Tony20100/langchain_full huggingface dataset:
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]¶
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
bool
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 ChatAnthropic¶
Anthropic
Build an Agent
ChatAnthropic
How to add fallbacks to a runnable
How to attach callbacks to a runnable
How to configure runtime chain internals
How to create a custom Output Parser
How to create a dynamic (self-constructing) chain
How to create custom callback handlers
How to parse XML output
How to pass callbacks in at runtime
How to propagate callbacks  constructor
How to route between sub-chains
How to stream chat model responses
How to track token usage in ChatModels
How to use callbacks in async environments
How to use prompting alone (no tool calling) to do extraction
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PlayWright Browser
Python
Response metadata
🦜️🏓 LangServe
langchain_aws.chat_models.bedrock.BedrockChat¶
class langchain_aws.chat_models.bedrock.BedrockChat[source]¶
Bases: ChatBedrock
[Deprecated]
Notes
Deprecated since version 0.1.0: Use ChatBedrock instead.
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 config: Any = None¶
An optional botocore.config.Config instance to pass to the client.
param credentials_profile_name: Optional[str] = None¶
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param endpoint_url: Optional[str] = None¶
Needed if you don’t want to default to us-east-1 endpoint
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_tools(tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], *, tool_choice: Optional[Union[dict, str, Literal['auto', 'none'], 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 has a tool calling API.
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]¶
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.
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¶
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) –
# -> [4, 6, 8]
Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) – 
name (Optional[str]) – 
Return type
RunnableSerializable[Input, Other]
predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
[Deprecated]
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
text (str) – 
stop (Optional[Sequence[str]]) – 
kwargs (Any) – 
Return type
str
predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
[Deprecated]
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
messages (List[BaseMessage]) – 
stop (Optional[Sequence[str]]) – 
kwargs (Any) – 
Return type
BaseMessage
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
set_system_prompt_with_tools(xml_tools_system_prompt: str) → None¶
Workaround to bind. Sets the system prompt with tools
Parameters
xml_tools_system_prompt (str) – 
Return type
None
langchain_google_genai.chat_models.ChatGoogleGenerativeAI¶
class langchain_google_genai.chat_models.ChatGoogleGenerativeAI[source]¶
Bases: _BaseGoogleGenerativeAI, BaseChatModel
Google Generative AI Chat models API.
To use, you must have either:
The GOOGLE_API_KEY` environment variable set with your API key, or
Pass your API key using the google_api_key kwarg to the ChatGoogle
constructor.
Example
from langchain_google_genai import ChatGoogleGenerativeAI
chat = ChatGoogleGenerativeAI(model="gemini-pro")
chat.invoke("Write me a ballad about LangChain")
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 additional_headers: Optional[Dict[str, str]] = None¶
A key-value dictionary representing additional headers for the model call
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_options: Optional[Dict] = None¶
A dictionary of client options to pass to the Google API client, such as api_endpoint.
param convert_system_message_to_human: bool = False¶
Whether to merge any leading SystemMessage into the following HumanMessage.
Gemini does not support system messages; any unsupported messages will
Gemini does not support system messages; any unsupported messages will
raise an error.
param credentials: Any = None¶
The default custom credentials (google.auth.credentials.Credentials) to use
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param google_api_key: Optional[SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param max_output_tokens: Optional[int] = None¶
Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64.
param max_retries: int = 6¶
The maximum number of retries to make when generating.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: str [Required]¶
Model name to use.
The name of the model to use.
Supported examples:
gemini-pro
models/text-bison-001
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 safety_settings: Optional[Dict[HarmCategory, HarmBlockThreshold]] = None¶
The default safety settings to use for all generations.
For example:
from google.generativeai.types.safety_types import HarmBlockThreshold, HarmCategory
safety_settings = {HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.7¶
Run inference with this temperature. Must by in the closed interval
[0.0, 1.0].
param timeout: Optional[float] = None¶
The maximum number of seconds to wait for a response.
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 transport: Optional[str] = None¶
A string, one of: [rest, grpc, grpc_asyncio].
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]¶
Parameters
kwargs (Any) – 
Return type
Runnable[Input, Output]
bind_tools(tools: Sequence[Union[ToolDict, Tool]], tool_config: Optional[Union[Dict, _ToolConfigDict]] = None, *, tool_choice: Optional[Union[dict, List[str], str, Literal['auto', 'none', 'any'], Literal[True], bool]] = None, **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.
Assumes model is compatible with google-generativeAI tool-calling API.
Parameters
tools (Sequence[Union[ToolDict, Tool]]) – A list of tool definitions to bind to this chat model.
Can be a 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.
tool_config (Optional[Union[Dict, _ToolConfigDict]]) – 
tool_choice (Optional[Union[dict, List[str], str, Literal['auto', 'none', 'any'], ~typing.Literal[True], bool]]) – 
**kwargs – 
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) –
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.
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"
).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) –
Return type
Type[BaseModel]
classmethod get_lc_namespace() → List[str]¶
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
str
get_num_tokens(text: str) → int[source]¶
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[BaseMessage]) → int¶
Get the number of tokens in the messages.
Useful for checking if an input will fit in a model’s context window.
Parameters
messages (List[BaseMessage]) – The message inputs to tokenize.
Returns
The sum of the number of tokens across the messages.
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
A pydantic model that can be used to validate output.
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[source]¶
Is this class serializable?
Return type
bool
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 ChatGoogleGenerativeAI¶
Google
Google AI chat models
langchain_community.chat_models.human.HumanInputChatModel¶
class langchain_community.chat_models.human.HumanInputChatModel[source]¶
Bases: BaseChatModel
ChatModel which returns user input as the response.
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 input_func: Callable [Optional]¶
param input_kwargs: Mapping[str, Any] = {}¶
param message_func: Callable [Optional]¶
param message_kwargs: Mapping[str, Any] = {}¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param separator: str = '\n'¶
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
langchain_community.chat_models.premai.chat_with_retry¶
langchain_community.chat_models.premai.chat_with_retry(llm: ChatPremAI, project_id: int, messages: List[dict], stream: bool = False, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) → Any[source]¶
Using tenacity for retry in completion call
Parameters
llm (ChatPremAI) – 
project_id (int) – 
messages (List[dict]) – 
stream (bool) – 
run_manager (Optional[CallbackManagerForLLMRun]) – 
kwargs (Any) – 
Return type
Any