-[47+ classification and regression algorithms](https://postgresml.org/docs/introduction/apis/sql-extensions/pgml.train/)

-[47+ classification and regression algorithms](https://postgresml.org/docs/introduction/apis/sql-extension/pgml.train/)

-[8 - 40X faster inference than HTTP based model serving](https://postgresml.org/blog/postgresml-is-8x-faster-than-python-http-microservices)

-[Millions of transactions per second](https://postgresml.org/blog/scaling-postgresml-to-one-million-requests-per-second)

-[Horizontal scalability](https://github.com/postgresml/pgcat)

There are two stages in building a knowledge based chatbot:

- Build a knowledge base by ingesting documents, chunking documents, generating embeddings and indexing these embeddings for fast query

- Generate responses to user queries by retrieving relevant documents and generating responses using OpenAI and[OpenSourceAI API](https://postgresml.org/docs/introduction/apis/client-sdks/opensourceai)

- Generate responses to user queries by retrieving relevant documents and generating responses using OpenAI and[OpenSourceAI API](https://postgresml.org/docs/introduction/apis/client-sdk/opensourceai)

This tool automates the above two stages and provides a command line interface to build and deploy a knowledge based chatbot.

Advanced search engines like Google use this technique to extract the meaning of search queries and rank the results based on what the user actually_wants_, unlike simple keyword matches which can easily give irrelevant results.

To accomplish this, for each document in our app, we include an embedding column stored as a vector. A vector is just an array of floating point numbers. For each item in our to-do list, we automatically generate the embedding using the PostgresML[`pgml.embed()`](https://postgresml.org/docs/introduction/apis/sql-extensions/pgml.embed) function. This function runs inside the database and doesn't require the Django app to install the model locally.

To accomplish this, for each document in our app, we include an embedding column stored as a vector. A vector is just an array of floating point numbers. For each item in our to-do list, we automatically generate the embedding using the PostgresML[`pgml.embed()`](https://postgresml.org/docs/introduction/apis/sql-extension/pgml.embed) function. This function runs inside the database and doesn't require the Django app to install the model locally.

An embedding model running inside PostgresML is able to extract the meaning of search queries & compare it to the meaning of the documents it stores, just like a human being would if they were able to search millions of documents in just a few milliseconds.

SDK is specifically designed to provide powerful, flexible document search.`Pipeline`s are required to perform search. See the[Pipelines](https://postgresml.org/docs/introduction/apis/client-sdks/pipelines) for more information about using`Pipeline`s.

SDK is specifically designed to provide powerful, flexible document search.`Pipeline`s are required to perform search. See the[Pipelines](https://postgresml.org/docs/introduction/apis/client-sdk/pipelines) for more information about using`Pipeline`s.

This section will assume we have previously ran the following code:

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/introduction/apis/client-sdks/search)[](https://postgresml.org/docs/introduction/apis/client-sdks/search#metadata-filtering)for more examples on filtering documents.

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

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

*[Vector Search](https://postgresml.org/docs/introduction/apis/client-sdks/search)

*[Document Search](https://postgresml.org/docs/introduction/apis/client-sdks/document-search)

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

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

See their respective pages for more information on searching.

SDK is specifically designed to provide powerful, flexible vector search.`Pipeline`s are required to perform search. See[Pipelines](https://postgresml.org/docs/introduction/apis/client-sdks/pipelines)for more information about using`Pipeline`s.

SDK is specifically designed to provide powerful, flexible vector search.`Pipeline`s are required to perform search. See[Pipelines](https://postgresml.org/docs/introduction/apis/client-sdk/pipelines)for more information about using`Pipeline`s.

This section will assume we have previously ran the following code:

*[pgml.embed.md](pgml.embed.md"mention") returns vector embeddings for nearest neighbor searches and other vector database use cases

*[pgml.generate.md](pgml.generate.md"mention") returns streaming text responses for chatbots

*[pgml.transform](../../introduction/apis/sql-extensions/pgml.transform"mention") allows you to perform dozens of natural language processing (NLP) tasks with thousands of models, like sentiment analysis, question and answering, translation, summarization and text generation

*[pgml.transform](../../introduction/apis/sql-extension/pgml.transform"mention") allows you to perform dozens of natural language processing (NLP) tasks with thousands of models, like sentiment analysis, question and answering, translation, summarization and text generation

*[pgml.tune.md](pgml.tune.md"mention") fine tunes an open source model on your own data

);

See[pgml.train](../../introduction/apis/sql-extensions/pgml.train"mention") for more information.

See[pgml.train](../../introduction/apis/sql-extension/pgml.train"mention") for more information.

ORDER BY predictionDESC;

See[pgml.predict](../../introduction/apis/sql-extensions/pgml.predict"mention") for more information.

See[pgml.predict](../../introduction/apis/sql-extension/pgml.predict"mention") for more information.

If you've already been through the[pgml.train](../../../introduction/apis/sql-extensions/pgml.train"mention") examples, you can see the predictive results of those models:

If you've already been through the[pgml.train](../../../introduction/apis/sql-extension/pgml.train"mention") examples, you can see the predictive results of those models:

All clustering algorithms implemented by PostgresML are online versions. You may use the[pgml.predict](../../../introduction/apis/sql-extensions/pgml.predict"mention")function to cluster novel datapoints after the clustering model has been trained.

All clustering algorithms implemented by PostgresML are online versions. You may use the[pgml.predict](../../../introduction/apis/sql-extension/pgml.predict"mention")function to cluster novel datapoints after the clustering model has been trained.

| Algorithm| Reference|

| ----------------------| -----------------------------------------------------------------------------------------------------------------|

{% hint style="info"%}

We are utilizing the OpenSourceAIclassin our pgml library. Thisis actually a dropin replacementfor OpenAI. [Find the docs here](https://postgresml.org/docs/introduction/apis/client-sdks/opensourceai).

We are utilizing the OpenSourceAIclassin our pgml library. Thisis actually a dropin replacementfor OpenAI. [Find the docs here](https://postgresml.org/docs/introduction/apis/client-sdk/opensourceai).

{% endhint%}

This program let's us have conversations like the following:

Note that we have alist of dictionaries called`history` we use to store the chat history,and instead of feeding text into our model, we are inputting the`history`list. Our library automatically converts thislist of dictionaries into theformat expected by the model. Notice the`roles`in the dictionaries are the sameas the`roles` of the messagesin the previous example. Thislist of dictionarieswith keys`role`and`content`as a storage systemfor messagesis pretty standardand used by usas wellas OpenAIand HuggingFace.

{% hint style="info"%}

Once again we are using`pgml` to abstract away the complicated piecesfor our machine learning task. This isn't a guide on how to use our libraries, but for more information [check out our docs](https://postgresml.org/docs/introduction/apis/client-sdks/getting-started).

Once again we are using`pgml` to abstract away the complicated piecesfor our machine learning task. This isn't a guide on how to use our libraries, but for more information [check out our docs](https://postgresml.org/docs/introduction/apis/client-sdk/getting-started).

{% endhint%}

Our search returned the exact section of the Wikipedia article we wanted! Let's talk a little bit about what is going on here.