Tony20100/langchain_full huggingface dataset:
Possible options are:
CosmosDBSimilarityType.COS (cosine distance),
CosmosDBSimilarityType.L2 (Euclidean distance), and
CosmosDBSimilarityType.IP (inner product).
m (int) – The max number of connections per layer (16 by default, minimum
value is 2, maximum value is 100). Higher m is suitable for datasets
with high dimensionality and/or high accuracy requirements.
ef_construction (int) – the size of the dynamic candidate list for constructing
the graph (64 by default, minimum value is 4, maximum
value is 1000). Higher ef_construction will result in
better index quality and higher accuracy, but it will
also increase the time required to build the index.
ef_construction has to be at least 2 * m
Returns
An object describing the created index
Return type
dict[str, Any]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
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]
delete_document_by_id(document_id: Optional[str] = None) → None[source]¶
Removes a Specific Document by Id
Parameters
document_id (Optional[str]) – The document identifier
Return type
None
delete_index() → None[source]¶
Deletes the index specified during instance construction if it exists
Return type
None
classmethod from_connection_string(connection_string: str, namespace: str, embedding: Embeddings, application_name: str = 'LANGCHAIN_PYTHON', **kwargs: Any) → AzureCosmosDBVectorSearch[source]¶
Creates an Instance of AzureCosmosDBVectorSearch from a Connection String
Parameters
connection_string (str) – The MongoDB vCore instance connection string
namespace (str) – The namespace (database.collection)
embedding (Embeddings) – The embedding utility
**kwargs (Any) – Dynamic keyword arguments
application_name (str) – 
**kwargs – 
Returns
an instance of the vector store
Return type
AzureCosmosDBVectorSearch
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) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection: Optional[Collection[CosmosDBDocumentType]] = None, **kwargs: Any) → AzureCosmosDBVectorSearch[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
collection (Optional[Collection[CosmosDBDocumentType]]) – 
kwargs (Any) – 
Return type
AzureCosmosDBVectorSearch
get_collection() → Collection[CosmosDBDocumentType][source]¶
Return type
Collection[CosmosDBDocumentType]
get_index_name() → str[source]¶
Returns the index name
Returns
Returns the index name
Return type
str
index_exists() → bool[source]¶
Returns the index name
Return type
str
index_exists() → bool[source]¶
Verifies if the specified index name during instanceconstruction exists on the collection
Returns
Returns True on success and False if no such index existson the collection
Return type
bool
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, ef_search: int = 40, score_threshold: float = 0.0, **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.
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.
kind (CosmosDBVectorSearchType) – 
ef_search (int) – 
score_threshold (float) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, ef_search: int = 40, score_threshold: float = 0.0, **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.
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.
kind (CosmosDBVectorSearchType) – 
ef_search (int) – 
score_threshold (float) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
Return type
List[Document]
similarity_search(query: str, k: int = 4, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, ef_search: int = 40, score_threshold: float = 0.0, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
kind (CosmosDBVectorSearchType) – 
ef_search (int) – 
score_threshold (float) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query.
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
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, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, ef_search: int = 40, score_threshold: float = 0.0) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) – 
kind (CosmosDBVectorSearchType) – 
ef_search (int) – 
score_threshold (float) – 
Return type
List[Tuple[Document, float]]
Examples using AzureCosmosDBVectorSearch¶
Azure Cosmos DB
Microsoft
langchain_community.vectorstores.zep_cloud.ZepCloudVectorStore¶
class langchain_community.vectorstores.zep_cloud.ZepCloudVectorStore(collection_name: str, api_key: str)[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
collection_name (str) – The name of the collection in the Zep store.
api_key (str) – The API key for the Zep API.
Attributes
embeddings
Unavailable for ZepCloud
Methods
__init__(collection_name, api_key)
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(...)
Unsupported in Zep Cloud
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type[, metadata, k])
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, ...])
Unsupported in Zep Cloud
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
asimilarity_search_with_score(*args, **kwargs)
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.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Unsupported in Zep Cloud
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, ...])
Unsupported in Zep Cloud
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_key: str) → None[source]¶
Parameters
collection_name (str) – 
api_key (str) – 
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
documents (List[Document]) – Documents to add to the vectorstore.
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
documents (List[Document]) – Documents to add to the vectorstore.
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
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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
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]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async amax_marginal_relevance_search(query: str, 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
query (str) –
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]¶
Unsupported in Zep Cloud
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
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]
kwargs (Any) – 
Return type
List[Document]
async asimilarity_search(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
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]¶
Unsupported in Zep Cloud
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.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = '', api_key: Optional[str] = None, **kwargs: Any) → ZepCloudVectorStore[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.
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_key (str) – The API key for the Zep API.
**kwargs – Additional parameters specific to the vectorstore.
embedding (Embeddings) – 
Returns
An instance of ZepVectorStore.
Return type
ZepVectorStore
Returns
An instance of ZepVectorStore.
Return type
ZepVectorStore
max_marginal_relevance_search(query: str, 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
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]¶
Unsupported in Zep Cloud
Parameters
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
kwargs (Any) – 
Return type
List[Document]
search(query: str, search_type: SearchType, 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 (SearchType) – 
metadata (Optional[Dict[str, Any]]) – 
k (int) – 
kwargs (Any) – 
Return type
List[Document]
similarity_search(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
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]¶
Unsupported in Zep Cloud
Parameters
embedding (List[float]) – 
k (int) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
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.
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 ZepCloudVectorStore¶
Zep
Zep Cloud