Tony20100/langchain_full huggingface dataset:
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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.
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]]
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Parameters
embedding (List[float]) – 
k (int) – 
filter (Optional[dict]) – 
Return type
List[Tuple[Document, float]]
Examples using Hologres¶
Alibaba Cloud
Hologres
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_embeddings(text_embeddings, embedding)
Construct FAISS wrapper from raw documents asynchronously.
afrom_texts(texts, embedding[, metadatas, ids])
Construct FAISS wrapper from raw documents asynchronously.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance asynchronously.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance asynchronously.
amax_marginal_relevance_search_with_score_by_vector(...)
Return docs and their similarity scores selected using the maximal marginal
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, filter, fetch_k])
Return docs most similar to query asynchronously.
asimilarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector asynchronously.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(query[, k, ...])
Return docs most similar to query asynchronously.
asimilarity_search_with_score_by_vector(...)
Return docs most similar to query asynchronously.
delete([ids])
Delete by ID.
deserialize_from_bytes(serialized, ...)
Deserialize FAISS index, docstore, and index_to_docstore_id from bytes.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct FAISS wrapper from raw documents.
from_embeddings(text_embeddings, embedding)
Construct FAISS wrapper from raw documents.
from_texts(texts, embedding[, metadatas, ids])
Construct FAISS wrapper from raw documents.
load_local(folder_path, embeddings[, ...])
Load FAISS index, docstore, and index_to_docstore_id from disk.
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.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs and their similarity scores selected using the maximal marginal
merge_from(target)
Merge another FAISS object with the current one.
save_local(folder_path[, index_name])
Save FAISS index, docstore, and index_to_docstore_id to disk.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
serialize_to_bytes()
Serialize FAISS index, docstore, and index_to_docstore_id to bytes.
similarity_search(query[, k, filter, fetch_k])
Return docs most similar to query.
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].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
Return docs most similar to query.
Parameters
embedding_function (Union[Callable[[str], List[float]], Embeddings]) – 
index (Any) – 
docstore (Docstore) –
index (Any) – 
docstore (Docstore) – 
index_to_docstore_id (Dict[int, str]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
normalize_L2 (bool) – 
distance_strategy (DistanceStrategy) – 
__init__(embedding_function: Union[Callable[[str], List[float]], Embeddings], index: Any, docstore: Docstore, index_to_docstore_id: Dict[int, str], relevance_score_fn: Optional[Callable[[float], float]] = None, normalize_L2: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶
Initialize with necessary components.
Parameters
embedding_function (Union[Callable[[str], List[float]], Embeddings]) – 
index (Any) – 
docstore (Docstore) – 
index_to_docstore_id (Dict[int, str]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
normalize_L2 (bool) – 
distance_strategy (DistanceStrategy) – 
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, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstoreasynchronously.
Parameters
texts (Iterable[str]) – Iterable of strings to 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.
ids (Optional[List[str]]) – Optional list of unique IDs.
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
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_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add the given texts and embeddings to the vectorstore.
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – Iterable pairs of string and embedding to
add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique IDs.
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to 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.
ids (Optional[List[str]]) – Optional list of unique IDs.
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
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
async classmethod afrom_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[Iterable[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents asynchronously.
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – 
embedding (Embeddings) – 
metadatas (Optional[Iterable[dict]]) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
FAISS
kwargs (Any) – 
Return type
FAISS
async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents asynchronously.
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the FAISS database
This is intended to be a quick way to get started.
Example
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = await FAISS.afrom_texts(texts, embeddings)
Parameters
texts (list[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
FAISS
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance asynchronously.
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 before filtering (if needed) 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
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Union[Callable, Dict[str, Any]]]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance asynchronously.
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 before filtering 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.
filter (Optional[Union[Callable, Dict[str, Any]]]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_with_score_by_vector(embedding: List[float], *, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None) → List[Tuple[Document, float]][source]¶
Return docs and their similarity scores selected using the maximal marginalrelevance asynchronously.
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 before filtering 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.
filter (Optional[Union[Callable, Dict[str, Any]]]) – 
Returns
List of Documents and similarity scores selected by maximal marginalrelevance and score for each.
Return type
List[Tuple[Document, float]]
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
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
kwargs (Any) – 
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(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]
async asimilarity_search(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query asynchronously.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Union[Callable, Dict[str, Any]]]) – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector asynchronously.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding.
Return type
List[Document]
async asimilarity_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]]
async asimilarity_search_with_score(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query asynchronously.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of documents most similar to the query text with
L2 distance in float. Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query asynchronously.
Parameters
Return docs most similar to query asynchronously.
Parameters
embedding (List[float]) – Embedding vector to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
**kwargs (Any) – kwargs to be passed to similarity search. Can include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of documents most similar to the query text and L2 distance
in float for each. Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by ID. These are the IDs in the vectorstore.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod deserialize_from_bytes(serialized: bytes, embeddings: Embeddings, **kwargs: Any) → FAISS[source]¶
Deserialize FAISS index, docstore, and index_to_docstore_id from bytes.
Parameters
serialized (bytes) – 
embeddings (Embeddings) – 
kwargs (Any) – 
Return type
FAISS
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
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_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[Iterable[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the FAISS database
This is intended to be a quick way to get started.
Example
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = zip(texts, text_embeddings)
faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – 
embedding (Embeddings) – 
metadatas (Optional[Iterable[dict]]) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
FAISS
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the FAISS database
This is intended to be a quick way to get started.
Example
from langchain_community.vectorstores import FAISS
Example
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, embeddings)
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
FAISS
classmethod load_local(folder_path: str, embeddings: Embeddings, index_name: str = 'index', *, allow_dangerous_deserialization: bool = False, **kwargs: Any) → FAISS[source]¶
Load FAISS index, docstore, and index_to_docstore_id from disk.
Parameters
folder_path (str) – folder path to load index, docstore,
and index_to_docstore_id from.
embeddings (Embeddings) – Embeddings to use when generating queries
index_name (str) – for saving with a specific index file name
allow_dangerous_deserialization (bool) – whether to allow deserialization
of the data which involves loading a pickle file.
Pickle files can be modified by malicious actors to deliver a
malicious payload that results in execution of
arbitrary code on your machine.
kwargs (Any) – 
Return type
FAISS
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **kwargs: Any) → List[Document][source]¶
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.