From 0f761c0cdaca55a66a62657b00129421c8f23d4b Mon Sep 17 00:00:00 2001 From: Dj Walker-Morgan Date: Wed, 29 May 2024 15:54:16 +0100 Subject: [PATCH 1/7] Updated image, some small text changes Signed-off-by: Dj Walker-Morgan --- .../ai-ml/images/pgai-overview.png | 4 ++-- .../ai-ml/install-tech-preview.mdx | 18 ++++++++++-------- .../edb-postgres-ai/ai-ml/overview.mdx | 8 +++++--- 3 files changed, 17 insertions(+), 13 deletions(-) diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png index 1d82930782f..d219ec2ffa1 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png +++ b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6202298c01b91caa7a8ea8b81fe3265cf4a9bc13bf6715ebc6008a92613211fd -size 67416 +oid sha256:aa0ca7bad0dfc1b494df9353390adebf324f4b7ccd26630955d378b2f949f5ea +size 67791 diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx index 89535cad785..e6c97ab137d 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx @@ -10,9 +10,11 @@ The preview release of pgai is distributed as a self-contained Docker container ## Configuring and running the container image If you haven’t already, sign up for an EDB account and log in to the EDB container registry. + + Login to docker with your the username tech-preview and your EDB Repo 2.0 Subscription Token as your password: -``` +```shell docker login docker.enterprisedb.com -u tech-preview -p __OUTPUT__ Login Succeeded @@ -20,7 +22,7 @@ Login Succeeded Download the pgai container image: -``` +```shell docker pull docker.enterprisedb.com/tech-preview/pgai __OUTPUT__ ... @@ -30,26 +32,26 @@ docker.enterprisedb.com/tech-preview/pgai:latest Specify a password to use for Postgres in the environment variable PGPASSWORD. The tech preview container will set up Postgres with this password and use it to connect to it. In bash or zsh set it as follows: -``` +```shell export PGPASSWORD= ``` You can use the pgai extension with encoder LLMs in Open AI or with open encoder LLMs from HuggingFace. If you want to use Open AI you also must provide your API key for that in the OPENAI_API_KEY environment variable: -``` +```shell export OPENAI_API_KEY= ``` You can use the pgai extension with AI data stored in Postgres tables or on S3 compatible object storage. To work with object storage you need to specify the ACCESS_KEY and SECRET_KEY environment variables:. -``` +```shell export ACCESS_KEY= export SECRET_KEY= ``` Start the pgai tech preview container with the following command. It makes the tech preview PostgreSQL database available on local port 15432: -``` +```shell docker run -d --name pgai \ -e ACCESS_KEY=$ACCESS_KEY \ -e SECRET_KEY=$SECRET_KEY \ @@ -65,13 +67,13 @@ docker run -d --name pgai \ If you haven’t yet, install the Postgres command-line tools. If you’re on a Mac, using Homebrew, you can install it as follows: -``` +```shell brew install libpq ``` Connect to the tech preview PostgreSQL running in the container. Note that this relies on $PGPASSWORD being set - if you’re using a different terminal for this part, make sure you re-export the password: -``` +```shell $ psql -h localhost -p 15432 -U postgres postgres __OUTPUT__ psql (16.1, server 16.3 (Debian 16.3-1.pgdg120+1)) diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx index becb50a0f74..91df905c519 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx @@ -11,9 +11,11 @@ The pgai extension is currently available as a tech preview. It will be continuo ![PGAI Overview](images/pgai-overview.png) -pgai introduced the concept of a “retriever” that you can create for a given type and location of AI data. Currently pgai supports unstructured plain text documents as well as a set of image formats. This data can either reside in regular columns of a Postgres table or it can reside in an S3 compatible object storage bucket. +pgai introduces the concept of a “retriever” that you can create for a given type and location of AI data. Currently pgai supports unstructured plain text documents as well as a set of image formats. This data can either reside in regular columns of a Postgres table or it can reside in an S3 compatible object storage bucket. -A retriever encapsulates all processing that is needed to make the AI data in the provided source location searchable and retrievable via similarity. The application just needs to create a retriever via the pgai.create_retriever() function. When auto_embedding=TRUE is specified the pgai extension will automatically generate embeddings for all the data in the source location. Otherwise it will be up to the application to request a bulk generation of embeddings via pgai.refresh_retriever(). +A retriever encapsulates all processing that is needed to make the AI data in the provided source location searchable and retrievable through similarity. The application just needs to create a retriever via the `pgai.create_retriever()` function. When `auto_embedding=TRUE` is specified the pgai extension will automatically generate embeddings for all the data in the source location. + +Otherwise it will be up to the application to request a bulk generation of embeddings using `pgai.refresh_retriever()`. Auto embedding is currently supported for AI data stored in Postgres tables and it automates the embedding updates using Postgres triggers. You can also combine the two options by using pgai.refresh_retriever() to embed all previously existing data and also setting `auto_embedding=TRUE` to generate embeddings for all new and changed data from now on. @@ -21,7 +23,7 @@ All embedding generation, storage, indexing and management is handled by the pga Once a retriever is created and all embeddings are up to date, the application can just use pgai.retrieve() to run a similarity search and retrieval by providing a query input. When the retriever is created for text data, the query input is also a text term. For image retrievers the query input is an image. The pgai retriever makes sure to use the same encoder LLM for the query input, conducts a similarity search and finally returns the ranked list of similar data from the source location. -pgai currently supports a broad list of open encoder LLMs from HuggingFace as well as a set of OpenAI encoders. Just consult the list of supported encoder LLMs in the pgai.encoders meta table. HuggingFace LLMs are running locally on the Postgres node, while OpenAI encoders involve a call out to the OpenAI cloud service. +pgai currently supports a broad list of open encoder LLMs from HuggingFace as well as a set of OpenAI encoders. Consult the list of supported encoder LLMs in the pgai.encoders meta table. HuggingFace LLMs are running locally on the Postgres node, while OpenAI encoders involve a call out to the OpenAI cloud service. From a6605b076445b463bfc549eccf8e192fa6b6fd45 Mon Sep 17 00:00:00 2001 From: Dj Walker-Morgan Date: Wed, 29 May 2024 16:48:20 +0100 Subject: [PATCH 2/7] model corrections, full sync with final docs Signed-off-by: Dj Walker-Morgan --- .../ai-ml/install-tech-preview.mdx | 7 ++-- ...functions.mdx => additional_functions.mdx} | 37 +++++++++++++++++-- .../working-with-ai-data-in-S3.mdx | 35 +----------------- .../working-with-ai-data-in-postgres.mdx | 12 +++--- 4 files changed, 45 insertions(+), 46 deletions(-) rename advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/{standalone-embedding-functions.mdx => additional_functions.mdx} (52%) diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx index e6c97ab137d..527fa43c002 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx @@ -74,7 +74,7 @@ brew install libpq Connect to the tech preview PostgreSQL running in the container. Note that this relies on $PGPASSWORD being set - if you’re using a different terminal for this part, make sure you re-export the password: ```shell -$ psql -h localhost -p 15432 -U postgres postgres +psql -h localhost -p 15432 -U postgres postgres __OUTPUT__ psql (16.1, server 16.3 (Debian 16.3-1.pgdg120+1)) Type "help" for help. @@ -86,15 +86,16 @@ postgres=# Install the pgai extension: ```sql -postgres=# create extension pgai cascade; +create extension pgai cascade; __OUTPUT__ NOTICE: installing required extension "plpython3u" NOTICE: installing required extension "vector" CREATE EXTENSION +postgres=# ``` ```sql -postgres=# \dx +\dx __OUTPUT__ List of installed extensions Name | Version | Schema | Description diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/standalone-embedding-functions.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/additional_functions.mdx similarity index 52% rename from advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/standalone-embedding-functions.mdx rename to advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/additional_functions.mdx index 809273d4102..893657651e0 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/standalone-embedding-functions.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/additional_functions.mdx @@ -1,10 +1,12 @@ --- -title: Stand-alone Embedding Functions in pgai -navTitle: Stand-alone Embedding Functions -description: Use the pgai extension to generate embeddings for images and text. +title: Additional functions and stand-alone embedding in pgai +navTitle: Additional functions +description: Other pgai extension functions and how to generate embeddings for images and text. --- -Use generate_single_image_embedding function to get embeddings for the given image. Currently, model_provider can only be openai or huggingface. You can check the list of valid embedding models and model providers from the Encoders Supported PGAI section. +## Standalone embedding + +Use the `generate_single_image_embedding` function to get embeddings for the given image. Currently, `model_provider` can only be `openai` or `huggingface`. You can check the list of valid embedding models and model providers from the Encoders Supported PGAI section. ```sql SELECT pgai.generate_single_image_embedding( @@ -37,6 +39,7 @@ __OUTPUT__ (1 row) ``` +## Supported encoders You can check the list of valid embedding models and model providers from pgai.encoders table @@ -50,4 +53,30 @@ __OUTPUT__ (2 rows) ``` +## Available functions + +You can find the complete list of currently available functions of the pgai extension by selecting from `information_schema.routines` any `routine_name` belonging to the pgai routine schema: + + +``` +SELECT routine_name from information_schema.routines WHERE routine_schema='pgai'; +__OUTPUT__ + routine_name +--------------------------------- + init + create_pg_retriever + create_s3_retriever + _embed_table_update + refresh_retriever + retrieve + retrieve_via_s3 + register_prompt_template + render_prompt + generate + ag + rag + generate_text_embedding + generate_single_image_embedding +(14 rows) +``` diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-S3.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-S3.mdx index 905359f81c8..fab9c220596 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-S3.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-S3.mdx @@ -14,12 +14,11 @@ First let’s create a retriever for images stored on s3-compatible object stora SELECT pgai.create_s3_retriever( 'image_embeddings', -- Name of the similarity retrieval setup 'public', -- Schema of the source table - 'img_id', -- Primary key 'clip-vit-base-patch32', -- Embeddings encoder model for similarity data 'img', -- data type, could be either img or text 'torsten', -- S3 bucket name - 'https://s3.us-south.cloud-object-storage.appdomain.cloud', -- s3 endpoint address - '' -- prefix + '', -- prefix + 'https://s3.us-south.cloud-object-storage.appdomain.cloud' -- s3 endpoint address ); __OUTPUT__ create_s3_retriever @@ -57,33 +56,3 @@ __OUTPUT__ (1 row) ``` -If you set the `ACCESS_KEY` and `SECRET_KEY` you can use the following queries to run without an `s3_endpoint` setting using a command like this: - -```sql -SELECT pgai.create_s3_retriever( - 'img_file_embeddings', -- Name of the similarity retrieval setup - 'demo', -- Schema of the source table - 'img_id', -- Primary key - 'clip-vit-base-patch32', -- Embeddings encoder model for similarity data - 'img', -- data type - 'bilge-ince-test' -- S3 bucket name -); -__OUTPUT__ - create_s3_retriever ---------------------- - -(1 row) -``` - -```sql -SELECT pgai.refresh_retriever('img_file_embeddings'); -``` - -```sql -SELECT data from pgai.retrieve_via_s3('img_file_embeddings', - 1, - 'bilge-ince-test', - 'kirpis_small.jpg', - 'http://s3.eu-central-1.amazonaws.com' - ); -``` diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-postgres.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-postgres.mdx index f0af183386a..aec055f14c4 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-postgres.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/using-tech-preview/working-with-ai-data-in-postgres.mdx @@ -22,14 +22,14 @@ CREATE TABLE ``` -Now let’s create a retriever with the just created products table as the source. We specify product_id as the unique key column to and we define the product_name and description columns to use for the similarity search by the retriever. We use the text-embeddings-3-small open encoder model from HuggingFace. We set auto_embedding to True so that any future insert, update or delete to the source table will automatically generate, update or delete also the corresponding embedding. We provide a name for the retriever so that we can identify and reference it subsequent operations: +Now let’s create a retriever with the just created products table as the source. We specify product_id as the unique key column to and we define the product_name and description columns to use for the similarity search by the retriever. We use the `all-MiniLM-L6-v2` open encoder model from HuggingFace. We set `auto_embedding` to True so that any future insert, update or delete to the source table will automatically generate, update or delete also the corresponding embedding. We provide a name for the retriever so that we can identify and reference it subsequent operations: ```sql SELECT pgai.create_pg_retriever( 'product_embeddings_auto', -- Retriever name 'public', -- Schema 'product_id', -- Primary key - 'text-embedding-3-small', -- embedding model + 'all-MiniLM-L6-v2', -- embedding model 'text', -- data type 'products', -- Source table ARRAY['product_name', 'description'], -- Columns to vectorize @@ -87,7 +87,7 @@ SELECT pgai.create_pg_retriever( 'product_embeddings_bulk', -- Retriever name 'public', -- Schema 'product_id', -- Primary key - 'text-embedding-3-small', -- embedding model + 'all-MiniLM-L6-v2', -- embedding model 'text', -- data type 'products', -- Source table ARRAY['product_name', 'description'], -- Columns to vectorize @@ -103,7 +103,7 @@ __OUTPUT__ We created this second retriever on the products table after we have inserted the AI records there. If we run a retrieve operation now we would not get back any results: -``` +```sql SELECT data FROM pgai.retrieve( 'I like it', -- The query text to retrieve the top similar data 5, -- top K @@ -117,7 +117,7 @@ __OUTPUT__ That’s why we first need to run a bulk generation of embeddings. This is achieved via the `refresh_retriever()` function: -``` +```sql SELECT pgai.refresh_retriever( 'product_embeddings_bulk' -- name of the retriever ); @@ -229,4 +229,4 @@ __OUTPUT__ We used the two different retrievers for the same source data just to demonstrate the workings of auto embedding compared to explicit `refresh_retriever()`. In practice you may want to combine auto embedding and refresh_retriever() in a single retriever to conduct an initial embedding of data that existed before you created the retriever and then rely on auto embedding for any future data that is ingested, updated or deleted. -You should consider relying on refresh_retriever() only, without auto embedding, if you typically ingest a lot of AI data at once in batch manner. +You should consider relying on `refresh_retriever()` only, without auto embedding, if you typically ingest a lot of AI data at once in a batched manner. From e94c95017b4a47ab6209cc697d49f6f966940609 Mon Sep 17 00:00:00 2001 From: Dj Walker-Morgan Date: Thu, 30 May 2024 17:26:02 +0100 Subject: [PATCH 3/7] Full image replacement Signed-off-by: Dj Walker-Morgan --- .../edb_postgres_ai_platform_artificial_intelligence-v4.png | 3 +++ advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png | 3 --- advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png delete mode 100644 advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png b/advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png new file mode 100644 index 00000000000..bab6416ed98 --- /dev/null +++ b/advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acc112c5b73f7e848148a0668f09055047ee7ee846e97654153dfe60aea231f8 +size 242510 diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png deleted file mode 100644 index d219ec2ffa1..00000000000 --- a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:aa0ca7bad0dfc1b494df9353390adebf324f4b7ccd26630955d378b2f949f5ea -size 67791 diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx index 91df905c519..fc13572e447 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx @@ -9,7 +9,7 @@ At the heart of EDB Postgres® AI is the EDB Postgres AI database (pgai). This b The pgai extension is currently available as a tech preview. It will be continuously extended with new functions. Here comes a description of the major functionality available to date. -![PGAI Overview](images/pgai-overview.png) +![PGAI Overview](images/edb_postgres_ai_platform_artificial_intelligence-v4.png) pgai introduces the concept of a “retriever” that you can create for a given type and location of AI data. Currently pgai supports unstructured plain text documents as well as a set of image formats. This data can either reside in regular columns of a Postgres table or it can reside in an S3 compatible object storage bucket. From 14b6329371dce7a0eb925e8c0d7790f4ad070530 Mon Sep 17 00:00:00 2001 From: Dj Walker-Morgan Date: Fri, 31 May 2024 08:27:19 +0100 Subject: [PATCH 4/7] Revert "Full image replacement" This reverts commit 0d3887a8ffc861083eedb31604e418bd45a36b0a. --- .../edb_postgres_ai_platform_artificial_intelligence-v4.png | 3 --- advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png | 3 +++ advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) delete mode 100644 advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png create mode 100644 advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png b/advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png deleted file mode 100644 index bab6416ed98..00000000000 --- a/advocacy_docs/edb-postgres-ai/ai-ml/images/edb_postgres_ai_platform_artificial_intelligence-v4.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:acc112c5b73f7e848148a0668f09055047ee7ee846e97654153dfe60aea231f8 -size 242510 diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png new file mode 100644 index 00000000000..d219ec2ffa1 --- /dev/null +++ b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aa0ca7bad0dfc1b494df9353390adebf324f4b7ccd26630955d378b2f949f5ea +size 67791 diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx index fc13572e447..91df905c519 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx @@ -9,7 +9,7 @@ At the heart of EDB Postgres® AI is the EDB Postgres AI database (pgai). This b The pgai extension is currently available as a tech preview. It will be continuously extended with new functions. Here comes a description of the major functionality available to date. -![PGAI Overview](images/edb_postgres_ai_platform_artificial_intelligence-v4.png) +![PGAI Overview](images/pgai-overview.png) pgai introduces the concept of a “retriever” that you can create for a given type and location of AI data. Currently pgai supports unstructured plain text documents as well as a set of image formats. This data can either reside in regular columns of a Postgres table or it can reside in an S3 compatible object storage bucket. From 1f0de8e01b371168d668cfde6b6427997ec9ad21 Mon Sep 17 00:00:00 2001 From: Dj Walker-Morgan Date: Fri, 31 May 2024 11:15:50 +0100 Subject: [PATCH 5/7] Update image with background to show white text Signed-off-by: Dj Walker-Morgan --- advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png index d219ec2ffa1..afe4e5b5c43 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png +++ b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:aa0ca7bad0dfc1b494df9353390adebf324f4b7ccd26630955d378b2f949f5ea -size 67791 +oid sha256:1c96c8a593ede10be25dfd55acddf812ec28358166d8772769a54f0c316cb239 +size 62873 From 6a3b18fd40164f0a2c19dadfa368cb25d935bc4b Mon Sep 17 00:00:00 2001 From: Dj Walker-Morgan Date: Fri, 31 May 2024 14:58:25 +0100 Subject: [PATCH 6/7] Moved filenames around to avoid stacked LFS commit issue Signed-off-by: Dj Walker-Morgan --- .../{pgai-overview.png => pgai-overview-withbackground.png} | 0 advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename advocacy_docs/edb-postgres-ai/ai-ml/images/{pgai-overview.png => pgai-overview-withbackground.png} (100%) diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png b/advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview-withbackground.png similarity index 100% rename from advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview.png rename to advocacy_docs/edb-postgres-ai/ai-ml/images/pgai-overview-withbackground.png diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx index 91df905c519..8b118c057dc 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/overview.mdx @@ -9,7 +9,7 @@ At the heart of EDB Postgres® AI is the EDB Postgres AI database (pgai). This b The pgai extension is currently available as a tech preview. It will be continuously extended with new functions. Here comes a description of the major functionality available to date. -![PGAI Overview](images/pgai-overview.png) +![PGAI Overview](images/pgai-overview-withbackground.png) pgai introduces the concept of a “retriever” that you can create for a given type and location of AI data. Currently pgai supports unstructured plain text documents as well as a set of image formats. This data can either reside in regular columns of a Postgres table or it can reside in an S3 compatible object storage bucket. From 4fcd62cd6c16560277582eba6ce12a6dcc454b98 Mon Sep 17 00:00:00 2001 From: Dj Walker-Morgan <126472455+djw-m@users.noreply.github.com> Date: Mon, 3 Jun 2024 09:34:31 +0100 Subject: [PATCH 7/7] Update advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx Co-authored-by: gvasquezvargas --- advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx b/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx index 527fa43c002..23c4d5a7843 100644 --- a/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx +++ b/advocacy_docs/edb-postgres-ai/ai-ml/install-tech-preview.mdx @@ -12,7 +12,7 @@ The preview release of pgai is distributed as a self-contained Docker container If you haven’t already, sign up for an EDB account and log in to the EDB container registry. -Login to docker with your the username tech-preview and your EDB Repo 2.0 Subscription Token as your password: +Log in to docker with your the username tech-preview and your EDB Repo 2.0 Subscription Token as your password: ```shell docker login docker.enterprisedb.com -u tech-preview -p