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pgml-cms/docs/api/client-sdk/collections.md

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---
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description: >-
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Organizational building blocks of the SDK. Manage all documents and related
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chunks, embeddings, tsvectors, and pipelines.
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---
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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.
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{% endtab %}
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{% endtabs %}
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Documents can be merged by setting the `merge` option. On conflict, new document keys will override old document keys.
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Documents can be merged by setting the `merge` option. On conflict, new document keys will override old document keys.
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{% tabs %}
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{% tab title="JavaScript" %}

pgml-cms/docs/api/client-sdk/pipelines.md

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---
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description: >-
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Pipelines are composed of a model, splitter, and additional optional
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arguments.
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---
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# Pipelines
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`Pipeline`s define the schema for the transformation of documents. Different `Pipeline`s can be used for different tasks.
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{% endtab %}
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Enabling a `Pipeline` will cause it to automatically run on all documents it may have missed while disabled.
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Enabling a `Pipeline` will cause it to automatically run on all documents it may have missed while disabled.
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## **Remove a Pipeline**
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pgml-cms/docs/api/client-sdk/tutorials/semantic-search.md

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---
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description: Example for Semantic Search
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description: >-
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JavaScript and Python code snippets for using instructor models in more
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advanced search use cases.
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---
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# Semantic Search

pgml-cms/docs/api/sql-extension/pgml.deploy.md

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

pgml-cms/docs/api/sql-extension/pgml.embed.md

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---
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Generate high quality embeddings with faster end-to-end vector operations
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without an additional vector database.
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---
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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/api/sql-extension/pgml.predict/README.md

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---
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description: >-
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Batch predict from data in a table. Online predict with parameters passed in a
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query. Automatically reuse pre-processing steps from training.
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---
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# pgml.predict()
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## API
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### Classification Example
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If you've already been through the [pgml.train](../../../api/sql-extension/pgml.train/ "mention") examples, you can see the predictive results of those models:
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If you've already been through the [pgml.train](../pgml.train "mention") examples, you can see the predictive results of those models:
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```sql
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SELECT

pgml-cms/docs/api/sql-extension/pgml.train/README.md

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---
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description: >-
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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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with the function parameters.
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Pre-process and pull data to train a model using any of 50 different ML
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algorithms.
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# pgml.train()

pgml-cms/docs/api/sql-extension/pgml.transform/README.md

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description: >-
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Perform dozens of state-of-the-art natural language processing (NLP) tasks
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with thousands of models. Serve with the same Postgres infrastructure.
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pgml-cms/docs/api/sql-extension/pgml.tune.md

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description: Fine tune open-source models on your own data.
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---
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# pgml.tune()
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## Fine Tuning

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