langchain_community.vectorstores.zep.ZepVectorStore¶

class langchain_community.vectorstores.zep.ZepVectorStore(collection_name: str, api_url: str, *, api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, embedding: Optional[Embeddings] = None)[source]¶

Zep vector store.

It provides methods for adding texts or documents to the store, searching for similar documents, and deleting documents.

Search scores are calculated using cosine similarity normalized to [0, 1].

Parameters

  • api_url (str) – The URL of the Zep API.

  • collection_name (str) – The name of the collection in the Zep store.

  • api_key (Optional[str]) – The API key for the Zep API.

  • config (Optional[CollectionConfig]) – The configuration for the collection. Required if the collection does not already exist.

  • embedding (Optional[Embeddings]) – Optional embedding function to use to embed the texts. Required if the collection is not auto-embedded.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(collection_name, api_url, *[, ...])

aadd_documents(documents, **kwargs)

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

aadd_texts(texts[, metadatas, document_ids])

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, document_ids])

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[, metadata, k])

Return docs most similar to query using specified search type.

asimilarity_search(query[, k, metadata])

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 most similar to query.

asimilarity_search_with_score(*args, **kwargs)

Run similarity search with distance asynchronously.

delete([ids])

Delete by Zep vector UUIDs.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

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

Class method that returns a ZepVectorStore instance initialized from texts.

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[, metadata, k])

Return docs most similar to query using specified search type.

similarity_search(query[, k, metadata])

Return docs most similar to 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(query[, k, ...])

Run similarity search with distance.
__init__(collection_name: str, api_url: str, *, api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, embedding: Optional[Embeddings] = None) → None[source]¶
Parameters

  • collection_name (str) –

  • api_url (str) –

  • api_key (Optional[str]) –

  • config (Optional[CollectionConfig]) –

  • embedding (Optional[Embeddings]) –

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[str, Any]]] = None, document_ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶

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

Parameters

  • texts (Iterable[str]) –

  • metadatas (Optional[List[Dict[str, Any]]]) –

  • document_ids (Optional[List[str]]) –

  • 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[str, Any]]] = None, document_ids: Optional[List[str]] = 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[str, Any]]]) – Optional list of metadatas associated with the texts.

  • document_ids (Optional[List[str]]) – Optional list of document ids 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.

Parameters

  • query (str) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶

Return docs selected using the maximal marginal relevance.

Parameters

  • embedding (List[float]) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • metadata (Optional[Dict[str, Any]]) –

  • 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, metadata: Optional[Dict[str, Any]] = None, k: int = 3, **kwargs: Any) → List[Document][source]¶

Return docs most similar to query using specified search type.

Parameters

  • query (str) –

  • search_type (str) –

  • metadata (Optional[Dict[str, Any]]) –

  • k (int) –

  • kwargs (Any) –

Return type

List[Document]

Return docs most similar to query.

Parameters

  • query (str) –

  • k (int) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

Return type

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶

Return docs most similar to embedding vector.

Parameters

  • embedding (List[float]) –

  • k (int) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

Return type

List[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Return docs most similar to query.

Parameters

  • query (str) –

  • k (int) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

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) → None[source]¶

Delete by Zep vector UUIDs.

Parameters

  • ids (Optional[List[str]]) – The UUIDs of the vectors to delete.

  • kwargs (Any) –

Raises

ValueError – If no UUIDs are provided.

Return type

None

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_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, collection_name: str = '', api_url: str = '', api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, **kwargs: Any) → ZepVectorStore[source]¶

Class method that returns a ZepVectorStore instance initialized from texts.

If the collection does not exist, it will be created.

Parameters

  • texts (List[str]) – The list of texts to add to the vectorstore.

  • embedding (Optional[Embeddings]) – Optional embedding function to use to embed the texts.

  • metadatas (Optional[List[Dict[str, Any]]]) – Optional list of metadata associated with the texts.

  • collection_name (str) – The name of the collection in the Zep store.

  • api_url (str) – The URL of the Zep API.

  • api_key (Optional[str]) – The API key for the Zep API.

  • config (Optional[CollectionConfig]) – The configuration for the collection.

  • **kwargs – Additional parameters specific to the vectorstore.

Returns

An instance of ZepVectorStore.

Return type

ZepVectorStore

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. Zep determines this automatically and this parameter is

    ignored.

  • 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.

  • metadata (Optional[Dict[str, Any]]) – Optional, metadata to filter the resulting set of retrieved docs

  • 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, metadata: Optional[Dict[str, Any]] = 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. Zep determines this automatically and this parameter is

    ignored.

  • 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.

  • metadata (Optional[Dict[str, Any]]) – Optional, metadata to filter the resulting set of retrieved docs

  • kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

search(query: str, search_type: str, metadata: Optional[Dict[str, Any]] = None, k: int = 3, **kwargs: Any) → List[Document][source]¶

Return docs most similar to query using specified search type.

Parameters

  • query (str) –

  • search_type (str) –

  • metadata (Optional[Dict[str, Any]]) –

  • k (int) –

  • kwargs (Any) –

Return type

List[Document]

Return docs most similar to query.

Parameters

  • query (str) –

  • k (int) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶

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.

  • metadata (Optional[Dict[str, Any]]) – Optional, metadata filter

  • 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(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Run similarity search with distance.

Parameters

  • query (str) –

  • k (int) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

Examples using ZepVectorStore¶