Tony20100/langchain_full huggingface dataset:
needed (functions are) – 
index_url (Optional[str]) – URL of the UpstashVector index.
index_token (Optional[str]) – Token of the UpstashVector index.
embedding (Optional[Union[Embeddings, bool]]) – Embeddings object or a boolean. When false, no embedding
is applied. If true, Upstash embeddings are used. When Upstash
embeddings are used, text is sent directly to Upstash and
embedding is applied there instead of embedding in Langchain.
namespace (str) – Namespace to use from the index.
Example
from langchain_community.vectorstores.upstash import UpstashVectorStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = UpstashVectorStore(
    embedding=embeddings,
    index_url="...",
    index_token="...",
    namespace="..."
)
# With an existing index
from upstash_vector import Index
index = Index(url="...", token="...")
vectorstore = UpstashVectorStore(
    embedding=embeddings,
    index=index,
    namespace="..."
)
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([text_key, index, async_index, ...])
Constructor for UpstashVectorStore.
aadd_documents(documents[, ids, batch_size, ...])
Get the embeddings for the documents and add them to the vectorstore.
aadd_texts(texts[, metadatas, ids, ...])
Get the embeddings for the texts and add them to the vectorstore.
add_documents(documents[, ids, batch_size, ...])
Get the embeddings for the documents and add them to the vectorstore.
Get the embeddings for the documents and add them to the vectorstore.
add_texts(texts[, metadatas, ids, ...])
Get the embeddings for the texts and add them to the vectorstore.
adelete([ids, delete_all, batch_size, namespace])
Delete by vector IDs
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas, ...])
Create a new UpstashVectorStore from a list of texts.
ainfo()
Get statistics about the index.
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, filter, namespace])
Return documents most similar to query.
asimilarity_search_by_vector(embedding[, k, ...])
Return documents closest to the given embedding.
asimilarity_search_by_vector_with_score(...)
Return texts whose embedding is closest to the given embedding
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(query[, k, ...])
Retrieve texts most similar to query and convert the result to Document objects.
delete([ids, delete_all, batch_size, namespace])
Delete by vector IDs
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Create a new UpstashVectorStore from a list of texts.
info()
Get statistics about the index.
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, filter, namespace])
Return documents most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return documents closest to the given embedding.
similarity_search_by_vector_with_score(embedding)
Return texts whose embedding is closest to the given embedding
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Retrieve texts most similar to query and convert the result to Document objects.
__init__(text_key: str = 'text', index: Optional[Index] = None, async_index: Optional[AsyncIndex] = None, index_url: Optional[str] = None, index_token: Optional[str] = None, embedding: Optional[Union[Embeddings, bool]] = None, *, namespace: str = '')[source]¶
Constructor for UpstashVectorStore.
If index or index_url and index_token are not provided, the constructor will
attempt to create an index using the environment variables
UPSTASH_VECTOR_REST_URL`and `UPSTASH_VECTOR_REST_TOKEN.
Parameters
text_key (str) – Key to store the text in metadata.
index (Optional[Index]) – UpstashVector Index object.
index (Optional[Index]) – UpstashVector Index object.
async_index (Optional[AsyncIndex]) – UpstashVector AsyncIndex object, provide only if async
needed (functions are) – 
index_url (Optional[str]) – URL of the UpstashVector index.
index_token (Optional[str]) – Token of the UpstashVector index.
embedding (Optional[Union[Embeddings, bool]]) – Embeddings object or a boolean. When false, no embedding
is applied. If true, Upstash embeddings are used. When Upstash
embeddings are used, text is sent directly to Upstash and
embedding is applied there instead of embedding in Langchain.
namespace (str) – Namespace to use from the index.
Example
from langchain_community.vectorstores.upstash import UpstashVectorStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = UpstashVectorStore(
    embedding=embeddings,
    index_url="...",
    index_token="...",
    namespace="..."
)
# With an existing index
from upstash_vector import Index
index = Index(url="...", token="...")
vectorstore = UpstashVectorStore(
    embedding=embeddings,
    index=index,
    namespace="..."
)
async aadd_documents(documents: Iterable[Document], ids: Optional[List[str]] = None, batch_size: int = 32, embedding_chunk_size: int = 1000, *, namespace: Optional[str] = None, **kwargs: Any) → List[str][source]¶
Get the embeddings for the documents and add them to the vectorstore.
Documents are sent to the embeddings object
in batches of size embedding_chunk_size.
The embeddings are then upserted into the vectorstore
in batches of size batch_size.
Parameters
in batches of size batch_size.
Parameters
documents (Iterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – Batch size to use when upserting the embeddings.
request. (Upstash supports at max 1000 vectors per) – 
embedding_batch_size – Chunk size to use when embedding the texts.
namespace (Optional[str]) – Namespace to use from the index.
ids (Optional[List[str]]) – 
embedding_chunk_size (int) – 
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, embedding_chunk_size: int = 1000, *, namespace: Optional[str] = None, **kwargs: Any) → List[str][source]¶
Get the embeddings for the texts and add them to the vectorstore.
Texts are sent to the embeddings object
in batches of size embedding_chunk_size.
The embeddings are then upserted into the vectorstore
in batches of size batch_size.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of ids to associate with the texts.
batch_size (int) – Batch size to use when upserting the embeddings.
request. (Upstash supports at max 1000 vectors per) – 
embedding_batch_size – Chunk size to use when embedding the texts.
namespace (Optional[str]) – Namespace to use from the index.
embedding_chunk_size (int) – 
kwargs (Any) –
embedding_chunk_size (int) – 
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], ids: Optional[List[str]] = None, batch_size: int = 32, embedding_chunk_size: int = 1000, *, namespace: Optional[str] = None, **kwargs: Any) → List[str][source]¶
Get the embeddings for the documents and add them to the vectorstore.
Documents are sent to the embeddings object
in batches of size embedding_chunk_size.
The embeddings are then upserted into the vectorstore
in batches of size batch_size.
Parameters
documents (List[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – Batch size to use when upserting the embeddings.
request. (Upstash supports at max 1000 vectors per) – 
embedding_batch_size – Chunk size to use when embedding the texts.
namespace (Optional[str]) – Namespace to use from the index.
ids (Optional[List[str]]) – 
embedding_chunk_size (int) – 
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, embedding_chunk_size: int = 1000, *, namespace: Optional[str] = None, **kwargs: Any) → List[str][source]¶
Get the embeddings for the texts and add them to the vectorstore.
Texts are sent to the embeddings object
in batches of size embedding_chunk_size.
The embeddings are then upserted into the vectorstore
in batches of size batch_size.
The embeddings are then upserted into the vectorstore
in batches of size batch_size.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of ids to associate with the texts.
batch_size (int) – Batch size to use when upserting the embeddings.
request. (Upstash supports at max 1000 vectors per) – 
embedding_batch_size – Chunk size to use when embedding the texts.
namespace (Optional[str]) – Namespace to use from the index.
embedding_chunk_size (int) – 
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, delete_all: Optional[bool] = None, batch_size: Optional[int] = 1000, *, namespace: Optional[str] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs
Parameters
ids (Optional[List[str]]) – List of ids to delete.
delete_all (Optional[bool]) – Delete all vectors in the index.
batch_size (Optional[int]) – Batch size to use when deleting the embeddings.
namespace (Optional[str]) – Namespace to use from the index.
request. (Upstash supports at max 1000 deletions per) – 
kwargs (Any) – 
Return type
None
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.
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, ids: Optional[List[str]] = None, embedding_chunk_size: int = 1000, batch_size: int = 32, text_key: str = 'text', index: Optional[Index] = None, async_index: Optional[AsyncIndex] = None, index_url: Optional[str] = None, index_token: Optional[str] = None, *, namespace: str = '', **kwargs: Any) → UpstashVectorStore[source]¶
Create a new UpstashVectorStore from a list of texts.
Example
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
embedding_chunk_size (int) – 
batch_size (int) – 
text_key (str) – 
index (Optional[Index]) – 
async_index (Optional[AsyncIndex]) – 
index_url (Optional[str]) – 
index_token (Optional[str]) – 
namespace (str) – 
kwargs (Any) – 
Return type
UpstashVectorStore
async ainfo() → InfoResult[source]¶
Get statistics about the index.
Returns
total number of vectors
total number of vectors waiting to be indexed
total size of the index on disk in bytes
dimension count for the index
similarity function selected for the index
Return type
InfoResult
dimension count for the index
similarity function selected for the index
Return type
InfoResult
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[str] = None, *, namespace: Optional[str] = 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.
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.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_by_vector(embedding: Union[List[float], str], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[str] = None, *, namespace: Optional[str] = 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 (Union[List[float], str]) – Embedding to look up documents similar to.
embedding (Union[List[float], str]) – 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.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
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(
# 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.
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]
async asimilarity_search(query: str, k: int = 4, filter: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return documents most similar to query.
Parameters
query (str) – Text to look up documents similar to.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents most similar to the query
Return type
List[Document]
async asimilarity_search_by_vector(embedding: Union[List[float], str], k: int = 4, filter: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return documents closest to the given embedding.
Parameters
embedding (Union[List[float], str]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents most similar to the query
Return type
List[Document]
async asimilarity_search_by_vector_with_score(embedding: Union[List[float], str], k: int = 4, filter: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return texts whose embedding is closest to the given embedding
Parameters
embedding (Union[List[float], str]) – 
k (int) – 
filter (Optional[str]) – 
namespace (Optional[str]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
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].
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]]
async asimilarity_search_with_score(query: str, k: int = 4, filter: Optional[str] = None, *, namespace: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Retrieve texts most similar to query and
convert the result to Document objects.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, delete_all: Optional[bool] = None, batch_size: Optional[int] = 1000, *, namespace: Optional[str] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs
Parameters
Delete by vector IDs
Parameters
ids (Optional[List[str]]) – List of ids to delete.
delete_all (Optional[bool]) – Delete all vectors in the index.
batch_size (Optional[int]) – Batch size to use when deleting the embeddings.
namespace (Optional[str]) – Namespace to use from the index.
request. (Upstash supports at max 1000 deletions per) – 
kwargs (Any) – 
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, ids: Optional[List[str]] = None, embedding_chunk_size: int = 1000, batch_size: int = 32, text_key: str = 'text', index: Optional[Index] = None, async_index: Optional[AsyncIndex] = None, index_url: Optional[str] = None, index_token: Optional[str] = None, *, namespace: str = '', **kwargs: Any) → UpstashVectorStore[source]¶
Create a new UpstashVectorStore from a list of texts.
Example
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
embedding_chunk_size (int) – 
batch_size (int) – 
text_key (str) – 
index (Optional[Index]) – 
async_index (Optional[AsyncIndex]) –
index (Optional[Index]) – 
async_index (Optional[AsyncIndex]) – 
index_url (Optional[str]) – 
index_token (Optional[str]) – 
namespace (str) – 
kwargs (Any) – 
Return type
UpstashVectorStore
info() → InfoResult[source]¶
Get statistics about the index.
Returns
total number of vectors
total number of vectors waiting to be indexed
total size of the index on disk in bytes
dimension count for the index
similarity function selected for the index
Return type
InfoResult
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[str] = None, *, namespace: Optional[str] = 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.
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.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
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: Union[List[float], str], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[str] = None, *, namespace: Optional[str] = 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 (Union[List[float], str]) – 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.
filter (Optional[str]) – Optional metadata filter in str format
namespace (Optional[str]) – Namespace to use from the index.
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]