Supabase vector self querying retriever (#10304)

## Description
Adds Supabase Vector as a self-querying retriever.

- Designed to be backwards compatible with existing `filter` logic on
`SupabaseVectorStore`.
- Adds new filter `postgrest_filter` to `SupabaseVectorStore`
`similarity_search()` methods
- Supports entire PostgREST [filter query
language](https://postgrest.org/en/stable/references/api/tables_views.html#read)
(used by self-querying retriever, but also works as an escape hatch for
more query control)
- `SupabaseVectorTranslator` converts Langchain filter into the above
PostgREST query
- Adds Jupyter Notebook for the self-querying retriever
- Adds tests

## Tag maintainer
@hwchase17

## Twitter handle
[@ggrdson](https://twitter.com/ggrdson)

docs/extras/integrations/vectorstores/supabase.ipynb

@@ -28,43 +28,41 @@
     "The following function determines cosine similarity, but you can adjust to your needs.\n",
     "\n",
     "```sql\n",
-    "       -- Enable the pgvector extension to work with embedding vectors\n",
-    "       create extension vector;\n",
+    "-- Enable the pgvector extension to work with embedding vectors\n",
+    "create extension if not exists vector;\n",
     "\n",
-    "       -- Create a table to store your documents\n",
-    "       create table documents (\n",
-    "       id uuid primary key,\n",
-    "       content text, -- corresponds to Document.pageContent\n",
-    "       metadata jsonb, -- corresponds to Document.metadata\n",
-    "       embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed\n",
-    "       );\n",
+    "-- Create a table to store your documents\n",
+    "create table\n",
+    "  documents (\n",
+    "    id uuid primary key,\n",
+    "    content text, -- corresponds to Document.pageContent\n",
+    "    metadata jsonb, -- corresponds to Document.metadata\n",
+    "    embedding vector (1536) -- 1536 works for OpenAI embeddings, change if needed\n",
+    "  );\n",
     "\n",
-    "       CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)\n",
-    "           RETURNS TABLE(\n",
-    "               id uuid,\n",
-    "               content text,\n",
-    "               metadata jsonb,\n",
-    "               -- we return matched vectors to enable maximal marginal relevance searches\n",
-    "               embedding vector(1536),\n",
-    "               similarity float)\n",
-    "           LANGUAGE plpgsql\n",
-    "           AS $$\n",
-    "           # variable_conflict use_column\n",
-    "       BEGIN\n",
-    "           RETURN query\n",
-    "           SELECT\n",
-    "               id,\n",
-    "               content,\n",
-    "               metadata,\n",
-    "               embedding,\n",
-    "               1 -(documents.embedding <=> query_embedding) AS similarity\n",
-    "           FROM\n",
-    "               documents\n",
-    "           ORDER BY\n",
-    "               documents.embedding <=> query_embedding\n",
-    "           LIMIT match_count;\n",
-    "       END;\n",
-    "       $$;\n",
+    "-- Create a function to search for documents\n",
+    "create function match_documents (\n",
+    "  query_embedding vector (1536),\n",
+    "  filter jsonb default '{}'\n",
+    ") returns table (\n",
+    "  id uuid,\n",
+    "  content text,\n",
+    "  metadata jsonb,\n",
+    "  similarity float\n",
+    ") language plpgsql as $$\n",
+    "#variable_conflict use_column\n",
+    "begin\n",
+    "  return query\n",
+    "  select\n",
+    "    id,\n",
+    "    content,\n",
+    "    metadata,\n",
+    "    1 - (documents.embedding <=> query_embedding) as similarity\n",
+    "  from documents\n",
+    "  where metadata @> filter\n",
+    "  order by documents.embedding <=> query_embedding;\n",
+    "end;\n",
+    "$$;\n",
     "```"
    ]
   },