Tony20100/langchain_full huggingface dataset:
langchain_upstage.layout_analysis_parsers.UpstageLayoutAnalysisParser¶
class langchain_upstage.layout_analysis_parsers.UpstageLayoutAnalysisParser(api_key: Optional[str] = None, output_type: Union[Literal['text', 'html'], dict] = 'html', split: Literal['none', 'element', 'page'] = 'none', use_ocr: bool = False, exclude: list = [])[source]¶
Upstage Layout Analysis Parser.
To use, you should have the environment variable UPSTAGE_API_KEY
set with your API key or pass it as a named parameter to the constructor.
Example
from langchain_upstage import UpstageLayoutAnalysisParser
loader = UpstageLayoutAnalysisParser(split="page", output_type="text")
Initializes an instance of the Upstage class.
Parameters
api_key (str, optional) – The API key for accessing the Upstage API.
Defaults to None, in which case it will be
fetched from the environment variable
UPSTAGE_API_KEY.
output_type (Union[OutputType, dict], optional) – The type of output to be
generated by the parser.
Defaults to “html”.
split (SplitType, optional) – The type of splitting to be applied.
Defaults to “none” (no splitting).
use_ocr (bool, optional) – Extract text from images in the document.
Defaults to False. (Use text info in PDF file)
exclude (list, optional) – Exclude specific elements from the output.
Defaults to [] (all included).
Methods
__init__([api_key, output_type, split, ...])
Initializes an instance of the Upstage class.
lazy_parse(blob[, is_batch])
Lazily parses a document and yields Document objects based on the specified split type.
parse(blob)
parse(blob)
Eagerly parse the blob into a document or documents.
__init__(api_key: Optional[str] = None, output_type: Union[Literal['text', 'html'], dict] = 'html', split: Literal['none', 'element', 'page'] = 'none', use_ocr: bool = False, exclude: list = [])[source]¶
Initializes an instance of the Upstage class.
Parameters
api_key (str, optional) – The API key for accessing the Upstage API.
Defaults to None, in which case it will be
fetched from the environment variable
UPSTAGE_API_KEY.
output_type (Union[OutputType, dict], optional) – The type of output to be
generated by the parser.
Defaults to “html”.
split (SplitType, optional) – The type of splitting to be applied.
Defaults to “none” (no splitting).
use_ocr (bool, optional) – Extract text from images in the document.
Defaults to False. (Use text info in PDF file)
exclude (list, optional) – Exclude specific elements from the output.
Defaults to [] (all included).
lazy_parse(blob: Blob, is_batch: bool = False) → Iterator[Document][source]¶
Lazily parses a document and yields Document objects based on the specified
split type.
Parameters
blob (Blob) – The input document blob to parse.
is_batch (bool, optional) – Whether to parse the document in batches.
Defaults to False (single page parsing)
Yields
Document – The parsed document object.
Raises
ValueError – If an invalid split type is provided.
Return type
Iterator[Document]
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
langchain_nvidia_ai_endpoints.reranking.NVIDIARerank¶
class langchain_nvidia_ai_endpoints.reranking.NVIDIARerank[source]¶
Bases: BaseDocumentCompressor
LangChain Document Compressor that uses the NVIDIA NeMo Retriever Reranking API.
Create a new NVIDIARerank document compressor.
This class provides access to a NVIDIA NIM for reranking. By default, it
connects to a hosted NIM, but can be configured to connect to a local NIM
using the base_url parameter. An API key is required to connect to the
hosted NIM.
Parameters
model (str) – The model to use for reranking.
nvidia_api_key (str) – The API key to use for connecting to the hosted NIM.
api_key (str) – Alternative to nvidia_api_key.
base_url (str) – The base URL of the NIM to connect to.
API Key:
- The recommended way to provide the API key is through the NVIDIA_API_KEY
environment variable.
param base_url: str = 'https://integrate.api.nvidia.com/v1'¶
Base url for model listing an invocation
param max_batch_size: int = 32¶
The maximum batch size.
Constraints
minimum = 1
param model: str = 'nv-rerank-qa-mistral-4b:1'¶
The model to use for reranking.
param top_n: int = 5¶
The number of documents to return.
Constraints
minimum = 0
async acompress_documents(documents: Sequence[Document], query: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Document]¶
Compress retrieved documents given the query context.
Parameters
Compress retrieved documents given the query context.
Parameters
documents (Sequence[Document]) – 
query (str) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
Return type
Sequence[Document]
compress_documents(documents: Sequence[Document], query: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Document][source]¶
Compress documents using the NVIDIA NeMo Retriever Reranking microservice API.
Parameters
documents (Sequence[Document]) – A sequence of documents to compress.
query (str) – The query to use for compressing the documents.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run during the compression process.
Returns
A sequence of compressed documents.
Return type
Sequence[Document]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod get_available_models(**kwargs: Any) → List[Model][source]¶
Get a list of available models that work with NVIDIARerank.
Parameters
kwargs (Any) – 
Return type
List[Model]
Parameters
kwargs (Any) – 
Return type
List[Model]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
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
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
property available_models: List[Model]¶
Get a list of available models that work with NVIDIARerank.
langchain_nvidia_ai_endpoints.reranking.Ranking¶
class langchain_nvidia_ai_endpoints.reranking.Ranking[source]¶
Bases: BaseModel
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 index: int [Required]¶
param logit: float [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
langchain.smith.evaluation.string_run_evaluator.ChainStringRunMapper¶
class langchain.smith.evaluation.string_run_evaluator.ChainStringRunMapper[source]¶
Bases: StringRunMapper
Extract items to evaluate from the run object from a chain.
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 input_key: Optional[str] = None¶
The key from the model Run’s inputs to use as the eval input.
If not provided, will use the only input key or raise an
error if there are multiple.
param prediction_key: Optional[str] = None¶
The key from the model Run’s outputs to use as the eval prediction.
If not provided, will use the only output key or raise an error
if there are multiple.
__call__(run: Run) → Dict[str, str]¶
Maps the Run to a dictionary.
Parameters
run (Run) – 
Return type
Dict[str, str]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
to the object.
Return type
List[str]
map(run: Run) → Dict[str, str][source]¶
Maps the Run to a dictionary.
Parameters
run (Run) – 
Return type
Dict[str, str]
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
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
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Return type
unicode
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented¶
Return type
SerializedNotImplemented
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
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”}
property output_keys: List[str]¶
The keys to extract from the run.
langchain.smith.evaluation.config.RunEvalConfig¶
class langchain.smith.evaluation.config.RunEvalConfig[source]¶
Bases: BaseModel
Configuration for a run evaluation.
Parameters
evaluators (List[Union[EvaluatorType, EvalConfig, RunEvaluator, Callable]]) – Configurations for which evaluators to apply to the dataset run.
Each can be the string of an EvaluatorType, such
as EvaluatorType.QA, the evaluator type string (“qa”), or a configuration for a
given evaluator (e.g., RunEvalConfig.QA).
custom_evaluators (Optional[List[Union[RunEvaluator, StringEvaluator]]]) – Custom evaluators to apply to the dataset run.
reference_key (Optional[str]) – The key in the dataset run to use as the reference string.
If not provided, it will be inferred automatically.
prediction_key (Optional[str]) – The key from the traced run’s outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically.
input_key (Optional[str]) – The key from the traced run’s inputs dictionary to use to represent the
input. If not provided, it will be inferred automatically.
eval_llm (Optional[BaseLanguageModel]) – The language model to pass to any evaluators that use a language model.
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_evaluators: Optional[List[Callable[[Sequence[Run], Optional[Sequence[Example]]], Union[EvaluationResult, EvaluationResults, dict]]]] = None¶
Evaluators that run on an aggregate/batch level.
These generate 1 or more metrics that are assigned to the full test run.
As a result, they are not associated with individual traces.
As a result, they are not associated with individual traces.
param custom_evaluators: Optional[List[Union[Callable[[Run, Optional[Example]], Union[EvaluationResult, EvaluationResults, dict]], RunEvaluator, StringEvaluator]]] = None¶
Custom evaluators to apply to the dataset run.
param eval_llm: Optional[BaseLanguageModel] = None¶
The language model to pass to any evaluators that require one.
param evaluators: List[Union[EvaluatorType, str, EvalConfig, Callable[[Run, Optional[Example]], Union[EvaluationResult, EvaluationResults, dict]], RunEvaluator, StringEvaluator]] [Optional]¶
Configurations for which evaluators to apply to the dataset run.
Each can be the string of an
EvaluatorType, such
as EvaluatorType.QA, the evaluator type string (“qa”), or a configuration for a
given evaluator
(e.g.,
RunEvalConfig.QA).
param input_key: Optional[str] = None¶
The key from the traced run’s inputs dictionary to use to represent the
input. If not provided, it will be inferred automatically.
param prediction_key: Optional[str] = None¶
The key from the traced run’s outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically.
param reference_key: Optional[str] = None¶
The key in the dataset run to use as the reference string.
If not provided, we will attempt to infer automatically.
class CoTQA[source]¶
Bases: SingleKeyEvalConfig
Configuration for a context-based QA evaluator.
Parameters
prompt (Optional[BasePromptTemplate]) – The prompt template to use for generating the question.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.
Create a new model by parsing and validating input data from keyword arguments.
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 evaluator_type: EvaluatorType = EvaluatorType.CONTEXT_QA¶
param input_key: Optional[str] = None¶
The key from the traced run’s inputs dictionary to use to represent the
input. If not provided, it will be inferred automatically.
param llm: Optional[BaseLanguageModel] = None¶
param prediction_key: Optional[str] = None¶
The key from the traced run’s outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically.
param prompt: Optional[BasePromptTemplate] = None¶
param reference_key: Optional[str] = None¶
The key in the dataset run to use as the reference string.
If not provided, we will attempt to infer automatically.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_kwargs() → Dict[str, Any]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type