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# `langchain_community.vectorstores.jaguar`.Jaguar

class langchain_community.vectorstores.jaguar.Jaguar(pod: str, store: str, vector_index: str, vector_type: str, vector_dimension: int, url: str, embedding: Embeddings)

  Jaguar API vector store.

  See http://www.jaguardb.com See http://github.com/fserv/jaguar-sdk

  Example

      from langchain_community.vectorstores.jaguar import Jaguar

      vectorstore = Jaguar(
          pod = 'vdb',
          store = 'mystore',
          vector_index = 'v',
          vector_type = 'cosine_fraction_float',
          vector_dimension = 1536,
          url='http://192.168.8.88:8080/fwww/',
          embedding=openai_model
      )

  Attributes

  `embeddings` | Access the query embedding object if available.

  Methods

  `__init__`(pod, store, vector_index, ...) |
  `aadd_documents`(documents, **kwargs) | Run more documents through the embeddings and add to the vectorstore.
  `aadd_texts`(texts[, metadatas]) | 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]) | Add texts through the embeddings and add to the vectorstore. :param texts: list of text strings to add to the jaguar vector store. :param metadatas: Optional list of metadatas associated with the texts. [{"m1": "v11", "m2": "v12", "m3": "v13", "filecol": "path_file1.jpg" }, {"m1": "v21", "m2": "v22", "m3": "v23", "filecol": "path_file2.jpg" }, {"m1": "v31", "m2": "v32", "m3": "v33", "filecol": "path_file3.jpg" }, {"m1": "v41", "m2": "v42", "m3": "v43", "filecol": "path_file4.jpg" }] :param kwargs: vector_index=name_of_vector_index file_column=name_of_file_column.
  `adelete`([ids]) | Delete by vector ID or other criteria.
  `afrom_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `afrom_texts`(texts, embedding[, metadatas]) | Return VectorStore initialized from texts and embeddings.
  `amax_marginal_relevance_search`(query[, k, ...]) | Return docs selected using the maximal marginal relevance.
  `amax_marginal_relevance_search_by_vector`(...) | Return docs selected using the maximal marginal relevance.
  `as_retriever`(**kwargs) | Return VectorStoreRetriever initialized from this VectorStore.
  `asearch`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `asimilarity_search`(query[, k]) | 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 and relevance scores in the range [0, 1], asynchronously.
  `asimilarity_search_with_score`(*args, **kwargs) | Run similarity search with distance asynchronously.
  `clear`() | Delete all records in jaguardb Args: No args Returns: None
  `count`() | Count records of a store in jaguardb Args: no args Returns: (int) number of records in pod store
  `create`(metadata_str, text_size) | create the vector store on the backend database :param metadata_str: columns and their types :type metadata_str: str
  `delete`(zids, **kwargs) | Delete records in jaguardb by a list of zero-ids :param pod: name of a Pod :type pod: str :param ids: a list of zid as string :type ids: List[str]
  `drop`() | Drop or remove a store in jaguardb Args: no args Returns: None
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts, embedding, url, pod, ...) | Return VectorStore initialized from texts and embeddings.
  `is_anomalous`(query, **kwargs) | Detect if given text is anomalous from the dataset :param query: Text to detect if it is anomaly
  `login`([jaguar_api_key]) | login to jaguardb server with a jaguar_api_key or let self._jag find a key :param pod: name of a Pod :type pod: str :param store: name of a vector store :type store: str :param optional jaguar_api_key: API key of user to jaguardb server :type optional jaguar_api_key: str
  `logout`() | Logout to cleanup resources Args: no args Returns: None
  `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.
  `prt`(msg) |
  `run`(query[, withFile]) | Run any query statement in jaguardb :param query: query statement to jaguardb :type query: str
  `search`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `similarity_search`(query[, k, where, metadatas]) | Return Jaguar documents most similar to query, along with scores. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 5. :param where: the where clause in select similarity. For example a where can be "rating > 3.0 and (state = 'NV' or state = 'CA')".
  `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, ...]) | Return Jaguar documents most similar to query, along with scores. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 3. :param lambda_val: lexical match parameter for hybrid search. :param where: the where clause in select similarity. For example a where can be "rating > 3.0 and (state = 'NV' or state = 'CA')" :param args: extra options passed to select similarity :param kwargs: vector_index=vcol, vector_type=cosine_fraction_float.

  Parameters

    - pod (str) –

    - store (str) –

    - vector_index (str) –

    - vector_type (str) –

    - vector_dimension (int) –

    - url (str) –

    - embedding (Embeddings) –

  __init__(pod: str, store: str, vector_index: str, vector_type: str, vector_dimension: int, url: str, embedding: Embeddings)
    Parameters

      - pod (str) –

      - store (str) –

      - vector_index (str) –

      - vector_type (str) –

      - vector_dimension (int) –

      - url (str) –

      - embedding (Embeddings) –

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

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

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]

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

    Parameters

      - texts (Iterable[str]) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      List[str]

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

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

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  add_texts(texts: List[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]

    Add texts through the embeddings and add to the vectorstore. :param texts: list of text strings to add to the jaguar vector store. :param metadatas: Optional list of metadatas associated with the texts.

    > [{“m1”: “v11”, “m2”: “v12”, “m3”: “v13”, “filecol”: “path_file1.jpg” },

    >   {“m1”: “v21”, “m2”: “v22”, “m3”: “v23”, “filecol”: “path_file2.jpg” }, {“m1”: “v31”, “m2”: “v32”, “m3”: “v33”, “filecol”: “path_file3.jpg” }, {“m1”: “v41”, “m2”: “v42”, “m3”: “v43”, “filecol”: “path_file4.jpg” }]

    Parameters

      - kwargs (Any) – vector_index=name_of_vector_index file_column=name_of_file_column

      - texts (List[str]) –

      - metadatas (Optional[List[dict]]) –

    Returns

      List of ids from adding the texts into the vectorstore

    Return type

      List[str]

  async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

    Delete by vector ID or other criteria.

    Parameters

      - ids (Optional[List[str]]) – List of ids to delete.

      - **kwargs (Any) – Other keyword arguments that subclasses might use.

    Returns

      True if deletion is successful, False otherwise, None if not implemented.

    Return type

      Optional[bool]

  async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - embedding (Embeddings) –

      - kwargs (Any) –

    Return type

      VST

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

    Return VectorStore initialized from texts and embeddings.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      VST

  async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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.

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    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, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - fetch_k (int) –

      - lambda_mult (float) –

      - kwargs (Any) –

    Return type

      List[Document]

  as_retriever(**kwargs: Any) → VectorStoreRetriever

    Return VectorStoreRetriever initialized from this VectorStore.

    Parameters

      - search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

      - search_kwargs (Optional[Dict]) –

        Keyword arguments to pass to the search function. Can include things like:

        > k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

        > > for similarity_score_threshold

        > fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

        > > 1 for minimum diversity and 0 for maximum. (Default: 0.5)

        > filter: Filter by document metadata

      - kwargs (Any) –

    Returns

      Retriever class for VectorStore.

    Return type

      VectorStoreRetriever

    Examples:

        # Retrieve more documents with higher diversity
        # Useful if your dataset has many similar documents
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 6, 'lambda_mult': 0.25}
        )

        # Fetch more documents for the MMR algorithm to consider
        # But only return the top 5
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 5, 'fetch_k': 50}
        )

        # Only retrieve documents that have a relevance score
        # Above a certain threshold
        docsearch.as_retriever(
            search_type="similarity_score_threshold",
            search_kwargs={'score_threshold': 0.8}
        )

        # Only get the single most similar document from the dataset
        docsearch.as_retriever(search_kwargs={'k': 1})

        # Use a filter to only retrieve documents from a specific paper
        docsearch.as_retriever(
            search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
        )

  async asearch(query: str, search_type: str, **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, **kwargs: Any) → List[Document]

    Return docs most similar to query.

    Parameters

      - query (str) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

    Return docs most similar to embedding vector.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

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

    Run similarity search with distance asynchronously.

    Parameters

      - args (Any) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]

  clear() → None

    Delete all records in jaguardb Args: No args Returns: None

    Return type

      None

  count() → int

    Count records of a store in jaguardb Args: no args Returns: (int) number of records in pod store

    Return type

      int

  create(metadata_str: str, text_size: int) → None

    create the vector store on the backend database :param metadata_str: columns and their types :type metadata_str: str

    Returns

      True if successful; False if not successful

    Parameters

      - metadata_str (str) –

      - text_size (int) –

    Return type

      None

  delete(zids: List[str], **kwargs: Any) → None

    Delete records in jaguardb by a list of zero-ids :param pod: name of a Pod :type pod: str :param ids: a list of zid as string :type ids: List[str]

    Returns

      Do not return anything

    Parameters

      - zids (List[str]) –

      - kwargs (Any) –

    Return type

      None

  drop() → None

    Drop or remove a store in jaguardb Args: no args Returns: None

    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, url: str, pod: str, store: str, vector_index: str, vector_type: str, vector_dimension: int, metadatas: Optional[List[dict]] = None, jaguar_api_key: Optional[str] = '', **kwargs: Any) → Jaguar

    Return VectorStore initialized from texts and embeddings.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

      - url (str) –

      - pod (str) –

      - store (str) –

      - vector_index (str) –

      - vector_type (str) –

      - vector_dimension (int) –

      - metadatas (Optional[List[dict]]) –

      - jaguar_api_key (Optional[str]) –

      - kwargs (Any) –

    Return type

      Jaguar

  is_anomalous(query: str, **kwargs: Any) → bool

    Detect if given text is anomalous from the dataset :param query: Text to detect if it is anomaly

    Returns

      True or False

    Parameters

      - query (str) –

      - kwargs (Any) –

    Return type

      bool

  login(jaguar_api_key: Optional[str] = '') → bool

    login to jaguardb server with a jaguar_api_key or let self._jag find a key :param pod: name of a Pod :type pod: str :param store: name of a vector store :type store: str :param optional jaguar_api_key: API key of user to jaguardb server :type optional jaguar_api_key: str

    Returns

      True if successful; False if not successful

    Parameters

      jaguar_api_key (Optional[str]) –

    Return type

      bool

  logout() → None

    Logout to cleanup resources Args: no args Returns: None

    Return type

      None

  max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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.

      - 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, **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 (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.

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  prt(msg: str) → None
    Parameters

      msg (str) –

    Return type

      None

  run(query: str, withFile: bool = False) → dict

    Run any query statement in jaguardb :param query: query statement to jaguardb :type query: str

    Returns

      None for invalid token, or json result string

    Parameters

      - query (str) –

      - withFile (bool) –

    Return type

      dict

  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 = 3, where: Optional[str] = None, metadatas: Optional[List[str]] = None, **kwargs: Any) → List[Document]

    Return Jaguar documents most similar to query, along with scores. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 5. :param where: the where clause in select similarity. For example a

    > where can be “rating > 3.0 and (state = ‘NV’ or state = ‘CA’)”

    Returns

      List of Documents most similar to the query

    Parameters

      - query (str) –

      - k (int) –

      - where (Optional[str]) –

      - metadatas (Optional[List[str]]) –

      - kwargs (Any) –

    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

      List of Tuples of (doc, similarity_score)

    Return type

      List[Tuple[Document, float]]

  similarity_search_with_score(query: str, k: int = 3, fetch_k: int = -1, where: Optional[str] = None, args: Optional[str] = None, metadatas: Optional[List[str]] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Return Jaguar documents most similar to query, along with scores. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 3. :param lambda_val: lexical match parameter for hybrid search. :param where: the where clause in select similarity. For example a

    > where can be “rating > 3.0 and (state = ‘NV’ or state = ‘CA’)”

    Parameters

      - args (Optional[str]) – extra options passed to select similarity

      - kwargs (Any) – vector_index=vcol, vector_type=cosine_fraction_float

      - query (str) –

      - k (int) –

      - fetch_k (int) –

      - where (Optional[str]) –

      - metadatas (Optional[List[str]]) –

    Returns

      List of Documents most similar to the query and score for each. List of Tuples of (doc, similarity_score):

      > [ (doc, score), (doc, score), …]

    Return type

      List[Tuple[Document, float]]

# Examples using Jaguar

- Jaguar

- cosine: distance metric