Tony20100/langchain_full huggingface dataset:
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 client: Any = None¶
param connect_timeout: Optional[int] = 60¶
Timeout for connect to volc engine maas endpoint. Default is 60 seconds.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param endpoint: Optional[str] = 'maas-api.ml-platform-cn-beijing.volces.com'¶
Endpoint of the VolcEngineMaas LLM.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: str = 'skylark-lite-public'¶
Model name. you could check this model details here
https://www.volcengine.com/docs/82379/1133187
and you could choose other models by change this field
param model_kwargs: Dict[str, Any] [Optional]¶
model special arguments, you could check detail on model page
param model_version: Optional[str] = None¶
Model version. Only used in moonshot large language model.
you could check details here https://www.volcengine.com/docs/82379/1158281
param read_timeout: Optional[int] = 60¶
Timeout for read response from volc engine maas endpoint.
Default is 60 seconds.
param region: Optional[str] = 'Region'¶
Default is 60 seconds.
param region: Optional[str] = 'Region'¶
Region of the VolcEngineMaas LLM.
param streaming: bool = False¶
Whether to stream the results.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = 0.95¶
A non-negative float that tunes the degree of randomness in generation.
param top_p: Optional[float] = 0.8¶
Total probability mass of tokens to consider at each step.
param verbose: bool [Optional]¶
Whether to print out response text.
param volc_engine_maas_ak: Optional[SecretStr] = None¶
access key for volc engine
Constraints
type = string
writeOnly = True
format = password
param volc_engine_maas_sk: Optional[SecretStr] = None¶
secret key for volc engine
Constraints
type = string
writeOnly = True
format = password
__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 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
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.
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 VolcEngineMaasChat¶
VolcEngineMaasChat
langchain_community.chat_models.baidu_qianfan_endpoint.convert_message_to_dict¶
langchain_community.chat_models.baidu_qianfan_endpoint.convert_message_to_dict(message: BaseMessage) → dict[source]¶
Convert a message to a dictionary that can be passed to the API.
Parameters
message (BaseMessage) – 
Return type
dict
Examples using convert_message_to_dict¶
Twitter (via Apify)
langchain_community.chat_models.litellm_router.ChatLiteLLMRouter¶
class langchain_community.chat_models.litellm_router.ChatLiteLLMRouter[source]¶
Bases: ChatLiteLLM
LiteLLM Router as LangChain Model.
Construct Chat LiteLLM Router.
param anthropic_api_key: Optional[str] = None¶
param api_base: Optional[str] = None¶
param azure_api_key: Optional[str] = None¶
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 cohere_api_key: Optional[str] = None¶
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param custom_llm_provider: Optional[str] = None¶
param max_retries: int = 6¶
param max_tokens: int = 256¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: str = 'gpt-3.5-turbo'¶
param model_kwargs: Dict[str, Any] [Optional]¶
Run inference with this temperature. Must by in the closed
interval [0.0, 1.0].
param model_name: Optional[str] = None¶
param model_name: Optional[str] = None¶
Model name to use.
param n: int = 1¶
Number of chat completions to generate for each prompt. Note that the API may
not return the full n completions if duplicates are generated.
param openai_api_key: Optional[str] = None¶
param openrouter_api_key: Optional[str] = None¶
param organization: Optional[str] = None¶
param replicate_api_key: Optional[str] = None¶
param request_timeout: Optional[Union[float, Tuple[float, float]]] = None¶
param router: Any = None¶
param streaming: bool = False¶
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = 1¶
param top_k: Optional[int] = None¶
Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive.
param top_p: Optional[float] = None¶
Decode using nucleus sampling: consider the smallest set of tokens whose
probability sum is at least top_p. Must be in the closed interval [0.0, 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
kwargs (Any) – 
Return type
Runnable[LanguageModelInput, 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(run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) → Any¶
Use tenacity to retry the completion call.
Parameters
run_manager (Optional[CallbackManagerForLLMRun]) – 
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.
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.
Returns
A pydantic model that can be used to validate config.
Return type
Type[BaseModel]
configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
Configure alternatives for runnables that can be set at runtime.
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
    model_name="claude-3-sonnet-20240229"
model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenAI
print(
    model.with_config(
        configurable={"llm": "openai"}
    ).invoke("which organization created you?").content
)
Parameters
which (ConfigurableField) – 
default_key (str) – 
prefix_keys (bool) – 
kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) – 
Return type
RunnableSerializable[Input, Output]
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
Configure particular runnable fields at runtime.
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)
# max_tokens = 20
print(
    "max_tokens_20: ",
    model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
    configurable={"output_token_number": 200}
    ).invoke("tell me something about chess").content
)
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 ChatLiteLLMRouter¶
ChatLiteLLMRouter
langchain_community.chat_models.fake.FakeMessagesListChatModel¶
class langchain_community.chat_models.fake.FakeMessagesListChatModel[source]¶
Bases: BaseChatModel
Fake ChatModel for testing purposes.
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 i: int = 0¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param responses: List[BaseMessage] [Required]¶
param sleep: Optional[float] = None¶
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
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.
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]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
langchain_ai21.chat_models.ChatAI21¶
class langchain_ai21.chat_models.ChatAI21[source]¶
Bases: BaseChatModel, AI21Base
ChatAI21 chat model.
Example
from langchain_ai21 import ChatAI21
model = ChatAI21()
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_host: Optional[str] = None¶
param api_key: Optional[SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
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 count_penalty: Optional[Any] = None¶
A penalty applied to tokens based on their frequency
in the generated responses.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param frequency_penalty: Optional[Any] = None¶
A penalty applied to tokens that are frequently generated.
param max_tokens: int = 16¶
The maximum number of tokens to generate for each response.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param min_tokens: int = 0¶
Metadata to add to the run trace.
param min_tokens: int = 0¶
The minimum number of tokens to generate for each response.
param model: str [Required]¶
Model type you wish to interact with.
You can view the options at https://github.com/AI21Labs/ai21-python?tab=readme-ov-file#model-types
param n: int = 1¶
Number of chat completions to generate for each prompt.
param num_results: int = 1¶
The number of responses to generate for a given prompt.
param num_retries: Optional[int] = None¶
param presence_penalty: Optional[Any] = None¶
A penalty applied to tokens that are already present in the prompt.
param stop: Optional[List[str]] = None¶
Default stop sequences.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.7¶
A value controlling the “creativity” of the model’s responses.
param timeout_sec: Optional[float] = None¶
param top_k_return: int = 0¶
The number of top-scoring tokens to consider for each generation step.
param top_p: float = 1¶
A value controlling the diversity of the model’s responses.
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]]) –
langchain_aws.chat_models.bedrock.ChatPromptAdapter¶
class langchain_aws.chat_models.bedrock.ChatPromptAdapter[source]¶
Adapter class to prepare the inputs from Langchain to prompt format
that Chat model expects.
Methods
__init__()
convert_messages_to_prompt(provider, ...)
format_messages(provider, messages)
__init__()¶
classmethod convert_messages_to_prompt(provider: str, messages: List[BaseMessage], model: str) → str[source]¶
Parameters
provider (str) – 
messages (List[BaseMessage]) – 
model (str) – 
Return type
str
classmethod format_messages(provider: str, messages: List[BaseMessage]) → Tuple[Optional[str], List[Dict]][source]¶
Parameters
provider (str) – 
messages (List[BaseMessage]) – 
Return type
Tuple[Optional[str], List[Dict]]
langchain_community.chat_models.premai.ChatPremAI¶
class langchain_community.chat_models.premai.ChatPremAI[source]¶
Bases: BaseChatModel, BaseModel
PremAI Chat models.
To use, you will need to have an API key. You can find your existing API Key
or generate a new one here: https://app.premai.io/api_keys/
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 client: Any = None¶
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param max_retries: int = 1¶
Max number of retries to call the API
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: Optional[str] = None (alias 'model_name')¶
Name of the model. This is an optional parameter.
The default model is the one deployed from Prem’s LaunchPad: https://app.premai.io/projects/8/launchpad
If model name is other than default model then it will override the calls
from the model deployed from launchpad.
param premai_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Prem AI API Key. Get it here: https://app.premai.io/api_keys/
Constraints
type = string
writeOnly = True
format = password
param project_id: int [Required]¶
The project ID in which the experiments or deployments are carried out.
You can find all your projects here: https://app.premai.io/projects/
param repositories: Optional[dict] = None¶
Add valid repository ids. This will be overriding existing connected
repositories (if any) and will use RAG with the connected repos.
param streaming: Optional[bool] = False¶
Whether to stream the responses or not.
param system_prompt: Optional[str] = ''¶
Acts like a default instruction that helps the LLM act or generate
in a specific way.This is an Optional Parameter. By default the
system prompt would be using Prem’s Launchpad models system prompt.
Changing the system prompt would override the default system prompt.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = None¶
Model temperature. Value should be >= 0 and <= 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]]) –