Tony20100/langchain_full huggingface dataset:
Parameters
input_type (Optional[Type[Input]]) – 
output_type (Optional[Type[Output]]) – 
Return type
Runnable[Input, Output]
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property async_prediction_client: PredictionServiceAsyncClient¶
Returns PredictionServiceClient.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property endpoint_path: str¶
property input_schema: Type[BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
property prediction_client: PredictionServiceClient¶
Returns PredictionServiceClient.
task_executor: ClassVar[Optional[Executor]] = FieldInfo(exclude=True, extra={})¶
Examples using GemmaVertexAIModelGarden¶
Google Cloud Vertex AI
langchain_google_vertexai.gemma.GemmaChatLocalKaggle¶
class langchain_google_vertexai.gemma.GemmaChatLocalKaggle[source]¶
Bases: _GemmaLocalKaggleBase, BaseChatModel
Needed for mypy typing to recognize model_name as a valid arg.
param cache: Union[BaseCache, bool, None] = None¶
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED] Callback manager to add to the run trace.
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param keras_backend: str = 'jax'¶
param max_tokens: Optional[int] = None¶
The maximum number of tokens to generate.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_name: str = 'gemma_2b_en' (alias 'model')¶
Gemma model name.
param parse_response: bool = False¶
Whether to post-process the chat response and clean repeations
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = None¶
The temperature to use for sampling.
param top_k: Optional[int] = None¶
The top-k value to use for sampling.
param top_p: Optional[float] = None¶
param top_p: Optional[float] = None¶
The top-p value to use for sampling.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → BaseMessage¶
[Deprecated]
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
messages (List[BaseMessage]) – 
stop (Optional[List[str]]) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
BaseMessage
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
langchain_google_vertexai.gemma.GemmaLocalHF¶
class langchain_google_vertexai.gemma.GemmaLocalHF[source]¶
Bases: _GemmaLocalHFBase, BaseLLM
Local gemma model loaded from HuggingFace.
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 cache: Union[BaseCache, bool, None] = None¶
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
param cache_dir: Optional[str] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED]
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param hf_access_token: str [Required]¶
param max_tokens: Optional[int] = None¶
The maximum number of tokens to generate.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_name: str = 'gemma_2b_en' (alias 'model')¶
Gemma model name.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = None¶
The temperature to use for sampling.
param top_k: Optional[int] = None¶
The top-k value to use for sampling.
param top_p: Optional[float] = None¶
param top_p: Optional[float] = None¶
The top-p value to use for sampling.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
prompt (str) – 
stop (Optional[List[str]]) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
str
async abatch(inputs: List[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]]) –
langchain_google_community.gmail.search.SearchArgsSchema¶
class langchain_google_community.gmail.search.SearchArgsSchema[source]¶
Bases: BaseModel
Input for SearchGmailTool.
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 max_results: int = 10¶
The maximum number of results to return.
param query: str [Required]¶
The Gmail query. Example filters include from:sender, to:recipient, subject:subject, -filtered_term, in:folder, is:important|read|starred, after:year/mo/date, before:year/mo/date, label:label_name “exact phrase”. Search newer/older than using d (day), m (month), and y (year): newer_than:2d, older_than:1y. Attachments with extension example: filename:pdf. Multiple term matching example: from:amy OR from:david.
param resource: Resource = Resource.MESSAGES¶
Whether to search for threads or messages.
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¶
langchain_google_community.gmail.utils.build_resource_service¶
langchain_google_community.gmail.utils.build_resource_service(credentials: Optional[Credentials] = None, service_name: str = 'gmail', service_version: str = 'v1') → Resource[source]¶
Build a Gmail service.
Parameters
credentials (Optional[Credentials]) – 
service_name (str) – 
service_version (str) – 
Return type
Resource
Examples using build_resource_service¶
Gmail
langchain_google_community.gmail.get_message.SearchArgsSchema¶
class langchain_google_community.gmail.get_message.SearchArgsSchema[source]¶
Bases: BaseModel
Input for GetMessageTool.
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 message_id: str [Required]¶
The unique ID of the email message, retrieved from a search.
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
langchain_google_community.gmail.create_draft.GmailCreateDraft¶
class langchain_google_community.gmail.create_draft.GmailCreateDraft[source]¶
Bases: GmailBaseTool
Tool that creates a draft email for Gmail.
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[CreateDraftSchema] = <class 'langchain_google_community.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.
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 metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
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.
langchain_google_community.gmail.toolkit.GmailToolkit¶
class langchain_google_community.gmail.toolkit.GmailToolkit[source]¶
Bases: BaseToolkit
Toolkit for interacting with Gmail.
Security Note: This toolkit contains tools that can read and modifythe state of a service; e.g., by reading, creating, updating, deleting
data associated with this service.
For example, this toolkit can be used to send emails on behalf of the
associated account.
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 api_resource: Resource [Optional]¶
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
langchain_google_community.gmail.base.GmailBaseTool¶
class langchain_google_community.gmail.base.GmailBaseTool[source]¶
Bases: BaseTool
Base class for Gmail tools.
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: 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 handle_validation_error: Optional[Union[bool, str, Callable[[ValidationError], str]]] = False¶
Handle the content of the ValidationError thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
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 tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
langchain_google_community.gmail.send_message.SendMessageSchema¶
class langchain_google_community.gmail.send_message.SendMessageSchema[source]¶
Bases: BaseModel
Input for SendMessageTool.
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.
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
langchain_google_community.gmail.utils.get_gmail_credentials¶
langchain_google_community.gmail.utils.get_gmail_credentials(token_file: Optional[str] = None, client_secrets_file: Optional[str] = None, scopes: Optional[List[str]] = None) → Credentials[source]¶
Get credentials.
Parameters
token_file (Optional[str]) – 
client_secrets_file (Optional[str]) – 
scopes (Optional[List[str]]) – 
Return type
Credentials
Examples using get_gmail_credentials¶
Gmail
langchain_google_community.gmail.loader.GMailLoader¶
class langchain_google_community.gmail.loader.GMailLoader(creds: Any, n: int = 100, raise_error: bool = False)[source]¶
Load data from GMail.
There are many ways you could want to load data from GMail.
This loader is currently fairly opinionated in how to do so.
The way it does it is it first looks for all messages that you have sent.
It then looks for messages where you are responding to a previous email.
It then fetches that previous email, and creates a training example
of that email, followed by your email.
Note that there are clear limitations here. For example,
all examples created are only looking at the previous email for context.
To use:
Set up a Google Developer Account:Go to the Google Developer Console, create a project,
and enable the Gmail API for that project.
This will give you a credentials.json file that you’ll need later.
Methods
__init__(creds[, n, raise_error])
lazy_load()
Lazy load the chat sessions.
load()
Eagerly load the chat sessions into memory.
Parameters
creds (Any) – 
n (int) – 
raise_error (bool) – 
__init__(creds: Any, n: int = 100, raise_error: bool = False) → None[source]¶
Parameters
creds (Any) – 
n (int) – 
raise_error (bool) – 
Return type
None
lazy_load() → Iterator[ChatSession][source]¶
Lazy load the chat sessions.
Return type
Iterator[ChatSession]
load() → List[ChatSession]¶
Eagerly load the chat sessions into memory.
Return type
List[ChatSession]
Examples using GMailLoader¶
GMail
langchain_google_community.gmail.create_draft.CreateDraftSchema¶
class langchain_google_community.gmail.create_draft.CreateDraftSchema[source]¶
Bases: BaseModel
Input for CreateDraftTool.
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[List[str]] = None¶
The list of BCC recipients.
param cc: Optional[List[str]] = None¶
The list of CC recipients.
param message: str [Required]¶
The message to include in the draft.
param subject: str [Required]¶
The subject of the message.
param to: List[str] [Required]¶
The list of recipients.
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
langchain_google_community.gmail.get_message.GmailGetMessage¶
class langchain_google_community.gmail.get_message.GmailGetMessage[source]¶
Bases: GmailBaseTool
Tool that gets a message by ID from Gmail.
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[SearchArgsSchema] = <class 'langchain_google_community.gmail.get_message.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 fetch an email by message ID. Returns the thread ID, snippet, body, subject, and sender.'¶
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 handle_validation_error: Optional[Union[bool, str, Callable[[ValidationError], str]]] = False¶
Handle the content of the ValidationError thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means