`langchain_community.vectorstores.vdms`.VDMS¶

class langchain_community.vectorstores.vdms.VDMS(client: vdms.vdms, *, embedding: Optional[Embeddings] = None, collection_name: str = 'langchain', distance_strategy: DISTANCE_METRICS = 'L2', engine: ENGINES = 'FaissFlat', relevance_score_fn: Optional[Callable[[float], float]] = None)[source]¶

Intel Lab’s VDMS for vector-store workloads.

To use, you should have both: - the vdms python package installed - a host (str) and port (int) associated with a deployed VDMS Server

Visit https://github.com/IntelLabs/vdms/wiki more information.

IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.

Parameters

client (vdms.vdms) – VDMS Client used to connect to VDMS server
collection_name (str) – Name of data collection [Default: langchain]
distance_strategy (DISTANCE_METRICS) – Method used to calculate distances. VDMS supports “L2” (euclidean distance) or “IP” (inner product) [Default: L2]
engine (ENGINES) – Underlying implementation for indexing and computing distances. VDMS supports TileDBDense, TileDBSparse, FaissFlat, FaissIVFFlat, and Flinng [Default: FaissFlat]
embedding (Optional[Embeddings]) – Any embedding function implementing langchain_core.embeddings.Embeddings interface.
relevance_score_fn (Optional[Callable[[float], float]]) – Function for obtaining relevance score

Example

from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.vdms import VDMS, VDMS_Client

vectorstore = VDMS(
    client=VDMS_Client("localhost", 55555),
    embedding=HuggingFaceEmbeddings(),
    collection_name="langchain-demo",
    distance_strategy="L2",
    engine="FaissFlat",
)

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(client, *[, embedding, ...])
`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_images`(uris[, metadatas, ids, ...])	Run more images through the embeddings and add to the vectorstore.
`add_texts`(texts[, metadatas, ids, batch_size])	Run more texts through the embeddings and add to the vectorstore.
`adelete`([ids])	Delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
`amax_marginal_relevance_search`(query[, k, ...])	Return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
`asimilarity_search`(query[, k])	Return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Return docs and relevance scores in the range [0, 1], asynchronously.
`asimilarity_search_with_score`(args, *kwargs)	Run similarity search with distance asynchronously.
`count`(collection_name)
`decode_image`(base64_image)
`delete`([ids, collection_name, constraints])	Delete by ID.
`encode_image`(image_path)
`from_documents`(documents[, embedding, ids, ...])	Create a VDMS vectorstore from a list of documents.
`from_texts`(texts[, embedding, metadatas, ...])	Create a VDMS vectorstore from a raw documents.
`get`(collection_name[, constraints, limit, ...])	Gets the collection.
`get_descriptor_response`(command_str, setname)
`get_k_candidates`(setname, fetch_k[, ...])
`max_marginal_relevance_search`(query[, k, ...])	Return docs selected using the maximal marginal relevance.
`max_marginal_relevance_search_by_vector`(...)	Return docs selected using the maximal marginal relevance.
`max_marginal_relevance_search_with_score`(query)	Return docs selected using the maximal marginal relevance.
`max_marginal_relevance_search_with_score_by_vector`(...)	Return docs selected using the maximal marginal relevance.
`query_collection_embeddings`([...])
`search`(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
`similarity_search`(query[, k, fetch_k, filter])	Run similarity search with VDMS.
`similarity_search_by_vector`(embedding[, k, ...])	Return docs most similar to embedding vector.
`similarity_search_with_relevance_scores`(query)	Return docs and relevance scores in the range [0, 1].
`similarity_search_with_score`(query[, k, ...])	Run similarity search with VDMS with distance.
`similarity_search_with_score_by_vector`(embedding)	Return docs most similar to embedding vector and similarity score.
`update_document`(collection_name, ...)	Update a document in the collection.
`update_documents`(collection_name, ids, documents)	Update a document in the collection.

__init__(client: vdms.vdms, *, embedding: Optional[Embeddings] = None, collection_name: str = 'langchain', distance_strategy: DISTANCE_METRICS = 'L2', engine: ENGINES = 'FaissFlat', relevance_score_fn: Optional[Callable[[float], float]] = None) → None[source]¶

Parameters

client (vdms.vdms) –
embedding (Optional[Embeddings]) –
collection_name (str) –
distance_strategy (DISTANCE_METRICS) –
engine (ENGINES) –
relevance_score_fn (Optional[Callable[[float], float]]) –

Return type

None

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.
documents (List[Document]) –
kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶

Run more texts through the embeddings and add to the vectorstore.

Parameters

texts (Iterable[str]) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Return type

List[str]

add_documents(documents: List[Document], **kwargs: Any) → List[str]¶

Run more documents through the embeddings and add to the vectorstore.

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.
documents (List[Document]) –
kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

add_images(uris: List[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, add_path: Optional[bool] = True, **kwargs: Any) → List[str][source]¶

Run more images through the embeddings and add to the vectorstore.

Images are added as embeddings (AddDescriptor) instead of separate entity (AddImage) within VDMS to leverage similarity search capability

Parameters

uris (List[str]) – List of paths to the images to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique IDs.
batch_size (int) – Number of concurrent requests to send to the server.
add_path (Optional[bool]) – Bool to add image path as metadata
kwargs (Any) –

Returns

List of ids from adding images into the vectorstore.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) → List[str][source]¶

Run more texts through the embeddings and add to the vectorstore.

Parameters

texts (Iterable[str]) – List of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique IDs.
batch_size (int) – Number of concurrent requests to send to the server.
kwargs (Any) –

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶

Delete by vector ID or other criteria.

Parameters

ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶

Return VectorStore initialized from documents and embeddings.

Parameters

documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –

Return type

VST

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶

Return VectorStore initialized from texts and embeddings.

Parameters

texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Return type

VST

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.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

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.

Parameters

embedding (List[float]) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –

Return type

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever¶

Return VectorStoreRetriever initialized from this VectorStore.

Parameters

search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.
search_kwargs (Optional[Dict]) –
Keyword arguments to pass to the search function. Can include things like:

k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

for similarity_score_threshold

fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

1 for minimum diversity and 0 for maximum. (Default: 0.5)

filter: Filter by document metadata
kwargs (Any) –

Returns

Retriever class for VectorStore.

Return type

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶

Return docs most similar to query using specified search type.

Parameters

query (str) –
search_type (str) –
kwargs (Any) –

Return type

List[Document]

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶

Return docs most similar to query.

Parameters

query (str) –
k (int) –
kwargs (Any) –

Return type

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) –
k (int) –
kwargs (Any) –

Return type

List[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶

Return docs and relevance scores in the range [0, 1], asynchronously.

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]]¶

Run similarity search with distance asynchronously.

Parameters

args (Any) –
kwargs (Any) –

Return type

List[Tuple[Document, float]]

count(collection_name: str) → int[source]¶

Parameters: collection_name (str) –
Return type: int

decode_image(base64_image: str) → bytes[source]¶

Parameters: base64_image (str) –
Return type: bytes

delete(ids: Optional[List[str]] = None, collection_name: Optional[str] = None, constraints: Optional[Dict] = None, **kwargs: Any) → bool[source]¶

Delete by ID. These are the IDs in the vectorstore.

Parameters

ids (Optional[List[str]]) – List of ids to delete.
collection_name (Optional[str]) –
constraints (Optional[Dict]) –
kwargs (Any) –

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

encode_image(image_path: str) → str[source]¶

Parameters: image_path (str) –
Return type: str

classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, batch_size: int = 32, collection_name: str = 'langchain', **kwargs: Any) → VDMS[source]¶

Create a VDMS vectorstore from a list of documents.

Parameters

collection_name (str) – Name of the collection to create.
documents (List[Document]) – List of documents to add to vectorstore.
embedding (Embeddings) – Embedding function. Defaults to None.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
batch_size (int) – Number of concurrent requests to send to the server.
kwargs (Any) –

Returns

VDMS vectorstore.

Return type

VDMS

classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, collection_name: str = 'langchain', **kwargs: Any) → VDMS[source]¶

Create a VDMS vectorstore from a raw documents.

Parameters

texts (List[str]) – List of texts to add to the collection.
embedding (Embeddings) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
batch_size (int) – Number of concurrent requests to send to the server.
collection_name (str) – Name of the collection to create.
kwargs (Any) –

Returns

VDMS vectorstore.

Return type

VDMS

get(collection_name: str, constraints: Optional[Dict] = None, limit: Optional[int] = None, include: List[str] = ['metadata']) → Tuple[Any, Any][source]¶

Gets the collection. Get embeddings and their associated data from the data store. If no constraints provided returns all embeddings up to limit.

Parameters

constraints (Optional[Dict]) – A dict used to filter results by. E.g. {“color” : [“==”, “red”], “price”: [“>”, 4.00]}. Optional.
limit (Optional[int]) – The number of documents to return. Optional.
include (List[str]) – A list of what to include in the results. Can contain “embeddings”, “metadatas”, “documents”. Ids are always included. Defaults to [“metadatas”, “documents”]. Optional.
collection_name (str) –

Return type

Tuple[Any, Any]

get_descriptor_response(command_str: str, setname: str, k_neighbors: int = 3, fetch_k: int = 15, constraints: Optional[dict] = None, results: Optional[Dict[str, Any]] = None, query_embedding: Optional[List[float]] = None, normalize_distance: bool = False) → Tuple[List[Dict[str, Any]], List][source]¶

Parameters

command_str (str) –
setname (str) –
k_neighbors (int) –
fetch_k (int) –
constraints (Optional[dict]) –
results (Optional[Dict[str, Any]]) –
query_embedding (Optional[List[float]]) –
normalize_distance (bool) –

Return type

Tuple[List[Dict[str, Any]], List]

get_k_candidates(setname: str, fetch_k: Optional[int], results: Optional[Dict[str, Any]] = None, all_blobs: Optional[List] = None, normalize: Optional[bool] = False) → Tuple[List[Dict[str, Any]], List, float][source]¶

Parameters

setname (str) –
fetch_k (Optional[int]) –
results (Optional[Dict[str, Any]]) –
all_blobs (Optional[List]) –
normalize (Optional[bool]) –

Return type

Tuple[List[Dict[str, Any]], List, float]

max_marginal_relevance_search(query: str, k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

max_marginal_relevance_search_with_score(query: str, k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Tuple[Document, float]][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.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Tuple[Document, float]]

max_marginal_relevance_search_with_score_by_vector(embedding: List[float], k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Tuple[Document, float]][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.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Tuple[Document, float]]

query_collection_embeddings(query_embeddings: Optional[List[List[float]]] = None, collection_name: Optional[str] = None, n_results: int = 3, fetch_k: int = 15, filter: Optional[Dict[str, Any]] = None, results: Optional[Dict[str, Any]] = None, normalize_distance: bool = False, **kwargs: Any) → List[Tuple[Dict[str, Any], List]][source]¶

Parameters

query_embeddings (Optional[List[List[float]]]) –
collection_name (Optional[str]) –
n_results (int) –
fetch_k (int) –
filter (Optional[Dict[str, Any]]) –
results (Optional[Dict[str, Any]]) –
normalize_distance (bool) –
kwargs (Any) –

Return type

List[Tuple[Dict[str, Any], List]]

search(query: str, search_type: str, **kwargs: Any) → List[Document]¶

Return docs most similar to query using specified search type.

Parameters

query (str) –
search_type (str) –
kwargs (Any) –

Return type

List[Document]

similarity_search(query: str, k: int = 3, fetch_k: int = 15, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶

Run similarity search with VDMS.

Parameters

query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 3.
fetch_k (int) – Number of candidates to fetch for knn (>= k).
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

List of documents most similar to the query text.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 3, fetch_k: int = 15, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶

Return docs most similar to embedding vector. :param embedding: Embedding to look up documents similar to. :type embedding: List[float] :param k: Number of Documents to return. Defaults to 3. :type k: int :param fetch_k: Number of candidates to fetch for knn (>= k). :type fetch_k: int :param filter: Filter by metadata. Defaults to None. :type filter: Optional[Dict[str, str]]

Returns

List of Documents most similar to the query vector.

Parameters

embedding (List[float]) –
k (int) –
fetch_k (int) –
filter (Optional[Dict[str, List]]) –
kwargs (Any) –

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 = 3, fetch_k: int = 15, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Run similarity search with VDMS with distance.

Parameters

query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 3.
fetch_k (int) – Number of candidates to fetch for knn (>= k).
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

Return type

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 3, fetch_k: int = 15, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Return docs most similar to embedding vector and similarity score.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 3.
fetch_k (int) – Number of candidates to fetch for knn (>= k).
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –

Returns

List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

Return type

List[Tuple[Document, float]]

update_document(collection_name: str, document_id: str, document: Document) → None[source]¶

Update a document in the collection.

Parameters

document_id (str) – ID of the document to update.
document (Document) – Document to update.
collection_name (str) –

Return type

None

update_documents(collection_name: str, ids: List[str], documents: List[Document]) → None[source]¶

Update a document in the collection.

Parameters

ids (List[str]) – List of ids of the document to update.
documents (List[Document]) – List of documents to update.
collection_name (str) –

Return type

None

langchain_community.vectorstores.vdms.VDMS¶

Examples using VDMS¶

`langchain_community.vectorstores.vdms`.VDMS¶