from __future__ import annotations
from itertools import repeat
from typing import (
TYPE_CHECKING,
Any,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
import supabase
[docs]class SupabaseVectorStore(VectorStore):
"""VectorStore for a Supabase postgres database. Assumes you have the `pgvector`
extension installed and a `match_documents` (or similar) function. For more details:
https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase
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.
"""
_client: supabase.client.Client
# This is the embedding function. Don't confuse with the embedding vectors.
# We should perhaps rename the underlying Embedding base class to EmbeddingFunction
# or something
_embedding: Embeddings
table_name: str
query_name: str
def __init__(
self,
client: supabase.client.Client,
embedding: Embeddings,
table_name: str,
query_name: Union[str, None] = None,
) -> None:
"""Initialize with supabase client."""
try:
import supabase # noqa: F401
except ImportError:
raise ValueError(
"Could not import supabase python package. "
"Please install it with `pip install supabase`."
)
self._client = client
self._embedding: Embeddings = embedding
self.table_name = table_name or "documents"
self.query_name = query_name or "match_documents"
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict[Any, Any]]] = None,
**kwargs: Any,
) -> List[str]:
docs = self._texts_to_documents(texts, metadatas)
vectors = self._embedding.embed_documents(list(texts))
return self.add_vectors(vectors, docs)
[docs] @classmethod
def from_texts(
cls: Type["SupabaseVectorStore"],
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",
**kwargs: Any,
) -> "SupabaseVectorStore":
"""Return VectorStore initialized from texts and embeddings."""
if not client:
raise ValueError("Supabase client is required.")
if not table_name:
raise ValueError("Supabase document table_name is required.")
embeddings = embedding.embed_documents(texts)
docs = cls._texts_to_documents(texts, metadatas)
_ids = cls._add_vectors(client, table_name, embeddings, docs)
return cls(
client=client,
embedding=embedding,
table_name=table_name,
query_name=query_name,
)
[docs] def add_vectors(
self, vectors: List[List[float]], documents: List[Document]
) -> List[str]:
return self._add_vectors(self._client, self.table_name, vectors, documents)
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
vectors = self._embedding.embed_documents([query])
return self.similarity_search_by_vector(vectors[0], k)
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
result = self.similarity_search_by_vector_with_relevance_scores(embedding, k)
documents = [doc for doc, _ in result]
return documents
[docs] def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
vectors = self._embedding.embed_documents([query])
return self.similarity_search_by_vector_with_relevance_scores(vectors[0], k)
[docs] def similarity_search_by_vector_with_relevance_scores(
self, query: List[float], k: int
) -> List[Tuple[Document, float]]:
match_documents_params = dict(query_embedding=query, match_count=k)
res = self._client.rpc(self.query_name, match_documents_params).execute()
match_result = [
(
Document(
metadata=search.get("metadata", {}), # type: ignore
page_content=search.get("content", ""),
),
search.get("similarity", 0.0),
)
for search in res.data
if search.get("content")
]
return match_result
[docs] def similarity_search_by_vector_returning_embeddings(
self, query: List[float], k: int
) -> List[Tuple[Document, float, np.ndarray[np.float32, Any]]]:
match_documents_params = dict(query_embedding=query, match_count=k)
res = self._client.rpc(self.query_name, match_documents_params).execute()
match_result = [
(
Document(
metadata=search.get("metadata", {}), # type: ignore
page_content=search.get("content", ""),
),
search.get("similarity", 0.0),
# Supabase returns a vector type as its string represation (!).
# This is a hack to convert the string to numpy array.
np.fromstring(
search.get("embedding", "").strip("[]"), np.float32, sep=","
),
)
for search in res.data
if search.get("content")
]
return match_result
@staticmethod
def _texts_to_documents(
texts: Iterable[str],
metadatas: Optional[Iterable[dict[Any, Any]]] = None,
) -> List[Document]:
"""Return list of Documents from list of texts and metadatas."""
if metadatas is None:
metadatas = repeat({})
docs = [
Document(page_content=text, metadata=metadata)
for text, metadata in zip(texts, metadatas)
]
return docs
@staticmethod
def _add_vectors(
client: supabase.client.Client,
table_name: str,
vectors: List[List[float]],
documents: List[Document],
) -> List[str]:
"""Add vectors to Supabase table."""
rows: List[dict[str, Any]] = [
{
"content": documents[idx].page_content,
"embedding": embedding,
"metadata": documents[idx].metadata, # type: ignore
}
for idx, embedding in enumerate(vectors)
]
# According to the SupabaseVectorStore JS implementation, the best chunk size
# is 500
chunk_size = 500
id_list: List[str] = []
for i in range(0, len(rows), chunk_size):
chunk = rows[i : i + chunk_size]
result = client.from_(table_name).insert(chunk).execute() # type: ignore
if len(result.data) == 0:
raise Exception("Error inserting: No rows added")
# VectorStore.add_vectors returns ids as strings
ids = [str(i.get("id")) for i in result.data if i.get("id")]
id_list.extend(ids)
return id_list
[docs] def max_marginal_relevance_search_by_vector(
self,
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.
Args:
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.
"""
result = self.similarity_search_by_vector_returning_embeddings(
embedding, fetch_k
)
matched_documents = [doc_tuple[0] for doc_tuple in result]
matched_embeddings = [doc_tuple[2] for doc_tuple in result]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
matched_embeddings,
k=k,
lambda_mult=lambda_mult,
)
filtered_documents = [matched_documents[i] for i in mmr_selected]
return filtered_documents
[docs] def max_marginal_relevance_search(
self,
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.
Args:
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` requires that `query_name` returns matched
embeddings alongside the match documents. The following function function
demonstrates how to do this:
```sql
CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536),
match_count int)
RETURNS TABLE(
id bigint,
content text,
metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGIN
RETURN query
SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROM
docstore
ORDER BY
docstore.embedding <=> query_embedding
LIMIT match_count;
END;
$$;```
"""
embedding = self._embedding.embed_documents([query])
docs = self.max_marginal_relevance_search_by_vector(
embedding[0], k, fetch_k, lambda_mult=lambda_mult
)
return docs