Tony20100/langchain_full huggingface dataset:
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 ChatPremAI¶
ChatPremAI
PremAI
langchain_community.chat_models.google_palm.achat_with_retry¶
async langchain_community.chat_models.google_palm.achat_with_retry(llm: ChatGooglePalm, **kwargs: Any) → Any[source]¶
Use tenacity to retry the async completion call.
Parameters
llm (ChatGooglePalm) – 
kwargs (Any) – 
Return type
Any
langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector¶
class langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector[source]¶
Bases: _VectorStoreExampleSelector
Select examples based on Max Marginal Relevance.
This was shown to improve performance in this paper:
https://arxiv.org/pdf/2211.13892.pdf
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 example_keys: Optional[List[str]] = None¶
Optional keys to filter examples to.
param fetch_k: int = 20¶
Number of examples to fetch to rerank.
param input_keys: Optional[List[str]] = None¶
Optional keys to filter input to. If provided, the search is based on
the input variables instead of all variables.
param k: int = 4¶
Number of examples to select.
param vectorstore: VectorStore [Required]¶
VectorStore that contains information about examples.
param vectorstore_kwargs: Optional[Dict[str, Any]] = None¶
Extra arguments passed to similarity_search function of the vectorstore.
async aadd_example(example: Dict[str, str]) → str¶
Add new example to vectorstore.
Parameters
example (Dict[str, str]) – 
Return type
str
add_example(example: Dict[str, str]) → str¶
Add new example to vectorstore.
Parameters
example (Dict[str, str]) – 
Return type
str
Parameters
example (Dict[str, str]) – 
Return type
str
async classmethod afrom_examples(examples: List[dict], embeddings: Embeddings, vectorstore_cls: Type[VectorStore], *, k: int = 4, input_keys: Optional[List[str]] = None, fetch_k: int = 20, example_keys: Optional[List[str]] = None, vectorstore_kwargs: Optional[dict] = None, **vectorstore_cls_kwargs: Any) → MaxMarginalRelevanceExampleSelector[source]¶
Asynchronously create k-shot example selector using example list and
embeddings.
Reshuffles examples dynamically based on Max Marginal Relevance.
Parameters
examples (List[dict]) – List of examples to use in the prompt.
embeddings (Embeddings) – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls (Type[VectorStore]) – A vector store DB interface class, e.g. FAISS.
k (int) – Number of examples to select
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
input_keys (Optional[List[str]]) – If provided, the search is based on the input variables
instead of all variables.
example_keys (Optional[List[str]]) – If provided, keys to filter examples to.
vectorstore_kwargs (Optional[dict]) – Extra arguments passed to similarity_search function
of the vectorstore.
vectorstore_cls_kwargs (Any) – optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
Return type
MaxMarginalRelevanceExampleSelector
async aselect_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Asynchronously select examples based on Max Marginal Relevance.
Parameters
input_variables (Dict[str, str]) – The input variables to use for search.
Returns
input_variables (Dict[str, str]) – The input variables to use for search.
Returns
The selected examples.
Return type
List[dict]
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
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_examples(examples: List[dict], embeddings: Embeddings, vectorstore_cls: Type[VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, fetch_k: int = 20, example_keys: Optional[List[str]] = None, vectorstore_kwargs: Optional[dict] = None, **vectorstore_cls_kwargs: Any) → MaxMarginalRelevanceExampleSelector[source]¶
Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on Max Marginal Relevance.
Parameters
examples (List[dict]) – List of examples to use in the prompt.
embeddings (Embeddings) – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls (Type[VectorStore]) – A vector store DB interface class, e.g. FAISS.
k (int) – Number of examples to select
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
input_keys (Optional[List[str]]) – If provided, the search is based on the input variables
instead of all variables.
example_keys (Optional[List[str]]) – If provided, keys to filter examples to.
vectorstore_kwargs (Optional[dict]) – Extra arguments passed to similarity_search function
of the vectorstore.
vectorstore_cls_kwargs (Any) – optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
Return type
MaxMarginalRelevanceExampleSelector
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
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) –
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
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
select_examples(input_variables: Dict[str, str]) → List[dict][source]¶
select_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Select examples based on Max Marginal Relevance.
Parameters
input_variables (Dict[str, str]) – The input variables to use for search.
Returns
The selected examples.
Return type
List[dict]
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
Examples using MaxMarginalRelevanceExampleSelector¶
How to select examples by maximal marginal relevance (MMR)
langchain_community.example_selectors.ngram_overlap.ngram_overlap_score¶
langchain_community.example_selectors.ngram_overlap.ngram_overlap_score(source: List[str], example: List[str]) → float[source]¶
Compute ngram overlap score of source and example as sentence_bleu score
from NLTK package.
Use sentence_bleu with method1 smoothing function and auto reweighting.
Return float value between 0.0 and 1.0 inclusive.
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf
Parameters
source (List[str]) – 
example (List[str]) – 
Return type
float
langchain_core.example_selectors.base.BaseExampleSelector¶
class langchain_core.example_selectors.base.BaseExampleSelector[source]¶
Interface for selecting examples to include in prompts.
Methods
__init__()
aadd_example(example)
Add new example to store.
add_example(example)
Add new example to store.
aselect_examples(input_variables)
Select which examples to use based on the inputs.
select_examples(input_variables)
Select which examples to use based on the inputs.
__init__()¶
async aadd_example(example: Dict[str, str]) → Any[source]¶
Add new example to store.
Parameters
example (Dict[str, str]) – 
Return type
Any
abstract add_example(example: Dict[str, str]) → Any[source]¶
Add new example to store.
Parameters
example (Dict[str, str]) – 
Return type
Any
async aselect_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Select which examples to use based on the inputs.
Parameters
input_variables (Dict[str, str]) – 
Return type
List[dict]
abstract select_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Select which examples to use based on the inputs.
Parameters
input_variables (Dict[str, str]) – 
Return type
List[dict]
Examples using BaseExampleSelector¶
How to use example selectors
langchain_community.example_selectors.ngram_overlap.NGramOverlapExampleSelector¶
class langchain_community.example_selectors.ngram_overlap.NGramOverlapExampleSelector[source]¶
Bases: BaseExampleSelector, BaseModel
Select and order examples based on ngram overlap score (sentence_bleu score
from NLTK package).
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf
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 example_prompt: PromptTemplate [Required]¶
Prompt template used to format the examples.
param examples: List[dict] [Required]¶
A list of the examples that the prompt template expects.
param threshold: float = -1.0¶
Threshold at which algorithm stops. Set to -1.0 by default.
For negative threshold:
select_examples sorts examples by ngram_overlap_score, but excludes none.
For threshold greater than 1.0:
select_examples excludes all examples, and returns an empty list.
For threshold equal to 0.0:
select_examples sorts examples by ngram_overlap_score,
and excludes examples with no ngram overlap with input.
async aadd_example(example: Dict[str, str]) → Any¶
Add new example to store.
Parameters
example (Dict[str, str]) – 
Return type
Any
add_example(example: Dict[str, str]) → None[source]¶
Add new example to list.
Parameters
example (Dict[str, str]) – 
Return type
None
async aselect_examples(input_variables: Dict[str, str]) → List[dict]¶
Select which examples to use based on the inputs.
Parameters
input_variables (Dict[str, str]) – 
Return type
Parameters
input_variables (Dict[str, str]) – 
Return type
List[dict]
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
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
select_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Return list of examples sorted by ngram_overlap_score with input.
Descending order.
Excludes any examples with ngram_overlap_score less than or equal to threshold.
Parameters
input_variables (Dict[str, str]) – 
Return type
List[dict]
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
Examples using NGramOverlapExampleSelector¶
How to select examples by n-gram overlap
langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector¶
class langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector[source]¶
Bases: _VectorStoreExampleSelector
Select examples based on semantic similarity.
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 example_keys: Optional[List[str]] = None¶
Optional keys to filter examples to.
param input_keys: Optional[List[str]] = None¶
Optional keys to filter input to. If provided, the search is based on
the input variables instead of all variables.
param k: int = 4¶
Number of examples to select.
param vectorstore: VectorStore [Required]¶
VectorStore that contains information about examples.
param vectorstore_kwargs: Optional[Dict[str, Any]] = None¶
Extra arguments passed to similarity_search function of the vectorstore.
async aadd_example(example: Dict[str, str]) → str¶
Add new example to vectorstore.
Parameters
example (Dict[str, str]) – 
Return type
str
add_example(example: Dict[str, str]) → str¶
Add new example to vectorstore.
Parameters
example (Dict[str, str]) – 
Return type
str
async classmethod afrom_examples(examples: List[dict], embeddings: Embeddings, vectorstore_cls: Type[VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, *, example_keys: Optional[List[str]] = None, vectorstore_kwargs: Optional[dict] = None, **vectorstore_cls_kwargs: Any) → SemanticSimilarityExampleSelector[source]¶
Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
Reshuffles examples dynamically based on query similarity.
Parameters
examples (List[dict]) – List of examples to use in the prompt.
embeddings (Embeddings) – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls (Type[VectorStore]) – A vector store DB interface class, e.g. FAISS.
k (int) – Number of examples to select
input_keys (Optional[List[str]]) – If provided, the search is based on the input variables
instead of all variables.
example_keys (Optional[List[str]]) – If provided, keys to filter examples to.
vectorstore_kwargs (Optional[dict]) – Extra arguments passed to similarity_search function
of the vectorstore.
vectorstore_cls_kwargs (Any) – optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
Return type
SemanticSimilarityExampleSelector
async aselect_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Asynchronously select examples based on semantic similarity.
Parameters
input_variables (Dict[str, str]) – 
Return type
List[dict]
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
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_examples(examples: List[dict], embeddings: Embeddings, vectorstore_cls: Type[VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, *, example_keys: Optional[List[str]] = None, vectorstore_kwargs: Optional[dict] = None, **vectorstore_cls_kwargs: Any) → SemanticSimilarityExampleSelector[source]¶
Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples (List[dict]) – List of examples to use in the prompt.
embeddings (Embeddings) – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls (Type[VectorStore]) – A vector store DB interface class, e.g. FAISS.
k (int) – Number of examples to select
input_keys (Optional[List[str]]) – If provided, the search is based on the input variables
instead of all variables.
example_keys (Optional[List[str]]) – If provided, keys to filter examples to.
vectorstore_kwargs (Optional[dict]) – Extra arguments passed to similarity_search function
of the vectorstore.
vectorstore_cls_kwargs (Any) – optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
Return type
SemanticSimilarityExampleSelector
classmethod from_orm(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
select_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Select examples based on semantic similarity.
Parameters
input_variables (Dict[str, str]) – 
Return type
List[dict]
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
Examples using SemanticSimilarityExampleSelector¶
How to best prompt for Graph-RAG
How to better prompt when doing SQL question-answering
How to select examples by maximal marginal relevance (MMR)
How to select examples by similarity
How to use few shot examples
langchain_core.example_selectors.length_based.LengthBasedExampleSelector¶
class langchain_core.example_selectors.length_based.LengthBasedExampleSelector[source]¶
Bases: BaseExampleSelector, BaseModel
Select examples based on length.
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 example_prompt: PromptTemplate [Required]¶
Prompt template used to format the examples.
param examples: List[dict] [Required]¶
A list of the examples that the prompt template expects.
param get_text_length: Callable[[str], int] = <function _get_length_based>¶
Function to measure prompt length. Defaults to word count.
param max_length: int = 2048¶
Max length for the prompt, beyond which examples are cut.
async aadd_example(example: Dict[str, str]) → None[source]¶
Add new example to list.
Parameters
example (Dict[str, str]) – 
Return type
None
add_example(example: Dict[str, str]) → None[source]¶
Add new example to list.
Parameters
example (Dict[str, str]) – 
Return type
None
async aselect_examples(input_variables: Dict[str, str]) → List[dict][source]¶
Select which examples to use based on the input lengths.
Parameters
input_variables (Dict[str, str]) – 
Return type
List[dict]
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