langchain_community.vectorstores.oraclevs
.OracleVS¶
- class langchain_community.vectorstores.oraclevs.OracleVS(client: Connection, embedding_function: Union[Callable[[str], List[float]], Embeddings], table_name: str, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, query: Optional[str] = 'What is a Oracle database', params: Optional[Dict[str, Any]] = None)[source]¶
OracleVS vector store.
To use, you should have both: - the
oracledb
python package installed - a connection string associated with a OracleDBCluster having deployed anSearch index
Example
from langchain.vectorstores import OracleVS from langchain.embeddings.openai import OpenAIEmbeddings import oracledb with oracledb.connect(user = user, passwd = pwd, dsn = dsn) as connection: print ("Database version:", connection.version) embeddings = OpenAIEmbeddings() query = "" vectors = OracleVS(connection, table_name, embeddings, query)
Attributes
embeddings
A property that returns an Embeddings instance embedding_function is an instance of Embeddings, otherwise returns None.
client
Initialize with necessary components.
Methods
__init__
(client, embedding_function, table_name)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, ids])Add more texts to the vectorstore index.
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.
delete
([ids])Delete by vector IDs.
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_texts
(texts, embedding[, metadatas])Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
Return docs and their similarity scores 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 docs most similar to query.
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, filter])Return docs most similar to query.
- Parameters
client (Connection) –
embedding_function (Union[Callable[[str], List[float]], Embeddings]) –
table_name (str) –
distance_strategy (DistanceStrategy) –
query (Optional[str]) –
params (Optional[Dict[str, Any]]) –
- __init__(client: Connection, embedding_function: Union[Callable[[str], List[float]], Embeddings], table_name: str, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, query: Optional[str] = 'What is a Oracle database', params: Optional[Dict[str, Any]] = None)[source]¶
- Parameters
client (Connection) –
embedding_function (Union[Callable[[str], List[float]], Embeddings]) –
table_name (str) –
distance_strategy (DistanceStrategy) –
query (Optional[str]) –
params (Optional[Dict[str, Any]]) –
- 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: Iterable[str], metadatas: Optional[List[Dict[Any, Any]]] = None, ids: Optional[List[str]] = None, **kwargs: Any) List[str] [source]¶
Add more texts to the vectorstore index. :param texts: Iterable of strings to add to the vectorstore. :param metadatas: Optional list of metadatas associated with the texts. :param ids: Optional list of ids for the texts that are being added to :param the vector store.: :param kwargs: vectorstore specific parameters
- Parameters
texts (Iterable[str]) –
metadatas (Optional[List[Dict[Any, Any]]]) –
ids (Optional[List[str]]) –
kwargs (Any) –
- 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
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]]
- delete(ids: Optional[List[str]] = None, **kwargs: Any) None [source]¶
Delete by vector IDs. :param self: An instance of the class :param ids: List of ids to delete. :param **kwargs:
- Parameters
ids (Optional[List[str]]) –
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: Iterable[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) OracleVS [source]¶
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (Iterable[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) List[Document] [source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
self – An instance of the class
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[Dict[str, Any]]) – Optional[Dict[str, Any]]
**kwargs (Any) –
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
max_marginal_relevance_search requires that query returns matched embeddings alongside the match documents.
- max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) List[Document] [source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
self – An instance of the class
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.
filter (Optional[Dict[str, Any]]) – Optional[Dict[str, Any]]
**kwargs (Any) – Any
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- max_marginal_relevance_search_with_score_by_vector(embedding: List[float], *, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None) List[Tuple[Document, float]] [source]¶
Return docs and their similarity scores selected using the maximal marginal
relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
self – An instance of the class
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 before filtering to pass to MMR algorithm.
filter (Optional[Dict[str, Any]]) – (Optional[Dict[str, str]]): Filter by metadata. Defaults
None. (to) –
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.
- Returns
List of Documents and similarity scores selected by maximal marginal relevance and score for each.
- Return type
List[Tuple[Document, float]]
- 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[Dict[str, Any]] = None, **kwargs: Any) List[Document] [source]¶
Return docs most similar to query.
- Parameters
query (str) –
k (int) –
filter (Optional[Dict[str, Any]]) –
kwargs (Any) –
- Return type
List[Document]
- similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict[str, Any]] = None, **kwargs: Any) List[Document] [source]¶
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.
filter (Optional[dict[str, Any]]) –
kwargs (Any) –
- Returns
List of Documents most similar to the query vector.
- Return type
List[Document]
- similarity_search_by_vector_returning_embeddings(embedding: List[float], k: int, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) List[Tuple[Document, float, ndarray[float32, Any]]] [source]¶
- Parameters
embedding (List[float]) –
k (int) –
filter (Optional[Dict[str, Any]]) –
kwargs (Any) –
- Return type
List[Tuple[Document, float, ndarray[float32, Any]]]
- similarity_search_by_vector_with_relevance_scores(embedding: List[float], k: int = 4, filter: Optional[dict[str, Any]] = None, **kwargs: Any) List[Tuple[Document, float]] [source]¶
- Parameters
embedding (List[float]) –
k (int) –
filter (Optional[dict[str, Any]]) –
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[dict[str, Any]] = None, **kwargs: Any) List[Tuple[Document, float]] [source]¶
Return docs most similar to query.
- Parameters
query (str) –
k (int) –
filter (Optional[dict[str, Any]]) –
kwargs (Any) –
- Return type
List[Tuple[Document, float]]