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docs supabase update (#4935)
# docs: updated `Supabase` notebook - the title of the notebook was inconsistent (included redundant "Vectorstore"). Removed this "Vectorstore" - added `Postgress` to the title. It is important. The `Postgres` name is much more popular than `Supabase`. - added description for the `Postrgress` - added more info to the `Supabase` description
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@ -5,7 +5,7 @@
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"id": "683953b3",
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"metadata": {},
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"source": [
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"# SupabaseVectorStore"
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"# Supabase (Postgres)"
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]
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},
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{
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@ -13,7 +13,9 @@
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"id": "cc80fa84-1f2f-48b4-bd39-3e6412f012f1",
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"metadata": {},
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"source": [
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">[Supabase](https://supabase.com/docs) is an open source Firebase alternative.\n",
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">[Supabase](https://supabase.com/docs) is an open source Firebase alternative. `Supabase` is built on top of `PostgreSQL`, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.\n",
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"\n",
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">[PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) also known as `Postgres`, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance.\n",
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"\n",
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"This notebook shows how to use `Supabase` and `pgvector` as your VectorStore.\n",
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"\n",
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@ -125,21 +127,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"id": "90afc6df",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# If you're storing your Supabase and OpenAI API keys in a .env file, you can load them with dotenv\n",
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"from dotenv import load_dotenv\n",
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@ -164,18 +157,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"id": "aac9563e",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-04-19 20:12:28,593:INFO - NumExpr defaulting to 8 threads.\n"
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]
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}
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],
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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