viruPVS/langchain-docs huggingface dataset:
multilingual Tensorflow Universal Sentence Encoder will be used.
gcs_client¶
The GCS client.
gcs_bucket_name¶
The GCS bucket name.
credentials¶
Created GCP credentials.
Type
Optional
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(project_id, index, endpoint, ...[, ...])
Google Vertex AI Vector Search (previously Matching Engine)
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])
Run more texts through the embeddings and add to the vectorstore.
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_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.
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
delete([ids])
Delete by vector ID or other criteria.
from_components(project_id, region, ...[, ...])
Takes the object creation out of the constructor.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Use from components instead.
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])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to the embedding.
similarity_search_by_vector_with_score(embedding)
Return docs most similar to the embedding and their cosine distance.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, filter])
Return docs most similar to query and their cosine distance from the query.
__init__(project_id: str, index: MatchingEngineIndex, endpoint: MatchingEngineIndexEndpoint, embedding: Embeddings, gcs_client: storage.Client, gcs_bucket_name: str, credentials: Optional[Credentials] = None)[source]¶
Google Vertex AI Vector Search (previously Matching Engine)implementation of the vector store.
While the embeddings are stored in the Matching Engine, the embedded
documents will be stored in GCS.
An existing Index and corresponding Endpoint are preconditions for
using this module.
See usage in
docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb.
Note that this implementation is mostly meant for reading if you are
planning to do a real time implementation. While reading is a real time
operation, updating the index takes close to one hour.
project_id¶
The GCS project id.
index¶
The created index class. See
~:func:MatchingEngine.from_components.
endpoint¶
The created endpoint class. See
~:func:MatchingEngine.from_components.
embedding¶
A Embeddings that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
gcs_client¶
The GCS client.
gcs_bucket_name¶
The GCS bucket name.
credentials¶
Created GCP credentials.
Type
Optional
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
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.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – 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.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_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.
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_components(project_id: str, region: str, gcs_bucket_name: str, index_id: str, endpoint_id: str, credentials_path: Optional[str] = None, embedding: Optional[Embeddings] = None) → MatchingEngine[source]¶
Takes the object creation out of the constructor.
Parameters
project_id – The GCP project id.
region – The default location making the API calls. It must have
regional. (the same location as the GCS bucket and must be) – 
gcs_bucket_name – The location where the vectors will be stored in
created. (order for the index to be) – 
index_id – The id of the created index.
endpoint_id – The id of the created endpoint.
credentials_path – (Optional) The path of the Google credentials on
system. (the local file) – 
embedding – The Embeddings that will be used for
texts. (embedding the) – 
Returns
A configured MatchingEngine with the texts added to the index.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → MatchingEngine[source]¶
Use from components instead.
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 – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
Returns
List of Documents selected by maximal marginal relevance.
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 – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
Returns
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[List[Namespace]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query – The string that will be used to search for similar documents.
k – The amount of neighbors that will be retrieved.
filter – Optional. A list of Namespaces for filtering the matching results.
For example:
[Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])]
will match datapoints that satisfy “red color” but not include
datapoints with “squared shape”. Please refer to
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json
for more detail.
Returns
A list of k matching documents.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[List[Namespace]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to the embedding.
Parameters
embedding – Embedding to look up documents similar to.
k – The amount of neighbors that will be retrieved.
filter – Optional. A list of Namespaces for filtering the matching results.
For example:
[Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])]
will match datapoints that satisfy “red color” but not include
datapoints with “squared shape”. Please refer to
datapoints with “squared shape”. Please refer to
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json
for more detail.
Returns
A list of k matching documents.
similarity_search_by_vector_with_score(embedding: List[float], k: int = 4, filter: Optional[List[Namespace]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to the embedding and their cosine distance.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Optional. A list of Namespaces for filtering
the matching results.
For example:
[Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])]
will match datapoints that satisfy “red color” but not include
datapoints with “squared shape”. Please refer to
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json
for more detail.
Returns
List of documents most similar to
the query text and cosine distance in float for each.
Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
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 – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – 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
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[List[Namespace]] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query and their cosine distance from the query.
Parameters
query – String query look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Optional. A list of Namespaces for filtering
the matching results.
For example:
[Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])]
will match datapoints that satisfy “red color” but not include
datapoints with “squared shape”. Please refer to
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json
for more detail.
Returns
List of documents most similar to
the query text and cosine distance in float for each.
Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
Examples using MatchingEngine¶
Google Vertex AI MatchingEngine
langchain_community.vectorstores.sklearn.SKLearnVectorStore¶
class langchain_community.vectorstores.sklearn.SKLearnVectorStore(embedding: Embeddings, *, persist_path: Optional[str] = None, serializer: Literal['json', 'bson', 'parquet'] = 'json', metric: str = 'cosine', **kwargs: Any)[source]¶
Simple in-memory vector store based on the scikit-learn library
NearestNeighbors implementation.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, *[, persist_path, ...])
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, ids])
Run more texts through the embeddings and add to the vectorstore.
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_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])
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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param lambda_mult: 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.
max_marginal_relevance_search_by_vector(...)
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param lambda_mult: 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.
persist()
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, 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])
Run similarity search with distance.
__init__(embedding: Embeddings, *, persist_path: Optional[str] = None, serializer: Literal['json', 'bson', 'parquet'] = 'json', metric: str = 'cosine', **kwargs: Any) → None[source]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
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.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
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 of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – 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.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
**kwargs – 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)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, persist_path: Optional[str] = None, **kwargs: Any) → SKLearnVectorStore[source]¶
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param lambda_mult: 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.
Returns
List of Documents selected by maximal marginal relevance.
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][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param embedding: Embedding to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param lambda_mult: 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.
Returns
List of Documents selected by maximal marginal relevance.
persist() → None[source]¶
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
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 – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – 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)
similarity_search_with_score(query: str, *, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Examples using SKLearnVectorStore¶
scikit-learn