Tony20100/langchain_full huggingface dataset:
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 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 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
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the
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
static get_available_models() → Set[str][source]¶
Get available models from EverlyAI API.
Return type
Set[str]
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.
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]¶
Get the namespace of the langchain object.
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
str
get_num_tokens(text: str) → int¶
Get the number of tokens present in the text.
Useful for checking if an input will fit in a model’s context window.
Parameters
text (str) – The string input to tokenize.
Returns
The integer number of tokens in the text.
Return type
int
get_num_tokens_from_messages(messages: list[langchain_core.messages.base.BaseMessage]) → int[source]¶
Calculate num tokens with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
Parameters
messages (list[langchain_core.messages.base.BaseMessage]) – 
Return type
int
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
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
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]¶
Get the tokens present in the text with tiktoken package.
Parameters
text (str) – 
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
bool
Return whether this model can be serialized by Langchain.
Return type
bool
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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
Return type
List[str]
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
Example
from langchain_core.runnables import RunnableLambda
def _lambda(x: int) -> int:
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.
Examples using ChatEverlyAI¶
ChatEverlyAI
langchain_community.chat_models.yuan2.acompletion_with_retry¶
async langchain_community.chat_models.yuan2.acompletion_with_retry(llm: ChatYuan2, **kwargs: Any) → Any[source]¶
Use tenacity to retry the async completion call.
Parameters
llm (ChatYuan2) – 
kwargs (Any) – 
Return type
Any
langchain_community.chat_models.kinetica.KineticaUtil¶
class langchain_community.chat_models.kinetica.KineticaUtil[source]¶
Kinetica utility functions.
Methods
__init__()
create_kdbc([url, user, passwd])
Create a connectica connection object and verify connectivity.
__init__()¶
classmethod create_kdbc(url: Optional[str] = None, user: Optional[str] = None, passwd: Optional[str] = None) → gpudb.GPUdb[source]¶
Create a connectica connection object and verify connectivity.
If None is passed for one or more of the parameters then an attempt will be made
to retrieve the value from the related environment variable.
Parameters
url (Optional[str]) – The Kinetica URL or KINETICA_URL if None.
user (Optional[str]) – The Kinetica user or KINETICA_USER if None.
passwd (Optional[str]) – The Kinetica password or KINETICA_PASSWD if None.
Returns
The Kinetica connection object.
Return type
gpudb.GPUdb
langchain_mistralai.chat_models.acompletion_with_retry¶
async langchain_mistralai.chat_models.acompletion_with_retry(llm: ChatMistralAI, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any) → Any[source]¶
Use tenacity to retry the async completion call.
Parameters
llm (ChatMistralAI) – 
run_manager (Optional[AsyncCallbackManagerForLLMRun]) – 
kwargs (Any) – 
Return type
Any
langchain_community.chat_models.maritalk.ChatMaritalk¶
class langchain_community.chat_models.maritalk.ChatMaritalk[source]¶
Bases: BaseChatModel
MariTalk Chat models API.
This class allows interacting with the MariTalk chatbot API.
To use it, you must provide an API key either through the constructor.
Example
from langchain_community.chat_models import ChatMaritalk
chat = ChatMaritalk(api_key="your_api_key_here")
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_key: str [Required]¶
Your MariTalk API key.
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 do_sample: bool = True¶
Whether or not to use sampling; use True to enable.
param max_tokens: int = 512¶
The maximum number of tokens to generate in the reply.
Constraints
exclusiveMinimum = 0
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: str [Required]¶
Metadata to add to the run trace.
param model: str [Required]¶
Chose one of the available models:
- sabia-2-medium
- sabia-2-small
- sabia-2-medium-2024-03-13
- sabia-2-small-2024-03-13
- maritalk-2024-01-08 (deprecated)
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.7¶
Run inference with this temperature.
Must be in the closed interval [0.0, 1.0].
Constraints
exclusiveMinimum = 0.0
exclusiveMaximum = 1.0
param top_p: float = 0.95¶
Nucleus sampling parameter controlling the size of
the probability mass considered for sampling.
Constraints
exclusiveMinimum = 0.0
exclusiveMaximum = 1.0
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]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
Return type
List[str]
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
Example
from langchain_core.runnables import RunnableLambda
def _lambda(x: int) -> int:
    return x + 1
runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]
Return type
Runnable[List[Input], List[Output]]
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
parse_messages_for_model(messages: List[BaseMessage]) → List[Dict[str, Union[str, List[Union[str, Dict[Any, Any]]]]]][source]¶
Parses messages from LangChain’s format to the format expected by
the MariTalk API.
Parameters
messages (List[BaseMessage]) – A list of messages in LangChain
parsed. (format to be) – 
Returns
A list of messages formatted for the MariTalk API.
Return type
List[Dict[str, Union[str, List[Union[str, Dict[Any, Any]]]]]]
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
stop_after_attempt (int) – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
Return type
Runnable[Input, Output]
with_structured_output(schema: Union[Dict, Type[BaseModel]], **kwargs: Any) → Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]]¶
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.
langchain_community.chat_models.tongyi.ChatTongyi¶
class langchain_community.chat_models.tongyi.ChatTongyi[source]¶
Bases: BaseChatModel
Alibaba Tongyi Qwen chat models API.
To use, you should have the dashscope python package installed,
and set env DASHSCOPE_API_KEY with your API key, or pass
it as a named parameter to the constructor.
Example
from langchain_community.chat_models import ChatTongyi
Tongyi_chat = ChatTongyi()
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 dashscope_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Dashscope api key provide by Alibaba Cloud.
Constraints
type = string
writeOnly = True
format = password
param max_retries: int = 10¶
Maximum number of retries to make when generating.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
Metadata to add to the run trace.
param model_kwargs: Dict[str, Any] [Optional]¶
param model_name: str = 'qwen-turbo' (alias 'model')¶
Model name to use.
callable multimodal model:
- qwen-vl-v1
- qwen-vl-chat-v1
- qwen-audio-turbo
- qwen-vl-plus
- qwen-vl-max
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 top_p: float = 0.8¶
Total probability mass of tokens to consider at each step.
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;
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}
Parameters
kwargs (Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) – 
Return type
RunnableSerializable[Any, Any]
async astream(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[BaseMessageChunk]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
Parameters
input (LanguageModelInput) – 
config (Optional[RunnableConfig]) – 
stop (Optional[List[str]]) – 
kwargs (Any) – 
Return type
AsyncIterator[BaseMessageChunk]
async astream_completion_with_retry(**kwargs: Any) → Any[source]¶
Because the dashscope SDK doesn’t provide an async API,
we wrap stream_generate_with_retry with an async generator.
Parameters
kwargs (Any) – 
Return type
Any
astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1', 'v2'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[StreamEvent]¶
[Beta] Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information
# Output is 'One two'
Parameters
kwargs (Any) – 
Return type
Runnable[Input, Output]
bind_tools(tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], **kwargs: Any) → Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage][source]¶
Bind tool-like objects to this chat model.
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.
**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
completion_with_retry(**kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call.
Parameters
kwargs (Any) – 
Return type
Any
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
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]¶
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¶
Is this class serializable?
Return type
bool