Tony20100/langchain_full huggingface dataset:
langchain_core.outputs.llm_result.LLMResult¶
class langchain_core.outputs.llm_result.LLMResult[source]¶
Bases: BaseModel
Class that contains all results for a batched LLM call.
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 generations: List[List[Generation]] [Required]¶
List of generated outputs. This is a List[List[]] because
each input could have multiple candidate generations.
param llm_output: Optional[dict] = None¶
Arbitrary LLM provider-specific output.
param run: Optional[List[RunInfo]] = None¶
List of metadata info for model call for each input.
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
flatten() → List[LLMResult][source]¶
Flatten generations into a single list.
Unpack List[List[Generation]] -> List[LLMResult] where each returned LLMResultcontains only a single Generation. If token usage information is available,
it is kept only for the LLMResult corresponding to the top-choice
Generation, to avoid over-counting of token usage downstream.
Returns
List of LLMResults where each returned LLMResult contains a singleGeneration.
Return type
List[LLMResult]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
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
Examples using LLMResult¶
Google Cloud Vertex AI
How to attach callbacks to a runnable
How to pass callbacks in at runtime
How to propagate callbacks  constructor
How to use callbacks in async environments
langchain_prompty.parsers.PromptyChatParser¶
class langchain_prompty.parsers.PromptyChatParser(prompty: Prompty)[source]¶
Methods
__init__(prompty)
inline_image(image_item)
invoke(data)
parse_content(content)
for parsing inline images
Parameters
prompty (Prompty) – 
__init__(prompty: Prompty) → None[source]¶
Parameters
prompty (Prompty) – 
Return type
None
inline_image(image_item: str) → str[source]¶
Parameters
image_item (str) – 
Return type
str
invoke(data: BaseModel) → BaseModel[source]¶
Parameters
data (BaseModel) – 
Return type
BaseModel
parse_content(content: str) → Union[str, List][source]¶
for parsing inline images
Parameters
content (str) – 
Return type
Union[str, List]
langchain_google_genai.google_vector_store.ServerSideEmbedding¶
class langchain_google_genai.google_vector_store.ServerSideEmbedding[source]¶
Do nothing embedding model where the embedding is done by the server.
Methods
__init__()
aembed_documents(texts)
Asynchronous Embed search docs.
aembed_query(text)
Asynchronous Embed query text.
embed_documents(texts)
Embed search docs.
embed_query(text)
Embed query text.
__init__()¶
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed query text.
Parameters
text (str) – 
Return type
List[float]
langchain_google_genai.google_vector_store.GoogleVectorStore¶
class langchain_google_genai.google_vector_store.GoogleVectorStore(*, corpus_id: str, document_id: Optional[str] = None, **kwargs: Any)[source]¶
Google GenerativeAI Vector Store.
Currently, it computes the embedding vectors on the server side.
Example: Add texts to an existing corpus.
store = GoogleVectorStore(corpus_id=”123”)
store.add_documents(documents, document_id=”456”)
Example: Create a new corpus.
store = GoogleVectorStore.create_corpus(corpus_id=”123”, display_name=”My Google corpus”)
Example: Query the corpus for relevant passages.
store.as_retriever()             .get_relevant_documents(“Who caught the gingerbread man?”)
Example: Ask the corpus for grounded responses!
aqa = store.as_aqa()
response = aqa.invoke(“Who caught the gingerbread man?”)
print(response.answer)
print(response.attributed_passages)
print(response.answerability_probability)
You can also operate at Google’s Document level.
Example: Add texts to an existing Google Vector Store Document.
doc_store = GoogleVectorStore(corpus_id=”123”, document_id=”456”)
doc_store.add_documents(documents)
Example: Create a new Google Vector Store Document.
doc_store = GoogleVectorStore.create_document(corpus_id=”123”, document_id=”456”, display_name=”My Google document”)
Example: Query the Google document.
doc_store.as_retriever()             .get_relevant_documents(“Who caught the gingerbread man?”)
For more details, see the class’s methods.
Returns an existing Google Semantic Retriever corpus or document.
If just the corpus ID is provided, the vector store operates over all
documents within that corpus.
documents within that corpus.
If the document ID is provided, the vector store operates over just that
document.
Raises
DoesNotExistsException if the IDs do not match to anything on Google – server. In this case, consider using create_corpus or
    create_document to create one.
Parameters
corpus_id (str) – 
document_id (Optional[str]) – 
kwargs (Any) – 
Attributes
corpus_id
Returns the corpus ID managed by this vector store.
document_id
Returns the document ID managed by this vector store.
embeddings
Access the query embedding object if available.
name
Returns the name of the Google entity.
Methods
__init__(*, corpus_id[, document_id])
Returns an existing Google Semantic Retriever corpus or document.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, document_id])
Add texts to the vector store.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_aqa(**kwargs)
Construct a Google Generative AI AQA engine.
as_aqa(**kwargs)
Construct a Google Generative AI AQA engine.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
create_corpus([corpus_id, display_name])
Create a Google Semantic Retriever corpus.
create_document(corpus_id[, document_id, ...])
Create a Google Semantic Retriever document.
delete([ids])
Delete chunnks.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts[, embedding, metadatas, ...])
Returns a vector store of an existing document with the specified text.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Search the vector store for relevant texts.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, filter])
Run similarity search with distance.
__init__(*, corpus_id: str, document_id: Optional[str] = None, **kwargs: Any)[source]¶
Returns an existing Google Semantic Retriever corpus or document.
If just the corpus ID is provided, the vector store operates over all
documents within that corpus.
If the document ID is provided, the vector store operates over just that
document.
Raises
DoesNotExistsException if the IDs do not match to anything on Google – server. In this case, consider using create_corpus or
    create_document to create one.
Parameters
corpus_id (str) – 
document_id (Optional[str]) – 
kwargs (Any) – 
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, *, document_id: Optional[str] = None, **kwargs: Any) → List[str][source]¶
Add texts to the vector store.
If the vector store points to a corpus (instead of a document), you must
also provide a document_id.
Returns
Chunk’s names created on Google servers.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[Dict[str, Any]]]) – 
document_id (Optional[str]) – 
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
as_aqa(**kwargs: Any) → Runnable[str, AqaOutput][source]¶
Construct a Google Generative AI AQA engine.
All arguments are optional.
Parameters
answer_style – See
google.ai.generativelanguage.GenerateAnswerRequest.AnswerStyle.
safety_settings – See google.ai.generativelanguage.SafetySetting.
temperature – 0.0 to 1.0.
kwargs (Any) – 
Return type
Runnable[str, AqaOutput]
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
classmethod create_corpus(corpus_id: Optional[str] = None, display_name: Optional[str] = None) → GoogleVectorStore[source]¶
Create a Google Semantic Retriever corpus.
Parameters
corpus_id (Optional[str]) – The ID to use to create the new corpus. If not provided,
Google server will provide one.
display_name (Optional[str]) – The title of the new corpus. If not provided, Google
server will provide one.
Returns
An instance of vector store that points to the newly created corpus.
Return type
GoogleVectorStore
classmethod create_document(corpus_id: str, document_id: Optional[str] = None, display_name: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) → GoogleVectorStore[source]¶
Create a Google Semantic Retriever document.
Parameters
corpus_id (str) – ID of an existing corpus.
document_id (Optional[str]) – The ID to use to create the new Google Semantic
Retriever document. If not provided, Google server will provide
one.
display_name (Optional[str]) – The title of the new document. If not provided, Google
server will provide one.
metadata (Optional[Dict[str, Any]]) – 
Returns
An instance of vector store that points to the newly created
document.
Return type
GoogleVectorStore
document.
Return type
GoogleVectorStore
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete chunnks.
Note that the “ids” are not corpus ID or document ID. Rather, these
are the entity names returned by add_texts.
Returns
True if successful. Otherwise, you should get an exception anyway.
Parameters
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict[str, Any]]] = None, *, corpus_id: Optional[str] = None, document_id: Optional[str] = None, **kwargs: Any) → GoogleVectorStore[source]¶
Returns a vector store of an existing document with the specified text.
Parameters
corpus_id (Optional[str]) – REQUIRED. Must be an existing corpus.
document_id (Optional[str]) – REQUIRED. Must be an existing document.
texts (List[str]) – Texts to be loaded into the vector store.
embedding (Optional[Embeddings]) – 
metadatas (Optional[List[dict[str, Any]]]) – 
kwargs (Any) – 
Returns
A vector store pointing to the specified Google Semantic Retriever
Document.
Raises
DoesNotExistsException if the IDs do not match to anything at – Google server.
Return type
Return type
GoogleVectorStore
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
kwargs (Any) – 
Returns
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Search the vector store for relevant texts.
Parameters
query (str) – 
k (int) – 
filter (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Parameters
query (str) – 
k (int) – 
filter (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
langchain_google_vertexai.model_garden.ChatAnthropicVertex¶
class langchain_google_vertexai.model_garden.ChatAnthropicVertex[source]¶
Bases: _VertexAICommon, BaseChatModel
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 access_token: Optional[str] = None¶
param additional_headers: Optional[Dict[str, str]] = None¶
A key-value dictionary representing additional headers for the model call
param api_endpoint: Optional[str] = None¶
Desired API endpoint, e.g., us-central1-aiplatform.googleapis.com
param api_transport: Optional[str] = None¶
The desired API transport method, can be either ‘grpc’ or ‘rest’
param cache: Union[BaseCache, bool, None] = None¶
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED] Callback manager to add to the run trace.
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param client_cert_source: Optional[Callable[[], Tuple[bytes, bytes]]] = None¶
A callback which returns client certificate bytes and private key bytes both
param credentials: Any = None¶
The default custom credentials (google.auth.credentials.Credentials) to use
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param full_model_name: Optional[str] = None¶