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].

asimilarity_search_with_score(*args, **kwargs)

Run similarity search with distance.

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

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

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

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.

  • **kwargs (Any) – vectorstore specific parameters.

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

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

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

Parameters
  • documents (List[Document]) – Documents to add to the vectorstore.

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

add_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]) – List of Documents to add to the vectorstore.

  • embedding (Embeddings) – Embedding function to use.

  • 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]) – Texts to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

  • embedding (Embeddings) – Embedding function to use.

  • kwargs (Any) –

Return type

VST

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.

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.

  • kwargs (Any) –

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
  • 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) – Input text.

  • search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

  • **kwargs (Any) – Arguments to pass to the search method.

Return type

List[Document]

Return docs most similar to query.

Parameters
  • query (str) – Input text.

  • k (int) – Number of Documents to return. Defaults to 4.

  • kwargs (Any) –

Returns

List of Documents most similar to the query.

Return type

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **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) –

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]]

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]]

Run similarity search with distance.

Returns

List of Tuples of (doc, similarity_score)

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]

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) – Input text

  • search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

  • **kwargs (Any) – Arguments to pass to the search method.

Return type

List[Document]

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

Examples using VDMS