchema¶
class langchain.tools.gmail.send_message.SendMessageSchema(*, message: str, to: Union[str, List[str]], subject: str, cc: Optional[Union[str, List[str]]] = None, bcc: Optional[Union[str, List[str]]] = None)[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param bcc: Optional[Union[str, List[str]]] = None¶
The list of BCC recipients.
param cc: Optional[Union[str, List[str]]] = None¶
The list of CC recipients.
param message: str [Required]¶
The message to send.
param subject: str [Required]¶
The subject of the message.
param to: Union[str, List[str]] [Required]¶
The list of recipients. | [
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51f63bfaf344-0 | langchain.tools.file_management.write.WriteFileTool¶
class langchain.tools.file_management.write.WriteFileTool(*, name: str = 'write_file', description: str = 'Write file to disk', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, root_dir: ~typing.Optional[str] = None)[source]¶
Bases: BaseFileToolMixin, BaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.write.WriteFileInput'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Write file to disk'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.file_management.write.WriteFileTool.html |
51f63bfaf344-1 | Handle the content of the ToolException thrown.
param name: str = 'write_file'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param root_dir: Optional[str] = None¶
The final path will be chosen relative to root_dir if specified.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
get_relative_path(file_path: str) → Path¶
Get the relative path, returning an error if unsupported.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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a854a8c844d5-0 | langchain.tools.jira.tool.JiraAction¶
class langchain.tools.jira.tool.JiraAction(*, name: str = '', description: str = '', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: JiraAPIWrapper = None, mode: str)[source]¶
Bases: BaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_wrapper: langchain.utilities.jira.JiraAPIWrapper [Optional]¶
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param 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.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param mode: str [Required]¶
param name: str = ''¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.jira.tool.JiraAction.html |
a854a8c844d5-1 | Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.jira.tool.JiraAction.html |
9e5ccca02cfe-0 | langchain.tools.base.create_schema_from_function¶
langchain.tools.base.create_schema_from_function(model_name: str, func: Callable) → Type[BaseModel][source]¶
Create a pydantic schema from a function’s signature.
:param model_name: Name to assign to the generated pydandic schema
:param func: Function to generate the schema from
Returns
A pydantic model with the same arguments as the function | [
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f5805b4a2c69-0 | langchain.tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool¶
class langchain.tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool(*, name: str = 'azure_cognitive_services_image_analysis', description: str = 'A wrapper around Azure Cognitive Services Image Analysis. Useful for when you need to analyze images. Input should be a url to an image.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, azure_cogs_key: str = '', azure_cogs_endpoint: str = '', vision_service: Any = None, analysis_options: Any = None)[source]¶
Bases: BaseTool
Tool that queries the Azure Cognitive Services Image Analysis API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Azure Cognitive Services Image Analysis. Useful for when you need to analyze images. Input should be a url to an image.'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool.html |
f5805b4a2c69-1 | Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'azure_cognitive_services_image_analysis'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
validator validate_environment » all fields[source]¶
Validate that api key and endpoint exists in environment.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool.html |
f5805b4a2c69-2 | Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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1cc64330acdf-0 | langchain.tools.office365.messages_search.SearchEmailsInput¶
class langchain.tools.office365.messages_search.SearchEmailsInput(*, folder: str = None, query: str, max_results: int = 10, truncate: bool = True)[source]¶
Bases: BaseModel
Input for SearchEmails Tool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param folder: str = None¶
If the user wants to search in only one folder, the name of the folder. Default folders are “inbox”, “drafts”, “sent items”, “deleted ttems”, but users can search custom folders as well.
param max_results: int = 10¶
The maximum number of results to return.
param query: str [Required]¶
The Microsoift Graph v1.0 $search query. Example filters include from:sender, from:sender, to:recipient, subject:subject, recipients:list_of_recipients, body:excitement, importance:high, received>2022-12-01, received<2021-12-01, sent>2022-12-01, sent<2021-12-01, hasAttachments:true attachment:api-catalog.md, cc:samanthab@contoso.com, bcc:samanthab@contoso.com, body:excitement date range example: received:2023-06-08..2023-06-09 matching example: from:amy OR from:david.
param truncate: bool = True¶
Whether the email body is trucated to meet token number limits. Set to False for searches that will retrieve very few results, otherwise, set to True | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.office365.messages_search.SearchEmailsInput.html |
dafea9d1f77b-0 | langchain.tools.office365.send_message.O365SendMessage¶
class langchain.tools.office365.send_message.O365SendMessage(*, name: str = 'send_email', description: str = 'Use this tool to send an email with the provided message fields.', args_schema: ~typing.Type[~langchain.tools.office365.send_message.SendMessageSchema] = <class 'langchain.tools.office365.send_message.SendMessageSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, account: Account = None)[source]¶
Bases: O365BaseTool
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param account: Account [Optional]¶
param args_schema: Type[langchain.tools.office365.send_message.SendMessageSchema] = <class 'langchain.tools.office365.send_message.SendMessageSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to send an email with the provided message fields.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.office365.send_message.O365SendMessage.html |
dafea9d1f77b-1 | You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param name: str = 'send_email'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.office365.send_message.O365SendMessage.html |
b81c7bd1ed05-0 | langchain.tools.python.tool.PythonREPLTool¶
class langchain.tools.python.tool.PythonREPLTool(*, name: str = 'Python_REPL', description: str = 'A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, python_repl: PythonREPL = None, sanitize_input: bool = True)[source]¶
Bases: BaseTool
A tool for running python code in a REPL.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | [
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] | https://langchain.readthedocs.io/en/latest/tools/langchain.tools.python.tool.PythonREPLTool.html |
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