Tony20100/langchain_full huggingface dataset:
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
Return type
Type[BaseModel]
get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶
Parameters
config (Optional[RunnableConfig]) – 
Return type
List[BasePromptTemplate]
get_token_ids(text: str) → List[int][source]¶
Return the ordered ids of the tokens in a text.
Parameters
text (str) – The string input to tokenize.
Returns
A list of ids corresponding to the tokens in the text, in order they occurin the text.
Return type
List[int]
invoke(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → BaseMessage¶
Transform a single input into an output. Override to implement.
Parameters
input (LanguageModelInput) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
stop (Optional[List[str]]) – 
kwargs (Any) – 
Returns
The output of the runnable.
Return type
BaseMessage
classmethod is_lc_serializable() → bool[source]¶
Return whether this model can be serialized by Langchain.
Return type
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: Any¶
Get the output type for this runnable.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict[str, Any]¶
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.
langchain_community.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint¶
class langchain_community.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint[source]¶
Bases: BaseChatModel, AzureMLBaseEndpoint
Azure ML Online Endpoint chat models.
Example
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 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 content_formatter: Any = None¶
The content formatter that provides an input and output
transform function to handle formats between the LLM and
the endpoint
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param deployment_name: str = ''¶
Deployment Name for Endpoint. NOT REQUIRED to call endpoint. Should be passed
to constructor or specified as env var AZUREML_DEPLOYMENT_NAME.
param endpoint_api_key: SecretStr = SecretStr('')¶
Authentication Key for Endpoint. Should be passed to constructor or specified as
env var AZUREML_ENDPOINT_API_KEY.
Constraints
type = string
writeOnly = True
format = password
param endpoint_api_type: AzureMLEndpointApiType = AzureMLEndpointApiType.dedicated¶
Type of the endpoint being consumed. Possible values are serverless for
pay-as-you-go and dedicated for dedicated endpoints.
param endpoint_url: str = ''¶
URL of pre-existing Endpoint. Should be passed to constructor or specified as
env var AZUREML_ENDPOINT_URL.
param max_retries: int = 1¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Optional[dict] = None¶
Keyword arguments to pass to the model.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param timeout: int = 50¶
Request timeout for calls to the endpoint
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
Not implemented on this class.
Parameters
schema (Union[Dict, Type[BaseModel]]) – 
kwargs (Any) – 
Return type
Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]]
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
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: Any¶
Get the output type for this runnable.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
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.
Examples using AzureMLChatOnlineEndpoint¶
AzureMLChatOnlineEndpoint
langchain_community.chat_models.moonshot.MoonshotChat¶
class langchain_community.chat_models.moonshot.MoonshotChat[source]¶
Bases: MoonshotCommon, ChatOpenAI
Moonshot large language models.
To use, you should have the openai python package installed, and the
environment variable MOONSHOT_API_KEY set with your API key.
(Moonshot’s chat API is compatible with OpenAI’s SDK.)
Referenced from https://platform.moonshot.cn/docs
Example
from langchain_community.chat_models.moonshot import MoonshotChat
moonshot = MoonshotChat(model="moonshot-v1-8k")
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 base_url: str = 'https://api.moonshot.cn/v1'¶
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 default_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param http_client: Union[Any, None] = None¶
param http_client: Union[Any, None] = None¶
Optional httpx.Client.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param max_tokens: Optional[int] = 1024¶
Maximum number of tokens to generate.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param model_name: str = 'moonshot-v1-8k' (alias 'model')¶
Model name. Available models listed here: https://platform.moonshot.cn/pricing
param moonshot_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Moonshot API key. Get it here: https://platform.moonshot.cn/console/api-keys
Constraints
type = string
writeOnly = True
format = password
param n: int = 1¶
Number of chat completions to generate for each prompt.
param openai_api_base: Optional[str] = None (alias 'base_url')¶
Base URL path for API requests, leave blank if not using a proxy or service
emulator.
param openai_api_key: Optional[str] = None (alias 'api_key')¶
Automatically inferred from env var OPENAI_API_KEY if not provided.
param openai_organization: Optional[str] = None (alias 'organization')¶
Automatically inferred from env var OPENAI_ORG_ID if not provided.
param openai_proxy: Optional[str] = None¶
param request_timeout: Union[float, Tuple[float, float], Any, None] = None (alias 'timeout')¶
Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None.
None.
param streaming: bool = False¶
Whether to stream the results or not.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.3¶
Temperature parameter (higher values make the model more creative).
param tiktoken_model_name: Optional[str] = None¶
The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
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
property lc_secrets: dict¶
A map of constructor argument names to secret ids.
For example,{“moonshot_api_key”: “MOONSHOT_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.
Examples using MoonshotChat¶
MoonshotChat
langchain_together.chat_models.ChatTogether¶
class langchain_together.chat_models.ChatTogether[source]¶
Bases: BaseChatOpenAI
ChatTogether chat model.
To use, you should have the environment variable TOGETHER_API_KEY
set with your API key or pass it as a named parameter to the constructor.
Example
from langchain_together import ChatTogether
model = ChatTogether()
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 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 default_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param http_async_client: Union[Any, None] = None¶
Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you’d like a custom client for sync invocations.
param http_client: Union[Any, None] = None¶
Optional httpx.Client. Only used for sync invocations. Must specify
Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you’d like a custom client for async invocations.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param max_tokens: Optional[int] = None¶
Maximum number of tokens to generate.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param model_name: str = 'meta-llama/Llama-3-8b-chat-hf' (alias 'model')¶
Model name to use.
param n: int = 1¶
Number of chat completions to generate for each prompt.
param openai_api_base: Optional[str] = None (alias 'base_url')¶
Base URL path for API requests, leave blank if not using a proxy or service
emulator.
param openai_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Automatically inferred from env var OPENAI_API_KEY if not provided.
Constraints
type = string
writeOnly = True
format = password
param openai_organization: Optional[str] = None (alias 'organization')¶
Automatically inferred from env var OPENAI_ORG_ID if not provided.
param openai_proxy: Optional[str] = None¶
param request_timeout: Union[float, Tuple[float, float], Any, None] = None (alias 'timeout')¶
Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None.
param streaming: bool = False¶
Whether to stream the results or not.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.7¶
What sampling temperature to use.
param tiktoken_model_name: Optional[str] = None¶
The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
param together_api_base: Optional[str] = 'https://api.together.ai/v1/' (alias 'base_url')¶
param together_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Automatically inferred from env are TOGETHER_API_KEY if not provided.
Constraints
type = string
writeOnly = True
format = password
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]]) –
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
Iterator[Tuple[int, Union[Output, Exception]]]
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
Useful when a runnable in a chain requires an argument that is not
in the output of the previous runnable or included in the user input.
Example:
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
    llm
    | StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'
# With bind.
chain = (
    llm.bind(stop=["three"])
    | StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
Parameters
kwargs (Any) – 
Return type
Runnable[Input, Output]
bind_functions(functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], function_call: Optional[Union[_FunctionCall, str, Literal['auto', 'none']]] = None, **kwargs: Any) → Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]¶
Bind functions (and other objects) to this chat model.
Bind functions (and other objects) to this chat model.
Assumes model is compatible with OpenAI function-calling API.
NOTE: Using bind_tools is recommended instead, as the functions andfunction_call request parameters are officially marked as deprecated by
OpenAI.
Parameters
functions (Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]]) – A list of function definitions to bind to this chat model.
Can be  a dictionary, pydantic model, or callable. Pydantic
models and callables will be automatically converted to
their schema dictionary representation.
function_call (Optional[Union[_FunctionCall, str, Literal['auto', 'none']]]) – Which function to require the model to call.
Must be the name of the single provided function or
“auto” to automatically determine which function to call
(if any).
**kwargs (Any) – Any additional parameters to pass to the
Runnable constructor.
Return type
Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]
bind_tools(tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], *, tool_choice: Optional[Union[dict, str, Literal['auto', 'none', 'required', 'any'], bool]] = None, **kwargs: Any) → Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]¶
Bind tool-like objects to this chat model.
Assumes model is compatible with OpenAI tool-calling API.
Parameters
tools (Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]]) – A list of tool definitions to bind to this chat model.
Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
models, callables, and BaseTools will be automatically converted to
their schema dictionary representation.
tool_choice (Optional[Union[dict, str, Literal['auto', 'none', 'required', 'any'], bool]]) – Which tool to require the model to call.
Options are:
name of the tool (str): calls corresponding tool;
“auto”: automatically selects a tool (including no tool);
“none”: does not call a tool;
“any” or “required”: force at least one tool to be called;
True: forces tool call (requires tools be length 1);
False: no effect;
or a dict of the form:
{“type”: “function”, “function”: {“name”: <<tool_name>>}}.
**kwargs (Any) – Any additional parameters to pass to the
Runnable constructor.
Return type
Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]
call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
[Deprecated]
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
message (str) – 
stop (Optional[List[str]]) – 
kwargs (Any) – 
Return type
str
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include (Optional[Sequence[str]]) – A list of fields to include in the config schema.
first occurrence of any of these substrings.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
Return type
LLMResult
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) – 
Return type
Graph
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
Return type
Type[BaseModel]
classmethod get_lc_namespace() → List[str][source]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
Return type
List[str]
get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶
Get the name of the runnable.
Parameters
suffix (Optional[str]) – 
name (Optional[str]) – 
Return type