langchain_community.vectorstores.bageldb
.Bagel¶
- class langchain_community.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_community.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.
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.
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 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.
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.
Return docs most similar to embedding vector and similarity score.
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.
- 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.
- 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
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.
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
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.
- 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
- 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
- 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.
- 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 – 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.
- similarity_search(query: str, k: int = 5, where: Optional[Dict[str, str]] = None, **kwargs: Any) List[Document] [source]¶
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]]