langchain.vectorstores.bageldb.Bagel

class langchain.vectorstores.bageldb.Bagel(cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, embedding_function: Optional[Embeddings] = None, cluster_metadata: Optional[Dict] = None, client: Optional[bagel.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)[source]

BagelDB.ai vector store.

To use, you should have the betabageldb python package installed.

Example

from langchain.vectorstores import Bagel
vectorstore = Bagel(cluster_name="langchain_store")

Initialize with bagel client

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__([cluster_name, client_settings, ...])

Initialize with bagel client

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, ids, embeddings])

Add texts along with their corresponding embeddings and optional metadata to the BagelDB cluster.

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 IDs.

delete_cluster()

Delete the cluster.

from_documents(documents[, embedding, ids, ...])

Create a Bagel vectorstore from a list of documents.

from_texts(texts[, embedding, metadatas, ...])

Create and initialize a Bagel instance from list of texts.

get([ids, where, limit, offset, ...])

Gets the collection.

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.

search(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

similarity_search(query[, k, where])

Run a similarity search with BagelDB.

similarity_search_by_vector(embedding[, k, ...])

Return docs most similar to embedding vector.

similarity_search_by_vector_with_relevance_scores(...)

Return docs most similar to embedding vector and similarity score.

similarity_search_with_relevance_scores(query)

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

similarity_search_with_score(query[, k, where])

Run a similarity search with BagelDB and return documents with their corresponding similarity scores.

update_document(document_id, document)

Update a document in the cluster.

__init__(cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, embedding_function: Optional[Embeddings] = None, cluster_metadata: Optional[Dict] = None, client: Optional[bagel.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) None[source]

Initialize with bagel client

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.

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.

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.

Returns

List of IDs of the added texts.

Return type

List[str]

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

Add texts along with their corresponding embeddings and optional metadata to the BagelDB cluster.

Parameters
  • texts (Iterable[str]) – Texts to be added.

  • embeddings (Optional[List[float]]) – List of embeddingvectors

  • metadatas (Optional[List[dict]]) – Optional list of metadatas.

  • ids (Optional[List[str]]) – List of unique ID for the texts.

Returns

List of unique ID representing the added texts.

Return type

List[str]

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

Delete by vector ID or other criteria.

Parameters
  • ids – List of ids to delete.

  • **kwargs – 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.

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

Return VectorStore initialized from texts and embeddings.

Return docs selected using the maximal marginal relevance.

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.

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

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.

Return docs most similar to query.

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

Return docs most similar to embedding vector.

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 – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs

    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)

async asimilarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]]

Run similarity search with distance asynchronously.

delete(ids: Optional[List[str]] = None, **kwargs: Any) None[source]

Delete by IDs.

Parameters

ids – List of ids to delete.

delete_cluster() None[source]

Delete the cluster.

classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, client: Optional[bagel.Client] = None, cluster_metadata: Optional[Dict] = None, **kwargs: Any) Bagel[source]

Create a Bagel vectorstore from a list of documents.

Parameters
  • documents (List[Document]) – List of Document objects to add to the Bagel vectorstore.

  • embedding (Optional[List[float]]) – List of embedding.

  • ids (Optional[List[str]]) – List of IDs. Defaults to None.

  • cluster_name (str) – The name of the BagelDB cluster.

  • client_settings (Optional[bagel.config.Settings]) – Client settings.

  • client (Optional[bagel.Client]) – Bagel client instance.

  • cluster_metadata (Optional[Dict]) – Metadata associated with the Bagel cluster. Defaults to None.

Returns

Bagel vectorstore.

Return type

Bagel

classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, cluster_name: str = 'langchain', client_settings: Optional[bagel.config.Settings] = None, cluster_metadata: Optional[Dict] = None, client: Optional[bagel.Client] = None, text_embeddings: Optional[List[List[float]]] = None, **kwargs: Any) Bagel[source]

Create and initialize a Bagel instance from list of texts.

Parameters
  • texts (List[str]) – List of text content to be added.

  • cluster_name (str) – The name of the BagelDB cluster.

  • client_settings (Optional[bagel.config.Settings]) – Client settings.

  • cluster_metadata (Optional[Dict]) – Metadata of the cluster.

  • embeddings (Optional[Embeddings]) – List of embedding.

  • metadatas (Optional[List[dict]]) – List of metadata.

  • ids (Optional[List[str]]) – List of unique ID. Defaults to None.

  • client (Optional[bagel.Client]) – Bagel client instance.

Returns

Bagel vectorstore.

Return type

Bagel

get(ids: Optional[OneOrMany[ID]] = None, where: Optional[Where] = None, limit: Optional[int] = None, offset: Optional[int] = None, where_document: Optional[WhereDocument] = None, include: Optional[List[str]] = None) Dict[str, Any][source]

Gets the collection.

Return docs selected using the maximal marginal relevance.

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

Parameters
  • query – Text to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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 – Embedding to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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

Return docs most similar to query using specified search type.

Run a similarity search with BagelDB.

Parameters
  • query (str) – The query text to search for similar documents/texts.

  • k (int) – The number of results to return.

  • where (Optional[Dict[str, str]]) – Metadata filters to narrow down.

Returns

List of documents objects representing the documents most similar to the query text.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document][source]

Return docs most similar to embedding vector.

similarity_search_by_vector_with_relevance_scores(query_embeddings: List[float], k: int = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Return docs most similar to embedding vector and similarity score.

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 – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs

    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)

similarity_search_with_score(query: str, k: int = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Run a similarity search with BagelDB and return documents with their corresponding similarity scores.

Parameters
  • query (str) – The query text to search for similar documents.

  • k (int) – The number of results to return.

  • where (Optional[Dict[str, str]]) – Filter using metadata.

Returns

List of tuples, each containing a Document object representing a similar document and its corresponding similarity score.

Return type

List[Tuple[Document, float]]

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

Update a document in the cluster.

Parameters
  • document_id (str) – ID of the document to update.

  • document (Document) – Document to update.

Examples using Bagel