langchain_community.vectorstores.vald
.Vald¶
- class langchain_community.vectorstores.vald.Vald(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None)[source]¶
Wrapper around Vald vector database.
To use, you should have the
vald-client-python
python package installed.Example
from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores import Vald texts = ['foo', 'bar', 'baz'] vald = Vald.from_texts( texts=texts, embedding=HuggingFaceEmbeddings(), host="localhost", port=8080, skip_strict_exist_check=False, )
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__
(embedding[, host, port, ...])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, grpc_metadata, ...])- param skip_strict_exist_check
Deprecated. This is not used basically.
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, skip_strict_exist_check, ...])- param skip_strict_exist_check
Deprecated. This is not used basically.
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_texts
(texts, embedding[, metadatas, ...])- param skip_strict_exist_check
Deprecated. This is not used basically.
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs 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, radius, ...])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, ...])Run similarity search with distance.
similarity_search_with_score_by_vector
(embedding)- __init__(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None)[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: Iterable[str], metadatas: Optional[List[dict]] = None, grpc_metadata: Optional[Any] = None, skip_strict_exist_check: bool = False, **kwargs: Any) List[str] [source]¶
- Parameters
skip_strict_exist_check – Deprecated. This is not used basically.
- 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.
- delete(ids: Optional[List[str]] = None, skip_strict_exist_check: bool = False, grpc_metadata: Optional[Any] = None, **kwargs: Any) Optional[bool] [source]¶
- Parameters
skip_strict_exist_check – Deprecated. This is not used basically.
- 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, metadatas: Optional[List[dict]] = None, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None, grpc_metadata: Optional[Any] = None, skip_strict_exist_check: bool = False, **kwargs: Any) Vald [source]¶
- Parameters
skip_strict_exist_check – Deprecated. This is not used basically.
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[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
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, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[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
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.
- 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 = 4, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) List[Document] [source]¶
Return docs most similar to query.
- similarity_search_by_vector(embedding: List[float], k: int = 4, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) List[Document] [source]¶
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)