Last week's whirlwind of events with OpenAI CEO and founder Sam Altman stirred up quite a buzz in the industry. The whole deal left many of us scratching our heads about where OpenAI is headed. Between the corporate drama, valid worries about privacy and transparency, and ongoing issues around model performance, censorship, and the use of marketing scare tactics; it's no wonder there's a growing sense of dissatisfaction and distrust in proprietary models. 

Last week's whirlwind of events with OpenAI CEO and founder Sam Altman stirred up quite a buzz in the industry. The whole deal left many of us scratching our heads about where OpenAI is headed. Between the corporate drama, valid worries about privacy and transparency, and ongoing issues around model performance, censorship, and the use of marketing scare tactics; it's no wonder there's a growing sense of dissatisfaction and distrust in proprietary models.

On the bright side, the open-source realm has emerged as a potent contender, not just in reaction to OpenAI's shortcomings but as a genuine advancement in its own right. We're all about making the benefits of open-source models accessible to as many folks as possible. So, we've made switching from OpenAI to open-source as easy as possible with a drop-in replacement. It lets users specify any model they’d like in just a few lines of code. We call it the OpenAI Switch Kit. Read on to learn more about why we think you’ll like it, or just try it now and see what you think.

We think so. Open-source models have made remarkable strides, not only catching up to proprietary counterparts but also surpassing them across multiple domains. The advantages are clear:

***Performance & reliability:** Open-source models are increasingly comparable or superior across a wide range of tasks and performance metrics. Mistral and Llama-based models, for example, are easily faster than GPT 4. Reliability is another concern you may reconsider leaving in the hands of OpenAI. OpenAI’s API has suffered from several recent outages, and their rate limits can interrupt your app if there is a surge in usage. Open-source models enable greater control over your model’s latency, scalability and availability. Ultimately, the outcome of greater control is that your organization can produce a more dependable integration and a highly reliable production application. 

***Safety & privacy:** Open-source models are the clear winner when it comes to security sensitive AI applications. There are[enormous risks](https://www.infosecurity-magazine.com/news-features/chatgpts-datascraping-scrutiny/) associated with transmitting private data to external entities such as OpenAI. By contrast, open-source models retain sensitive information within an organization's own cloud environments. The data never has to leave your premises, so the risk is bypassed altogether – it’s enterprise security by default. At PostgresML, we offer such private hosting of LLM’s in your own cloud. 

***Model censorship:** A growing number of experts inside and outside of leading AI companies argue that model restrictions have gone too far. The Atlantic recently published an[article on AI’s “Spicy-Mayo Problem''](https://www.theatlantic.com/ideas/archive/2023/11/ai-safety-regulations-uncensored-models/676076/) which delves into the issues surrounding AI censorship. The titular example describes a chatbot refusing to return commands asking for a “dangerously spicy” mayo recipe. Censorship can affect baseline performance, and in the case of apps for creative work such as Sudowrite, unrestricted open-source models can actually be a key differentiating value for users. 

***Flexibility & customization:** Closed-source models like GPT3.5 Turbo are fine for generalized tasks, but leave little room for customization. Fine-tuning is highly restricted. Additionally, the headwinds at OpenAI have exposed the[dangerous reality of AI vendor lock-in](https://techcrunch.com/2023/11/21/openai-dangers-vendor-lock-in/). Open-source models such as MPT-7B, Llama V2 and Mistral 7B are designed with extensive flexibility for fine tuning, so organizations can create custom specifications and optimize model performance for their unique needs. This level of customization and flexibility opens the door for advanced techniques like DPO, PPO LoRa and more. 

***Performance & reliability:** Open-source models are increasingly comparable or superior across a wide range of tasks and performance metrics. Mistral and Llama-based models, for example, are easily faster than GPT 4. Reliability is another concern you may reconsider leaving in the hands of OpenAI. OpenAI’s API has suffered from several recent outages, and their rate limits can interrupt your app if there is a surge in usage. Open-source models enable greater control over your model’s latency, scalability and availability. Ultimately, the outcome of greater control is that your organization can produce a more dependable integration and a highly reliable production application.

***Safety & privacy:** Open-source models are the clear winner when it comes to security sensitive AI applications. There are[enormous risks](https://www.infosecurity-magazine.com/news-features/chatgpts-datascraping-scrutiny/) associated with transmitting private data to external entities such as OpenAI. By contrast, open-source models retain sensitive information within an organization's own cloud environments. The data never has to leave your premises, so the risk is bypassed altogether – it’s enterprise security by default. At PostgresML, we offer such private hosting of LLM’s in your own cloud.

***Model censorship:** A growing number of experts inside and outside of leading AI companies argue that model restrictions have gone too far. The Atlantic recently published an[article on AI’s “Spicy-Mayo Problem''](https://www.theatlantic.com/ideas/archive/2023/11/ai-safety-regulations-uncensored-models/676076/) which delves into the issues surrounding AI censorship. The titular example describes a chatbot refusing to return commands asking for a “dangerously spicy” mayo recipe. Censorship can affect baseline performance, and in the case of apps for creative work such as Sudowrite, unrestricted open-source models can actually be a key differentiating value for users.

***Flexibility & customization:** Closed-source models like GPT3.5 Turbo are fine for generalized tasks, but leave little room for customization. Fine-tuning is highly restricted. Additionally, the headwinds at OpenAI have exposed the[dangerous reality of AI vendor lock-in](https://techcrunch.com/2023/11/21/openai-dangers-vendor-lock-in/). Open-source models such as MPT-7B, Llama V2 and Mistral 7B are designed with extensive flexibility for fine tuning, so organizations can create custom specifications and optimize model performance for their unique needs. This level of customization and flexibility opens the door for advanced techniques like DPO, PPO LoRa and more.

If you are using PostgresML cloud, use the hostname and credentials from the cloud platform. 

If you are using PostgresML cloud, use the hostname and credentials from the cloud platform.

!!! note

PostgresML provides cloud hosted AI application databases, that bring the latest machine learning and vector capabilities to the heart of everyone’s favorite tech stack. We're looking for a Head of Growth, with a Technical Product Manager skill set to help shape the core product, inside and outside the company. 

PostgresML provides cloud hosted AI application databases, that bring the latest machine learning and vector capabilities to the heart of everyone’s favorite tech stack. We're looking for a Head of Growth, with a Technical Product Manager skill set to help shape the core product, inside and outside the company.

Reach out if you want to:

PostgresML is a complete MLOps platform built on PostgreSQL.

The data for ML & AI systems is inherently larger and more dynamic than the models. It's more efficient, manageable and reliable to move the models to the database, rather than continuously moving the data to the models\_.\_ PostgresML allows you to take advantage of the fundamental relationship between data and models, by extending the database with the following capabilities and goals:

The data for ML & AI systems is inherently larger and more dynamic than the models. It's more efficient, manageable and reliable to move the models to the database, rather than continuously moving the data to the models. PostgresML allows you to take advantage of the fundamental relationship between data and models, by extending the database with the following capabilities and goals:

***Model Serving** -_**GPU accelerated**_ inference engine for interactive applications, with no additional networking latency or reliability costs.

***Model Store** - Download_**open-source**_ models including state of the art LLMs from HuggingFace, and track changes in performance between versions.

Just like`vector_search`,`search` takes in two arguments. The first is a`JSON` object specifying the`query` and`limit` and the second is the`Pipeline`. The`query` object can have three fields:`full_text_search`,`semantic_search` and`filter`. Both`full_text_search` and`semantic_search` function similarly. They take in the text to compare against, titled`query`, an optional`boost` parameter used to boost the effectiveness of the ranking, and`semantic_search` also takes in an optional`parameters` key which specify parameters to pass to the embedding model when embedding the passed in text.

Lets break this query down a little bit more. We are asking for a maximum of 10 documents ranked by`full_text_search` on the`abstract` and`semantic_search` on the`abstract` and`body`. We are also filtering out all documents that do not have the key`user_id` equal to`1`. The`full_text_search` provides a score for the`abstract`, and`semantic_search` provides scores for the`abstract` and the`body`. The`boost` parameter is a multiplier applied to these scores before they are summed together and sorted by`score``DESC`. 

Lets break this query down a little bit more. We are asking for a maximum of 10 documents ranked by`full_text_search` on the`abstract` and`semantic_search` on the`abstract` and`body`. We are also filtering out all documents that do not have the key`user_id` equal to`1`. The`full_text_search` provides a score for the`abstract`, and`semantic_search` provides scores for the`abstract` and the`body`. The`boost` parameter is a multiplier applied to these scores before they are summed together and sorted by`score``DESC`.

The`filter` is structured the same way it is when performing`vector_search` see[filtering with vector\_search](https://postgresml.org/docs/api/client-sdk/search)[](https://postgresml.org/docs/api/client-sdk/search#metadata-filtering)for more examples on filtering documents.

`Pipeline`s define the schema for the transformation of documents. Different`Pipeline`s can be used for different tasks. 

`Pipeline`s define the schema for the transformation of documents. Different`Pipeline`s can be used for different tasks.

New`Pipeline`s require schema. Here are a few examples of variations of schema along with common use cases. 

New`Pipeline`s require schema. Here are a few examples of variations of schema along with common use cases.

For the following section we will assume we have documents that have the structure:

{% endtab %}

{% endtabs %}

This`Pipeline` splits and embeds the`body` text enabling semantic search using vectors. This is a very popular`Pipeline` for RAG. 

This`Pipeline` splits and embeds the`body` text enabling semantic search using vectors. This is a very popular`Pipeline` for RAG.

We support most every open source model on[Hugging Face](https://huggingface.co/), and OpenAI's embedding models. To use a model from OpenAI specify the`source` as`openai`, and make sure and set the environment variable`OPENAI_API_KEY`.

There are two different forms of search that can be done after adding a`Pipeline` to a`Collection` 

There are two different forms of search that can be done after adding a`Pipeline` to a`Collection`

*[Vector Search](https://postgresml.org/docs/api/client-sdk/search)

*[Document Search](https://postgresml.org/docs/api/client-sdk/document-search)

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. 

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.

[Link to full JavaScript implementation](../../../../../pgml-sdks/pgml/javascript/examples/semantic\_search.js)

Fill-mask refers to a task where certain words in a sentence are hidden or "masked", and the objective is to predict what words should fill in those masked positions. Such models are valuable when we want to gain statistical insights about the language used to train the model. 

Fill-mask refers to a task where certain words in a sentence are hidden or "masked", and the objective is to predict what words should fill in those masked positions. Such models are valuable when we want to gain statistical insights about the language used to train the model.

SELECTpgml.transform(

Text-to-text generation methods, such as T5, are neural network architectures designed to perform various natural language processing tasks, including summarization, translation, and question answering. T5 is a transformer-based architecture pre-trained on a large corpus of text data using denoising autoencoding. This pre-training process enables the model to learn general language patterns and relationships between different tasks, which can be fine-tuned for specific downstream tasks. During fine-tuning, the T5 model is trained on a task-specific dataset to learn how to perform the specific task. 

Text-to-text generation methods, such as T5, are neural network architectures designed to perform various natural language processing tasks, including summarization, translation, and question answering. T5 is a transformer-based architecture pre-trained on a large corpus of text data using denoising autoencoding. This pre-training process enables the model to learn general language patterns and relationships between different tasks, which can be fine-tuned for specific downstream tasks. During fine-tuning, the T5 model is trained on a task-specific dataset to learn how to perform the specific task.

_Translation_

You can connect to your database from any Postgres compatible client. PostgresML is intended to serve in the traditional role of an application database, along with it's extended role as an MLOps platform to make it easy to build and maintain AI applications. 

You can connect to your database from any Postgres compatible client. PostgresML is intended to serve in the traditional role of an application database, along with it's extended role as an MLOps platform to make it easy to build and maintain AI applications.