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Merge branch 'master' into dan-product-left-nav-update
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‎pgml-cms/docs/README.md

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<figure><imgsrc=".gitbook/assets/ml_system.svg"alt="Machine Learning Infrastructure (2.0) by a16z"><figcaptionclass="mt-2"><p>PostgresML handles all of the functions <ahref="https://a16z.com/emerging-architectures-for-modern-data-infrastructure/">described by a16z</a></p></figcaption></figure>
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These capabilities are primarily provided by two open-source software projects, that may be used independently, but are designed to be used with the rest of the Postgres ecosystem:
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These capabilities are primarily provided by two open-source software projects, that may be used independently, but are designed to be usedtogetherwith the rest of the Postgres ecosystem:
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***pgml** - an open source extension for PostgreSQL. It adds support for GPUs and the latest ML & AI algorithms_inside_ the database with a SQL API and no additional infrastructure, networking latency, or reliability costs
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***PgCat** - an open source pooler for PostgreSQL. It abstracts the scalability and reliability concerns of managing a distributed cluster of Postgres databases. Client applications connect only to the pooler, which handles load balancing, sharding, and failover, outside of any single database server.
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*[**pgml**](/docs/api/sql-extension/) - an open source extension for PostgreSQL. It adds support for GPUs and the latest ML & AI algorithms_inside_ the database with a SQL API and no additional infrastructure, networking latency, or reliability costs.
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*[**PgCat**](/docs/product/pgcat/) - an open source connection pooler for PostgreSQL. It abstracts the scalability and reliability concerns of managing a distributed cluster of Postgres databases. Client applications connect only to the pooler, which handles load balancing, sharding, and failover, outside of any single database server.
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<figure><imgsrc=".gitbook/assets/architecture.png"alt="PostgresML architectural diagram"><figcaption></figcaption></figure>
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To learn more about how we designed PostgresML, take a look at our[architecture overview](/docs/resources/architecture/).
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##Client SDK
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The PostgresML team also provides[native language SDKs](https://github.com/postgresml/postgresml/tree/master/pgml-sdks/pgml) which implement best practices for common ML & AI applications. The JavaScript and Python SDKs are generated from the a core Rust library, which provides a uniform API, correctness and efficiency across all environments.
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The PostgresML team also provides[native language SDKs](/docs/api/client-sdk/) which implement best practices for common ML & AI applications. The JavaScript and Python SDKs are generated from the a core Rust library, which provides a uniform API, correctness and efficiency across all environments.
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While using the SDK is completely optional, SDK clients can perform advanced machine learning tasks in a single SQL request, without having to transfer additional data, models, hardware or dependencies to the client application.
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‎pgml-cms/docs/SUMMARY.md

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##API
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*[Overview](api/apis.md)
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*[SQL Extension](api/sql-extension/README.md)
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*[pgml.deploy()](api/sql-extension/pgml.deploy.md)
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*[SQL extension](api/sql-extension/README.md)
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*[pgml.embed()](api/sql-extension/pgml.embed.md)
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*[pgml.transform()](api/sql-extension/pgml.transform/README.md)
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*[Fill-Mask](api/sql-extension/pgml.transform/fill-mask.md)
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*[Question answering](api/sql-extension/pgml.transform/question-answering.md)
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*[Summarization](api/sql-extension/pgml.transform/summarization.md)
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*[Text classification](api/sql-extension/pgml.transform/text-classification.md)
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*[Text Generation](api/sql-extension/pgml.transform/text-generation.md)
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*[Text-to-Text Generation](api/sql-extension/pgml.transform/text-to-text-generation.md)
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*[Token Classification](api/sql-extension/pgml.transform/token-classification.md)
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*[Translation](api/sql-extension/pgml.transform/translation.md)
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*[Zero-shot Classification](api/sql-extension/pgml.transform/zero-shot-classification.md)
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*[pgml.deploy()](api/sql-extension/pgml.deploy.md)
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*[pgml.chunk()](api/sql-extension/pgml.chunk.md)
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*[pgml.generate()](api/sql-extension/pgml.generate.md)
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*[pgml.predict()](api/sql-extension/pgml.predict/README.md)
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*[Data Pre-processing](api/sql-extension/pgml.train/data-pre-processing.md)
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*[Hyperparameter Search](api/sql-extension/pgml.train/hyperparameter-search.md)
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*[Joint Optimization](api/sql-extension/pgml.train/joint-optimization.md)
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*[pgml.transform()](api/sql-extension/pgml.transform/README.md)
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*[Fill Mask](api/sql-extension/pgml.transform/fill-mask.md)
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*[Question Answering](api/sql-extension/pgml.transform/question-answering.md)
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*[Summarization](api/sql-extension/pgml.transform/summarization.md)
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*[Text Classification](api/sql-extension/pgml.transform/text-classification.md)
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*[Text Generation](api/sql-extension/pgml.transform/text-generation.md)
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*[Text-to-Text Generation](api/sql-extension/pgml.transform/text-to-text-generation.md)
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*[Token Classification](api/sql-extension/pgml.transform/token-classification.md)
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*[Translation](api/sql-extension/pgml.transform/translation.md)
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*[Zero-shot Classification](api/sql-extension/pgml.transform/zero-shot-classification.md)
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*[pgml.tune()](api/sql-extension/pgml.tune.md)
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*[Client SDK](api/client-sdk/README.md)
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*[Collections](api/client-sdk/collections.md)
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##Resources
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*[Architecture](resources/architecture/README.md)
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*[Why PostgresML?](resources/architecture/why-postgresml.md)
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*[FAQs](resources/faqs.md)
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*[Data Storage & Retrieval](resources/data-storage-and-retrieval/tabular-data.md)
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*[Tabular data](resources/data-storage-and-retrieval/tabular-data.md)
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*[Documents](resources/data-storage-and-retrieval/documents.md)
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*[Partitioning](resources/data-storage-and-retrieval/partitioning.md)
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*[LLM based pipelines with PostgresML and dbt (data build tool)](resources/data-storage-and-retrieval/llm-based-pipelines-with-postgresml-and-dbt-data-build-tool.md)
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*[Benchmarks](resources/benchmarks/README.md)
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*[Benchmarks](resources/benchmarks/postgresml-is-8-40x-faster-than-python-http-microservices.md)
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*[PostgresML is 8-40x faster than Python HTTP microservices](resources/benchmarks/postgresml-is-8-40x-faster-than-python-http-microservices.md)
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*[Scaling to 1 Million Requests per Second](resources/benchmarks/million-requests-per-second.md)
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*[MindsDB vs PostgresML](resources/benchmarks/mindsdb-vs-postgresml.md)
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*[Contributing](resources/developer-docs/contributing.md)
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*[Distributed Training](resources/developer-docs/distributed-training.md)
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*[GPU Support](resources/developer-docs/gpu-support.md)
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*[Deploying PostgresML](resources/developer-docs/deploying-postgresml/README.md)
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*[Monitoring](resources/developer-docs/deploying-postgresml/monitoring.md)
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*[Self-hosting](resources/developer-docs/self-hosting/README.md)
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*[Pooler](resources/developer-docs/self-hosting/pooler.md)
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*[Building from source](resources/developer-docs/self-hosting/building-from-source.md)

‎pgml-cms/docs/api/apis.md

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The following functions are implemented and maintained by the PostgresML extension:
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| Functionname| Description|
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| Function| Description|
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|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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|[pgml.embed()](sql-extension/pgml.embed)| Generate embeddings inside the database using open source embedding models from Hugging Face.|
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|[pgml.transform()](sql-extension/pgml.transform/)| Download and run latest Hugging Face transformer models, like Llama, Mixtral, and many more to perform various NLP tasks like text generation, summarization, sentiment analysis and more.|
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| pgml.transform_stream()| Streaming version of[pgml.transform()](sql-extension/pgml.transform/). Retrieve tokens as they are generated by the LLM, decreasing time to first token.|
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|[pgml.train()](sql-extension/pgml.train/)| Train a machine learning model on data from a Postgres table or view. Supports XGBoost, LightGBM, Catboost and all Scikit-learn algorithms.|
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|[pgml.deploy()](sql-extension/pgml.deploy)| Deploy a version of the model created with pgml.train().|
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|[pgml.predict()](sql-extension/pgml.predict/)| Perform real time inference using a model trained with pgml.train() on live application data.|
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The client SDK implements best practices and common use cases, using the PostgresML SQL functions and standard PostgreSQL features to do it. The SDK core is written in Rust, which manages creating and running queries, connection pooling, and error handling.
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For each additional language we support (current JavaScript and Python), we create and publish language-native bindings. This architecture ensures all programming languages we support have identical APIs and similar performance when interacting with PostgresML.
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For each additional language we support (currently JavaScript and Python), we create and publish language-native bindings. This architecture ensures all programming languages we support have identical APIs and similar performance when interacting with PostgresML.
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###Use cases
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‎pgml-cms/docs/api/client-sdk/tutorials/semantic-search-1.md

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This tutorial demonstrates using the`pgml` SDK to create a collection, add documents, build a pipeline for vector search, make a sample query, and archive the collection when finished. In this tutorial we use[hkunlp/instructor-base](https://huggingface.co/hkunlp/instructor-base), a more advanced embeddings model that takes parameters when doing embedding and recall.
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[Link to full JavaScript implementation](../../../../../pgml-sdks/pgml/javascript/examples/question\_answering.js)
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[Link to full JavaScript implementation](https://github.com/postgresml/postgresml/blob/master/pgml-sdks/pgml/javascript/examples/question_answering.js)
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[Link to full Python implementation](../../../../../pgml-sdks/pgml/python/examples/question\_answering.py)
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[Link to full Python implementation](https://github.com/postgresml/postgresml/blob/master/pgml-sdks/pgml/python/examples/question_answering.py)
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##Imports and Setup
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The SDK is imported and environment variables are loaded.
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{% tabs %}
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{% tab title="JavasScript" %}
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{% tab title="JavaScript" %}
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```js
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require("dotenv").config();

‎pgml-cms/docs/api/client-sdk/tutorials/semantic-search.md

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This tutorial demonstrates using the`pgml` SDK to create a collection, add documents, build a pipeline for vector search, make a sample query, and archive the collection when finished.
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[Link to full JavaScript implementation](../../../../../pgml-sdks/pgml/javascript/examples/semantic\_search.js)
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[Link to full JavaScript implementation](https://github.com/postgresml/postgresml/blob/master/pgml-sdks/pgml/javascript/examples/semantic_search.js)
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[Link to full Python implementation](../../../../../pgml-sdks/pgml/python/examples/semantic\_search.py)
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[Link to full Python implementation](https://github.com/postgresml/postgresml/blob/master/pgml-sdks/pgml/python/examples/semantic_search.py)
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##Imports and Setup
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The SDK is imported and environment variables are loaded.
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{% tabs %}
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{% tab title="JavasScript" %}
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{% tab title="JavaScript" %}
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```js
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constpgml=require("pgml");
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require("dotenv").config();

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