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Construct OpenSearchVectorSearch wrapper from raw documents.
Example
from langchain import OpenSearchVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
opensearch_vector_search = OpenSearchVectorSearch.from_texts(
texts,
embeddings,
opensearch_url="http://localhost:9200"
)
OpenSearch by default supports Approximate Search powered by nmslib, faiss
and lucene engines recommended for large datasets. Also supports brute force
search through Script Scoring and Painless Scripting.
Optional Args:vector_field: Document field embeddings are stored in. Defaults to
“vector_field”.
text_field: Document field the text of the document is stored in. Defaults
to “text”.
Optional Keyword Args for Approximate Search:engine: “nmslib”, “faiss”, “lucene”; default: “nmslib”
space_type: “l2”, “l1”, “cosinesimil”, “linf”, “innerproduct”; default: “l2”
ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
Keyword Args for Script Scoring or Painless Scripting:is_appx_search: False
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → list[langchain.schema.Document][source]¶
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html
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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.
Defaults to 20.
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
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, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
By default, supports Approximate Search.
|
https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html
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Return docs most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
Optional Args:vector_field: Document field embeddings are stored in. Defaults to
“vector_field”.
text_field: Document field the text of the document is stored in. Defaults
to “text”.
metadata_field: Document field that metadata is stored in. Defaults to
“metadata”.
Can be set to a special value “*” to include the entire document.
Optional Args for Approximate Search:search_type: “approximate_search”; default: “approximate_search”
boolean_filter: A Boolean filter consists of a Boolean query that
contains a k-NN query and a filter.
subquery_clause: Query clause on the knn vector field; default: “must”
lucene_filter: the Lucene algorithm decides whether to perform an exact
k-NN search with pre-filtering or an approximate search with modified
post-filtering.
Optional Args for Script Scoring Search:search_type: “script_scoring”; default: “approximate_search”
space_type: “l2”, “l1”, “linf”, “cosinesimil”, “innerproduct”,
“hammingbit”; default: “l2”
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {“match_all”: {}}
Optional Args for Painless Scripting Search:search_type: “painless_scripting”; default: “approximate_search”
space_type: “l2Squared”, “l1Norm”, “cosineSimilarity”; default: “l2Squared”
pre_filter: script_score query to pre-filter documents before identifying
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html
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pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {“match_all”: {}}
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.
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]¶
Return docs and it’s scores most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents along with its scores most similar to the query.
Optional Args:same as similarity_search
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html
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langchain.vectorstores.elastic_vector_search.ElasticKnnSearch¶
class langchain.vectorstores.elastic_vector_search.ElasticKnnSearch(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: Optional[str] = 'vector', query_field: Optional[str] = 'text')[source]¶
Bases: ElasticVectorSearch
A class for performing k-Nearest Neighbors (k-NN) search on an Elasticsearch index.
The class is designed for a text search scenario where documents are text strings
and their embeddings are vector representations of those strings.
Initializes an instance of the ElasticKnnSearch class and sets up theElasticsearch client.
Parameters
index_name – The name of the Elasticsearch index.
embedding – An instance of the Embeddings class, used to generate vector
representations of text strings.
es_connection – An existing Elasticsearch connection.
es_cloud_id – The Cloud ID of the Elasticsearch instance. Required if
creating a new connection.
es_user – The username for the Elasticsearch instance. Required if
creating a new connection.
es_password – The password for the Elasticsearch instance. Required if
creating a new connection.
Methods
__init__(index_name, embedding[, ...])
Initializes an instance of the ElasticKnnSearch class and sets up the
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.
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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.
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)
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 most similar to query.
client_search(client, index_name, ...)
create_index(client, index_name, mapping)
delete(ids)
Delete by vector IDs.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct ElasticVectorSearch wrapper from raw documents.
knn_hybrid_search([query, k, query_vector, ...])
Performs a hybrid k-nearest neighbor (k-NN) and text-based search on the
knn_search([query, k, query_vector, ...])
Performs a k-nearest neighbor (k-NN) search on the Elasticsearch index.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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Performs a k-nearest neighbor (k-NN) search on the Elasticsearch index.
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 embedding vector.
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.
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]
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, refresh_indices: bool = True, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]¶
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.
refresh_indices – bool to refresh ElasticSearch indices
Returns
List of ids from adding the texts into the vectorstore.
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.
async amax_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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
client_search(client: Any, index_name: str, script_query: Dict, size: int) → Any¶
create_index(client: Any, index_name: str, mapping: Dict) → None¶
delete(ids: List[str]) → None¶
Delete by vector IDs.
Parameters
ids – List of ids to delete.
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, elasticsearch_url: Optional[str] = None, index_name: Optional[str] = None, refresh_indices: bool = True, **kwargs: Any) → ElasticVectorSearch¶
Construct ElasticVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in the Elasticsearch instance.
Adds the documents to the newly created Elasticsearch index.
This is intended to be a quick way to get started.
Example
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
texts,
embeddings,
elasticsearch_url="http://localhost:9200"
)
knn_hybrid_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, knn_boost: Optional[float] = 0.9, query_boost: Optional[float] = 0.1, fields: Optional[Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...]]] = None) → Dict[Any, Any][source]¶
Performs a hybrid k-nearest neighbor (k-NN) and text-based search on theElasticsearch index.
The search can be conducted using either a raw query vector or a model ID.
The method first generates
the body of the k-NN search query and the text-based query, which can be
interpreted by Elasticsearch.
It then performs the hybrid search on the Elasticsearch index and returns the
results.
Parameters
query – The query or queries to be used for the search. Required if
query_vector is not provided.
k – The number of nearest neighbors to return. Defaults to 10.
query_vector – The query vector to be used for the search. Required if
query is not provided.
model_id – The ID of the model to use for generating the query vector, if
query is provided.
size – The number of search hits to return. Defaults to 10.
source – Whether to include the source of each hit in the results.
knn_boost – The boost factor for the k-NN part of the search.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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knn_boost – The boost factor for the k-NN part of the search.
query_boost – The boost factor for the text-based part of the search.
fields – The fields to include in the source of each hit. If None, all fields are
included. Defaults to None.
vector_query_field – Field name to use in knn search if not default ‘vector’
query_field – Field name to use in search if not default ‘text’
Returns
The search results.
Raises
ValueError – If neither query_vector nor model_id is provided, or if
both are provided.
knn_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, fields: Optional[Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...]]] = None) → Dict[source]¶
Performs a k-nearest neighbor (k-NN) search on the Elasticsearch index.
The search can be conducted using either a raw query vector or a model ID.
The method first generates
the body of the search query, which can be interpreted by Elasticsearch.
It then performs the k-NN
search on the Elasticsearch index and returns the results.
Parameters
query – The query or queries to be used for the search. Required if
query_vector is not provided.
k – The number of nearest neighbors to return. Defaults to 10.
query_vector – The query vector to be used for the search. Required if
query is not provided.
model_id – The ID of the model to use for generating the query vector, if
query is provided.
size – The number of search hits to return. Defaults to 10.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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size – The number of search hits to return. Defaults to 10.
source – Whether to include the source of each hit in the results.
fields – The fields to include in the source of each hit. If None, all
fields are included.
vector_query_field – Field name to use in knn search if not default ‘vector’
Returns
The search results.
Raises
ValueError – If neither query_vector nor model_id is provided, or if
both are provided.
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.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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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.
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[dict] = None, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the 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.
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
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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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, filter: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html
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langchain.vectorstores.sklearn.SKLearnVectorStore¶
class langchain.vectorstores.sklearn.SKLearnVectorStore(embedding: Embeddings, *, persist_path: Optional[str] = None, serializer: Literal['json', 'bson', 'parquet'] = 'json', metric: str = 'cosine', **kwargs: Any)[source]¶
Bases: VectorStore
A simple in-memory vector store based on the scikit-learn library
NearestNeighbors implementation.
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.
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)
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)
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Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
delete(ids)
Delete by vector ID.
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(...)
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])
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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])
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 classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
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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.
async amax_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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
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Parameters
ids – List of ids to delete.
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 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
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html
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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.
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
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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]¶
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html
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langchain.vectorstores.tair.Tair¶
class langchain.vectorstores.tair.Tair(embedding_function: Embeddings, url: str, index_name: str, content_key: str = 'content', metadata_key: str = 'metadata', search_params: Optional[dict] = None, **kwargs: Any)[source]¶
Bases: VectorStore
Wrapper around Tair Vector store.
Methods
__init__(embedding_function, url, index_name)
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])
Add texts data to an existing index.
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)
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 most similar to query.
create_index_if_not_exist(dim, ...)
delete(ids)
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create_index_if_not_exist(dim, ...)
delete(ids)
Delete by vector ID.
drop_index([index_name])
Drop an existing index.
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_existing_index(embedding[, index_name, ...])
Connect to an existing Tair index.
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.
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])
Returns the most similar indexed documents to the query text.
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].
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
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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]¶
Add texts data to an existing index.
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.
async amax_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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
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Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
create_index_if_not_exist(dim: int, distance_type: str, index_type: str, data_type: str, **kwargs: Any) → bool[source]¶
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
static drop_index(index_name: str = 'langchain', **kwargs: Any) → bool[source]¶
Drop an existing index.
Parameters
index_name (str) – Name of the index to drop.
Returns
True if the index is dropped successfully.
Return type
bool
classmethod from_documents(documents: List[Document], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: str = 'langchain', content_key: str = 'content', metadata_key: str = 'metadata', **kwargs: Any) → Tair[source]¶
Return VectorStore initialized from documents and embeddings.
classmethod from_existing_index(embedding: Embeddings, index_name: str = 'langchain', content_key: str = 'content', metadata_key: str = 'metadata', **kwargs: Any) → Tair[source]¶
Connect to an existing Tair index.
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Connect to an existing Tair index.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: str = 'langchain', content_key: str = 'content', metadata_key: str = 'metadata', **kwargs: Any) → Tair[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]¶
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
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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.
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]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
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 – 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
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filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
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langchain.vectorstores.clickhouse.ClickhouseSettings¶
class langchain.vectorstores.clickhouse.ClickhouseSettings(_env_file: Optional[Union[str, PathLike, List[Union[str, PathLike]], Tuple[Union[str, PathLike], ...]]] = '<object object>', _env_file_encoding: Optional[str] = None, _env_nested_delimiter: Optional[str] = None, _secrets_dir: Optional[Union[str, PathLike]] = None, *, host: str = 'localhost', port: int = 8123, username: Optional[str] = None, password: Optional[str] = None, index_type: str = 'annoy', index_param: Optional[Union[List, Dict]] = ["'L2Distance'", 100], index_query_params: Dict[str, str] = {}, column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata': 'metadata', 'uuid': 'uuid'}, database: str = 'default', table: str = 'langchain', metric: str = 'angular')[source]¶
Bases: BaseSettings
ClickHouse Client Configuration
Attribute:
clickhouse_host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
clickhouse_port (int) : URL port to connect with HTTP. Defaults to 8443.
username (str) : Username to login. Defaults to None.
password (str) : Password to login. Defaults to None.
index_type (str): index type string.
index_param (list): index build parameter.
index_query_params(dict): index query parameters.
database (str) : Database name to find the table. Defaults to ‘default’.
table (str) : Table name to operate on.
Defaults to ‘vector_table’.
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table (str) : Table name to operate on.
Defaults to ‘vector_table’.
metric (str)Metric to compute distance,supported are (‘angular’, ‘euclidean’, ‘manhattan’, ‘hamming’,
‘dot’). Defaults to ‘angular’.
https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169
column_map (Dict)Column type map to project column name onto langchainsemantics. Must have keys: text, id, vector,
must be same size to number of columns. For example:
.. code-block:: python
{‘id’: ‘text_id’,
‘uuid’: ‘global_unique_id’
‘embedding’: ‘text_embedding’,
‘document’: ‘text_plain’,
‘metadata’: ‘metadata_dictionary_in_json’,
}
Defaults to identity map.
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 column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata': 'metadata', 'uuid': 'uuid'}¶
param database: str = 'default'¶
param host: str = 'localhost'¶
param index_param: Optional[Union[List, Dict]] = ["'L2Distance'", 100]¶
param index_query_params: Dict[str, str] = {}¶
param index_type: str = 'annoy'¶
param metric: str = 'angular'¶
param password: Optional[str] = None¶
param port: int = 8123¶
param table: str = 'langchain'¶
param username: Optional[str] = None¶
model Config[source]¶
Bases: object
env_file = '.env'¶
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model Config[source]¶
Bases: object
env_file = '.env'¶
env_file_encoding = 'utf-8'¶
env_prefix = 'clickhouse_'¶
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.ClickhouseSettings.html
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langchain.vectorstores.starrocks.get_named_result¶
langchain.vectorstores.starrocks.get_named_result(connection: Any, query: str) → List[dict[str, Any]][source]¶
Get a named result from a query.
:param connection: The connection to the database
:param query: The query to execute
Returns
The result of the query
Return type
List[dict[str, Any]]
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.get_named_result.html
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langchain.vectorstores.sklearn.JsonSerializer¶
class langchain.vectorstores.sklearn.JsonSerializer(persist_path: str)[source]¶
Bases: BaseSerializer
Serializes data in json using the json package from python standard library.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist_path
save(data)
Saves the data to the persist_path
classmethod extension() → str[source]¶
The file extension suggested by this serializer (without dot).
load() → Any[source]¶
Loads the data from the persist_path
save(data: Any) → None[source]¶
Saves the data to the persist_path
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.JsonSerializer.html
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langchain.vectorstores.atlas.AtlasDB¶
class langchain.vectorstores.atlas.AtlasDB(name: str, embedding_function: Optional[Embeddings] = None, api_key: Optional[str] = None, description: str = 'A description for your project', is_public: bool = True, reset_project_if_exists: bool = False)[source]¶
Bases: VectorStore
Wrapper around Atlas: Nomic’s neural database and rhizomatic instrument.
To use, you should have the nomic python package installed.
Example
from langchain.vectorstores import AtlasDB
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = AtlasDB("my_project", embeddings.embed_query)
Initialize the Atlas Client
Parameters
name (str) – The name of your project. If the project already exists,
it will be loaded.
embedding_function (Optional[Callable]) – An optional function used for
embedding your data. If None, data will be embedded with
Nomic’s embed model.
api_key (str) – Your nomic API key
description (str) – A description for your project.
is_public (bool) – Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool) – Whether to reset this project if it
already exists. Default False.
Generally userful during development and testing.
Methods
__init__(name[, embedding_function, ...])
Initialize the Atlas Client
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.
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Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, refresh])
Run more texts through the embeddings and add to the vectorstore.
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)
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 most similar to query.
create_index(**kwargs)
Creates an index in your project.
delete(ids)
Delete by vector ID.
from_documents(documents[, embedding, ids, ...])
Create an AtlasDB vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])
Create an AtlasDB vectorstore from a raw documents.
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])
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similarity_search(query[, k])
Run similarity search with AtlasDB
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].
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, refresh: bool = True, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
ids (Optional[List[str]]) – An optional list of ids.
refresh (bool) – Whether or not to refresh indices with the updated data.
Default True.
Returns
List of IDs of the added texts.
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Default True.
Returns
List of IDs of the added texts.
Return type
List[str]
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.
async amax_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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
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Return docs most similar to query.
create_index(**kwargs: Any) → Any[source]¶
Creates an index in your project.
See
https://docs.nomic.ai/atlas_api.html#nomic.project.AtlasProject.create_index
for full detail.
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, persist_directory: Optional[str] = None, description: str = 'A description for your project', is_public: bool = True, reset_project_if_exists: bool = False, index_kwargs: Optional[dict] = None, **kwargs: Any) → AtlasDB[source]¶
Create an AtlasDB vectorstore from a list of documents.
Parameters
name (str) – Name of the collection to create.
api_key (str) – Your nomic API key,
documents (List[Document]) – List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
ids (Optional[List[str]]) – Optional list of document IDs. If None,
ids will be auto created
description (str) – A description for your project.
is_public (bool) – Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool) – Whether to reset this project if
it already exists. Default False.
Generally userful during development and testing.
index_kwargs (Optional[dict]) – Dict of kwargs for index creation.
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index_kwargs (Optional[dict]) – Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, description: str = 'A description for your project', is_public: bool = True, reset_project_if_exists: bool = False, index_kwargs: Optional[dict] = None, **kwargs: Any) → AtlasDB[source]¶
Create an AtlasDB vectorstore from a raw documents.
Parameters
texts (List[str]) – The list of texts to ingest.
name (str) – Name of the project to create.
api_key (str) – Your nomic API key,
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
ids (Optional[List[str]]) – Optional list of document IDs. If None,
ids will be auto created
description (str) – A description for your project.
is_public (bool) – Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool) – Whether to reset this project if it
already exists. Default False.
Generally userful during development and testing.
index_kwargs (Optional[dict]) – Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
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Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
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
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
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Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Run similarity search with AtlasDB
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
Returns
List of documents most similar to the query text.
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 – 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)
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langchain.vectorstores.redis.Redis¶
class langchain.vectorstores.redis.Redis(redis_url: str, index_name: str, embedding_function: ~typing.Callable, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', relevance_score_fn: ~typing.Optional[~typing.Callable[[float], float]] = <function _default_relevance_score>, **kwargs: ~typing.Any)[source]¶
Bases: VectorStore
Wrapper around Redis vector database.
To use, you should have the redis python package installed.
Example
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Redis(
redis_url="redis://username:password@localhost:6379"
index_name="my-index",
embedding_function=embeddings.embed_query,
)
Initialize with necessary components.
Methods
__init__(redis_url, index_name, ...[, ...])
Initialize with necessary components.
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, embeddings, ...])
Add more texts to the vectorstore.
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.
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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)
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 most similar to query.
delete(ids, **kwargs)
Delete a Redis entry.
drop_index(index_name, delete_documents, ...)
Drop a Redis search index.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_existing_index(embedding, index_name[, ...])
Connect to an existing Redis index.
from_texts(texts, embedding[, metadatas, ...])
Create a Redis vectorstore from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Creates a new index for the embeddings in Redis. 3. Adds the documents to the newly created Redis index. This is intended to be a quick way to get started. .. rubric:: Example.
from_texts_return_keys(texts, embedding[, ...])
Create a Redis vectorstore from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Creates a new index for the embeddings in Redis. 3. Adds the documents to the newly created Redis index. 4. Returns the keys of the newly created documents. This is intended to be a quick way to get started. .. rubric:: Example.
max_marginal_relevance_search(query[, k, ...])
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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])
Returns the most similar indexed documents to the query text.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_limit_score(query[, k, ...])
Returns the most similar indexed documents to the query text within the score_threshold range.
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.
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]
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Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, batch_size: int = 1000, **kwargs: Any) → List[str][source]¶
Add more texts to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional) – Optional pre-generated
embeddings. Defaults to None.
keys (List[str]) or ids (List[str]) – Identifiers of entries.
Defaults to None.
batch_size (int, optional) – Batch size to use for writes. Defaults to 1000.
Returns
List of ids added to the vectorstore
Return type
List[str]
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.
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Return docs selected using the maximal marginal relevance.
async amax_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.
as_retriever(**kwargs: Any) → RedisVectorStoreRetriever[source]¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
static delete(ids: List[str], **kwargs: Any) → bool[source]¶
Delete a Redis entry.
Parameters
ids – List of ids (keys) to delete.
Returns
Whether or not the deletions were successful.
Return type
bool
static drop_index(index_name: str, delete_documents: bool, **kwargs: Any) → bool[source]¶
Drop a Redis search index.
Parameters
index_name (str) – Name of the index to drop.
delete_documents (bool) – Whether to drop the associated documents.
Returns
Whether or not the drop was successful.
Return type
bool
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Returns
Whether or not the drop was successful.
Return type
bool
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_existing_index(embedding: Embeddings, index_name: str, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', **kwargs: Any) → Redis[source]¶
Connect to an existing Redis index.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', **kwargs: Any) → Redis[source]¶
Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in Redis.
Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
.. rubric:: Example
classmethod from_texts_return_keys(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', distance_metric: Literal['COSINE', 'IP', 'L2'] = 'COSINE', **kwargs: Any) → Tuple[Redis, List[str]][source]¶
Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in Redis.
Adds the documents to the newly created Redis index.
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Adds the documents to the newly created Redis index.
Returns the keys of the newly created documents.
This is intended to be a quick way to get started.
.. rubric:: Example
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
List of Documents selected by maximal marginal relevance.
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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, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
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 – 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_limit_score(query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text within the
score_threshold range.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
score_threshold (float) – The minimum matching score required for a document
0.2. (to be considered a match. Defaults to) –
similarity (Because the similarity calculation algorithm is based on cosine) –
:param :
:param the smaller the angle:
:param the higher the similarity.:
Returns
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:param :
:param the smaller the angle:
:param the higher the similarity.:
Returns
A list of documents that are most similar to the query text,
including the match score for each document.
Return type
List[Document]
Note
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
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) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query and score for each
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langchain.vectorstores.utils.maximal_marginal_relevance¶
langchain.vectorstores.utils.maximal_marginal_relevance(query_embedding: ndarray, embedding_list: list, lambda_mult: float = 0.5, k: int = 4) → List[int][source]¶
Calculate maximal marginal relevance.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.utils.maximal_marginal_relevance.html
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langchain.vectorstores.faiss.dependable_faiss_import¶
langchain.vectorstores.faiss.dependable_faiss_import(no_avx2: Optional[bool] = None) → Any[source]¶
Import faiss if available, otherwise raise error.
If FAISS_NO_AVX2 environment variable is set, it will be considered
to load FAISS with no AVX2 optimization.
Parameters
no_avx2 – Load FAISS strictly with no AVX2 optimization
so that the vectorstore is portable and compatible with other devices.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.dependable_faiss_import.html
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langchain.vectorstores.vectara.VectaraRetriever¶
class langchain.vectorstores.vectara.VectaraRetriever(*, vectorstore: Vectara, search_type: str = 'similarity', search_kwargs: dict = None)[source]¶
Bases: VectorStoreRetriever
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 search_kwargs: dict [Optional]¶
Search params.
k: Number of Documents to return. Defaults to 5.
lambda_val: lexical match parameter for hybrid search.
filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be “doc.rating > 3.0 and part.lang = ‘deu’”} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
n_sentence_context: number of sentences before/after the matching segment to add
param search_type: str = 'similarity'¶
param vectorstore: Vectara [Required]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Add documents to vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Add documents to vectorstore.
add_texts(texts: List[str], metadatas: Optional[List[dict]] = None) → None[source]¶
Add text to the Vectara vectorstore.
Parameters
texts (List[str]) – The text
metadatas (List[dict]) – Metadata dicts, must line up with existing store
async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, **kwargs: Any) → List[Document]¶
Asynchronously get documents relevant to a query.
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Asynchronously get documents relevant to a query.
:param query: string to find relevant documents for
:param callbacks: Callback manager or list of callbacks
Returns
List of relevant documents
get_relevant_documents(query: str, *, callbacks: Callbacks = None, **kwargs: Any) → List[Document]¶
Retrieve documents relevant to a query.
:param query: string to find relevant documents for
:param callbacks: Callback manager or list of callbacks
Returns
List of relevant documents
validator validate_search_type » all fields¶
Validate search type.
allowed_search_types: ClassVar[Collection[str]] = ('similarity', 'similarity_score_threshold', 'mmr')¶
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
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langchain.vectorstores.hologres.Hologres¶
class langchain.vectorstores.hologres.Hologres(connection_string: str, embedding_function: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, logger: Optional[Logger] = None)[source]¶
Bases: VectorStore
VectorStore implementation using Hologres.
connection_string is a hologres connection string.
embedding_function any embedding function implementinglangchain.embeddings.base.Embeddings interface.
ndims is the number of dimensions of the embedding output.
table_name is the name of the table to store embeddings and data.(default: langchain_pg_embedding)
- NOTE: The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
pre_delete_table if True, will delete the table if it exists.(default: False)
- Useful for testing.
Methods
__init__(connection_string, embedding_function)
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_embeddings(texts, embeddings, metadatas, ...)
Add embeddings to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
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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)
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 most similar to query.
connection_string_from_db_params(host, port, ...)
Return connection string from database parameters.
create_table()
create_vector_extension()
delete(ids)
Delete by vector ID.
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct Hologres wrapper from raw documents and pre- generated embeddings.
from_existing_index(embedding[, ndims, ...])
Get intsance of an existing Hologres store.This method will return the instance of the store without inserting any new embeddings
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_connection_string(kwargs)
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])
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similarity_search(query[, k, filter])
Run similarity search with Hologres with distance.
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, filter])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
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_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any) → None[source]¶
Add embeddings to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
embeddings – List of list of embedding vectors.
metadatas – List of metadatas associated with the texts.
kwargs – vectorstore specific parameters
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kwargs – vectorstore specific parameters
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 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.
async amax_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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
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Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
classmethod connection_string_from_db_params(host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
create_table() → None[source]¶
create_vector_extension() → None[source]¶
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Hologres[source]¶
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
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“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', ids: Optional[List[str]] = None, pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Construct Hologres wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
Example
from langchain import Hologres
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
faiss = Hologres.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_existing_index(embedding: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Get intsance of an existing Hologres store.This method will
return the instance of the store without inserting any new
embeddings
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', ids: Optional[List[str]] = None, pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
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Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
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
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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[dict] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with Hologres with distance.
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.
Returns
List of Documents most similar to the query.
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 – Embedding to look up documents similar to.
k – 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 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
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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[dict] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – 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
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
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langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch¶
class langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
Bases: DocArrayIndex
Wrapper around in-memory storage for exact search.
To use it, you should have the docarray package with version >=0.32.0 installed.
You can install it with pip install “langchain[docarray]”.
Initialize a vector store from DocArray’s DocIndex.
Methods
__init__(doc_index, embedding)
Initialize a vector store from DocArray's DocIndex.
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.
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)
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])
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asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
delete(ids)
Delete by vector ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_params(embedding[, metric])
Initialize DocArrayInMemorySearch store.
from_texts(texts, embedding[, metadatas])
Create an DocArrayInMemorySearch store and insert data.
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])
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.
Attributes
doc_cls
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.
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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, **kwargs: Any) → List[str]¶
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.
Returns
List of ids from adding the texts into the vectorstore.
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.
async amax_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.
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Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
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_params(embedding: Embeddings, metric: Literal['cosine_sim', 'euclidian_dist', 'sgeuclidean_dist'] = 'cosine_sim', **kwargs: Any) → DocArrayInMemorySearch[source]¶
Initialize DocArrayInMemorySearch store.
Parameters
embedding (Embeddings) – Embedding function.
metric (str) – metric for exact nearest-neighbor search.
Can be one of: “cosine_sim”, “euclidean_dist” and “sqeuclidean_dist”.
Defaults to “cosine_sim”.
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Defaults to “cosine_sim”.
**kwargs – Other keyword arguments to be passed to the get_doc_cls method.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, **kwargs: Any) → DocArrayInMemorySearch[source]¶
Create an DocArrayInMemorySearch store and insert data.
Parameters
texts (List[str]) – Text data.
embedding (Embeddings) – Embedding function.
metadatas (Optional[List[Dict[Any, Any]]]) – Metadata for each text
if it exists. Defaults to None.
metric (str) – metric for exact nearest-neighbor search.
Can be one of: “cosine_sim”, “euclidean_dist” and “sqeuclidean_dist”.
Defaults to “cosine_sim”.
Returns
DocArrayInMemorySearch Vector Store
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.
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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
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, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the 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.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
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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]]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of documents most similar to the query text and
cosine distance in float for each.
Lower score represents more similarity.
property doc_cls: Type[BaseDoc]¶
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langchain.vectorstores.redis.RedisVectorStoreRetriever¶
class langchain.vectorstores.redis.RedisVectorStoreRetriever(*, vectorstore: Redis, search_type: str = 'similarity', search_kwargs: dict = None, k: int = 4, score_threshold: float = 0.4)[source]¶
Bases: VectorStoreRetriever, BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param k: int = 4¶
param score_threshold: float = 0.4¶
param search_kwargs: dict [Optional]¶
param search_type: str = 'similarity'¶
param vectorstore: Redis [Required]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str][source]¶
Add documents to vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str][source]¶
Add documents to vectorstore.
async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, **kwargs: Any) → List[Document]¶
Asynchronously get documents relevant to a query.
:param query: string to find relevant documents for
:param callbacks: Callback manager or list of callbacks
Returns
List of relevant documents
get_relevant_documents(query: str, *, callbacks: Callbacks = None, **kwargs: Any) → List[Document]¶
Retrieve documents relevant to a query.
:param query: string to find relevant documents for
:param callbacks: Callback manager or list of callbacks
Returns
List of relevant documents
validator validate_search_type » all fields[source]¶
Validate search type.
allowed_search_types: ClassVar[Collection[str]] = ('similarity', 'similarity_score_threshold', 'mmr')¶
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.RedisVectorStoreRetriever.html
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91132ea32b8b-1
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model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.RedisVectorStoreRetriever.html
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langchain.vectorstores.azuresearch.AzureSearch¶
class langchain.vectorstores.azuresearch.AzureSearch(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Callable, search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, semantic_query_language: str = 'en-us', **kwargs: Any)[source]¶
Bases: VectorStore
Initialize with necessary components.
Methods
__init__(azure_search_endpoint, ...[, ...])
Initialize with necessary components.
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])
Add texts data to an existing index.
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)
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 most similar to query.
delete(ids)
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Return docs most similar to query.
delete(ids)
Delete by vector ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
hybrid_search(query[, k])
Returns the most similar indexed documents to the query text.
hybrid_search_with_score(query[, k, filters])
Return docs most similar to query with an hybrid query.
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.
semantic_hybrid_search(query[, k])
Returns the most similar indexed documents to the query text.
semantic_hybrid_search_with_score(query[, ...])
Return docs most similar to query with an hybrid query.
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].
vector_search(query[, k])
Returns the most similar indexed documents to the query text.
vector_search_with_score(query[, k, filters])
Return docs most similar to query.
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
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(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, **kwargs: Any) → List[str][source]¶
Add texts data to an existing index.
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.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
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Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
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, azure_search_endpoint: str = '', azure_search_key: str = '', index_name: str = 'langchain-index', **kwargs: Any) → AzureSearch[source]¶
Return VectorStore initialized from texts and embeddings.
hybrid_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
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Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
hybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with an hybrid query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query and score for each
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.
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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
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.
semantic_hybrid_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
semantic_hybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with an hybrid query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query and score for each
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
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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.
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)
vector_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
vector_search_with_score(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
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k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query and score for each
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html
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langchain.vectorstores.milvus.Milvus¶
class langchain.vectorstores.milvus.Milvus(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: Optional[bool] = False)[source]¶
Bases: VectorStore
Wrapper around the Milvus vector database.
Initialize wrapper around the milvus vector database.
In order to use this you need to have pymilvus installed and a
running Milvus/Zilliz Cloud instance.
See the following documentation for how to run a Milvus instance:
https://milvus.io/docs/install_standalone-docker.md
If looking for a hosted Milvus, take a looka this documentation:
https://zilliz.com/cloud
IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
Parameters
embedding_function (Embeddings) – Function used to embed the text.
collection_name (str) – Which Milvus collection to use. Defaults to
“LangChainCollection”.
connection_args (Optional[dict[str, any]]) – The connection args used for
this class comes in the form of a dict, here are a few of the options:
address (str): The actual address of Milvus
instance. Example address: “localhost:19530”
uri (str): The uri of Milvus instance. Example uri:”http://randomwebsite:19530”,
“tcp:foobarsite:19530”,
“https://ok.s3.south.com:19530”.
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“https://ok.s3.south.com:19530”.
host (str): The host of Milvus instance. Default at “localhost”,PyMilvus will fill in the default host if only port is provided.
port (str/int): The port of Milvus instance. Default at 19530, PyMilvuswill fill in the default port if only host is provided.
user (str): Use which user to connect to Milvus instance. If user andpassword are provided, we will add related header in every RPC call.
password (str): Required when user is provided. The passwordcorresponding to the user.
secure (bool): Default is false. If set to true, tls will be enabled.
client_key_path (str): If use tls two-way authentication, need to
write the client.key path.
client_pem_path (str): If use tls two-way authentication, need towrite the client.pem path.
ca_pem_path (str): If use tls two-way authentication, need to writethe ca.pem path.
server_pem_path (str): If use tls one-way authentication, need towrite the server.pem path.
server_name (str): If use tls, need to write the common name.
consistency_level (str) – The consistency level to use for a collection.
Defaults to “Session”.
index_params (Optional[dict]) – Which index params to use. Defaults to
HNSW/AUTOINDEX depending on service.
search_params (Optional[dict]) – Which search params to use. Defaults to
default of index.
drop_old (Optional[bool]) – Whether to drop the current collection. Defaults
to False.
The connection args used for this class comes in the form of a dict,
here are a few of the options:
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here are a few of the options:
address (str): The actual address of Milvusinstance. Example address: “localhost:19530”
uri (str): The uri of Milvus instance. Example uri:“http://randomwebsite:19530”,
“tcp:foobarsite:19530”,
“https://ok.s3.south.com:19530”.
host (str): The host of Milvus instance. Default at “localhost”,PyMilvus will fill in the default host if only port is provided.
port (str/int): The port of Milvus instance. Default at 19530, PyMilvuswill fill in the default port if only host is provided.
user (str): Use which user to connect to Milvus instance. If user andpassword are provided, we will add related header in every RPC call.
password (str): Required when user is provided. The passwordcorresponding to the user.
secure (bool): Default is false. If set to true, tls will be enabled.
client_key_path (str): If use tls two-way authentication, need to
write the client.key path.
client_pem_path (str): If use tls two-way authentication, need towrite the client.pem path.
ca_pem_path (str): If use tls two-way authentication, need to writethe ca.pem path.
server_pem_path (str): If use tls one-way authentication, need towrite the server.pem path.
server_name (str): If use tls, need to write the common name.
Methods
__init__(embedding_function[, ...])
Initialize wrapper around the milvus vector database.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
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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, timeout, ...])
Insert text data into Milvus.
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)
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 most similar to query.
delete(ids)
Delete by vector ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Create a Milvus collection, indexes it with HNSW, and insert data.
max_marginal_relevance_search(query[, k, ...])
Perform a search and return results that are reordered by MMR.
max_marginal_relevance_search_by_vector(...)
Perform a search and return results that are reordered by MMR.
search(query, search_type, **kwargs)
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search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, param, expr, ...])
Perform a similarity search against the query string.
similarity_search_by_vector(embedding[, k, ...])
Perform a similarity search against the query string.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Perform a search on a query string and return results with score.
similarity_search_with_score_by_vector(embedding)
Perform a search on a query string and return results with score.
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, timeout: Optional[int] = None, batch_size: int = 1000, **kwargs: Any) → List[str][source]¶
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html
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Insert text data into Milvus.
Inserting data when the collection has not be made yet will result
in creating a new Collection. The data of the first entity decides
the schema of the new collection, the dim is extracted from the first
embedding and the columns are decided by the first metadata dict.
Metada keys will need to be present for all inserted values. At
the moment there is no None equivalent in Milvus.
Parameters
texts (Iterable[str]) – The texts to embed, it is assumed
that they all fit in memory.
metadatas (Optional[List[dict]]) – Metadata dicts attached to each of
the texts. Defaults to None.
timeout (Optional[int]) – Timeout for each batch insert. Defaults
to None.
batch_size (int, optional) – Batch size to use for insertion.
Defaults to 1000.
Raises
MilvusException – Failure to add texts
Returns
The resulting keys for each inserted element.
Return type
List[str]
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.
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Return docs selected using the maximal marginal relevance.
async amax_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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
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.
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Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'LangChainCollection', connection_args: dict[str, Any] = {'host': 'localhost', 'password': '', 'port': '19530', 'secure': False, 'user': ''}, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: bool = False, **kwargs: Any) → Milvus[source]¶
Create a Milvus collection, indexes it with HNSW, and insert data.
Parameters
texts (List[str]) – Text data.
embedding (Embeddings) – Embedding function.
metadatas (Optional[List[dict]]) – Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional) – Collection name to use. Defaults to
“LangChainCollection”.
connection_args (dict[str, Any], optional) – Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optional) – Which consistency level to use. Defaults
to “Session”.
index_params (Optional[dict], optional) – Which index_params to use. Defaults
to None.
search_params (Optional[dict], optional) – Which search params to use.
Defaults to None.
drop_old (Optional[bool], optional) – Whether to drop the collection with
that name if it exists. Defaults to False.
Returns
Milvus Vector Store
Return type
Milvus
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Returns
Milvus Vector Store
Return type
Milvus
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]¶
Perform a search and return results that are reordered by MMR.
Parameters
query (str) – The text being searched.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
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
param (dict, optional) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() keyword arguments.
Returns
Document results for search.
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, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]¶
Perform a search and return results that are reordered by MMR.
Parameters
embedding (str) – The embedding vector being searched.
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Parameters
embedding (str) – The embedding vector being searched.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
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
param (dict, optional) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() keyword arguments.
Returns
Document results for search.
Return type
List[Document]
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, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search against the query string.
Parameters
query (str) – The text to search.
k (int, optional) – How many results to return. Defaults to 4.
param (dict, optional) – The search params for the index type.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() keyword arguments.
Returns
Document results for search.
Return type
List[Document]
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Returns
Document results for search.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search against the query string.
Parameters
embedding (List[float]) – The embedding vector to search.
k (int, optional) – How many results to return. Defaults to 4.
param (dict, optional) – The search params for the index type.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() keyword arguments.
Returns
Document results for search.
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 – 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, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
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Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Parameters
query (str) – The text being searched.
k (int, optional) – The amount of results ot return. Defaults to 4.
param (dict) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() keyword arguments.
Return type
List[float], List[Tuple[Document, any, any]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Parameters
embedding (List[float]) – The embedding vector being searched.
k (int, optional) – The amount of results ot return. Defaults to 4.
param (dict) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.search() keyword arguments.
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Defaults to None.
kwargs – Collection.search() keyword arguments.
Returns
Result doc and score.
Return type
List[Tuple[Document, float]]
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langchain.vectorstores.matching_engine.MatchingEngine¶
class langchain.vectorstores.matching_engine.MatchingEngine(project_id: str, index: MatchingEngineIndex, endpoint: MatchingEngineIndexEndpoint, embedding: Embeddings, gcs_client: storage.Client, gcs_bucket_name: str, credentials: Optional[Credentials] = None)[source]¶
Bases: VectorStore
Vertex 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/modules/indexes/vectorstores/examples/matchingengine.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.
Vertex 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/modules/indexes/vectorstores/examples/matchingengine.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¶
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gcs_client¶
The GCS client.
gcs_bucket_name¶
The GCS bucket name.
credentials¶
Created GCP credentials.
Type
Optional
Methods
__init__(project_id, index, endpoint, ...[, ...])
Vertex Matching Engine implementation of the vector store.
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.
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)
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 most similar to query.
delete(ids)
Delete by vector ID.
from_components(project_id, region, ...[, ...])
Takes the object creation out of the constructor.
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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])
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].
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]
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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 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.
async amax_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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
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Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
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
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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.
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.
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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.
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.
Parameters
query – The string that will be used to search for similar documents.
k – The amount of neighbors that will be retrieved.
Returns
A list of k matching documents.
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.
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
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