Tony20100/langchain_full huggingface dataset:
Return a list of tools.
Return type
List[BaseTool]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
langchain_community.agent_toolkits.json.toolkit.JsonToolkit¶
class langchain_community.agent_toolkits.json.toolkit.JsonToolkit[source]¶
Bases: BaseToolkit
Toolkit for interacting with 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 spec: JsonSpec [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using JsonToolkit¶
JSON
langchain_community.agent_toolkits.openapi.spec.reduce_openapi_spec¶
langchain_community.agent_toolkits.openapi.spec.reduce_openapi_spec(spec: dict, dereference: bool = True) → ReducedOpenAPISpec[source]¶
Simplify/distill/minify a spec somehow.
I want a smaller target for retrieval and (more importantly)
I want smaller results from retrieval.
I was hoping https://openapi.tools/ would have some useful bits
to this end, but doesn’t seem so.
Parameters
spec (dict) – 
dereference (bool) – 
Return type
ReducedOpenAPISpec
Examples using reduce_openapi_spec¶
OpenAPI
langchain_community.agent_toolkits.sql.base.create_sql_agent¶
langchain_community.agent_toolkits.sql.base.create_sql_agent(llm: BaseLanguageModel, toolkit: Optional[SQLDatabaseToolkit] = None, agent_type: Optional[Union[AgentType, Literal['openai-tools', 'tool-calling']]] = None, callback_manager: Optional[BaseCallbackManager] = None, prefix: Optional[str] = None, suffix: Optional[str] = None, format_instructions: Optional[str] = None, input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, extra_tools: Sequence[BaseTool] = (), *, db: Optional[SQLDatabase] = None, prompt: Optional[BasePromptTemplate] = None, **kwargs: Any) → AgentExecutor[source]¶
Construct a SQL agent from an LLM and toolkit or database.
Parameters
llm (BaseLanguageModel) – Language model to use for the agent. If agent_type is “tool-calling” then
llm is expected to support tool calling.
toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. Must provide exactly one of
‘toolkit’ or ‘db’. Specify ‘toolkit’ if you want to use a different model
for the agent and the toolkit.
agent_type (Optional[Union[AgentType, Literal['openai-tools', 'tool-calling']]]) – One of “tool-calling”, “openai-tools”, “openai-functions”, or
“zero-shot-react-description”. Defaults to “zero-shot-react-description”.
“tool-calling” is recommended over the legacy “openai-tools” and
“tool-calling” is recommended over the legacy “openai-tools” and
“openai-functions” types.
callback_manager (Optional[BaseCallbackManager]) – DEPRECATED. Pass “callbacks” key into ‘agent_executor_kwargs’
instead to pass constructor callbacks to AgentExecutor.
prefix (Optional[str]) – Prompt prefix string. Must contain variables “top_k” and “dialect”.
suffix (Optional[str]) – Prompt suffix string. Default depends on agent type.
format_instructions (Optional[str]) – Formatting instructions to pass to
ZeroShotAgent.create_prompt() when ‘agent_type’ is
“zero-shot-react-description”. Otherwise ignored.
input_variables (Optional[List[str]]) – DEPRECATED.
top_k (int) – Number of rows to query for by default.
max_iterations (Optional[int]) – Passed to AgentExecutor init.
max_execution_time (Optional[float]) – Passed to AgentExecutor init.
early_stopping_method (str) – Passed to AgentExecutor init.
verbose (bool) – AgentExecutor verbosity.
agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args.
extra_tools (Sequence[BaseTool]) – Additional tools to give to agent on top of the ones that come with
SQLDatabaseToolkit.
db (Optional[SQLDatabase]) – SQLDatabase from which to create a SQLDatabaseToolkit. Toolkit is created
using ‘db’ and ‘llm’. Must provide exactly one of ‘db’ or ‘toolkit’.
prompt (Optional[BasePromptTemplate]) – Complete agent prompt. prompt and {prefix, suffix, format_instructions,
input_variables} are mutually exclusive.
**kwargs (Any) – Arbitrary additional Agent args.
Returns
An AgentExecutor with the specified agent_type agent.
Return type
AgentExecutor
Example
from langchain_openai import ChatOpenAI
from langchain_community.agent_toolkits import create_sql_agent
from langchain_community.agent_toolkits import create_sql_agent
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_uri(“sqlite:///Chinook.db”)
llm = ChatOpenAI(model=”gpt-3.5-turbo”, temperature=0)
agent_executor = create_sql_agent(llm, db=db, agent_type=”tool-calling”, verbose=True)
Examples using create_sql_agent¶
CnosDB
How to do question answering over CSVs
SQL Database
langchain_community.agent_toolkits.github.toolkit.UpdateFile¶
class langchain_community.agent_toolkits.github.toolkit.UpdateFile[source]¶
Bases: BaseModel
Schema for operations that require a file path and content as input.
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 formatted_file_update: str [Required]¶
Strictly follow the provided rules.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
langchain_community.agent_toolkits.openapi.spec.ReducedOpenAPISpec¶
class langchain_community.agent_toolkits.openapi.spec.ReducedOpenAPISpec(servers: List[dict], description: str, endpoints: List[Tuple[str, str, dict]])[source]¶
A reduced OpenAPI spec.
This is a quick and dirty representation for OpenAPI specs.
Parameters
servers (List[dict]) – 
description (str) – 
endpoints (List[Tuple[str, str, dict]]) – 
servers¶
The servers in the spec.
Type
List[dict]
description¶
The description of the spec.
Type
str
endpoints¶
The endpoints in the spec.
Type
List[Tuple[str, str, dict]]
Attributes
servers
description
endpoints
Methods
__init__(servers, description, endpoints)
__init__(servers: List[dict], description: str, endpoints: List[Tuple[str, str, dict]]) → None¶
Parameters
servers (List[dict]) – 
description (str) – 
endpoints (List[Tuple[str, str, dict]]) – 
Return type
None
langchain_community.agent_toolkits.github.toolkit.GetPR¶
class langchain_community.agent_toolkits.github.toolkit.GetPR[source]¶
Bases: BaseModel
Schema for operations that require a PR number as input.
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 pr_number: int = 0¶
The PR number as an integer, e.g. 12
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
langchain_community.agent_toolkits.github.toolkit.CreatePR¶
class langchain_community.agent_toolkits.github.toolkit.CreatePR[source]¶
Bases: BaseModel
Schema for operations that require a PR title and body as input.
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 formatted_pr: str [Required]¶
Follow the required formatting.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
langchain_community.agent_toolkits.steam.toolkit.SteamToolkit¶
class langchain_community.agent_toolkits.steam.toolkit.SteamToolkit[source]¶
Bases: BaseToolkit
Steam Toolkit.
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 tools: List[BaseTool] = []¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod from_steam_api_wrapper(steam_api_wrapper: SteamWebAPIWrapper) → SteamToolkit[source]¶
Parameters
steam_api_wrapper (SteamWebAPIWrapper) – 
Return type
SteamToolkit
get_tools() → List[BaseTool][source]¶
Get the tools in the toolkit.
Return type
List[BaseTool]
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using SteamToolkit¶
Steam Game Recommendation & Game Details
langchain_community.agent_toolkits.gitlab.toolkit.GitLabToolkit¶
class langchain_community.agent_toolkits.gitlab.toolkit.GitLabToolkit[source]¶
Bases: BaseToolkit
GitLab Toolkit.
Security Note: This toolkit contains tools that can read and modifythe state of a service; e.g., by creating, deleting, or updating,
reading underlying data.
For example, this toolkit can be used to create issues, pull requests,
and comments on GitLab.
See https://python.langchain.com/docs/security for more information.
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 tools: List[BaseTool] = []¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_gitlab_api_wrapper(gitlab_api_wrapper: GitLabAPIWrapper) → GitLabToolkit[source]¶
Parameters
gitlab_api_wrapper (GitLabAPIWrapper) – 
Return type
GitLabToolkit
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_tools() → List[BaseTool][source]¶
Get the tools in the toolkit.
Return type
List[BaseTool]
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using GitLabToolkit¶
Gitlab
langchain_community.agent_toolkits.openapi.planner.RequestsPatchToolWithParsing¶
class langchain_community.agent_toolkits.openapi.planner.RequestsPatchToolWithParsing[source]¶
Bases: BaseRequestsTool, BaseTool
Requests PATCH tool with LLM-instructed extraction of truncated responses.
Initialize the tool.
param allow_dangerous_requests: bool = False¶
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 = 'Use this when you want to PATCH content on a website.\nInput to the tool should be a json string with 3 keys: "url", "data", and "output_instructions".\nThe value of "url" should be a string.\nThe value of "data" should be a dictionary of key-value pairs of the body params available in the OpenAPI spec you want to PATCH the content with at the url.\nThe value of "output_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the PATCH request creates.\nAlways use double quotes for strings in the json string.'¶
Tool description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param handle_validation_error: Optional[Union[bool, str, Callable[[ValidationError], str]]] = False¶
Handle the content of the ValidationError thrown.
param llm_chain: Any [Optional]¶
LLMChain used to extract the response.
param metadata: Optional[Dict[str, Any]] = None¶