langchain_community.vectorstores.azure_cosmos_db
.AzureCosmosDBVectorSearch¶
- class langchain_community.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch(collection: Collection, embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent', application_name: str = 'LANGCHAIN_PYTHON')[source]¶
Azure Cosmos DB for MongoDB vCore vector store.
To use, you should have both: - the
pymongo
python package installed - a connection string associated with a MongoDB VCore ClusterExample
. code-block:: python
from langchain_community.vectorstores import AzureCosmosDBVectorSearch from langchain_community.embeddings.openai import OpenAIEmbeddings from pymongo import MongoClient
mongo_client = MongoClient(“<YOUR-CONNECTION-STRING>”) collection = mongo_client[“<db_name>”][“<collection_name>”] embeddings = OpenAIEmbeddings() vectorstore = AzureCosmosDBVectorSearch(collection, embeddings)
Constructor for AzureCosmosDBVectorSearch
- Parameters
collection (Collection) – MongoDB collection to add the texts to.
embedding (Embeddings) – Text embedding model to use.
index_name (str) – Name of the Atlas Search index.
text_key (str) – MongoDB field that will contain the text for each document.
embedding_key (str) – MongoDB field that will contain the embedding for each document.
application_name (str) –
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__
(collection, embedding, *[, ...])Constructor for AzureCosmosDBVectorSearch
aadd_documents
(documents, **kwargs)Async run more documents through the embeddings and add to the vectorstore.
aadd_texts
(texts[, metadatas])Async run more texts through the embeddings and add to the vectorstore.
add_documents
(documents, **kwargs)Add or update documents in the vectorstore.
add_texts
(texts[, metadatas])Run more texts through the embeddings and add to the vectorstore.
adelete
([ids])Async delete by vector ID or other criteria.
afrom_documents
(documents, embedding, **kwargs)Async return VectorStore initialized from documents and embeddings.
afrom_texts
(texts, embedding[, metadatas])Async return VectorStore initialized from texts and embeddings.
aget_by_ids
(ids, /)Async get documents by their IDs.
amax_marginal_relevance_search
(query[, k, ...])Async return docs selected using the maximal marginal relevance.
Async return docs selected using the maximal marginal relevance.
as_retriever
(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch
(query, search_type, **kwargs)Async return docs most similar to query using a specified search type.
asimilarity_search
(query[, k])Async return docs most similar to query.
asimilarity_search_by_vector
(embedding[, k])Async return docs most similar to embedding vector.
Async return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score
(*args, **kwargs)Async run similarity search with distance.
create_filter_index
(property_to_filter, ...)create_index
([num_lists, dimensions, ...])Creates an index using the index name specified at
delete
([ids])Delete by vector ID or other criteria.
delete_document_by_id
([document_id])Removes a Specific Document by Id
Deletes the index specified during instance construction if it exists
from_connection_string
(connection_string, ...)Creates an Instance of AzureCosmosDBVectorSearch from a Connection String
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_texts
(texts, embedding[, metadatas, ...])Return VectorStore initialized from texts and embeddings.
get_by_ids
(ids, /)Get documents by their IDs.
Returns the index name
Verifies if the specified index name during instance
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
search
(query, search_type, **kwargs)Return docs most similar to query using a specified search type.
similarity_search
(query[, k, kind, ...])Return docs most similar to query.
similarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score
(query[, k, ...])Run similarity search with distance.
- __init__(collection: Collection, embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent', application_name: str = 'LANGCHAIN_PYTHON')[source]¶
Constructor for AzureCosmosDBVectorSearch
- Parameters
collection (Collection) – MongoDB collection to add the texts to.
embedding (Embeddings) – Text embedding model to use.
index_name (str) – Name of the Atlas Search index.
text_key (str) – MongoDB field that will contain the text for each document.
embedding_key (str) – MongoDB field that will contain the embedding for each document.
application_name (str) –
- async aadd_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Async run more documents through the embeddings and add to the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of IDs does not match the number of documents.
- Return type
List[str]
- async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str] ¶
Async run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – vectorstore specific parameters.
- Returns
List of ids from adding the texts into the vectorstore.
- Raises
ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.
- Return type
List[str]
- add_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Add or update documents in the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of ids does not match the number of documents.
- Return type
List[str]
- add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, **kwargs: Any) List [source]¶
Run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[Dict[str, Any]]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters. One of the kwargs should be ids which is a list of ids associated with the texts.
- Returns
List of ids from adding the texts into the vectorstore.
- Raises
ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.
- Return type
List
- async adelete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool] ¶
Async delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.
**kwargs (Any) – Other keyword arguments that subclasses might use.
- Returns
True if deletion is successful, False otherwise, None if not implemented.
- Return type
Optional[bool]
- async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST ¶
Async return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ¶
Async return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from texts and embeddings.
- Return type
- async aget_by_ids(ids: Sequence[str], /) List[Document] ¶
Async get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document] ¶
Async return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
kwargs (Any) –
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- 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] ¶
Async return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- as_retriever(**kwargs: Any) VectorStoreRetriever ¶
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters
**kwargs (Any) –
Keyword arguments to pass to the search function. Can include: search_type (Optional[str]): Defines the type of search that
the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.
- search_kwargs (Optional[Dict]): Keyword arguments to pass to the
- search function. Can include things like:
k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold
for similarity_score_threshold
- fetch_k: Amount of documents to pass to MMR algorithm
(Default: 20)
- lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
- Returns
Retriever class for VectorStore.
- Return type
Examples:
# Retrieve more documents with higher diversity # Useful if your dataset has many similar documents docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25} ) # Fetch more documents for the MMR algorithm to consider # But only return the top 5 docsearch.as_retriever( search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50} ) # Only retrieve documents that have a relevance score # Above a certain threshold docsearch.as_retriever( search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8} ) # Only get the single most similar document from the dataset docsearch.as_retriever(search_kwargs={'k': 1}) # Use a filter to only retrieve documents from a specific paper docsearch.as_retriever( search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}} )
- async asearch(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Async return docs most similar to query using a specified search type.
- Parameters
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Async return docs most similar to query.
- Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Return type
List[Document]
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Async return docs most similar to embedding vector.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query vector.
- Return type
List[Document]
- async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ¶
Async return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
- Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
- Returns
List of Tuples of (doc, similarity_score)
- Return type
List[Tuple[Document, float]]
- async asimilarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]] ¶
Async run similarity search with distance.
- Parameters
*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Tuples of (doc, similarity_score).
- Return type
List[Tuple[Document, float]]
- create_filter_index(property_to_filter: str, index_name: str) dict[str, Any] [source]¶
- Parameters
property_to_filter (str) –
index_name (str) –
- Return type
dict[str, Any]
- create_index(num_lists: int = 100, dimensions: int = 1536, similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS, kind: str = 'vector-ivf', m: int = 16, ef_construction: int = 64) dict[str, Any] [source]¶
- Creates an index using the index name specified at
instance construction
- Setting the numLists parameter correctly is important for achieving
good accuracy and performance. Since the vector store uses IVF as the indexing strategy, you should create the index only after you have loaded a large enough sample documents to ensure that the centroids for the respective buckets are faily distributed.
- We recommend that numLists is set to documentCount/1000 for up
to 1 million documents and to sqrt(documentCount) for more than 1 million documents. As the number of items in your database grows, you should tune numLists to be larger in order to achieve good latency performance for vector search.
If you’re experimenting with a new scenario or creating a small demo, you can start with numLists set to 1 to perform a brute-force search across all vectors. This should provide you with the most accurate results from the vector search, however be aware that the search speed and latency will be slow. After your initial setup, you should go ahead and tune the numLists parameter using the above guidance.
- Parameters
kind (str) –
Type of vector index to create. Possible options are:
vector-ivf
- vector-hnsw: available as a preview feature only,
to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features
num_lists (int) – This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data. We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount) for more than 1 million documents. Using a numLists value of 1 is akin to performing brute-force search, which has limited performance
dimensions (int) – Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000
similarity (CosmosDBSimilarityType) –
Similarity metric to use with the IVF index.
- Possible options are:
CosmosDBSimilarityType.COS (cosine distance),
CosmosDBSimilarityType.L2 (Euclidean distance), and
CosmosDBSimilarityType.IP (inner product).
m (int) – The max number of connections per layer (16 by default, minimum value is 2, maximum value is 100). Higher m is suitable for datasets with high dimensionality and/or high accuracy requirements.
ef_construction (int) – the size of the dynamic candidate list for constructing the graph (64 by default, minimum value is 4, maximum value is 1000). Higher ef_construction will result in better index quality and higher accuracy, but it will also increase the time required to build the index. ef_construction has to be at least 2 * m
- Returns
An object describing the created index
- Return type
dict[str, Any]
- delete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool] [source]¶
Delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.
**kwargs (Any) – Other keyword arguments that subclasses might use.
- Returns
True if deletion is successful, False otherwise, None if not implemented.
- Return type
Optional[bool]
- delete_document_by_id(document_id: Optional[str] = None) None [source]¶
Removes a Specific Document by Id
- Parameters
document_id (Optional[str]) – The document identifier
- Return type
None
- delete_index() None [source]¶
Deletes the index specified during instance construction if it exists
- Return type
None
- classmethod from_connection_string(connection_string: str, namespace: str, embedding: Embeddings, application_name: str = 'LANGCHAIN_PYTHON', **kwargs: Any) AzureCosmosDBVectorSearch [source]¶
Creates an Instance of AzureCosmosDBVectorSearch from a Connection String
- Parameters
connection_string (str) – The MongoDB vCore instance connection string
namespace (str) – The namespace (database.collection)
embedding (Embeddings) – The embedding utility
**kwargs (Any) – Dynamic keyword arguments
application_name (str) –
**kwargs –
- Returns
an instance of the vector store
- Return type
- classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST ¶
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection: Optional[Collection] = None, **kwargs: Any) AzureCosmosDBVectorSearch [source]¶
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.
collection (Optional[Collection]) –
- Returns
VectorStore initialized from texts and embeddings.
- Return type
- get_by_ids(ids: Sequence[str], /) List[Document] ¶
Get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- get_index_name() str [source]¶
Returns the index name
- Returns
Returns the index name
- Return type
str
- index_exists() bool [source]¶
- Verifies if the specified index name during instance
construction exists on the collection
- Returns
- Returns True on success and False if no such index exists
on the collection
- Return type
bool
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, pre_filter: Optional[Dict] = None, ef_search: int = 40, score_threshold: float = 0.0, with_embedding: bool = False, **kwargs: Any) List[Document] [source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
**kwargs (Any) – Arguments to pass to the search method.
kind (CosmosDBVectorSearchType) –
pre_filter (Optional[Dict]) –
ef_search (int) –
score_threshold (float) –
with_embedding (bool) –
**kwargs –
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, pre_filter: Optional[Dict] = None, ef_search: int = 40, score_threshold: float = 0.0, with_embedding: bool = False, **kwargs: Any) List[Document] [source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
**kwargs (Any) – Arguments to pass to the search method.
kind (CosmosDBVectorSearchType) –
pre_filter (Optional[Dict]) –
ef_search (int) –
score_threshold (float) –
with_embedding (bool) –
**kwargs –
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- search(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Return docs most similar to query using a specified search type.
- Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- similarity_search(query: str, k: int = 4, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, pre_filter: Optional[Dict] = None, ef_search: int = 40, score_threshold: float = 0.0, with_embedding: bool = False, **kwargs: Any) List[Document] [source]¶
Return docs most similar to query.
- Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
kind (CosmosDBVectorSearchType) –
pre_filter (Optional[Dict]) –
ef_search (int) –
score_threshold (float) –
with_embedding (bool) –
**kwargs –
- Returns
List of Documents most similar to the query.
- Return type
List[Document]
- similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query vector.
- Return type
List[Document]
- similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
- Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs.
- Returns
List of Tuples of (doc, similarity_score).
- Return type
List[Tuple[Document, float]]
- similarity_search_with_score(query: str, k: int = 4, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, pre_filter: Optional[Dict] = None, ef_search: int = 40, score_threshold: float = 0.0, with_embedding: bool = False) List[Tuple[Document, float]] [source]¶
Run similarity search with distance.
- Parameters
*args – Arguments to pass to the search method.
**kwargs – Arguments to pass to the search method.
query (str) –
k (int) –
kind (CosmosDBVectorSearchType) –
pre_filter (Optional[Dict]) –
ef_search (int) –
score_threshold (float) –
with_embedding (bool) –
- Returns
List of Tuples of (doc, similarity_score).
- Return type
List[Tuple[Document, float]]