ZepVectorStore#

class langchain_community.vectorstores.zep.ZepVectorStore(collection_name: str, api_url: str, *, api_key: str | None = None, config: CollectionConfig | None = None, embedding: Embeddings | None = 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)	Async 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)	Add or update documents in the vectorstore.
`add_texts`(texts[, metadatas, document_ids])	Run more texts through the embeddings and add to the vectorstore.
`adelete`([ids])	Async delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`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)	Async run similarity search with distance.
`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.
`get_by_ids`(ids, /)	Get documents by their IDs.
`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: str | None = None, config: CollectionConfig | None = None, embedding: Embeddings | None = None) → None[source]#

Parameters:

collection_name (str) –
api_url (str) –
api_key (str | None) –
config (CollectionConfig | None) –
embedding (Embeddings | None) –

Return type:

None

async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

List[str]

async aadd_texts(texts: Iterable[str], metadatas: List[Dict[str, Any]] | None = None, document_ids: List[str] | None = None, **kwargs: Any) → List[str][source]#

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

Parameters:

texts (Iterable[str]) –
metadatas (List[Dict[str, Any]] | None) –
document_ids (List[str] | None) –
kwargs (Any) –

Return type:

List[str]

add_documents(documents: List[Document], **kwargs: Any) → List[str]#

Add or update documents in the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

List[str]

add_texts(texts: Iterable[str], metadatas: List[Dict[str, Any]] | None = None, document_ids: List[str] | None = 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 (List[Dict[str, Any]] | None) – Optional list of metadatas associated with the texts.
document_ids (List[str] | None) – 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: List[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.
**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#

Async return VectorStore initialized from documents and embeddings.

Parameters:

documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

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

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → List[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

New in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, metadata: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Parameters:

query (str) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
metadata (Dict[str, Any] | None) –
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: Dict[str, Any] | None = 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 (Dict[str, Any] | None) –
kwargs (Any) –

Return type:

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever#

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: 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, metadata: Dict[str, Any] | None = 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 (Dict[str, Any] | None) –
k (int) –
kwargs (Any) –

Return type:

List[Document]

async asimilarity_search(query: str, k: int = 4, metadata: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to query.

Parameters:

query (str) –
k (int) –
metadata (Dict[str, Any] | None) –
kwargs (Any) –

Return type:

List[Document]

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

Return docs most similar to embedding vector.

Parameters:

embedding (List[float]) –
k (int) –
metadata (Dict[str, Any] | None) –
kwargs (Any) –

Return type:

List[Document]

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

Return docs most similar to query.

Parameters:

query (str) –
k (int) –
metadata (Dict[str, Any] | None) –
kwargs (Any) –

Return type:

List[Tuple[Document, float]]

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

Async run similarity search with distance.

Parameters:

*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

delete(ids: List[str] | None = 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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

classmethod from_texts(texts: List[str], embedding: Embeddings | None = None, metadatas: List[dict] | None = None, collection_name: str = '', api_url: str = '', api_key: str | None = None, config: CollectionConfig | None = 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 (Any) – Additional parameters specific to the vectorstore.

Returns:

An instance of ZepVectorStore.

Return type:

ZepVectorStore

get_by_ids(ids: Sequence[str], /) → List[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

New in version 0.2.11.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, metadata: Dict[str, Any] | None = 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:

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 (Dict[str, Any] | None) – 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: Dict[str, Any] | None = 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 (Dict[str, Any] | None) – 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: Dict[str, Any] | None = 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 (Dict[str, Any] | None) –
k (int) –
kwargs (Any) –

Return type:

List[Document]

similarity_search(query: str, k: int = 4, metadata: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to query.

Parameters:

query (str) –
k (int) –
metadata (Dict[str, Any] | None) –
kwargs (Any) –

Return type:

List[Document]

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

Run similarity search with distance.

Parameters:

query (str) –
k (int) –
metadata (Dict[str, Any] | None) –
kwargs (Any) –

Return type:

List[Tuple[Document, float]]

Examples using ZepVectorStore

Zep