Feb 20, 2024 · Feb 6, 2024 · Feb 7, 2024 · Feb 8, 2024 · Feb 10, 2024 · Feb 10, 2024
diff --git a/pgml-cms/docs/introduction/apis/client-sdks/collections.md b/pgml-cms/docs/introduction/apis/client-sdks/collections.md
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 description: >-
  Organizational building blocks of the SDK. Manage all documents and related chunks, embeddings, tsvectors, and pipelines.
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 # Collections

 Collections are the organizational building blocks of the SDK. They manage all documents and related chunks, embeddings, tsvectors, and pipelines.
diff --git a/pgml-cms/docs/introduction/apis/client-sdks/pipelines.md b/pgml-cms/docs/introduction/apis/client-sdks/pipelines.md
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 description: >-
  Pipelines are composed of a model, splitter, and additional optional arguments.
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 # Pipelines

 Pipelines are composed of a Model, Splitter, and additional optional arguments. Collections can have any number of Pipelines. Each Pipeline is ran everytime documents are upserted.
diff --git a/...s/docs/introduction/apis/client-sdks/tutorials/extractive-question-answering.md b/...s/docs/introduction/apis/client-sdks/tutorials/extractive-question-answering.md
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 description: >-
  JavaScript and Python code snippets for end-to-end question answering.
 ---
 # Extractive Question Answering

 Here is the documentation for the JavaScript and Python code snippets performing end-to-end question answering:
diff --git a/...troduction/apis/client-sdks/tutorials/semantic-search-using-instructor-model.md b/...troduction/apis/client-sdks/tutorials/semantic-search-using-instructor-model.md
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 description: >-
  JavaScript and Python code snippets for using instructor models in more advanced search use cases.
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 # Semantic Search using Instructor model

 This shows using instructor models in the `pgml` SDK for more advanced use cases.
diff --git a/.../docs/introduction/apis/client-sdks/tutorials/summarizing-question-answering.md b/.../docs/introduction/apis/client-sdks/tutorials/summarizing-question-answering.md
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 description: >-
  JavaScript and Python code snippets for text summarization.
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 # Summarizing Question Answering

 Here are the Python and JavaScript examples for text summarization using `pgml` SDK
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md
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 description: >-
    Release trained models when ML quality metrics computed during training improve. Track model deployments over time and rollback if needed.
 ---

 # pgml.deploy()

 ## Deployments
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.embed.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.embed.md
 ---
 description: >-
    Generate high quality embeddings with faster end-to-end vector operations without an additional vector database.
 ---

 # pgml.embed()

 Embeddings are a numeric representation of text. They are used to represent words and sentences as vectors, an array of numbers. Embeddings can be used to find similar pieces of text, by comparing the similarity of the numeric vectors using a distance measure, or they can be used as input features for other machine learning models, since most algorithms can't use text directly.
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.predict/README.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.predict/README.md
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 description: >-
  Batch predict from data in a table. Online predict with parameters passed in a query. Automatically reuse pre-processing steps from training.
 ---

 # pgml.predict()

 ## API
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/README.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/README.md
 ---
 description: >-
  The training function is at the heart of PostgresML. It's a powerful single
  mechanism that can handle many different training tasks which are configurable
  with the function parameters.
  Pre-process and pull data to train a model using any of 50 different ML algorithms.
 ---

 # pgml.train()
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.transform/README.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.transform/README.md
 ---
 description: >-
  Perform dozens of state-of-the-art natural language processing (NLP) tasks with thousands of models. Serve with the same Postgres infrastructure.
 layout:
  title:
    visible: true
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.tune.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.tune.md
 ---
 description: >-
  Fine tune open-source models on your own data.
 ---

 # pgml.tune()

 ## Fine Tuning
diff --git a/...resources/benchmarks/ggml-quantized-llm-support-for-huggingface-transformers.md b/...resources/benchmarks/ggml-quantized-llm-support-for-huggingface-transformers.md
 ---
 description: >-
  Quantization allows PostgresML to fit larger models in less RAM.
 ---
 # GGML Quantized LLM support for Huggingface Transformers


diff --git a/...ms/docs/resources/benchmarks/making-postgres-30-percent-faster-in-production.md b/...ms/docs/resources/benchmarks/making-postgres-30-percent-faster-in-production.md
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 description: >-
  Anyone who runs Postgres at scale knows that performance comes with trade offs.
 ---
 # Making Postgres 30 Percent Faster in Production

 Anyone who runs Postgres at scale knows that performance comes with trade offs. The typical playbook is to place a pooler like PgBouncer in front of your database and turn on transaction mode. This makes multiple clients reuse the same server connection, which allows thousands of clients to connect to your database without causing a fork bomb.
diff --git a/pgml-cms/docs/resources/benchmarks/million-requests-per-second.md b/pgml-cms/docs/resources/benchmarks/million-requests-per-second.md
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 description: >-
  The question "Does it Scale?" has become somewhat of a meme in software engineering.
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 # Million Requests per Second

 The question "Does it Scale?" has become somewhat of a meme in software engineering. There is a good reason for it though, because most businesses plan for success. If your app, online store, or SaaS becomes popular, you want to be sure that the system powering it can serve all your new customers.
diff --git a/pgml-cms/docs/resources/benchmarks/mindsdb-vs-postgresml.md b/pgml-cms/docs/resources/benchmarks/mindsdb-vs-postgresml.md
 ---
 description: >-
    Compare two projects that both aim to provide an SQL interface to ML algorithms and the data they require.
 ---
 # MindsDB vs PostgresML

 ## Introduction
diff --git a/...sources/benchmarks/postgresml-is-8-40x-faster-than-python-http-microservices.md b/...sources/benchmarks/postgresml-is-8-40x-faster-than-python-http-microservices.md
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 description: >-
  PostgresML is a simpler alternative to that ever-growing complexity.
 ---
 # PostgresML is 8-40x faster than Python HTTP microservices

 Machine learning architectures can be some of the most complex, expensive and _difficult_ arenas in modern systems. The number of technologies and the amount of required hardware compete for tightening headcount, hosting, and latency budgets. Unfortunately, the trend in the industry is only getting worse along these lines, with increased usage of state-of-the-art architectures that center around data warehouses, microservices and NoSQL databases.
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,7 @@
		---
		description: >-
		Organizational building blocks of the SDK. Manage all documents and related chunks, embeddings, tsvectors, and pipelines.
		---
		# Collections

		Collections are the organizational building blocks of the SDK. They manage all documents and related chunks, embeddings, tsvectors, and pipelines.
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,7 @@
		---
		description: >-
		Pipelines are composed of a model, splitter, and additional optional arguments.
		---
		# Pipelines

		Pipelines are composed of a Model, Splitter, and additional optional arguments. Collections can have any number of Pipelines. Each Pipeline is ran everytime documents are upserted.
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,7 @@
		---
		description: >-
		JavaScript and Python code snippets for end-to-end question answering.
		---
		# Extractive Question Answering

		Here is the documentation for the JavaScript and Python code snippets performing end-to-end question answering:
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,8 @@
		---
		description: >-
		Release trained models when ML quality metrics computed during training improve. Track model deployments over time and rollback if needed.
		---

		# pgml.deploy()

		## Deployments
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,8 @@
		---
		description: >-
		Generate high quality embeddings with faster end-to-end vector operations without an additional vector database.
		---

		# pgml.embed()

		Embeddings are a numeric representation of text. They are used to represent words and sentences as vectors, an array of numbers. Embeddings can be used to find similar pieces of text, by comparing the similarity of the numeric vectors using a distance measure, or they can be used as input features for other machine learning models, since most algorithms can't use text directly.
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,8 @@
		---
		description: >-
		Batch predict from data in a table. Online predict with parameters passed in a query. Automatically reuse pre-processing steps from training.
		---

		# pgml.predict()

		## API
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,8 +1,6 @@
		---
		description: >-
		The training function is at the heart of PostgresML. It's a powerful single
		mechanism that can handle many different training tasks which are configurable
		with the function parameters.
		Pre-process and pull data to train a model using any of 50 different ML algorithms.
		---

		# pgml.train()
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,4 +1,6 @@
		---
		description: >-
		Perform dozens of state-of-the-art natural language processing (NLP) tasks with thousands of models. Serve with the same Postgres infrastructure.
		layout:
		title:
		visible: true
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,8 @@
		---
		description: >-
		Fine tune open-source models on your own data.
		---

		# pgml.tune()

		## Fine Tuning
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,3 +1,7 @@
		---
		description: >-
		Quantization allows PostgresML to fit larger models in less RAM.
		---
		# GGML Quantized LLM support for Huggingface Transformers


Expand Down