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Commitb444b82

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Dan doc landing page (#1320)
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‎pgml-cms/docs/introduction/apis/client-sdks/collections.md

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Organizational building blocks of the SDK. Manage all documents and related chunks, embeddings, tsvectors, and pipelines.
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#Collections
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Collections are the organizational building blocks of the SDK. They manage all documents and related chunks, embeddings, tsvectors, and pipelines.

‎pgml-cms/docs/introduction/apis/client-sdks/pipelines.md

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Pipelines are composed of a model, splitter, and additional optional arguments.
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#Pipelines
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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.

‎pgml-cms/docs/introduction/apis/client-sdks/tutorials/extractive-question-answering.md

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JavaScript and Python code snippets for end-to-end question answering.
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#Extractive Question Answering
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Here is the documentation for the JavaScript and Python code snippets performing end-to-end question answering:

‎pgml-cms/docs/introduction/apis/client-sdks/tutorials/semantic-search-using-instructor-model.md

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JavaScript and Python code snippets for using instructor models in more advanced search use cases.
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#Semantic Search using Instructor model
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This shows using instructor models in the`pgml` SDK for more advanced use cases.

‎pgml-cms/docs/introduction/apis/client-sdks/tutorials/summarizing-question-answering.md

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JavaScript and Python code snippets for text summarization.
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#Summarizing Question Answering
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Here are the Python and JavaScript examples for text summarization using`pgml` SDK

‎pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md

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Release trained models when ML quality metrics computed during training improve. Track model deployments over time and rollback if needed.
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#pgml.deploy()
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##Deployments

‎pgml-cms/docs/introduction/apis/sql-extensions/pgml.embed.md

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Generate high quality embeddings with faster end-to-end vector operations without an additional vector database.
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#pgml.embed()
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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.

‎pgml-cms/docs/introduction/apis/sql-extensions/pgml.predict/README.md

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Batch predict from data in a table. Online predict with parameters passed in a query. Automatically reuse pre-processing steps from training.
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#pgml.predict()
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##API

‎pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/README.md

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The training function is at the heart of PostgresML. It's a powerful single
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mechanism that can handle many different training tasks which are configurable
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Pre-process and pull data to train a model using any of 50 different ML algorithms.
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#pgml.train()

‎pgml-cms/docs/introduction/apis/sql-extensions/pgml.transform/README.md

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Perform dozens of state-of-the-art natural language processing (NLP) tasks with thousands of models. Serve with the same Postgres infrastructure.
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