id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
7e6a7dcc4d8f-1 | param usr_n_beg: str = '<s>[INST] '¶
param usr_n_end: str = ' [/INST]'¶
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) → BaseMess... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-2 | API.
Use this method when you want to:
take advantage of batched calls,
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 of PromptValues. A PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-3 | stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
async apredict_messages(messages: List[BaseMessage],... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-4 | Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-5 | Parameters
include – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[I... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-6 | classmethod from_orm(obj: Any) → Model¶
generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-7 | 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 confi... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-8 | Returns
A pydantic model that can be used to validate output.
get_token_ids(text: str) → List[int]¶
Return the ordered ids of the tokens in a text.
Parameters
text – The string input to tokenize.
Returns
A list of ids corresponding to the tokens in the text, in order they occurin the text.
invoke(input: Union[PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-9 | 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.
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 e... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-10 | Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Retur... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-11 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A se... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
7e6a7dcc4d8f-12 | between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Llama2Chat.html |
71e49b13b22b-0 | langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint¶
class langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint[source]¶
Bases: SimpleChatModel
AzureML Chat models API.
Example
azure_chat = AzureMLChatOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-1 | 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¶
Call self as a function.
async abatch(inputs: List[Input], config: ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-2 | 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 of PromptValues. A PromptValue is an object that can be
converted to match the format of any languag... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-3 | **kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
Asynchronously pass messages to the model and ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-4 | This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: As... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-5 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-6 | classmethod from_orm(obj: Any) → Model¶
generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-7 | 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 confi... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-8 | Returns
A pydantic model that can be used to validate output.
get_token_ids(text: str) → List[int]¶
Return the ordered ids of the tokens in a text.
Parameters
text – The string input to tokenize.
Returns
A list of ids corresponding to the tokens in the text, in order they occurin the text.
invoke(input: Union[PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-9 | 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.
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 e... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-10 | Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Retur... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-11 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A se... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
71e49b13b22b-12 | between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.azureml_endpoint.AzureMLChatOnlineEndpoint.html |
129f66a90403-0 | langchain.chat_models.google_palm.achat_with_retry¶
async langchain.chat_models.google_palm.achat_with_retry(llm: ChatGooglePalm, **kwargs: Any) → Any[source]¶
Use tenacity to retry the async completion call. | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.google_palm.achat_with_retry.html |
ce756d12e413-0 | langchain_experimental.chat_models.llm_wrapper.Vicuna¶
class langchain_experimental.chat_models.llm_wrapper.Vicuna[source]¶
Bases: ChatWrapper
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 ai_n_beg: ... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-1 | param usr_n_end: str = ' '¶
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¶
Call self as a function.
async abatch(i... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-2 | 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 of PromptValues. A PromptValue is an object that can be
converted to match the format of any languag... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-3 | **kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
Asynchronously pass messages to the model and ... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-4 | This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: As... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-5 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-6 | classmethod from_orm(obj: Any) → Model¶
generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-7 | 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 confi... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-8 | Returns
A pydantic model that can be used to validate output.
get_token_ids(text: str) → List[int]¶
Return the ordered ids of the tokens in a text.
Parameters
text – The string input to tokenize.
Returns
A list of ids corresponding to the tokens in the text, in order they occurin the text.
invoke(input: Union[PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-9 | 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.
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 e... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-10 | Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Retur... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-11 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A se... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
ce756d12e413-12 | between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types... | lang/api.python.langchain.com/en/latest/chat_models/langchain_experimental.chat_models.llm_wrapper.Vicuna.html |
1eb2c7a6e5e1-0 | langchain.chat_models.baichuan.ChatBaichuan¶
class langchain.chat_models.baichuan.ChatBaichuan[source]¶
Bases: BaseChatModel
Baichuan chat models API by Baichuan Intelligent Technology.
For more information, see https://platform.baichuan-ai.com/docs/api
Create a new model by parsing and validating input data from keywo... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-1 | Tags to add to the run trace.
param temperature: float = 0.3¶
What sampling temperature to use.
param top_k: int = 5¶
What search sampling control to use.
param top_p: float = 0.85¶
What probability mass to use.
param verbose: bool [Optional]¶
Whether to print out response text.
param with_search_enhance: bool = False¶... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-2 | Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are ag... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-3 | Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-4 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-5 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
config_schema(*, include: Optional[Sequence[str]] = None) → T... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-6 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-7 | text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-8 | Useful for checking if an input will fit in a model’s context window.
Parameters
messages – The message inputs to tokenize.
Returns
The sum of the number of tokens across the messages.
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-9 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
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_defa... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-10 | Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages.
Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
fir... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-11 | Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Defau... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-12 | on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
with_retry(*, ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
1eb2c7a6e5e1-13 | property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.baichuan.ChatBaichuan.html |
a8f5bc0fb7a9-0 | langchain.chat_models.cohere.get_cohere_chat_request¶
langchain.chat_models.cohere.get_cohere_chat_request(messages: List[BaseMessage], *, connectors: Optional[List[Dict[str, str]]] = None, **kwargs: Any) → Dict[str, Any][source]¶
Get the request for the Cohere chat API.
Parameters
messages – The messages.
connectors –... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.cohere.get_cohere_chat_request.html |
48dcc23c781c-0 | langchain.chat_models.ollama.ChatOllama¶
class langchain.chat_models.ollama.ChatOllama[source]¶
Bases: BaseChatModel, _OllamaCommon
Ollama locally runs large language models.
To use, follow the instructions at https://ollama.ai/.
Example
from langchain.chat_models import ChatOllama
ollama = ChatOllama(model="llama2")
C... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-1 | coherent text. (Default: 5.0)
param model: str = 'llama2'¶
Model name to use.
param num_ctx: Optional[int] = None¶
Sets the size of the context window used to generate the
next token. (Default: 2048)
param num_gpu: Optional[int] = None¶
The number of GPUs to use. On macOS it defaults to 1 to
enable metal support, 0 to ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-2 | param template: Optional[str] = None¶
full prompt or prompt template (overrides what is defined in the Modelfile)
param tfs_z: Optional[float] = None¶
Tail free sampling is used to reduce the impact of less probable
tokens from the output. A higher value (e.g., 2.0) will reduce the
impact more, while a value of 1.0 dis... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-3 | 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.
async agenerate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-4 | 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.
async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-5 | to the model provider API call.
Returns
Top model prediction as a message.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[BaseMessageChunk]¶
Default implementation of astream, which calls ainvo... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-6 | input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch w... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-7 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-8 | API.
Use this method when you want to:
take advantage of batched calls,
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 of PromptValues. A PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-9 | 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 – The string input to tokenize.
Returns
The integer number of tokens in the text.
get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶
Get the ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-10 | config – 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.
Returns
The output of the runnable.
classmethod is_... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-11 | classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
Pass a single string input to t... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-12 | stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[BaseMessageChunk]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-13 | fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
48dcc23c781c-14 | List configurable fields for this runnable.
property input_schema: Type[pydantic.main.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 constr... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.ollama.ChatOllama.html |
f5e302ad1bf1-0 | langchain.chat_models.everlyai.ChatEverlyAI¶
class langchain.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 ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-1 | 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 explicit... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-2 | 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 i... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-3 | Top Level call
async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult¶
Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batche... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-4 | Subclasses should override this method if they can run asynchronously.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
Asynchronously pass a string to the model and return a string prediction.
Use this method when calling pure text generation models and only the topcandidate gen... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-5 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-6 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
bind_functions(functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], function_call: Optional[str] = None, **kwargs: Any) → Run... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-7 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-8 | classmethod from_orm(obj: Any) → Model¶
generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-9 | static get_available_models() → Set[str][source]¶
Get available models from EverlyAI API.
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
me... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-10 | 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 – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
get_token_ids... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-11 | 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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-12 | Pass a message sequence to the model and return a message prediction.
Use this method when passing in chat messages. If you want to pass in raw text,use predict.
Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-13 | classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'E... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
f5e302ad1bf1-14 | Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.everlyai.ChatEverlyAI.html |
ca8e0f8f4346-0 | langchain.chat_models.vertexai.ChatVertexAI¶
class langchain.chat_models.vertexai.ChatVertexAI[source]¶
Bases: _VertexAICommon, BaseChatModel
Vertex AI Chat large language models API.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parse... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-1 | 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 = 0.0¶
Sampling temperature, it controls the degree of randomness in token selection.
param top_k: int = 40¶
How the model selects tokens for output, the ne... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-2 | Top Level call
async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult¶
Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batche... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-3 | Subclasses should override this method if they can run asynchronously.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
Asynchronously pass a string to the model and return a string prediction.
Use this method when calling pure text generation models and only the topcandidate gen... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-4 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-5 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
config_schema(*, include: Optional[Sequence[str]] = None) → T... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-6 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-7 | text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-8 | Useful for checking if an input will fit in a model’s context window.
Parameters
messages – The message inputs to tokenize.
Returns
The sum of the number of tokens across the messages.
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-9 | classmethod is_lc_serializable() → bool[source]¶
Is this class serializable?
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, excl... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-10 | Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages.
Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
fir... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-11 | Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Defau... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-12 | on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
with_retry(*, ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
ca8e0f8f4346-13 | 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”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
t... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.vertexai.ChatVertexAI.html |
a6d656e65025-0 | langchain.chat_models.mlflow_ai_gateway.ChatParams¶
class langchain.chat_models.mlflow_ai_gateway.ChatParams[source]¶
Bases: BaseModel
Parameters for the MLflow 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 fo... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.mlflow_ai_gateway.ChatParams.html |
a6d656e65025-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, 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, ex... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.mlflow_ai_gateway.ChatParams.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.