`langchain_community.vectorstores.thirdai_neuraldb`.NeuralDBVectorStore¶

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

Vectorstore that uses ThirdAI’s NeuralDB.

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

Example

from langchain_community.vectorstores import NeuralDBVectorStore
from thirdai import neural_db as ndb

db = ndb.NeuralDB()
vectorstore = NeuralDBVectorStore(db=db)

Attributes

`db`	NeuralDB 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.
`associate`(source, target)	The vectorstore associates a source phrase with a target phrase.
`associate_batch`(text_pairs)	Given a batch of (source, target) pairs, the vectorstore associates each source phrase with the corresponding target phrase.
`delete`([ids])	Delete by vector ID or other criteria.
`from_checkpoint`(checkpoint[, thirdai_key])	Create a NeuralDBVectorStore with a base model from a saved checkpoint
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_scratch`([thirdai_key])	Create a NeuralDBVectorStore from scratch.
`from_texts`(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
`insert`(sources[, train, fast_mode])	Inserts files / document sources into the vectorstore.
`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.
`save`(path)	Saves a NeuralDB instance to disk.
`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.
`upvote`(query, document_id)	The vectorstore upweights the score of a document for a specific query.
`upvote_batch`(query_id_pairs)	Given a batch of (query, document id) pairs, the vectorstore upweights the scores of the document for the corresponding queries.
`validate_environments`(values)	Validate ThirdAI environment variables.

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]

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

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

Parameters

texts (Iterable[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

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]]

associate(source: str, target: str)[source]¶

The vectorstore associates a source phrase with a target phrase. When the vectorstore sees the source phrase, it will also consider results that are relevant to the target phrase.

Parameters

source (str) – text to associate to target.
target (str) – text to associate source to.

associate_batch(text_pairs: List[Tuple[str, str]])[source]¶

Given a batch of (source, target) pairs, the vectorstore associates each source phrase with the corresponding target phrase.

Parameters

text_pairs (List[Tuple[str, str]]) – list of (source, target) text pairs. For each pair in
list (this) –
target. (the source will be associated with the) –

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_checkpoint(checkpoint: Union[str, Path], thirdai_key: Optional[str] = None)[source]¶

Create a NeuralDBVectorStore with a base model from a saved checkpoint

To use, set the THIRDAI_KEY environment variable with your ThirdAI API key, or pass thirdai_key as a named parameter.

Example

from langchain_community.vectorstores import NeuralDBVectorStore

vectorstore = NeuralDBVectorStore.from_checkpoint(
    checkpoint="/path/to/checkpoint.ndb",
    thirdai_key="your-thirdai-key",
)

vectorstore.insert([
    "/path/to/doc.pdf",
    "/path/to/doc.docx",
    "/path/to/doc.csv",
])

documents = vectorstore.similarity_search("AI-driven music therapy")

Parameters

checkpoint (Union[str, Path]) –
thirdai_key (Optional[str]) –

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

classmethod from_scratch(thirdai_key: Optional[str] = None, **model_kwargs)[source]¶

Create a NeuralDBVectorStore from scratch.

To use, set the THIRDAI_KEY environment variable with your ThirdAI API key, or pass thirdai_key as a named parameter.

Example

from langchain_community.vectorstores import NeuralDBVectorStore

vectorstore = NeuralDBVectorStore.from_scratch(
    thirdai_key="your-thirdai-key",
)

vectorstore.insert([
    "/path/to/doc.pdf",
    "/path/to/doc.docx",
    "/path/to/doc.csv",
])

documents = vectorstore.similarity_search("AI-driven music therapy")

Parameters: thirdai_key (Optional[str]) –

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → NeuralDBVectorStore[source]¶

Return VectorStore initialized from texts and embeddings.

Parameters

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

Return type

NeuralDBVectorStore

insert(sources: List[Any], train: bool = True, fast_mode: bool = True, **kwargs)[source]¶

Inserts files / document sources into the vectorstore.

Parameters

train (bool) – When True this means that the underlying model in the
files. (NeuralDB will undergo unsupervised pretraining on the inserted) –
True. (Defaults to) –
fast_mode (bool) – Much faster insertion with a slight drop in performance.
True. –
sources (List[Any]) –

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶

Return docs selected using the maximal marginal relevance.

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

Parameters

query (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]

save(path: str)[source]¶

Saves a NeuralDB instance to disk. Can be loaded into memory by calling NeuralDB.from_checkpoint(path)

Parameters: path (str) – path on disk to save the NeuralDB instance to.

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 = 10, **kwargs: Any) → List[Document][source]¶

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]]

upvote(query: str, document_id: Union[int, str])[source]¶

The vectorstore upweights the score of a document for a specific query. This is useful for fine-tuning the vectorstore to user behavior.

Parameters

query (str) – text to associate with document_id
document_id (Union[int, str]) – id of the document to associate query with.

upvote_batch(query_id_pairs: List[Tuple[str, int]])[source]¶

Given a batch of (query, document id) pairs, the vectorstore upweights the scores of the document for the corresponding queries. This is useful for fine-tuning the vectorstore to user behavior.

Parameters

query_id_pairs (List[Tuple[str, int]]) – list of (query, document id) pairs. For each pair in
list (this) –
query. (the model will upweight the document id for the) –

classmethod validate_environments(values: Dict) → Dict[source]¶

Validate ThirdAI environment variables.

Parameters: values (Dict) –
Return type: Dict

Examples using NeuralDBVectorStore¶

From scratch

langchain_community.vectorstores.thirdai_neuraldb.NeuralDBVectorStore¶

Examples using NeuralDBVectorStore¶

`langchain_community.vectorstores.thirdai_neuraldb`.NeuralDBVectorStore¶