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)[source]

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)[source]
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][source]

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
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

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]

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[source]

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

Return type

None

count() → int[source]

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[source]

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[source]

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[source]

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
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[source]

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[source]

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[source]

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[source]

Logout to cleanup resources Args: no args Returns: None

Return type

None

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[source]
Parameters

msg (str) –

Return type

None

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

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]

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]][source]

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