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# `langchain_community.vectorstores.upstash`.UpstashVectorStore

class langchain_community.vectorstores.upstash.UpstashVectorStore(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)

  Upstash Vector vector store

  To use, the `upstash-vector` python package must be installed.

  Also an Upstash Vector index is required. First create a new Upstash Vector index and copy the index_url and index_token variables. Then either pass them through the constructor or set the environment variables UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN.

  Example

      from langchain_openai import OpenAIEmbeddings
      from langchain_community.vectorstores import UpstashVectorStore

      embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
      vectorstore = UpstashVectorStore(
          embedding=embeddings,
          index_url="...",
          index_token="..."
      )

      # or

      import os

      os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
      os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."

      vectorstore = UpstashVectorStore(
          embedding=embeddings
      )

  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.

    - 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.

  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="..."
      )

      # With an existing index
      from upstash_vector import Index

      index = Index(url="...", token="...")
      vectorstore = UpstashVectorStore(
          embedding=embeddings,
          index=index
      )

  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.
  `add_texts`(texts[, metadatas, ids, ...]) | Get the embeddings for the texts and add them to the vectorstore.
  `adelete`([ids, delete_all, batch_size]) | 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]) | 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], asynchronously.
  `asimilarity_search_with_score`(query[, k, filter]) | Retrieve texts most similar to query and convert the result to Document objects.
  `delete`([ids, delete_all, batch_size]) | Delete by vector IDs
  `from_documents`(documents, embedding, **kwargs) | 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]) | 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, filter]) | 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)

    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.

      - 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.

    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="..."
        )

        # With an existing index
        from upstash_vector import Index

        index = Index(url="...", token="...")
        vectorstore = UpstashVectorStore(
            embedding=embeddings,
            index=index
        )

  async aadd_documents(documents: Iterable[Document], ids: Optional[List[str]] = None, batch_size: int = 32, embedding_chunk_size: int = 1000, **kwargs: Any) → List[str]

    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 (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.

      - 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, **kwargs: Any) → List[str]

    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.

      - 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, **kwargs: Any) → List[str]

    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.

      - 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, **kwargs: Any) → List[str]

    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.

      - 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, **kwargs: Any) → None

    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.

      - 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]) –

      - embedding (Embeddings) –

      - 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, **kwargs: Any) → UpstashVectorStore

    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]) –

      - kwargs (Any) –

    Return type

      UpstashVectorStore

  async ainfo() → InfoResult

    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

  async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[str] = None, **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 (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

      - 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, **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 (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

      - 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(
            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) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search(query: str, k: int = 4, filter: Optional[str] = None, **kwargs: Any) → List[Document]

    Return documents most similar to query.

    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

      - 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, **kwargs: Any) → List[Document]

    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

      - 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, **kwargs: Any) → List[Tuple[Document, float]]

    Return texts whose embedding is closest to the given embedding

    Parameters

      - embedding (Union[List[float], str]) –

      - k (int) –

      - filter (Optional[str]) –

      - 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], asynchronously.

    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, **kwargs: Any) → List[Tuple[Document, float]]

    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

      - 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, **kwargs: Any) → None

    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.

      - 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]) –

      - embedding (Embeddings) –

      - 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, **kwargs: Any) → UpstashVectorStore

    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]) –

      - kwargs (Any) –

    Return type

      UpstashVectorStore

  info() → InfoResult

    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, **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 (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

      - kwargs (Any) –

    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, **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 (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

      - 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) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  similarity_search(query: str, k: int = 4, filter: Optional[str] = None, **kwargs: Any) → List[Document]

    Return documents most similar to query.

    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

      - kwargs (Any) –

    Returns

      List of Documents most similar to the query and score for each

    Return type

      List[Document]

  similarity_search_by_vector(embedding: Union[List[float], str], k: int = 4, filter: Optional[str] = None, **kwargs: Any) → List[Document]

    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

      - kwargs (Any) –

    Returns

      List of Documents most similar to the query

    Return type

      List[Document]

  similarity_search_by_vector_with_score(embedding: Union[List[float], str], k: int = 4, filter: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Return texts whose embedding is closest to the given embedding

    Parameters

      - embedding (Union[List[float], str]) –

      - k (int) –

      - filter (Optional[str]) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]

  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, filter: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]]

    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

      - kwargs (Any) –

    Returns

      List of Documents most similar to the query and score for each

    Return type

      List[Tuple[Document, float]]