Tony20100/langchain_full huggingface dataset:
langchain_community.llms.solar.Solar¶
class langchain_community.llms.solar.Solar[source]¶
Bases: SolarCommon, LLM
Solar large language models.
To use, you should have the environment variable
SOLAR_API_KEY set with your API key.
Referenced from https://console.upstage.ai/services/solar
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param base_url: str = 'https://api.upstage.ai/v1/solar'¶
param cache: Union[BaseCache, bool, None] = None¶
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED]
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param max_tokens: int = 1024¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_name: str = 'solar-1-mini-chat' (alias 'model')¶
Model name. Available models listed here: https://console.upstage.ai/services/solar
param solar_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Solar API key. Get it here: https://console.upstage.ai/services/solar
Constraints
type = string
writeOnly = True
Constraints
type = string
writeOnly = True
format = password
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
prompt (str) – 
stop (Optional[List[str]]) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
str
async abatch(inputs: List[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
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[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]]) –
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: Type[str]¶
Get the input 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¶
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 Solar¶
Solar
langchain_community.llms.azureml_endpoint.LlamaContentFormatter¶
class langchain_community.llms.azureml_endpoint.LlamaContentFormatter[source]¶
Deprecated: Kept for backwards compatibility
Content formatter for Llama.
Attributes
accepts
The MIME type of the response data returned from the endpoint
content_formatter
content_type
The MIME type of the input data passed to the endpoint
format_error_msg
supported_api_types
Supported APIs for the given formatter.
Methods
__init__()
escape_special_characters(prompt)
Escapes any special characters in prompt
format_request_payload(prompt, model_kwargs, ...)
Formats the request according to the chosen api
format_response_payload(output, api_type)
Formats response
__init__() → None[source]¶
Return type
None
static escape_special_characters(prompt: str) → str¶
Escapes any special characters in prompt
Parameters
prompt (str) – 
Return type
str
format_request_payload(prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType) → bytes¶
Formats the request according to the chosen api
Parameters
prompt (str) – 
model_kwargs (Dict) – 
api_type (AzureMLEndpointApiType) – 
Return type
bytes
format_response_payload(output: bytes, api_type: AzureMLEndpointApiType) → Generation¶
Formats response
Parameters
output (bytes) – 
api_type (AzureMLEndpointApiType) – 
Return type
Generation
langchain_community.llms.konko.Konko¶
class langchain_community.llms.konko.Konko[source]¶
Bases: LLM
Konko AI models.
To use, you’ll need an API key. This can be passed in as init param
konko_api_key or set as environment variable KONKO_API_KEY.
Konko AI API reference: https://docs.konko.ai/reference/
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param base_url: str = 'https://api.konko.ai/v1/completions'¶
Base inference API URL.
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]
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 konko_api_key: SecretStr [Required]¶
Konko AI API key.
Constraints
type = string
writeOnly = True
format = password
param logprobs: Optional[int] = None¶
An integer that specifies how many top token log probabilities are included in
the response for each token generation step.
param max_tokens: Optional[int] = None¶
The maximum number of tokens to generate.
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: str [Required]¶
Model name. Available models listed here:
https://docs.konko.ai/reference/get_models
param repetition_penalty: Optional[float] = None¶
A number that controls the diversity of generated text by reducing the
likelihood of repeated sequences. Higher values decrease repetition.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = None¶
Model temperature.
param top_k: Optional[int] = None¶
Used to limit the number of choices for the next predicted word or token. It
specifies the maximum number of tokens to consider at each step, based on their
probability of occurrence. This technique helps to speed up the generation
process and can improve the quality of the generated text by focusing on the
most likely options.
param top_p: Optional[float] = None¶
Used to dynamically adjust the number of choices for each predicted token based
on the cumulative probabilities. A value of 1 will always yield the same
output. A temperature less than 1 favors more correctness and is appropriate
for question answering or summarization. A value greater than 1 introduces more
randomness in the output.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
prompt (str) – 
stop (Optional[List[str]]) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
str
async abatch(inputs: List[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
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[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Any) – 
Return type
List[str]
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.
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]]]
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, run_id: Optional[Union[UUID, List[Optional[UUID]]]] = None, **kwargs: Any) → LLMResult¶
Asynchronously pass a sequence of prompts to a model and return 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 agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts (List[str]) – List of string prompts.
stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[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.
tags (Optional[Union[List[str], List[List[str]]]]) – 
metadata (Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]) – 
run_name (Optional[Union[str, List[str]]]) – 
run_id (Optional[Union[UUID, List[Optional[UUID]]]]) – 
**kwargs – 
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
Return type
LLMResult
async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[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 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 agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
construct_payload(prompt: str, stop: Optional[List[str]] = None, **kwargs: Any) → Dict[str, Any][source]¶
Parameters
prompt (str) – 
stop (Optional[List[str]]) – 
kwargs (Any) – 
Return type
Dict[str, Any]
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
Parameters
kwargs (Any) – 
Return type
Dict
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
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: Type[str]¶
Get the input type for this runnable.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property default_params: Dict[str, Any]¶
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 Konko¶
Konko
langchain_community.llms.huggingface_pipeline.HuggingFacePipeline¶
class langchain_community.llms.huggingface_pipeline.HuggingFacePipeline[source]¶
Bases: BaseLLM
[Deprecated] HuggingFace Pipeline API.
To use, you should have the transformers python package installed.
Only supports text-generation, text2text-generation, summarization and
translation  for now.
Example using from_model_id:from langchain_community.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
    model_id="gpt2",
    task="text-generation",
    pipeline_kwargs={"max_new_tokens": 10},
)
Example passing pipeline in directly:from langchain_community.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
    "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
Notes
Deprecated since version 0.0.37.
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 batch_size: int = 4¶
Batch size to use when passing multiple documents to generate.
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.
Caching is not currently supported for streaming methods of models.
param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED]
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_id: str = 'gpt2'¶
Model name to use.
param model_kwargs: Optional[dict] = None¶
Keyword arguments passed to the model.
param pipeline_kwargs: Optional[dict] = None¶
Keyword arguments passed to the pipeline.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
prompt (str) – 
stop (Optional[List[str]]) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
str
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
Parameters
kwargs (Any) – 
Return type
Dict
classmethod from_model_id(model_id: str, task: str, backend: str = 'default', device: Optional[int] = -1, device_map: Optional[str] = None, model_kwargs: Optional[dict] = None, pipeline_kwargs: Optional[dict] = None, batch_size: int = 4, **kwargs: Any) → HuggingFacePipeline[source]¶
Construct the pipeline object from model_id and task.
Parameters
model_id (str) – 
task (str) – 
backend (str) – 
device (Optional[int]) – 
device_map (Optional[str]) – 
model_kwargs (Optional[dict]) –
device_map (Optional[str]) – 
model_kwargs (Optional[dict]) – 
pipeline_kwargs (Optional[dict]) – 
batch_size (int) – 
kwargs (Any) – 
Return type
HuggingFacePipeline
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, run_id: Optional[Union[UUID, List[Optional[UUID]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of prompts to a model and return 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 agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts (List[str]) – List of string prompts.
stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[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.
tags (Optional[Union[List[str], List[List[str]]]]) – 
metadata (Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]) – 
run_name (Optional[Union[str, List[str]]]) – 
run_id (Optional[Union[UUID, List[Optional[UUID]]]]) – 
**kwargs – 
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
Return type
LLMResult
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of prompts to the model 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 agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.