`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

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.

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]

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.

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

documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –

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]

max_marginal_relevance_search(query: str, 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

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]

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(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¶

langchain_community.vectorstores.supabase.SupabaseVectorStore¶

Examples using SupabaseVectorStore¶

`langchain_community.vectorstores.supabase`.SupabaseVectorStore¶