Tony20100/langchain_full huggingface dataset:
include_raw (bool) – 
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 ChatMistralAI¶
Build an Extraction Chain
MistralAI
Response metadata
langchain_community.chat_models.gpt_router.get_ordered_generation_requests¶
langchain_community.chat_models.gpt_router.get_ordered_generation_requests(models_priority_list: List[GPTRouterModel], **kwargs: Any) → List[source]¶
Return the body for the model router input.
Parameters
models_priority_list (List[GPTRouterModel]) – 
kwargs (Any) – 
Return type
List
langchain_community.chat_models.zhipuai.ChatZhipuAI¶
class langchain_community.chat_models.zhipuai.ChatZhipuAI[source]¶
Bases: BaseChatModel
ZhipuAI large language chat models API.
To use, you should have the PyJWT python package installed.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatZhipuAI
zhipuai_chat = ChatZhipuAI(temperature=0.5,
api_key=”your-api-key”,
model=”glm-4”
)
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 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_name: Optional[str] = 'glm-4' (alias 'model')¶
Model name to use, see ‘https://open.bigmodel.cn/dev/api#language’.
Model name to use, see ‘https://open.bigmodel.cn/dev/api#language’.
Alternatively, you can use any fine-tuned model from the GLM series.
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 = 0.95¶
What sampling temperature to use. The value ranges from 0.0 to 1.0 and cannot
be equal to 0.
The larger the value, the more random and creative the output; The smaller
the value, the more stable or certain the output will be.
You are advised to adjust top_p or temperature parameters based on application
scenarios, but do not adjust the two parameters at the same time.
param top_p: float = 0.7¶
Another method of sampling temperature is called nuclear sampling. The value
ranges from 0.0 to 1.0 and cannot be equal to 0 or 1.
The model considers the results with top_p probability quality tokens.
For example, 0.1 means that the model decoder only considers tokens from the
top 10% probability of the candidate set.
You are advised to adjust top_p or temperature parameters based on application
scenarios, but do not adjust the two parameters at the same time.
param verbose: bool [Optional]¶
Whether to print out response text.
param zhipuai_api_base: Optional[str] = None (alias 'api_base')¶
Base URL path for API requests, leave blank if not using a proxy or service
emulator.
param zhipuai_api_key: Optional[str] = None (alias 'api_key')¶
Automatically inferred from env var ZHIPUAI_API_KEY if not provided.
Automatically inferred from env var ZHIPUAI_API_KEY if not provided.
__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.
Parameters
inputs (Sequence[Input]) –
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 ChatZhipuAI¶
ZHIPU AI
langchain_huggingface.chat_models.huggingface.TGI_MESSAGE¶
class langchain_huggingface.chat_models.huggingface.TGI_MESSAGE(role: str, content: str, tool_calls: List[Dict])[source]¶
Attributes
role
content
tool_calls
Methods
__init__(role, content, tool_calls)
Parameters
role (str) – 
content (str) – 
tool_calls (List[Dict]) – 
__init__(role: str, content: str, tool_calls: List[Dict]) → None¶
Parameters
role (str) – 
content (str) – 
tool_calls (List[Dict]) – 
Return type
None
langchain_community.chat_models.solar.SolarChat¶
class langchain_community.chat_models.solar.SolarChat[source]¶
Bases: SolarCommon, ChatOpenAI
[Deprecated] Wrapper around Solar large language models.
To use, you should have the openai python package installed, and the
environment variable SOLAR_API_KEY set with your API key.
(Solar’s chat API is compatible with OpenAI’s SDK.)
Referenced from https://console.upstage.ai/services/solar
.. rubric:: Example
from langchain_community.chat_models.solar import SolarChat
solar = SolarChat(model="solar-1-mini-chat")
Notes
Deprecated since version 0.0.34.
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.upstage.ai/v1/solar'¶
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 default_query: Union[Mapping[str, object], 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: 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 = 'solar-1-mini-chat' (alias 'model')¶
Model name. Available models listed here: https://console.upstage.ai/services/solar
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.
param solar_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Solar API key. Get it here: https://console.upstage.ai/services/solar
Constraints
type = string
Constraints
type = string
writeOnly = True
format = password
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¶
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
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¶
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¶
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[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¶
A map of constructor argument names to secret ids.
property lc_secrets: dict¶
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 SolarChat¶
solar.md
langchain_community.chat_models.llama_edge.LlamaEdgeChatService¶
class langchain_community.chat_models.llama_edge.LlamaEdgeChatService[source]¶
Bases: BaseChatModel
Chat with LLMs via llama-api-server
For the information about llama-api-server, visit https://github.com/second-state/LlamaEdge
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 metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: str = 'NA'¶
model name, default is NA.
param request_timeout: int = 60¶
request timeout for chat http requests
param service_url: Optional[str] = None¶
URL of WasmChat service
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 verbose: bool [Optional]¶
Whether to print out response text.
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 LlamaEdgeChatService¶
LlamaEdge
langchain_community.chat_models.javelin_ai_gateway.ChatParams¶
class langchain_community.chat_models.javelin_ai_gateway.ChatParams[source]¶
Bases: BaseModel
Parameters for the Javelin AI Gateway LLM.
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_tokens: Optional[int] = None¶
param stop: Optional[List[str]] = None¶
param temperature: float = 0.0¶
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
langchain_community.chat_models.gpt_router.acompletion_with_retry¶
async langchain_community.chat_models.gpt_router.acompletion_with_retry(llm: GPTRouter, models_priority_list: List[GPTRouterModel], run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any) → Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse, None]][source]¶
Use tenacity to retry the async completion call.
Parameters
llm (GPTRouter) – 
models_priority_list (List[GPTRouterModel]) – 
run_manager (Optional[AsyncCallbackManagerForLLMRun]) – 
kwargs (Any) – 
Return type
Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse, None]]
langchain_community.chat_models.everlyai.ChatEverlyAI¶
class langchain_community.chat_models.everlyai.ChatEverlyAI[source]¶
Bases: ChatOpenAI
EverlyAI Chat large language models.
To use, you should have the openai python package installed, and the
environment variable EVERLYAI_API_KEY set with your API key.
Alternatively, you can use the everlyai_api_key keyword argument.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain_community.chat_models import ChatEverlyAI
chat = ChatEverlyAI(model_name="meta-llama/Llama-2-7b-chat-hf")
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 available_models: Optional[Set[str]] = None¶
Available models from EverlyAI API.
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_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param everlyai_api_base: str = 'https://everlyai.xyz/hosted'¶
Base URL path for API requests.
param everlyai_api_key: Optional[str] = None¶
EverlyAI Endpoints API keys.
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] = 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-2-7b-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[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')¶