langchain_community.vectorstores.thirdai_neuraldb.NeuralDBClientVectorStore¶

class langchain_community.vectorstores.thirdai_neuraldb.NeuralDBClientVectorStore(db: Any)[source]¶

Vectorstore that uses ThirdAI’s NeuralDB Enterprise Python Client for NeuralDBs.

To use, you should have the thirdai[neural_db] python package installed.

Example

from langchain_community.vectorstores import NeuralDBClientVectorStore
from thirdai.neural_db import ModelBazaar, NeuralDBClient

bazaar = ModelBazaar(base_url="http://{NEURAL_DB_ENTERPRISE_IP}/api/")
bazaar.log_in(email="user@thirdai.com", password="1234")

ndb_client = NeuralDBClient(
    deployment_identifier="user/model-0:user/deployment-0",
    base_url="http://{NEURAL_DB_ENTERPRISE_IP}/api/",
    bazaar=bazaar
)
vectorstore = NeuralDBClientVectorStore(db=ndb_client)
retriever = vectorstore.as_retriever(search_kwargs={'k':5})

Attributes

db

NeuralDB Client instance

embeddings

Access the query embedding object if available.

Methods

__init__(db)

aadd_documents(documents, **kwargs)

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

aadd_texts(texts[, metadatas])

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

add_documents(documents, **kwargs)

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

add_texts(texts[, metadatas])

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

adelete([ids])

Delete by vector ID or other criteria.

afrom_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

afrom_texts(texts, embedding[, metadatas])

Return VectorStore initialized from texts and embeddings.

amax_marginal_relevance_search(query[, k, ...])

Return docs selected using the maximal marginal relevance.

amax_marginal_relevance_search_by_vector(...)

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs)

Return VectorStoreRetriever initialized from this VectorStore.

asearch(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

asimilarity_search(query[, k])

Return docs most similar to query.

asimilarity_search_by_vector(embedding[, k])

Return docs most similar to embedding vector.

asimilarity_search_with_relevance_scores(query)

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

asimilarity_search_with_score(*args, **kwargs)

Run similarity search with distance asynchronously.

delete([ids])

Delete by vector ID or other criteria.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas])

Return VectorStore initialized from texts and embeddings.

insert(documents)

Inserts documents into the VectorStore and return the corresponding Sources.

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.

remove_documents(source_ids)

Deletes documents from the VectorStore using source ids.

search(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

similarity_search(query[, k])

Retrieve {k} contexts with for a given 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(*args, **kwargs)

Run similarity search with distance.
Parameters

db (Any) –

__init__(db: Any) → None[source]¶
Parameters

db (Any) –

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]

abstract 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[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]

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

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]

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]]

delete(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]

classmethod from_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

abstract classmethod from_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

insert(documents: List[Dict[str, Any]])[source]¶

Inserts documents into the VectorStore and return the corresponding Sources.

Parameters

  • documents (List[Dict[str, Any]]) – A list of dictionaries that

  • VectorStores. (represent documents to be inserted to the) –

  • format (The document dictionaries must be in the following) –

  • {"document_type" – “DOCUMENT_TYPE”, **kwargs} where “DOCUMENT_TYPE”

  • following (is one of the) –

  • "PDF" –

  • "CSV" –

  • "DOCX" –

  • "URL" –

  • "SentenceLevelPDF" –

  • "SentenceLevelDOCX" –

:param : :param “Unstructured”: :param “InMemoryText”.: :param The kwargs for each document type are shown below: :param class PDF: document_type: Literal[“PDF”]

path: str metadata: Optional[dict[str, Any]] = None on_disk: bool = False version: str = “v1” chunk_size: int = 100 stride: int = 40 emphasize_first_words: int = 0 ignore_header_footer: bool = True ignore_nonstandard_orientation: bool = True

Parameters

  • CSV (class) – document_type: Literal[“CSV”] path: str id_column: Optional[str] = None strong_columns: Optional[List[str]] = None weak_columns: Optional[List[str]] = None reference_columns: Optional[List[str]] = None save_extra_info: bool = True metadata: Optional[dict[str, Any]] = None has_offset: bool = False on_disk: bool = False

  • DOCX (class) – document_type: Literal[“DOCX”] path: str metadata: Optional[dict[str, Any]] = None on_disk: bool = False

  • URL (class) – document_type: Literal[“URL”] url: str save_extra_info: bool = True title_is_strong: bool = False metadata: Optional[dict[str, Any]] = None on_disk: bool = False

  • SentenceLevelPDF (class) – document_type: Literal[“SentenceLevelPDF”] path: str metadata: Optional[dict[str, Any]] = None on_disk: bool = False

  • SentenceLevelDOCX (class) – document_type: Literal[“SentenceLevelDOCX”] path: str metadata: Optional[dict[str, Any]] = None on_disk: bool = False

  • Unstructured (class) – document_type: Literal[“Unstructured”] path: str save_extra_info: bool = True metadata: Optional[dict[str, Any]] = None on_disk: bool = False

  • InMemoryText (class) – document_type: Literal[“InMemoryText”] name: str texts: list[str] metadatas: Optional[list[dict[str, Any]]] = None global_metadata: Optional[dict[str, Any]] = None on_disk: bool = False

  • "path" (For Document types with the arg) –

  • that (ensure) –

  • machine. (the path exists on your local) –

  • documents (List[Dict[str, Any]]) –

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]

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 (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]

remove_documents(source_ids: List[str])[source]¶

Deletes documents from the VectorStore using source ids.

Parameters

  • files (List[str]) – A list of source ids to delete from the VectorStore.

  • source_ids (List[str]) –

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]

Retrieve {k} contexts with for a given query

Parameters

  • query (str) – Query to submit to the model

  • k (int) – The max number of context results to retrieve. Defaults to 10.

  • kwargs (Any) –

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) –

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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶

Run similarity search with distance.

Parameters

  • args (Any) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]