langchain_community.vectorstores.vespa
.VespaStoreΒΆ
- class langchain_community.vectorstores.vespa.VespaStore(app: Any, embedding_function: Optional[Embeddings] = None, page_content_field: Optional[str] = None, embedding_field: Optional[str] = None, input_field: Optional[str] = None, metadata_fields: Optional[List[str]] = None)[source]ΒΆ
Vespa vector store.
To use, you should have the python client library
pyvespa
installed.Example
from langchain_community.vectorstores import VespaStore from langchain_community.embeddings.openai import OpenAIEmbeddings from vespa.application import Vespa # Create a vespa client dependent upon your application, # e.g. either connecting to Vespa Cloud or a local deployment # such as Docker. Please refer to the PyVespa documentation on # how to initialize the client. vespa_app = Vespa(url="...", port=..., application_package=...) # You need to instruct LangChain on which fields to use for embeddings vespa_config = dict( page_content_field="text", embedding_field="embedding", input_field="query_embedding", metadata_fields=["date", "rating", "author"] ) embedding_function = OpenAIEmbeddings() vectorstore = VespaStore(vespa_app, embedding_function, **vespa_config)
Initialize with a PyVespa client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__
(app[, embedding_function, ...])Initialize with a PyVespa client.
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 texts to the vectorstore.
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 ID or other criteria.
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_texts
(texts, embedding[, metadatas, ids])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.
search
(query, search_type, **kwargs)Return docs most similar to query using specified search type.
similarity_search
(query[, k])Return docs most similar to query.
similarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
Performs similarity search from a embeddings vector.
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score
(query[, k])Run similarity search with distance.
- Parameters
app (Any) β
embedding_function (Optional[Embeddings]) β
page_content_field (Optional[str]) β
embedding_field (Optional[str]) β
input_field (Optional[str]) β
metadata_fields (Optional[List[str]]) β
- __init__(app: Any, embedding_function: Optional[Embeddings] = None, page_content_field: Optional[str] = None, embedding_field: Optional[str] = None, input_field: Optional[str] = None, metadata_fields: Optional[List[str]] = None) None [source]ΒΆ
Initialize with a PyVespa client.
- Parameters
app (Any) β
embedding_function (Optional[Embeddings]) β
page_content_field (Optional[str]) β
embedding_field (Optional[str]) β
input_field (Optional[str]) β
metadata_fields (Optional[List[str]]) β
- Return type
None
- async aadd_documents(documents: List[Document], **kwargs: Any) List[str] ΒΆ
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) β Documents to add to the vectorstore.
documents (List[Document]) β
kwargs (Any) β
- 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] ΒΆ
Run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
- Return type
List[str]
- add_documents(documents: List[Document], **kwargs: Any) List[str] ΒΆ
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) β Documents to add to the vectorstore.
documents (List[Document]) β
kwargs (Any) β
- Returns
List of IDs of the added texts.
- Return type
List[str]
- add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) List[str] [source]ΒΆ
Add texts to the vectorstore.
- Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) β Optional list of ids associated with the texts.
kwargs (Any) β vectorstore specific parameters
- 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] ΒΆ
Delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) β List of ids to delete.
**kwargs (Any) β 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 ΒΆ
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) β
embedding (Embeddings) β
kwargs (Any) β
- Return type
VST
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ΒΆ
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) β
embedding (Embeddings) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
- 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] ΒΆ
Return docs selected using the maximal marginal relevance.
- Parameters
query (str) β
k (int) β
fetch_k (int) β
lambda_mult (float) β
kwargs (Any) β
- 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] ΒΆ
Return docs selected using the maximal marginal relevance.
- Parameters
embedding (List[float]) β
k (int) β
fetch_k (int) β
lambda_mult (float) β
kwargs (Any) β
- Return type
List[Document]
- as_retriever(**kwargs: Any) VectorStoreRetriever [source]ΒΆ
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters
search_type (Optional[str]) β Defines the type of search that the Retriever should perform. Can be βsimilarityβ (default), βmmrβ, or βsimilarity_score_thresholdβ.
search_kwargs (Optional[Dict]) β
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) β
- 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] ΒΆ
Return docs most similar to query using specified search type.
- Parameters
query (str) β
search_type (str) β
kwargs (Any) β
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ΒΆ
Return docs most similar to query.
- Parameters
query (str) β
k (int) β
kwargs (Any) β
- Return type
List[Document]
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ΒΆ
Return docs most similar to embedding vector.
- Parameters
embedding (List[float]) β
k (int) β
kwargs (Any) β
- Return type
List[Document]
- async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ΒΆ
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
- Parameters
query (str) β input text
k (int) β Number of Documents to return. Defaults to 4.
**kwargs (Any) β
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]] ΒΆ
Run similarity search with distance asynchronously.
- Parameters
args (Any) β
kwargs (Any) β
- Return type
List[Tuple[Document, float]]
- delete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool] [source]ΒΆ
Delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) β List of ids to delete.
**kwargs (Any) β Other keyword arguments that subclasses might use.
- Returns
True if deletion is successful, False otherwise, None if not implemented.
- Return type
Optional[bool]
- classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST ΒΆ
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) β
embedding (Embeddings) β
kwargs (Any) β
- Return type
VST
- classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) VespaStore [source]ΒΆ
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) β
embedding (Embeddings) β
metadatas (Optional[List[dict]]) β
ids (Optional[List[str]]) β
kwargs (Any) β
- Return type
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document] [source]ΒΆ
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
- 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] [source]ΒΆ
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]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- search(query: str, search_type: str, **kwargs: Any) List[Document] ΒΆ
Return docs most similar to query using specified search type.
- Parameters
query (str) β
search_type (str) β
kwargs (Any) β
- Return type
List[Document]
- similarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] [source]ΒΆ
Return docs most similar to query.
- Parameters
query (str) β
k (int) β
kwargs (Any) β
- Return type
List[Document]
- similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] [source]ΒΆ
Return docs most similar to embedding vector.
- Parameters
embedding (List[float]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
kwargs (Any) β
- Returns
List of Documents most similar to the query vector.
- Return type
List[Document]
- similarity_search_by_vector_with_score(query_embedding: List[float], k: int = 4, **kwargs: Any) List[Tuple[Document, float]] [source]ΒΆ
Performs similarity search from a embeddings vector.
- Parameters
query_embedding (List[float]) β Embeddings vector to search for.
k (int) β Number of results to return.
custom_query β Use this custom query instead default query (kwargs)
kwargs (Any) β other vector store specific parameters
- Returns
List of ids from adding the texts into the vectorstore.
- Return type
List[Tuple[Document, float]]
- similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]] ΒΆ
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
- Parameters
query (str) β input text
k (int) β Number of Documents to return. Defaults to 4.
**kwargs (Any) β
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]]