langchain_community.vectorstores.pgvecto_rs
.PGVecto_rs¶
- class langchain_community.vectorstores.pgvecto_rs.PGVecto_rs(embedding: Embeddings, dimension: int, db_url: str, collection_name: str, new_table: bool = False)[source]¶
VectorStore backed by pgvecto_rs.
Initialize a PGVecto_rs vectorstore.
- Parameters
embedding – Embeddings to use.
dimension – Dimension of the embeddings.
db_url – Database URL.
collection_name – Name of the collection.
new_table – Whether to create a new table or connect to an existing one.
true (If) –
exists (the table will be dropped if) –
recreated. (then) –
False. (Defaults to) –
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__
(embedding, dimension, db_url, ...)Initialize a PGVecto_rs vectorstore.
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])Run more texts through the embeddings and add 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_collection_name
(embedding, db_url, ...)Create new empty vectorstore with collection_name.
from_documents
(documents, embedding[, ...])Return VectorStore initialized from documents.
from_texts
(texts, embedding[, metadatas, ...])Return VectorStore initialized from texts and optional metadatas.
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, distance_func])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.
Return docs most similar to query vector, with its score.
- __init__(embedding: Embeddings, dimension: int, db_url: str, collection_name: str, new_table: bool = False) None [source]¶
Initialize a PGVecto_rs vectorstore.
- Parameters
embedding – Embeddings to use.
dimension – Dimension of the embeddings.
db_url – Database URL.
collection_name – Name of the collection.
new_table – Whether to create a new table or connect to an existing one.
true (If) –
exists (the table will be dropped if) –
recreated. (then) –
False. (Defaults to) –
- 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] [source]¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
documents (List[Document]) – List of documents to add to the vectorstore.
- Returns
List of ids of the added documents.
- add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str] [source]¶
Run more texts through the embeddings and add to the vectorstore.
- Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
- Returns
List of ids of the added texts.
- 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, **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]
- classmethod from_collection_name(embedding: Embeddings, db_url: str, collection_name: str) PGVecto_rs [source]¶
Create new empty vectorstore with collection_name. Or connect to an existing vectorstore in database if exists. Arguments should be the same as when the vectorstore was created.
- classmethod from_documents(documents: List[Document], embedding: Embeddings, db_url: str = '', collection_name: str = 'a8f85cd968a74c959834b4d956d8b33a', **kwargs: Any) PGVecto_rs [source]¶
Return VectorStore initialized from documents.
- classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, db_url: str = '', collection_name: str = '273f458472e6453cbcd102481bd9681b', **kwargs: Any) PGVecto_rs [source]¶
Return VectorStore initialized from texts and optional metadatas.
- 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.
- 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, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', **kwargs: Any) List[Document] [source]¶
Return docs most similar to query.
- similarity_search_by_vector(embedding: List[float], k: int = 4, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', filter: 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)
- similarity_search_with_score(query: str, k: int = 4, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', **kwargs: Any) List[Tuple[Document, float]] [source]¶
Run similarity search with distance.
- similarity_search_with_score_by_vector(query_vector: List[float], k: int = 4, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', filter: Union[None, Dict[str, Any], Any] = None, **kwargs: Any) List[Tuple[Document, float]] [source]¶
Return docs most similar to query vector, with its score.