+
-
+
diff --git a/apps/docs/components/Navigation/NavigationMenu/HomeMenuIconPicker.tsx b/apps/docs/components/Navigation/NavigationMenu/HomeMenuIconPicker.tsx
index a2b9fa52511..bc7c67c60d3 100644
--- a/apps/docs/components/Navigation/NavigationMenu/HomeMenuIconPicker.tsx
+++ b/apps/docs/components/Navigation/NavigationMenu/HomeMenuIconPicker.tsx
@@ -21,6 +21,7 @@ import {
IconMenuSwift,
IconMenuStatus,
IconMenuKotlin,
+ IconMenuAI,
} from './HomeMenuIcons'
function getMenuIcon(menuKey: string, width: number = 16, height: number = 16) {
@@ -41,6 +42,8 @@ function getMenuIcon(menuKey: string, width: number = 16, height: number = 16) {
return -
+
-
{filteredSections.map((section) => {
return (
- {item.notes}
+
+ {item.notes}
+
)}
{/* // parameters */}
diff --git a/apps/docs/docs/ref/csharp/release-notes.mdx b/apps/docs/docs/ref/csharp/release-notes.mdx
index 777621307dd..291636cfb08 100644
--- a/apps/docs/docs/ref/csharp/release-notes.mdx
+++ b/apps/docs/docs/ref/csharp/release-notes.mdx
@@ -3,6 +3,174 @@ id: release-notes
title: Release Notes
---
+## 0.11.0 - 2023-05-24
+
+- Update dependency: postgrest-csharp@3.2.0
+ - General codebase and QOL improvements. Exceptions are generally thrown through `PostgrestException` now instead
+ of `Exception`. A `FailureHint.Reason` is provided with failures if possible to parse.
+ - `AddDebugListener` is now available on the client to help with debugging
+ - Merges [#65](https://github.com/supabase-community/postgrest-csharp/pull/65) Cleanup + Add better exception handling
+ - Merges [#66](https://github.com/supabase-community/postgrest-csharp/pull/66) Local test Fixes
+ - Fixes [#67](https://github.com/supabase-community/postgrest-csharp/issues/67) Postgrest Reference attribute is
+ producing StackOverflow for circular references
+- Update dependency: gotrue-csharp@4.0.2
+ - [#58](https://github.com/supabase-community/gotrue-csharp/issues/58) - Add support for the `reauthentication` endpoint which allows for secure password changes.
+- Update dependency: realtime-csharp@6.0.1
+ - Updates publishing action for future packages, includes README and icon.
+ - Merges [#28](https://github.com/supabase-community/realtime-csharp/pull/28) and [#30](https://github.com/supabase-community/realtime-csharp/pull/30)
+ - The realtime client now takes a "fail-fast" approach. On establishing an initial connection, client will throw
+ a `RealtimeException` in `ConnectAsync()` if the socket server is unreachable. After an initial connection has been
+ established, the **client will continue attempting reconnections indefinitely until disconnected.**
+ - [Major, New] C# `EventHandlers` have been changed to `delegates`. This should allow for cleaner event data access over
+ the previous subclassed `EventArgs` setup. Events are scoped accordingly. For example, the `RealtimeSocket` error
+ handlers will receive events regarding socket connectivity; whereas the `RealtimeChannel` error handlers will receive
+ events according to `Channel` joining/leaving/etc. This is implemented with the following methods prefixed by (
+ Add/Remove/Clear):
+ - `RealtimeBroadcast.AddBroadcastEventHandler`
+ - `RealtimePresence.AddPresenceEventHandler`
+ - `RealtimeSocket.AddStateChangedHandler`
+ - `RealtimeSocket.AddMessageReceivedHandler`
+ - `RealtimeSocket.AddHeartbeatHandler`
+ - `RealtimeSocket.AddErrorHandler`
+ - `RealtimeClient.AddDebugHandler`
+ - `RealtimeClient.AddStateChangedHandler`
+ - `RealtimeChannel.AddPostgresChangeHandler`
+ - `RealtimeChannel.AddMessageReceivedHandler`
+ - `RealtimeChannel.AddErrorHandler`
+ - `Push.AddMessageReceivedHandler`
+ - [Major, new] `ClientOptions.Logger` has been removed in favor of `Client.AddDebugHandler()` which allows for
+ implementing custom logging solutions if desired.
+ - A simple logger can be set up with the following:
+ ```c#
+ client.AddDebugHandler((sender, message, exception) => Debug.WriteLine(message));
+ ```
+ - [Major] `Connect()` has been marked `Obsolete` in favor of `ConnectAsync()`
+ - Custom reconnection logic has been removed in favor of using the built-in logic from `Websocket.Client@4.6.1`.
+ - Exceptions that are handled within this library have been marked as `RealtimeException`s.
+ - The local, docker-composed test suite has been brought back (as opposed to remotely testing on live supabase servers)
+ to test against.
+ - Comments have been added throughout the entire codebase and an `XML` file is now generated on build.
+
+## 0.10.0 - 2023-05-14
+
+- Changes options to require `Supabase.SupabaseOptions.SessionPersistor` from using `ISupabaseSessionHandler`
+ to `IGotrueSessionPersistance
+ {examples.map((x) => (
+
+
+export const examples = [
+ {
+ name: 'Headless Vector Search',
+ description: 'A toolkit to perform vector similarity search on your knowledge base embeddings.',
+ href: '/guides/ai/examples/headless-vector-search',
+ },
+ {
+ name: 'Image Search with OpenAI CLIP',
+ description: 'Implement image search with the OpenAI CLIP Model and Supabase Vector.',
+ href: '/guides/ai/examples/image-search-openai-clip',
+ },
+ {
+ name: 'Hugging Face inference',
+ description: 'Generate image captions using Hugging Face.',
+ href: '/guides/ai/examples/huggingface-image-captioning',
+ },
+ {
+ name: 'OpenAI completions',
+ description: 'Generate GPT text completions using OpenAI in Edge Functions.',
+ href: '/guides/ai/examples/openai',
+ },
+ {
+ name: 'Building ChatGPT Plugins',
+ description: 'Use Supabase as a Retrieval Store for your ChatGPT plugin.',
+ href: '/guides/ai/examples/building-chatgpt-plugins',
+ },
+ {
+ name: 'Vector search with Next.js and OpenAI',
+ description:
+ 'Learn how to build a ChatGPT-style doc search powered by Next.js, OpenAI, and Supabase.',
+ href: '/guides/ai/examples/nextjs-vector-search',
+ },
+]
+
+## Case studies
+
+
+
+
+
+ {x.description}
+
+
+
+
+ ))}
+
+ {customers.map((x) => (
+
+
+export const customers = [
+ {
+ name: 'Mendable switches from Pinecone to Supabase for PostgreSQL vector embeddings',
+ description:
+ 'How Mendable boosts efficiency and accuracy of chat powered search for documentation using Supabase with pgvector',
+ href: 'https://supabase.com/customers/mendableai',
+ },
+ {
+ name: 'Markprompt: GDPR-Compliant AI Chatbots for Docs and Websites',
+ description:
+ "AI-powered chatbot platform, Markprompt, empowers developers to deliver efficient and GDPR-compliant prompt experiences on top of their content, by leveraging Supabase's secure and privacy-focused database and authentication solutions",
+ href: 'https://supabase.com/customers/markprompt',
+ },
+]
+
+export const Page = ({ children }) =>
+
+
+ {x.description}
+
+
+
+ ))}
+
+
+Phrases 1 and 2 would be plotted close to each other, since their meanings are similar. We would expect phrase 3 to live somewhere far away since it isn't related. If we had a fourth phrase, “Sally ate Swiss cheese”, this might exist somewhere between phrase 3 (cheese can go on sandwiches) and phrase 1 (mice like Swiss cheese).
+
+In this example we only have 2 dimensions: the X and Y axis. In reality, we would need many more dimensions to effectively capture the complexities of human language.
+
+## Using embeddings
+
+Compared to our 2-dimensional example above, most embedding models will output many more dimensions. For example OpenAI's `text-embedding-ada-002` model outputs 1536 dimensions.
+
+Why is this useful? Once we have generated embeddings on multiple texts, it is trivial to calculate how similar they are using vector math operations like cosine distance. A common use case for this is search. Your process might look something like this:
+
+1. Pre-process your knowledge base and generate embeddings for each page
+2. Store your embeddings to be referenced later (more on this)
+3. Build a search page that prompts your user for input
+4. Take user's input, generate a one-time embedding, then perform a similarity search against your pre-processed embeddings.
+5. Return the most similar pages to the user
+
+## See also
+
+- [Structured and Unstructured embeddings](/docs/guides/ai/structured-unstructured-embeddings)
+
+export const Page = ({ children }) => 
+
+
+
+### Step 1: Fork the ChatGPT Retrieval Plugin repository
+
+Fork the ChatGPT Retrieval Plugin repository to your GitHub account and clone it to your local machine. Read through the `README.md` file to understand the project structure.
+
+### Step 2: Install dependencies
+
+Choose your desired datastore provider and remove unused dependencies from `pyproject.toml`. For this example, we'll use Supabase. And install dependencies with Poetry:
+
+```bash
+poetry install
+```
+
+### Step 3: Create a Supabase project
+
+Create a [Supabase project](https://supabase.com/dashboard) and database by following the instructions [here](https://supabase.com/docs/guides/platform). Export the environment variables required for the retrieval plugin to work:
+
+```bash
+export OPENAI_API_KEY=
-
-
-
## Setup
- Open your Supabase project dashboard or [create a new project](https://app.supabase.com/projects).
diff --git a/apps/docs/pages/guides/ai/examples/image-search-openai-clip.mdx b/apps/docs/pages/guides/ai/examples/image-search-openai-clip.mdx
new file mode 100644
index 00000000000..0ca6fcd19d2
--- /dev/null
+++ b/apps/docs/pages/guides/ai/examples/image-search-openai-clip.mdx
@@ -0,0 +1,177 @@
+import Layout from '~/layouts/DefaultGuideLayout'
+
+export const meta = {
+ id: 'examples-image-search-python',
+ title: 'Image Search with OpenAI CLIP',
+ description: 'Implement image search with the OpenAI CLIP Model and Supabase Vector.',
+ subtitle: 'Implement image search with the OpenAI CLIP Model and Supabase Vector.',
+}
+
+The [OpenAI CLIP Model](https://github.com/openai/CLIP) was trained on a variety of (image, text)-pairs. You can use the CLIP model for:
+
+- Text-to-Image / Image-To-Text / Image-to-Image / Text-to-Text Search
+- You can fine-tune it on your own image and text data with the regular SentenceTransformers training code.
+
+[SentenceTransformers](https://www.sbert.net/examples/applications/image-search/README.html) provides models that allow you to embed images and text into the same vector space. You can use this to find similar images as well as to implement image search.
+
+You can find the full application code as a Python Poetry project on [GitHub](https://github.com/supabase/supabase/tree/master/examples/ai/image_search#image-search-with-supabase-vector).
+
+## Create a new Python Project with Poetry
+
+[Poetry](https://python-poetry.org/) provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
+
+```shell
+pip install poetry
+```
+
+Then initialize a new project:
+
+```shell
+poetry new image-search
+```
+
+## Setup Supabase project
+
+If you haven't already, [install the Supabase CLI](/docs/guides/cli), then initialize Supabase in the root of your newly created poetry project:
+
+```shell
+supabase init
+```
+
+Next, start your local Supabase stack:
+
+```shell
+supabase start
+```
+
+This will start up the Supabase stack locally and print out a bunch of environtment details, including your local `DB URL`. Make a note of that for later user.
+
+## Install the Dependencies
+
+We will need to add the following dependencies to our project:
+
+- [`vecs`](https://github.com/supabase/vecs#vecs): Supabase Vector Python Client.
+- [`sentence-transformers`](https://huggingface.co/sentence-transformers/clip-ViT-B-32): a framework for sentence, text and image embeddings (used with OpenAI CLIP model)
+- [`matplotlib`](https://matplotlib.org/): for displaying our image result
+
+```shell
+poetry add vecs sentence-transformers matplotlib
+```
+
+## Import the necessary dependencies
+
+At the top of your main python script, import the dependencies and store your `DB URL` from above in a variable:
+
+```python
+from PIL import Image
+from sentence_transformers import SentenceTransformer
+import vecs
+from matplotlib import pyplot as plt
+from matplotlib import image as mpimg
+
+DB_CONNECTION = "postgresql://postgres:postgres@localhost:54322/postgres"
+```
+
+## Create embeddings for your images
+
+In the root of your project, create a new folder called `images` and add some images. You can use the images from the example project on [GitHub](https://github.com/supabase/supabase/tree/master/examples/ai/image_search/images) or you can find license free images on [unsplash](https://unsplash.com).
+
+Next, create a `seed` method, which will create a new Supabase Vector Collection, generate embeddings for your images, and upsert the embeddings into your database:
+
+```python
+def seed():
+ # create vector store client
+ vx = vecs.create_client(DB_CONNECTION)
+
+ # create a collection of vectors with 3 dimensions
+ images = vx.create_collection(name="image_vectors", dimension=512)
+
+ # Load CLIP model
+ model = SentenceTransformer('clip-ViT-B-32')
+
+ # Encode an image:
+ img_emb1 = model.encode(Image.open('./images/one.jpg'))
+ img_emb2 = model.encode(Image.open('./images/two.jpg'))
+ img_emb3 = model.encode(Image.open('./images/three.jpg'))
+ img_emb4 = model.encode(Image.open('./images/four.jpg'))
+
+ # add records to the *images* collection
+ images.upsert(
+ vectors=[
+ (
+ "one.jpg", # the vector's identifier
+ img_emb1, # the vector. list or np.array
+ {"type": "jpg"} # associated metadata
+ ), (
+ "two.jpg",
+ img_emb2,
+ {"type": "jpg"}
+ ), (
+ "three.jpg",
+ img_emb3,
+ {"type": "jpg"}
+ ), (
+ "four.jpg",
+ img_emb4,
+ {"type": "jpg"}
+ )
+ ]
+ )
+ print("Inserted images")
+
+ # index the collection for fast search performance
+ images.create_index()
+ print("Created index")
+```
+
+Add this method as a script in your `pyproject.toml` file:
+
+```toml
+[tool.poetry.scripts]
+seed = "image_search.main:seed"
+search = "image_search.main:search"
+```
+
+After activating the virtual environtment with `poetry shell` you can now run your seed script via `poetry run seed`. You can inspect the generated embeddings in your local database by visiting the local Supabase dashboard at [localhost:54323](http://localhost:54323/project/default/editor), selecting the `vecs` schema, and the `image_vectors` database.
+
+## Perform an Image Search from a Text Query
+
+With Supabase Vector we can easily query our embeddings. We can use either an image as search input or alternative we can generate an embedding from a string input and use that as the query input:
+
+```python
+def search():
+ # create vector store client
+ vx = vecs.create_client(DB_CONNECTION)
+ images = vx.get_collection(name="image_vectors")
+
+ # Load CLIP model
+ model = SentenceTransformer('clip-ViT-B-32')
+ # Encode text query
+ query_string = "a bike in front of a red brick wall"
+ text_emb = model.encode(query_string)
+
+ # query the collection filtering metadata for "type" = "jpg"
+ results = images.query(
+ query_vector=text_emb, # required
+ limit=1, # number of records to return
+ filters={"type": {"$eq": "jpg"}}, # metadata filters
+ )
+ result = results[0]
+ print(result)
+ plt.title(result)
+ image = mpimg.imread('./images/' + result)
+ plt.imshow(image)
+ plt.show()
+```
+
+By limiting the query to one result, we can show the most relevant image to the user. Finally we use `matplotlib` to show the image result to the user.
+
+That's it, go ahead and test it out by running `poetry run search` and you will be presented with an image of a "bike in front of a red brick wall".
+
+## Conclusion
+
+With just a couple of lines of Python you are able to implement image search as well as reverse image search using OpenAI's CLIP model and Supabase Vector.
+
+export const Page = ({ children }) =>
-
-
-
-
-
-
+OpenAI provides a [completions API](https://platform.openai.com/docs/api-reference/completions) that allows you to use their generative GPT models in your own applications.
-Use the [OpenAI completions API](https://platform.openai.com/docs/api-reference/completions) in Supabase Edge Functions.
+OpenAI's API is intended to be used from the server-side. Supabase offers Edge Functions to make it easy to interact with third party APIs like OpenAI.
+
+## Setup Supabase project
+
+If you haven't already, [install the Supabase CLI](/docs/guides/cli) and initialize your project:
+
+```shell
+supabase init
+```
+
+## Create edge function
+
+Scaffold a new edge function called `openai` by running:
+
+```shell
+supabase functions new openai
+```
+
+A new edge function will now exist under `./supabase/functions/openai/index.ts`.
+
+We'll design the function to take your user's query (via POST request) and forward it to OpenAI's API.
```ts index.ts
import 'xhr_polyfill'
@@ -31,7 +46,7 @@ serve(async (req) => {
prompt: query,
max_tokens: 256,
temperature: 0,
- stream: true,
+ stream: false,
}
return fetch('https://api.openai.com/v1/completions', {
@@ -45,12 +60,28 @@ serve(async (req) => {
})
```
+Note that we are setting `stream` to `false` which will wait until the entire response is complete before returning. If you wish to stream GPT's response word-by-word back to your client, set `stream` to `true`.
+
+## Create OpenAI key
+
+You may have noticed we were passing `OPENAI_API_KEY` in the Authorization header to OpenAI. To generate this key, go to https://platform.openai.com/account/api-keys and create a new secret key.
+
+After getting the key, copy it into a new file called `.env.local` in your `./supabase` folder:
+
+```
+OPENAI_API_KEY=your-key-here
+```
+
## Run locally
+Serve the edge function locally by running:
+
```bash
supabase functions serve --env-file ./supabase/.env.local --no-verify-jwt
```
+Notice how we are passing in the `.env.local` file.
+
Use cURL or Postman to make a POST request to http://localhost:54321/functions/v1/openai.
```bash
@@ -59,8 +90,12 @@ curl -i --location --request POST http://localhost:54321/functions/v1/openai \
--data '{"query":"What is Supabase?"}'
```
+You should see a GPT response come back from OpenAI!
+
## Deploy
+Deploy your function to the cloud by runnning:
+
```bash
supabase functions deploy --no-verify-jwt openai
supabase secrets set --env-file ./supabase/.env.local
diff --git a/apps/docs/pages/guides/ai/google-colab.mdx b/apps/docs/pages/guides/ai/google-colab.mdx
new file mode 100644
index 00000000000..a3438246727
--- /dev/null
+++ b/apps/docs/pages/guides/ai/google-colab.mdx
@@ -0,0 +1,112 @@
+import Layout from '~/layouts/DefaultGuideLayout'
+
+export const meta = {
+ id: 'ai-google-colab',
+ title: 'Google Colab',
+ description: 'Use Google Colab to manage your Supabase Vector store.',
+ subtitle: 'Use Google Colab to manage your Supabase Vector store.',
+ sidebar_label: 'Google Colab',
+}
+
+Google Colab is a hosted Jupyter Notebook service. It provides free access to computing resources, including GPUs and TPUs, and is well-suited to machine learning, data science, and education. We can use Colab to manage collections using [Supabase Vecs](/docs/ai/vecs-python-client).
+
+In this tutorial we'll connect to a database running on the Supabase [platform](https://app.supabase.com/). If you don't already have a database, you can create one here: [database.new](https://database.new).
+
+## Create a new notebook
+
+Start by visiting [colab.research.google.com](https://colab.research.google.com/). There you can create a new notebook.
+
+
+
+## Install Vecs
+
+We'll use the Supabase Vector client, [Vecs](/docs/guides/ai/vecs-python-client), to manage our collections.
+
+At the top of the notebook add the notebook paste the following code and hit the "execute" button (`ctrl+enter`):
+
+```py
+pip install vecs
+```
+
+
+
+## Connect to your database
+
+Find the Postgres connection string for your Supabase project in the [database settings](https://app.supabase.com/_/settings/database) of the dashboard. Copy the "URI" format, which should look something like `postgresql:/postgres:
+
+## Resources
+
+- Official [Hugging Face site](https://huggingface.co/).
+- Official [Hugging Face JS docs](https://huggingface.co/docs/huggingface.js).
+- [Generate image captions](/docs/guides/ai/examples/huggingface-image-captioning) using Hugging Face.
+
+export const Page = ({ children }) => +- LangChain supports our database as a vector store, using pgvector. [Doc](https://hwchase17.github.io/langchainjs/docs/modules/indexes/vector_stores/supabase/)
- refine week: building a CRUD app with refine & Supabase. [Full series](https://refine.dev/week-of-refine/) diff --git a/apps/www/_blog/2023-04-14-launch-week-7-community-highlights.mdx b/apps/www/_blog/2023-04-14-launch-week-7-community-highlights.mdx index 5f74875cf0d..78c164718e0 100644 --- a/apps/www/_blog/2023-04-14-launch-week-7-community-highlights.mdx +++ b/apps/www/_blog/2023-04-14-launch-week-7-community-highlights.mdx @@ -38,7 +38,7 @@ Along with our new Clickhouse [Foreign Data Wrapper](https://supabase.github.io/ ### LangChain -It’s always fun when two open source communities join forces. LangChain is an open source framework for developing applications powered by language models. This week, to support the Supabase AI Hackathon, they released a [Supabase x Langchain template](https://github.com/langchain-ai/langchain-template-supabase), one of the fastest ways to build AI applications. Their community recently made two contributions for Supabase: adding Supabase as a [Vector Store](https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase), and a neat [Hybrid Search](https://js.langchain.com/docs/modules/indexes/retrievers/supabase-hybrid) algorithm using Postgres’ Full Text Search. The also +It’s always fun when two open source communities join forces. LangChain is an open source framework for developing applications powered by language models. This week, to support the Supabase AI Hackathon, they released a [Supabase x LangChain template](https://github.com/langchain-ai/langchain-template-supabase), one of the fastest ways to build AI applications. Their community recently made two contributions for Supabase: adding Supabase as a [Vector Store](https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase), and a neat [Hybrid Search](https://js.langchain.com/docs/modules/indexes/retrievers/supabase-hybrid) algorithm using Postgres’ Full Text Search. The also [Read the integration docs.](https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase) diff --git a/apps/www/_blog/2023-05-25-chatgpt-plugins-support-postgres.mdx b/apps/www/_blog/2023-05-25-chatgpt-plugins-support-postgres.mdx new file mode 100644 index 00000000000..c094a0f0826 --- /dev/null +++ b/apps/www/_blog/2023-05-25-chatgpt-plugins-support-postgres.mdx @@ -0,0 +1,268 @@ +--- +title: 'ChatGPT plugins now support Postgres & Supabase' +description: Supabase recently contributed to the OpenAI Retrieval Plugin repo with a Postgres and a Supabase implementation to help developers build ChatGPT plugins using pgvector. +tags: + - AI + - tutorial + - postgres +date: '2023-05-25' +toc_depth: 2 +author: egor_romanov,paul_copplestone +image: 2023-05-25-chatgpt-plugins-support-postgres/chatgpt-plugins-support-postgres.jpeg +thumb: 2023-05-25-chatgpt-plugins-support-postgres/chatgpt-plugins-support-postgres.jpeg +--- + +One of the challenges that ChatGPT faces is being able to answer questions from a private dataset. We can solve this with “retrieval plugins”, which allow ChatGPT to access information from a database. + +Supabase recently contributed to the OpenAI repo with a [Postgres](https://github.com/openai/chatgpt-retrieval-plugin#postgres) and a [Supabase](https://github.com/openai/chatgpt-retrieval-plugin#supabase) implementation to help developers build plugins using pgvector. + +Let’s dig into the specifics of Retrieval plugins, then we can implement an example - we’ll ingest all of the Postgres docs into a Supabase database, then get ChatGPT to answer questions. It’s a contrived example since ChatGPT already knows about Postgres, but what other data source would Supabase want to use? + +## What is ChatGPT Retrieval Plugin? + +ChatGPT recently released [Plugins](https://openai.com/blog/chatgpt-plugins) which help ChatGPT access up-to-date information, run computations, or use third-party services. + +A [Retrieval Plugin](https://github.com/openai/chatgpt-retrieval-plugin) is a Python project designed to inject external data into the ChatGPT. It allows ChatGPT to dynamically pull relevant information into conversations from your data sources. This could be PDF documents, Confluence, or Notion knowledge bases. + +A retrieval plugin does a few things: + +1. Turn documents into smaller chunks. +2. Converts chunks into embeddings using OpenAI's `text-embedding-ada-002` model. +3. Stores the embeddings into a vector database. +4. Queries the vector database for relevant documents when a question is asked. + +You can choose your preferred vector database provider from a list of [supported options](https://github.com/openai/chatgpt-retrieval-plugin#choosing-a-vector-database). + +## Adding Supabase and Postgres as Datastore options for ChatGPT Retrieval Plugin + +We've implemented two vector provider options: one for Postgres and one for Supabase. The main differences are: + +- The Postgres version uses the `psycopg2` python library to directly connect to the database. +- The Supabase version interacts with the database via PostgREST. This is helpful if you want to use [Row Level Security](https://supabase.com/docs/guides/auth/row-level-security) or if you are planning to use the data in the Retrieval store beyond ChatGPT. + +The Postgres implementation is great to start with because there are now a [large number of providers](https://github.com/pgvector/pgvector/issues/54) supporting pgvector. + +Both have the same schema so you can easily switch between them: + +```sql +create table if not exists documents ( + id text primary key default gen_random_uuid()::text, + source text, + source_id text, + content text, + document_id text, + author text, + url text, + created_at timestamptz default now(), + embedding vector(1536) +); +``` + +When you create the retrieval store inside your database, a stored function is implemented to query and find relevant information for your question to ChatGPT: + +```sql +create or replace function match_page_sections( + in_embedding vector(1536), + in_match_count int default 3, + in_document_id text default '%%', + in_source_id text default '%%', + in_source text default '%%', + in_author text default '%%', + in_start_date timestamptz default '-infinity', + in_end_date timestamptz default 'infinity' +) +returns table ( + id text, + source text, + source_id text, + document_id text, + url text, + created_at timestamptz, + author text, + content text, + embedding vector(1536), + similarity float +) +language plpgsql +as $$ +#variable_conflict use_variable +begin +return query + +select + documents.id, + documents.source, + documents.source_id, + documents.document_id, + documents.url, + documents.created_at, + documents.author, + documents.content, + documents.embedding, + (documents.embedding <#> in_embedding) * -1 as similarity +from + documents +where + in_start_date <= documents.created_at and + documents.created_at <= in_end_date and + (documents.source_id like in_source_id or documents.source_id is null) and + (documents.source like in_source or documents.source is null) and + (documents.author like in_author or documents.author is null) and + (documents.document_id like in_document_id or documents.document_id is null) +order by + documents.embedding <#> in_embedding +limit + in_match_count; +end; +$$; +``` + +We apply filters based on the source, author, document, and date, and find the closest embeddings using the inner product distance function. This function offers the best performance when the embeddings are normalized, which is the case for OpenAI embeddings. The similarity is calculated as: `(documents.embedding <#> in_embedding) * -1 as similarity`. And that’s it, you can now seamlessly use the Retrieval Plugin with a Postgres Database underneath, eliminating the need for any manual implementation on your end. + +## Example: Chat with Postgres Docs + +Let’s build an example where we can “ask ChatGPT questions” about the Postgres documentation. + +This will require several steps: + +1. Download all the [Postgres docs as a PDF](https://www.postgresql.org/files/documentation/pdf/15/postgresql-15-US.pdf) +2. Convert the docs into chunks of embedded text and store them in Supabase +3. Run our plugin locally so that we can ask questions about the Postgres docs. + +
+
+
+
+### Step 1: Fork the ChatGPT Retrieval Plugin repository
+
+Fork the ChatGPT Retrieval Plugin repository to your GitHub account and clone it to your local machine. Read through the `README.md` file to understand the project structure.
+
+### Step 2: Install dependencies
+
+Choose your desired datastore provider and remove unused dependencies from `pyproject.toml`. For this example, we'll use Supabase. And install dependencies with Poetry:
+
+```bash
+poetry install
+```
+
+### Step 3: Create a Supabase project
+
+Create a [Supabase project](https://supabase.com/dashboard) and database by following the instructions [here](https://supabase.com/docs/guides/platform). Export the environment variables required for the retrieval plugin to work:
+
+```bash
+export OPENAI_API_KEY=
+
+
+
+## Best Overall Project
+
+### Winner
+
+[Syno](https://github.com/ineffablesam/Syno) - by [@samuelP09301972](https://twitter.com/samuelP09301972)
+
+Syno changes the way you consume video content. Say goodbye to lengthy videos and say hello to concise, accurate summaries that capture the essence of each video. Experience the future of video summarization with Syno.
+
+
+
+### Runner Up
+
+[Study Genius](https://github.com/Salomondiei08/StudyAI-App.git) - by [@salomon_diei](https://twitter.com/salomon_diei)
+
+Study Genius is a mobile application that build a personalized learning experience for students. It provies a comprehensive platform for effective study, collaboration and personalized support for students.
+
+
+
+## Best FlutterFlow project
+
+### Winner
+
+[Lord Of The Memes](https://lord-of-the-memes.flutterflow.app/) - by [@mohonishc](https://twitter.com/mohonishc)
+
+Lord of the Memes is a fun interactive game to be played with friends where you have to react to various situations with meme gifs and compete to be crowned as the ultimate Meme Lord.
+
+
+
+### Runner Up
+
+[Colabity](https://app.flutterflow.io/project/cocolab-2n64lp) - by [@hichamics](https://twitter.com/hichamics)
+
+Colabity is an app that matches creative minds with each other to work on similar interest-based projects.
+
+
+
+## Best Dart Edge Project
+
+### Winner
+
+[ChatGlobe](https://chatglobe.vercel.app/) - by [@tmhk_tnht](https://twitter.com/tmhk_tnht)
+
+ChatGlobe an application that allows you to chat with people around the world in your own language. It translates the messages into multiple languages using Dart Edge and OpenAI API as they are sent.
+
+
+
+### Runner Up
+
+[Adventure Kingdom](https://github.com/Anujeshdahiya/adventurekingdom/tree/flutterflow) - by [@anujeshd](https://twitter.com/anujeshd), [@rishikhatri47](https://twitter.com/rishikhatri47), and [@Harshit_1320](https://twitter.com/Harshit_1320)
+
+Adventure Kingdom is an app for theme park in which you can check different park attraction informations.
+
+
+
+## Most technically impressive
+
+### Winner
+
+[Accident Detection](https://github.com/Guneetsinghtuli/supabase) - by [@Guneetsingh02](https://twitter.com/Guneetsingh02)
+
+Accident Detection is an app for detecting road accidents using machine learning techniques and utilizing inputs from a variety of sensors, such as accelerometers and GPS to detect and analyze data related to accidents, such as changes in speed, direction, and impact force.
+
+
+
+### Runner Up
+
+[IntelliTask](https://noga-dev.github.io/intellitask/) - by [@noga_dev](https://twitter.com/noga_dev)
+
+IntelliTask is a simple semi-automated todo app where the AI decides how important the task is and when it should be due.
+
+
+
+## Most visually pleasing
+
+### Winner
+
+[Caffeio](https://github.com/curregoz/caffeio-app) - by [@caurregoz](https://twitter.com/caurregoz)
+
+Caffeio an app for coffee aficionados. Explore a variety of coffee brewing methods like V60, AeroPress, and Chemex, with comprehensive guides and assistance to elevate your coffee experience. Uncover the captivating stories behind each method, deepening your appreciation for your favorites.
+
+
+
+### Runner Up
+
+[Decision Tales](https://zi30067ti310.zapp.page/) - by [@JudoUergens](https://twitter.com/JudoUergens)
+
+Decision Tales is an app that let's you follow your own path through fascinating stories. Each story has many possible outcomes, which you untangle with your decisions.
+
+
+
+## The Prizes
+
+[@samuelP09301972](https://twitter.com/samuelP09301972), the creator of Syno will receive a limited Flutter-themed Supabase keyboard.
+
+
+
+Each member of the winning and runner-up projects will receive a limited edition Flutter Hackathon swag pack including a t-shirt as proof of winning in the Flutter Hackathon.
+
+
+
+### Get Started Guides
+
+If you missed the hackathon, but still want to build awesome apps using Flutter and Supabase, here are some resources to get you started:
+
+- [Flutter Quick Start Guide](https://supabase.com/docs/guides/with-flutter)
+- [Supabase Flutter documentation](https://supabase.com/docs/reference/dart/introduction)
+- [Edge Functions Guide](https://supabase.com/docs/guides/functions)
+- [Setup Supabase on FlutterFlow guide](https://docs.flutterflow.io/data-and-backend/supabase/supabase-setup)
+- [How to Set Up Supabase Authentication in FlutterFlow video](https://www.youtube.com/watch?v=tL-sLPfWzVE)
diff --git a/apps/www/_customers/mendableai.mdx b/apps/www/_customers/mendableai.mdx
index 8b59a0c7246..625d5b5ffd4 100644
--- a/apps/www/_customers/mendableai.mdx
+++ b/apps/www/_customers/mendableai.mdx
@@ -28,7 +28,7 @@ misc: [{ label: 'Backed by', text: 'Y Combinator' }]
about: Mendable is Chat Powered Search for Documentation.
---
-[Mendable](http://mendable.ai/) provides a chat-powered search engine for technical documentation. Their AI-powered search tool makes it easier for developers and other technical users to find relevant information in complex documentation. Users can simply ask questions in natural language, and the tool returns the most relevant answers. Mendable's search engine also provides detailed analytics, which helps teams identify knowledge gaps and areas for improvement in their documentation. Mendable has integrated with some of the largest open source projects in the space such as Langchain and LlamaIndex.
+[Mendable](http://mendable.ai/) provides a chat-powered search engine for technical documentation. Their AI-powered search tool makes it easier for developers and other technical users to find relevant information in complex documentation. Users can simply ask questions in natural language, and the tool returns the most relevant answers. Mendable's search engine also provides detailed analytics, which helps teams identify knowledge gaps and areas for improvement in their documentation. Mendable has integrated with some of the largest open source projects in the space such as LangChain and LlamaIndex.
## The Challenge
diff --git a/apps/www/lib/authors.json b/apps/www/lib/authors.json
index 8eaff971cf1..d35168ab4c8 100644
--- a/apps/www/lib/authors.json
+++ b/apps/www/lib/authors.json
@@ -328,5 +328,12 @@
"position": "Product Management Leader @ AWS",
"author_url": "https://github.com/daltjoh-aws",
"author_image_url": "https://github.com/daltjoh-aws.png"
+ },
+ {
+ "author_id": "egor_romanov",
+ "author": "Egor Romanov",
+ "position": "Engineering",
+ "author_url": "https://github.com/egor-romanov",
+ "author_image_url": "https://github.com/egor-romanov.png"
}
]
diff --git a/apps/www/lib/redirects.js b/apps/www/lib/redirects.js
index 07b7367d81a..8302c7e8aba 100644
--- a/apps/www/lib/redirects.js
+++ b/apps/www/lib/redirects.js
@@ -1981,4 +1981,19 @@ module.exports = [
source: '/docs/guides/auth/auth-helpers/nextjs-server-components',
destination: '/docs/guides/auth/auth-helpers/nextjs',
},
+ {
+ permanent: true,
+ source: '/docs/guides/getting-started/openai/vector-search',
+ destination: '/docs/guides/ai/examples/docs-search',
+ },
+ {
+ permanent: true,
+ source: '/docs/guides/functions/examples/huggingface-image-captioning',
+ destination: '/docs/guides/ai/examples/huggingface-image-captioning',
+ },
+ {
+ permanent: true,
+ source: '/docs/guides/functions/examples/openai',
+ destination: '/docs/guides/ai/examples/openai',
+ },
]
diff --git a/apps/www/public/images/blog/2023-05-25-chatgpt-plugins-support-postgres/ask-chatgpt.png b/apps/www/public/images/blog/2023-05-25-chatgpt-plugins-support-postgres/ask-chatgpt.png
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diff --git a/apps/www/public/images/blog/flutter-hackathon-winners/syno.jpg b/apps/www/public/images/blog/flutter-hackathon-winners/syno.jpg
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diff --git a/docker/.env.example b/docker/.env.example
index 17bb463705f..21304c2557b 100644
--- a/docker/.env.example
+++ b/docker/.env.example
@@ -82,7 +82,8 @@ IMGPROXY_ENABLE_WEBP_DETECTION=true
############
# Functions - Configuration for Functions
############
-FUNCTIONS_HTTP_PORT=9002
+# NOTE: VERIFY_JWT applies to all functions. Per-function VERIFY_JWT is not supported yet.
+FUNCTIONS_VERIFY_JWT=false
############
# Logs - Configuration for Logflare
@@ -101,4 +102,4 @@ VECTOR_API_PORT=9001
# Google Cloud Project details
GOOGLE_PROJECT_ID=GOOGLE_PROJECT_ID
-GOOGLE_PROJECT_NUMBER=GOOGLE_PROJECT_NUMBER
\ No newline at end of file
+GOOGLE_PROJECT_NUMBER=GOOGLE_PROJECT_NUMBER
diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml
index 29c790d899d..3e28b3acdc4 100644
--- a/docker/docker-compose.yml
+++ b/docker/docker-compose.yml
@@ -250,10 +250,9 @@ services:
SUPABASE_URL: http://kong:8000
SUPABASE_ANON_KEY: ${ANON_KEY}
SUPABASE_SERVICE_ROLE_KEY: ${SERVICE_ROLE_KEY}
- SUPABASE_DB_URL: postgresql://postgres:${POSTGRES_PASSWORD}@{POSTGRES_DB}:${POSTGRES_PORT}/${POSTGRES_DB}"
- VERIFY_JWT: "false"
- ports:
- - ${FUNCTIONS_HTTP_PORT}:9000/tcp
+ SUPABASE_DB_URL: postgresql://postgres:${POSTGRES_PASSWORD}@{POSTGRES_DB}:${POSTGRES_PORT}/${POSTGRES_DB}
+ # TODO: Allow configuring VERIFY_JWT per function. This PR might help: https://github.com/supabase/cli/pull/786
+ VERIFY_JWT: "${FUNCTIONS_VERIFY_JWT}"
volumes:
- ./volumes/functions:/home/deno/functions:Z
command:
diff --git a/docker/volumes/api/kong.yml b/docker/volumes/api/kong.yml
index eb1a079eb27..ca90a29a3e4 100644
--- a/docker/volumes/api/kong.yml
+++ b/docker/volumes/api/kong.yml
@@ -153,6 +153,19 @@ services:
- /storage/v1/
plugins:
- name: cors
+
+ ## Edge Functions routes
+ - name: functions-v1
+ _comment: 'Edge Functions: /functions/v1/* -> http://functions:9000/*'
+ url: http://functions:9000/
+ routes:
+ - name: functions-v1-all
+ strip_path: true
+ paths:
+ - /functions/v1/
+ plugins:
+ - name: cors
+
## Analytics routes
- name: analytics-v1
_comment: 'Analytics: /analytics/v1/* -> http://logflare:4000/*'
diff --git a/docker/volumes/functions/hello/index.ts b/docker/volumes/functions/hello/index.ts
new file mode 100644
index 00000000000..f1e20b90e01
--- /dev/null
+++ b/docker/volumes/functions/hello/index.ts
@@ -0,0 +1,16 @@
+// Follow this setup guide to integrate the Deno language server with your editor:
+// https://deno.land/manual/getting_started/setup_your_environment
+// This enables autocomplete, go to definition, etc.
+
+import { serve } from "https://deno.land/std@0.177.1/http/server.ts"
+
+serve(async () => {
+ return new Response(
+ `"Hello from Edge Functions!"`,
+ { headers: { "Content-Type": "application/json" } },
+ )
+})
+
+// To invoke:
+// curl 'http://localhost:\"" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at an example review\n", + "data[\"text\"][5]" + ] + }, + { + "cell_type": "markdown", + "id": "955b2700-8242-40eb-ac3f-d479a0312693", + "metadata": {}, + "source": [ + "## Embedding Model\n", + "\n", + "Next, we can use the `sentence-transformers/all-MiniLM-L6-v2` model to create a 384 dimensional text embedding that represents the\n", + "semantic meaning of each review. These embeddings are what we'll use for near-duplicate detection." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ce9bc85b-e844-407c-a0ad-ccf6af3c8866", + "metadata": {}, + "outputs": [], + "source": [ + "from sentence_transformers import SentenceTransformer\n", + "\n", + "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" + ] + }, + { + "cell_type": "markdown", + "id": "9e82862d-440a-4f66-9ed7-0eaa6a0f4062", + "metadata": {}, + "source": [ + "## Initialize the Vecs Collection\n", + "\n", + "The [`vecs`](https://supabase.github.io/vecs/api/) library wraps a pythonic interface around PostgreSQL and pgvector.\n", + "A collection in `vecs` maps 1:1 with a PostgreSQL table." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d2771545-d209-4ceb-a222-ed139a4620f2", + "metadata": {}, + "outputs": [], + "source": [ + "import vecs\n", + "\n", + "# Substitute your connection string here\n", + "DB_CONNECTION = \"postgresql://postgres:password@localhost:5431/db\"\n", + "\n", + "# create vector store client\n", + "vx = vecs.create_client(DB_CONNECTION)\n", + "\n", + "# create a PostgreSQL/pgvector table named \"reviews\" to contain the review embeddings\n", + "reviews = vx.create_collection(name=\"reviews\", dimension=384)" + ] + }, + { + "cell_type": "markdown", + "id": "9ae030b4-cfd2-43bc-802f-e7ac4007d2ad", + "metadata": {}, + "source": [ + "## Create Embeddings for Each Review\n", + "\n", + "Now we can iterate over the dataset, producing embeddings for the reviews" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bd134310-9da1-4448-8358-9fc491c98e1e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "500it [07:06, 1.17it/s]\n" + ] + } + ], + "source": [ + "from typing import List, Dict, Tuple\n", + "from flupy import flu\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "\n", + "\n", + "batch_size = 50\n", + "\n", + "records: List[Tuple[str, np.ndarray, Dict]] = []\n", + "\n", + "# Iterate over the dataset in chunks\n", + "for chunk_ix, chunk in tqdm(flu(data['text']).chunk(batch_size).enumerate()):\n", + "\n", + " # Create embeddings for current chunk\n", + " embedding_chunk = model.encode(chunk)\n", + "\n", + " # Enumerate the embeddings and create a record to insert into the database\n", + " for row_ix, (text, embedding) in enumerate(zip(chunk, embedding_chunk)):\n", + " record_id = chunk_ix * batch_size + row_ix\n", + " records.append((f\"{record_id}\", embedding, {\"text\": text}))" + ] + }, + { + "cell_type": "markdown", + "id": "6ef285d7-dc7b-4576-ab2b-bf0a4ba06de4", + "metadata": {}, + "source": [ + "## Insert the Embeddings into Postgres" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6156e7f9-b78a-4ab9-8002-b09ec8716be2", + "metadata": {}, + "outputs": [], + "source": [ + "reviews.upsert(records)" + ] + }, + { + "cell_type": "markdown", + "id": "aa095a53-dd9c-4a3c-93bf-c54708c67765", + "metadata": {}, + "source": [ + "## Index the Collection\n", + "\n", + "Indexing the collection creates an index on the vector column in Postgres that significantly improves performance\n", + "of similarity queries." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6b96d3af-5592-4fbc-81ab-f77b4228ccaa", + "metadata": {}, + "outputs": [], + "source": [ + "reviews.create_index()" + ] + }, + { + "cell_type": "markdown", + "id": "371631e6-4995-484f-9d76-40ab5e7b2e16", + "metadata": {}, + "source": [ + "## Search for Near Duplicates\n", + "\n", + "Finally we can enumerate each review, searching for the most similar\n", + "reviews and displaying them if the results are near duplicates. We could then prune out the near-duplicate reviews\n", + "to make sure our viewers see a new and interesting opinion with each review they choose to read." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d2a5c699-7c04-48ad-9033-a310bbb0dffc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "824it [00:05, 187.11it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query_id: 817 \t result_id: 823 \t distance 0.1672 \n", + "\n", + " Query Text \n", + "\n", + " This has to be, by far, the absolute worst movie I have seen in the last 20 years. When I saw that Michael Madsen was in it I figured it couldn't be too bad a movie since he has been in some pretty decent films, and he was a pretty fair actor. WRONG! No one should waste their time on this film. I fast forwarded through 80 percent of it and I don't feel that I missed a thing. \n", + "\n", + " Result Text \n", + "\n", + " I agree totally with the last commenter this could be the worst movie ever made .I too had to fast forward through most of this movie. Michael Madsen must have done this movie as a favor to someone.The picture quality is grainy all the way through .And what little plot there is,is just plain stupid .I give this movie a 1 out of 10 if I could give it a lower score I would .Don't waste your time on this movie or you'll regret it. \n", + " --------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "8675it [00:47, 166.43it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query_id: 8663 \t result_id: 8669 \t distance 0.1532 \n", + "\n", + " Query Text \n", + "\n", + " Rodney Dangerfield is a great. He has done a lot of great works. But this one....is awful. The whole plot is whack. It could have been much better. The jokes in the movie aren't funny....their stupid. This was very not so hilarious. He can do much better than this. \n", + "\n", + " Result Text \n", + "\n", + " As much as I love Rodney Dangerfield, this was a terrible movie. The plot was kind of a holistic rip off of various movies, but unfortunately they forgot to rip off any good jokes. In addition it was annoying and boring and that's being kind. If you're looking for a good laugh, rent a copy of Private Parts. \n", + " --------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "10522it [00:58, 164.98it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query_id: 10486 \t result_id: 5437 \t distance 0.1634 \n", + "\n", + " Query Text \n", + "\n", + " Aside for being classic in the aspect of its cheesy lines and terrible acting, this film should never be watched unless you are looking for a good cure for your insomnia. I can't imagine anyone actually thinking this was a \"good movie.\" \n", + "\n", + " Result Text \n", + "\n", + " Aside from the horrendous acting and the ridiculous and ludicrous plot, this movie wasn't too bad. Unfortunately, that doesn't leave much movie not to suck. Do not waste your time on this film, even if you find yourself suffering from insomnia, as I did. Watch an infomercial instead. \n", + " --------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16405it [01:36, 97.07it/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query_id: 16401 \t result_id: 16424 \t distance 0.1583 \n", + "\n", + " Query Text \n", + "\n", + " This has to be the funniest stand up comedy I have ever seen. Eddie Izzard is a genius, he picks in Brits, Americans and everyone in between. His style is completely natural and completely hilarious. I doubt that anyone could sit through this and not laugh their a** off. Watch, enjoy, it's funny. \n", + "\n", + " Result Text \n", + "\n", + " Until I saw this special on HBO, I had never heard of Eddie Izzard. I sure am glad that I have now! He is one of the funniest comedians I have ever seen! Rarely has a comedian immersed himself so completely in his craft then Eddie. I could not stop laughing for the entire show. If you like to laugh you HAVE to see this special! \n", + " --------------------------------------------------------------------------------\n", + "query_id: 16401 \t result_id: 16408 \t distance 0.1663 \n", + "\n", + " Query Text \n", + "\n", + " This has to be the funniest stand up comedy I have ever seen. Eddie Izzard is a genius, he picks in Brits, Americans and everyone in between. His style is completely natural and completely hilarious. I doubt that anyone could sit through this and not laugh their a** off. Watch, enjoy, it's funny. \n", + "\n", + " Result Text \n", + "\n", + " This is another gem of a stand up show from Eddie Izzard . You cannot fail to laugh at the wide range of topics he talks about. He even takes the piss out of his American audiance at times and most of them didnt even realise it! A must see for anybody who likes comedians. 9 out of 10. \n", + " --------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18953it [01:54, 101.67it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query_id: 18936 \t result_id: 18942 \t distance 0.1325 \n", + "\n", + " Query Text \n", + "\n", + " World At War is perhaps the greatest documentary series of all time. The historical research is virtually flawless. Even after a quarter century, it is the most accurate and definitive documentary about WW2. An invaluable historical work that includes interviews with some of the most important and fascinating figures from the war. I highly recommend it as a learning experience. \n", + "\n", + " Result Text \n", + "\n", + " The world at war is one of the best documentaries about world war 2.
The 24 episodes cover the war and what it was like in the countries involved in it. The first episode tells us how the Hitler came to power, and how he was able to build up one of the strongest armies in the world. They also fucus on the military actions taken during the war, and the holocaust. One of the strongest and best documentaries ever made. All of you must watch this. Perfection! 10/10
\n", + " --------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21236it [02:09, 144.35it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query_id: 21202 \t result_id: 2618 \t distance 0.1586 \n", + "\n", + " Query Text \n", + "\n", + " I think Hollow Point is a funny film with some good moments I have never seen before in action movies. Well,both Tia Carrere and Thomas Ian Griffith aren't so good in acting, but Tia Carrere is nice and good looking girl, isn't it? But Donald Sutherland is superb in his role so-so mad gangster. \n", + "\n", + " Result Text \n", + "\n", + " Hollow point is an alright movie worth a half price rental or if nothing else is on a good time waster with no thought required. There are the requisite explosions and hammy acting and pretty ladies. A pretty good cast with Donald Sutherland, John Lithgow, and the lovely Tia Carrere. This cast plus a light hearted touch make for a not a great movie but a fun one..on a scale of one to ten ..a 4 \n", + " --------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "25000it [02:38, 157.92it/s]\n" + ] + } + ], + "source": [ + "for ix, text in tqdm(enumerate(data['text'])):\n", + "\n", + " # Load the next row from the dataset\n", + " query_results = reviews.fetch(ids=[f'{ix}'])\n", + " \n", + " (query_id, query_embedding, query_meta) = query_results[0]\n", + "\n", + " # Retrieve the original text from the row's metadata\n", + " query_text = query_meta[\"text\"]\n", + "\n", + " # To keep the output easy to read quickly, we'll restrict reviews to < 500 characters\n", + " # In the real-world you would not include this restriction\n", + " if len(query_text) < 500:\n", + "\n", + " # Query the review embeddings for the most similar 5 reviews\n", + " top_5 = reviews.query(\n", + " query_vector=query_embedding,\n", + " limit = 5,\n", + " include_metadata= True,\n", + " include_value=True\n", + " )\n", + "\n", + " # For each result\n", + " for result_id, result_distance, result_meta in top_5[1:]:\n", + " \n", + " result_text = result_meta[\"text\"]\n", + "\n", + " if (\n", + " # Since our query embedding is in the collection, the nearest result\n", + " # is always itself with a distance of 0. We exclude that record and \n", + " # review any others with a distance < 0.17\n", + " 0.01 < abs(result_distance) < 0.17\n", + " and len(result_text) < 500\n", + " and query_id < result_id\n", + " ):\n", + " print(\n", + " \"query_id:\", query_id,\n", + " \"\\t\", \"result_id:\", result_id,\n", + " \"\\t\", \"distance\", round(result_distance, 4),\n", + " \"\\n\\n\", \"Query Text\",\n", + " \"\\n\\n\", query_meta[\"text\"],\n", + " \"\\n\\n\", \"Result Text\",\n", + " \"\\n\\n\", result_meta[\"text\"],\n", + " \"\\n\", \"-\" * 80\n", + " )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/auth/nextjs/package.json b/examples/auth/nextjs/package.json index 523fe07bf51..97dd88bcd43 100644 --- a/examples/auth/nextjs/package.json +++ b/examples/auth/nextjs/package.json @@ -9,7 +9,7 @@ "lint": "next lint" }, "dependencies": { - "@supabase/auth-helpers-nextjs": "^0.7.0-next.8", + "@supabase/auth-helpers-nextjs": "^0.7.0", "@types/node": "^20.2.3", "@types/react": "^18.2.6", "@types/react-dom": "^18.2.4", diff --git a/examples/user-management/angular-user-management/package-lock.json b/examples/user-management/angular-user-management/package-lock.json index 88310a8268d..2ac8ec6b866 100644 --- a/examples/user-management/angular-user-management/package-lock.json +++ b/examples/user-management/angular-user-management/package-lock.json @@ -10619,9 +10619,9 @@ "dev": true }, "node_modules/socket.io-parser": { - "version": "4.2.1", - "resolved": "https://registry.npmjs.org/socket.io-parser/-/socket.io-parser-4.2.1.tgz", - "integrity": "sha512-V4GrkLy+HeF1F/en3SpUaM+7XxYXpuMUWLGde1kSSh5nQMN4hLrbPIkD+otwh6q9R6NOQBN4AMaOZ2zVjui82g==", + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/socket.io-parser/-/socket.io-parser-4.2.3.tgz", + "integrity": "sha512-JMafRntWVO2DCJimKsRTh/wnqVvO4hrfwOqtO7f+uzwsQMuxO6VwImtYxaQ+ieoyshWOTJyV0fA21lccEXRPpQ==", "dev": true, "dependencies": { "@socket.io/component-emitter": "~3.1.0", @@ -19733,9 +19733,9 @@ "dev": true }, "socket.io-parser": { - "version": "4.2.1", - "resolved": "https://registry.npmjs.org/socket.io-parser/-/socket.io-parser-4.2.1.tgz", - "integrity": "sha512-V4GrkLy+HeF1F/en3SpUaM+7XxYXpuMUWLGde1kSSh5nQMN4hLrbPIkD+otwh6q9R6NOQBN4AMaOZ2zVjui82g==", + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/socket.io-parser/-/socket.io-parser-4.2.3.tgz", + "integrity": "sha512-JMafRntWVO2DCJimKsRTh/wnqVvO4hrfwOqtO7f+uzwsQMuxO6VwImtYxaQ+ieoyshWOTJyV0fA21lccEXRPpQ==", "dev": true, "requires": { "@socket.io/component-emitter": "~3.1.0", diff --git a/package-lock.json b/package-lock.json index fb298385043..2436954f16f 100644 --- a/package-lock.json +++ b/package-lock.json @@ -14508,9 +14508,9 @@ } }, "node_modules/@supabase/shared-types": { - "version": "0.1.41", - "resolved": "https://registry.npmjs.org/@supabase/shared-types/-/shared-types-0.1.41.tgz", - "integrity": "sha512-qP1N39xRhi00L6g0FYJCWKAbPQbiVsMP/E3y5zdivOiL2VUFkbLKXYfvzzwhGj3eKdr7O/N/2VtZXWXm+DCx0g==" + "version": "0.1.43", + "resolved": "https://registry.npmjs.org/@supabase/shared-types/-/shared-types-0.1.43.tgz", + "integrity": "sha512-hv5FPcdAFwvWQHk5MgYoozlaNu6ybrr8TZp6+S23jj8jZNZwoY9qAVatG+tiD8LyVJvLd2GprBDsK1KfHLurNg==" }, "node_modules/@supabase/storage-js": { "version": "2.5.1", @@ -46420,7 +46420,7 @@ "@supabase/auth-helpers-nextjs": "^0.5.6", "@supabase/auth-helpers-react": "^0.3.1", "@supabase/react-data-grid": "^7.1.0-beta.7", - "@supabase/shared-types": "^0.1.40", + "@supabase/shared-types": "^0.1.43", "@supabase/supabase-js": "^2.21.0", "@tanstack/react-query": "^4.22.0", "@tanstack/react-query-devtools": "^4.22.0", @@ -57151,9 +57151,9 @@ } }, "@supabase/shared-types": { - "version": "0.1.41", - "resolved": "https://registry.npmjs.org/@supabase/shared-types/-/shared-types-0.1.41.tgz", - "integrity": "sha512-qP1N39xRhi00L6g0FYJCWKAbPQbiVsMP/E3y5zdivOiL2VUFkbLKXYfvzzwhGj3eKdr7O/N/2VtZXWXm+DCx0g==" + "version": "0.1.43", + "resolved": "https://registry.npmjs.org/@supabase/shared-types/-/shared-types-0.1.43.tgz", + "integrity": "sha512-hv5FPcdAFwvWQHk5MgYoozlaNu6ybrr8TZp6+S23jj8jZNZwoY9qAVatG+tiD8LyVJvLd2GprBDsK1KfHLurNg==" }, "@supabase/storage-js": { "version": "2.5.1", @@ -78525,7 +78525,7 @@ "@supabase/auth-helpers-react": "^0.3.1", "@supabase/postgres-meta": "^0.64.4", "@supabase/react-data-grid": "^7.1.0-beta.7", - "@supabase/shared-types": "^0.1.40", + "@supabase/shared-types": "^0.1.43", "@supabase/supabase-js": "^2.21.0", "@tailwindcss/typography": "^0.5.2", "@tanstack/react-query": "^4.22.0", diff --git a/packages/ui/src/components/Alert/Alert.test.js b/packages/ui/src/components/Alert/Alert.test.js deleted file mode 100644 index e6fdee11169..00000000000 --- a/packages/ui/src/components/Alert/Alert.test.js +++ /dev/null @@ -1,67 +0,0 @@ -import { fireEvent, render, screen } from '@testing-library/react' -import Alert from './Alert' - -const VARIANTS = ['success', 'danger', 'warning', 'info'] - -describe('#Alert', () => { - it('should render elements correctly', () => { - render(
Exceeded limit
+
+