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.
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.
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.
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.
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.
- __init__(pod: str, store: str, vector_index: str, vector_type: str, vector_dimension: int, url: str, embedding: Embeddings)[source]¶
- 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.
- 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.
- 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.
- 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 – vector_index=name_of_vector_index file_column=name_of_file_column
- Returns
List of ids from adding the texts into the vectorstore
- async adelete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool] ¶
Delete by vector ID or other criteria.
- Parameters
ids – List of ids to delete.
**kwargs – 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.
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ¶
Return VectorStore initialized from texts and embeddings.
- 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.
- 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.
- 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
- 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] ¶
Return docs most similar to query using specified search type.
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to query.
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- 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 – input text
k – Number of Documents to return. Defaults to 4.
**kwargs –
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)
- async asimilarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]] ¶
Run similarity search with distance asynchronously.
- count() int [source]¶
Count records of a store in jaguardb Args: no args Returns: (int) number of records in pod store
- 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
- 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
- classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST ¶
Return VectorStore initialized from documents and embeddings.
- 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.
- 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
- 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
- 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 – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
- Returns
List of Documents selected by maximal marginal relevance.
- 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 – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
- Returns
List of Documents selected by maximal marginal relevance.
- 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
- search(query: str, search_type: str, **kwargs: Any) List[Document] ¶
Return docs most similar to query using specified search type.
- 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
- similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
- Returns
List of Documents most similar to the query vector.
- 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 – input text
k – Number of Documents to return. Defaults to 4.
**kwargs –
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)
- 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 – extra options passed to select similarity
kwargs – vector_index=vcol, vector_type=cosine_fraction_float
- Returns
List of Documents most similar to the query and score for each. List of Tuples of (doc, similarity_score):
[ (doc, score), (doc, score), …]