langchain_community.vectorstores.supabase.SupabaseVectorStore

class langchain_community.vectorstores.supabase.SupabaseVectorStore(client: supabase.client.Client, embedding: Embeddings, table_name: str, chunk_size: int = 500, query_name: Union[str, None] = None)[source]

Supabase Postgres vector store.

It assumes you have the pgvector extension installed and a match_documents (or similar) function. For more details: https://integrations.langchain.com/vectorstores?integration_name=SupabaseVectorStore

You can implement your own match_documents function in order to limit the search space to a subset of documents based on your own authorization or business logic.

Note that the Supabase Python client does not yet support async operations.

If you’d like to use max_marginal_relevance_search, please review the instructions below on modifying the match_documents function to return matched embeddings.

Examples:

from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_core.documents import Document
from langchain_community.vectorstores import SupabaseVectorStore
from supabase.client import create_client

docs = [
    Document(page_content="foo", metadata={"id": 1}),
]
embeddings = OpenAIEmbeddings()
supabase_client = create_client("my_supabase_url", "my_supabase_key")
vector_store = SupabaseVectorStore.from_documents(
    docs,
    embeddings,
    client=supabase_client,
    table_name="documents",
    query_name="match_documents",
    chunk_size=500,
)

To load from an existing table:

from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import SupabaseVectorStore
from supabase.client import create_client


embeddings = OpenAIEmbeddings()
supabase_client = create_client("my_supabase_url", "my_supabase_key")
vector_store = SupabaseVectorStore(
    client=supabase_client,
    embedding=embeddings,
    table_name="documents",
    query_name="match_documents",
)

Initialize with supabase client.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(client, embedding, table_name[, ...])

Initialize with supabase 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])

Run more texts through the embeddings and add to the vectorstore.

add_vectors(vectors, documents, ids)

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.

amax_marginal_relevance_search_by_vector(...)

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.

asimilarity_search_with_relevance_scores(query)

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.

match_args(query, filter)

max_marginal_relevance_search(query[, k, ...])

Return docs selected using the maximal marginal relevance.

max_marginal_relevance_search_by_vector(...)

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, filter])

Return docs most similar to query.

similarity_search_by_vector(embedding[, k, ...])

Return docs most similar to embedding vector.

similarity_search_by_vector_returning_embeddings(...)

similarity_search_by_vector_with_relevance_scores(...)

similarity_search_with_relevance_scores(query)

Return docs and relevance scores in the range [0, 1].

similarity_search_with_score(*args, **kwargs)

Run similarity search with distance.

Parameters
  • client (supabase.client.Client) –

  • embedding (Embeddings) –

  • table_name (str) –

  • chunk_size (int) –

  • query_name (Union[str, None]) –

__init__(client: supabase.client.Client, embedding: Embeddings, table_name: str, chunk_size: int = 500, query_name: Union[str, None] = None) None[source]

Initialize with supabase client.

Parameters
  • client (supabase.client.Client) –

  • embedding (Embeddings) –

  • table_name (str) –

  • chunk_size (int) –

  • query_name (Union[str, None]) –

Return type

None

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]

Run more texts through the embeddings and add to the vectorstore.

Parameters
  • texts (Iterable[str]) – Iterable of strings to add to the vectorstore.

  • metadatas (Optional[List[Dict[Any, Any]]]) – Optional list of metadatas associated with the texts.

  • kwargs (Any) – vectorstore specific parameters

  • ids (Optional[List[str]]) –

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

add_vectors(vectors: List[List[float]], documents: List[Document], ids: List[str]) List[str][source]
Parameters
  • vectors (List[List[float]]) –

  • documents (List[Document]) –

  • ids (List[str]) –

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
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

Return docs selected using the maximal marginal relevance.

Parameters
  • query (str) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • kwargs (Any) –

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

VectorStoreRetriever

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]

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.

Parameters
  • ids (Optional[List[str]]) – List of ids to delete.

  • kwargs (Any) –

Return type

None

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST

Return VectorStore initialized from documents and embeddings.

Parameters
Return type

VST

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[supabase.client.Client] = None, table_name: Optional[str] = 'documents', query_name: Union[str, None] = 'match_documents', chunk_size: int = 500, ids: Optional[List[str]] = None, **kwargs: Any) SupabaseVectorStore[source]

Return VectorStore initialized from texts and embeddings.

Parameters
  • texts (List[str]) –

  • embedding (Embeddings) –

  • metadatas (Optional[List[dict]]) –

  • client (Optional[supabase.client.Client]) –

  • table_name (Optional[str]) –

  • query_name (Union[str, None]) –

  • chunk_size (int) –

  • ids (Optional[List[str]]) –

  • kwargs (Any) –

Return type

SupabaseVectorStore

match_args(query: List[float], filter: Optional[Dict[str, Any]]) Dict[str, Any][source]
Parameters
  • query (List[float]) –

  • filter (Optional[Dict[str, Any]]) –

Return type

Dict[str, Any]

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]

max_marginal_relevance_search requires that query_name returns matched embeddings alongside the match documents. The following function demonstrates how to do this:

```sql CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536),

match_count int)

RETURNS TABLE(

id uuid, content text, metadata jsonb, embedding vector(1536), similarity float)

LANGUAGE plpgsql AS $$ # variable_conflict use_column

BEGIN

RETURN query SELECT

id, content, metadata, embedding, 1 -(docstore.embedding <=> query_embedding) AS similarity

FROM

docstore

ORDER BY

docstore.embedding <=> query_embedding

LIMIT match_count;

END; $$; ```

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]

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]) – 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.

  • kwargs (Any) –

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

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]

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(query: List[float], k: int, filter: Optional[Dict[str, Any]] = None, postgrest_filter: Optional[str] = None) List[Tuple[Document, float, ndarray[float32, Any]]][source]
Parameters
  • query (List[float]) –

  • k (int) –

  • filter (Optional[Dict[str, Any]]) –

  • postgrest_filter (Optional[str]) –

Return type

List[Tuple[Document, float, ndarray[float32, Any]]]

similarity_search_by_vector_with_relevance_scores(query: List[float], k: int, filter: Optional[Dict[str, Any]] = None, postgrest_filter: Optional[str] = None, score_threshold: Optional[float] = None) List[Tuple[Document, float]][source]
Parameters
  • query (List[float]) –

  • k (int) –

  • filter (Optional[Dict[str, Any]]) –

  • postgrest_filter (Optional[str]) –

  • score_threshold (Optional[float]) –

Return type

List[Tuple[Document, float]]

similarity_search_with_relevance_scores(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) List[Tuple[Document, float]][source]

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

  • filter (Optional[Dict[str, Any]]) –

  • **kwargs

Returns

List of Tuples of (doc, similarity_score)

Return type

List[Tuple[Document, float]]

similarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]]

Run similarity search with distance.

Parameters
  • args (Any) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

Examples using SupabaseVectorStore