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)Async run more documents through the embeddings and add to the vectorstore.
aadd_texts
(texts[, metadatas])Async run more texts through the embeddings and add to the vectorstore.
add_documents
(documents, **kwargs)Add or update documents in 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])Async delete by vector ID or other criteria.
afrom_documents
(documents, embedding, **kwargs)Async return VectorStore initialized from documents and embeddings.
afrom_texts
(texts, embedding[, metadatas])Async return VectorStore initialized from texts and embeddings.
aget_by_ids
(ids, /)Async get documents by their IDs.
amax_marginal_relevance_search
(query[, k, ...])Async return docs selected using the maximal marginal relevance.
Async return docs selected using the maximal marginal relevance.
as_retriever
(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch
(query, search_type, **kwargs)Async return docs most similar to query using a specified search type.
asimilarity_search
(query[, k])Async return docs most similar to query.
asimilarity_search_by_vector
(embedding[, k])Async return docs most similar to embedding vector.
Async return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score
(*args, **kwargs)Async run similarity search with distance.
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.
get_by_ids
(ids, /)Get documents by their IDs.
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.
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 a 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.
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] ¶
Async run more documents through the embeddings and add to the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of IDs does not match the number of documents.
- Return type
List[str]
- async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str] ¶
Async run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – vectorstore specific parameters.
- Returns
List of ids from adding the texts into the vectorstore.
- Raises
ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.
- Return type
List[str]
- add_documents(documents: List[Document], **kwargs: Any) List[str] ¶
Add or update documents in the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of ids does not match the number of documents.
- 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] ¶
Async delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.
**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 ¶
Async 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) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ¶
Async return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from texts and embeddings.
- Return type
- async aget_by_ids(ids: Sequence[str], /) List[Document] ¶
Async get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document] ¶
Async 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. Default is 20.
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] ¶
Async 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. Default is 20.
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) – Arguments to pass to the search method.
- 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
**kwargs (Any) –
Keyword arguments to pass to the search function. Can include: 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
- Returns
Retriever class for VectorStore.
- Return type
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] ¶
Async return docs most similar to query using a 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.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Async return docs most similar to query.
- Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Return type
List[Document]
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Async 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) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query vector.
- Return type
List[Document]
- async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ¶
Async 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(*args: Any, **kwargs: Any) List[Tuple[Document, float]] ¶
Async run similarity search with distance.
- Parameters
*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Tuples of (doc, similarity_score).
- Return type
List[Tuple[Document, float]]
- 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
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- 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]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.
url (str) –
pod (str) –
store (str) –
vector_index (str) –
vector_type (str) –
vector_dimension (int) –
jaguar_api_key (Optional[str]) –
- Returns
VectorStore initialized from texts and embeddings.
- Return type
- get_by_ids(ids: Sequence[str], /) List[Document] ¶
Get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- 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
- 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. Default is 20.
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) – Arguments to pass to the search method.
- 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. Default is 20.
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) – Arguments to pass to the search method.
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- 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 a 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.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- similarity_search(query: str, k: int = 3, where: Optional[str] = None, metadatas: Optional[List[str]] = None, **kwargs: Any) List[Document] [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 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) – Arguments to pass to the search method.
- 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]]