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@@ -9,7 +9,7 @@ authors:
# Powering Rapid Data Applications Using Your Data Warehouse With VulcanSQL
-
+
Hello, data folks.
diff --git a/packages/doc/blog/querying-your-data-easily-and-smartly-through-huggingface.mdx b/packages/doc/blog/querying-your-data-easily-and-smartly-through-huggingface.mdx
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+---
+authors:
+ - name: Eason Kuo
+ title: contributor of VulcanSQL
+ url: https://github.com/kokokuo
+ image_url: https://avatars.githubusercontent.com/u/5389253?v=4
+ email: eason.kuo@cannerdata.com
+ - name: Jimmy Yeh
+ title: contributor of VulcanSQL
+ url: https://github.com/cyyeh
+ image_url: https://avatars.githubusercontent.com/u/11023068?v=4
+ email: jimmy.yeh@cannerdata.com
+---
+
+# Querying Your Data Easily and Smartly through Hugging Face
+
+*TLDR: VulcanSQL, a free and open-source data API framework built specifically for data applications,
+empowers data professionals to generate and distribute data APIs quickly and effortlessly.
+It takes your SQL templates and transforms them into data APIs, with no backend expertise necessary.*
+
+## Preface
+
+Normally, in order to retrieve the data we need from a data source,
+we have to write SQL statements. However, this process could be **time-consuming**
+especially when the data consumers have different requirements in short time.
+Now, to make this process easier and more flexible to data consumers,
+VulcanSQL has **integrated HuggingFace inference capabilities**. This allows us to **reduce
+the need for changing SQL templates** by simply allowing data consumers to ask questions
+and getting the results they need.
+
+<!--truncate-->
+
+## VulcanSQL HuggingFace Filters
+
+VulcanSQL leverages the Hugging Face Inference feature through the VulcanSQL
+[Filters](https://vulcansql.com/docs/develop/advanced#filters) statement.
+
+### What is Hugging Face
+
+Hugging Face is an AI community that builds tools to enable users to build, train,
+and deploy machine learning models. Hugging Face makes it easy to share tools, models,
+model weights, and datasets among other practitioners through its toolkit.
+
+
+
+Hugging Face provides the [Inference API](https://huggingface.co/inference-api) feature
+that allows users to run pre-trained AI models for various natural language processing (NLP)
+tasks, making it easier to integrate powerful language models into applications and services.
+
+
+
+### Table Question Answering Task Filter
+
+"[Table Question Answering](https://huggingface.co/tasks/table-question-answering)" is one of
+the NLP (Natural Language Processing) tasks provided by Hugging Face. Table Question Answering
+involves answering a question about the information in a given table. It allows for simulating SQL
+execution by inputting a table through its model.
+
+VulcanSQL currently integrates the table question answering feature by creating the filter named
+`huggingface_table_question_answering` and allows you to apply functions to variables using the
+pipe operator (`|`).
+
+**Sample 1 - send the data from the variable [`set` tag](https://www.notion.so/VulcanSQL-edb87d04de074125ab19275e6f63d844?pvs=21):**
+
+You could give the dataset with the **[`set` tag](https://www.notion.so/VulcanSQL-edb87d04de074125ab19275e6f63d844?pvs=21)**
+and give the question with the `query` field:
+
+```sql
+{% set data = [
+ {
+ "repository": "vulcan-sql",
+ "topic": ["analytics", "data-lake", "data-warehouse", "api-builder"],
+ "description":"Create and share Data APIs fast! Data API framework for DuckDB, ClickHouse, Snowflake, BigQuery, PostgreSQL"
+ },
+ {
+ "repository": "accio",
+ "topic": ["data-analytics", "data-lake", "data-warehouse", "bussiness-intelligence"],
+ "description": "Query Your Data Warehouse Like Exploring One Big View."
+ },
+ {
+ "repository": "hello-world",
+ "topic": [],
+ "description": "Sample repository for testing"
+ }
+] %}
+
+-- The source data for "huggingface_table_question_answering" needs to be an array of objects.
+SELECT {{ data | huggingface_table_question_answering(query="How many repositories related to data-lake topic?") }} as result
+```
+
+Here is a response returned by `huggingface_table_question_answering`:
+
+```json
+[
+ {
+ "result": "{\"answer\":\"COUNT > vulcan-sql, accio\",\"coordinates\":[[0,0],[1,0]],\"cells\":[\"vulcan-sql\",\"accio\"],\"aggregator\":\"COUNT\"}"
+ }
+]
+```
+
+The result will be converted to a JSON string from `huggingface_table_question_answering`.
+You could decompress the JSON string and use the result by yourself.
+
+**Sample 2 - send the data from the `req` tag:**
+
+You could also use the `req` tag to keep the query result from the previous SQL condition and save it
+to a variable named `repositories`. Then you can use `.value()` to get the data result and
+pass it to `huggingface_table_question_answering` with the pipe operator `|`.
+
+```sql
+{% req repositories %}
+ SELECT * FROM read_csv_auto('Top200StaredRepositories.csv')
+{% endreq %}
+
+{% set question = context.params.question %}
+
+SELECT {{ repositories.value() | huggingface_table_question_answering(query=question, wait_for_model=true) }} as result
+```
+
+You may see we also pass the value `true` to the `wait_for_model` field,
+which means waiting for the HuggingFace table question answering to load the pre-trained model; otherwise it may fail due to the model not being loaded completely.
+For more information, please see the [VulcanSQL's Hugging Face Table Question Answering Filter Extension Documentation](https://vulcansql.com/docs/extensions/huggingface/huggingface-table-question-answering).
+
+Now we could request API with different questions by the parameter `question` and get different results!
+
+**Scenario 1** - We asked `Find the repository has the most stars?`, and the Hugging Face model told us that freeCodeCamp has the most stars.
+
+
+
+**Scenario 2** - We asked `How many repositories use Python language? Give repository name`,
+and the Hugging Face model told us awesome-python, httpie and thefuck are using Python language.
+
+
+
+As you see, after using the **HuggingFace Table Answering Filter, the benefit is:**
+
+- **Don't need** to change the SQL template file.
+- **Don't need** to re-build the SQL template file.
+- **Don't need** to create another SQL template file to satisfy two query scenarios.
+
+:::info
+The quality of response depends on the model used in the HuggingFace Table Answering Filter and how we ask the questions to the model.
+:::
+
+However, VulcanSQL not only provides the Table Question Answering Filter feature
+but also publishes a popular Text Generation Filter for the Hugging Face Filters Extension.
+
+It means you could use the popular models [Meta Llama2](https://ai.meta.com/llama/)! 🥳
+
+### Text Generation Task Filter
+
+"[Text Generation](https://huggingface.co/tasks/text-generation)" is another NLP
+(Natural Language Processing) task provided by Hugging Face. Text generation means producing
+new text. These models can, for example, fill in incomplete text or paraphrase,
+or even answer your question according based on your input context.
+
+VulcanSQL also integrates the text generation feature by creating the filter named
+`huggingface_text_generation` and allows you to apply functions to variables using
+the pipe operator (`|`).
+
+Besides, Hugging Face provides the popular [Meta Llama2](https://huggingface.co/meta-llama) models,
+a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to
+70 billion parameters.
+
+
+
+In the next sample, we are going to demonstrate how VulcanSQL uses the `huggingface_text_generation` filter
+with the Llama2 model `meta-llama/Llama-2-13b-chat-hf` to answer your question.
+
+:::info
+If you would like to use the Meta Llama2 model, you have at least two options to choose from:
+
+1. Subscribe to the [Hugging Face Pro Account](https://huggingface.co/pricing#pro).
+2. Use [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints).
+
+For more information, please see [VulcanSQL's Text Generation Filter Extension Documentation](https://vulcansql.com/docs/extensions/huggingface/huggingface-text-generation).
+:::
+
+**Sample - send the data with the `req` tag:**
+
+The sample uses the HuggingFace access token of the [Pro Account](https://huggingface.co/pricing#pro) to get
+the result by using the `meta-llama/Llama-2-13b-chat-hf` model.
+
+```sql
+-- Using the `meta-llama/Llama-2-13b-chat-hf` model, data must have less than 4096 tokens, so need to limit data row and column for universities.
+{% req universities %}
+ SELECT rank, institution, "location code", "location" FROM read_csv_auto('2023-QS-World-University-Rankings.csv') LIMIT 100
+{% endreq %}
+
+{% set question = context.params.question %}
+
+SELECT {{ universities.value() | huggingface_text_generation(query=question, model="meta-llama/Llama-2-13b-chat-hf", wait_for_model=true) }} as result
+```
+
+**Scenario 1** - We asked `Which university is the top-ranked university?`, and the model gave us the top-ranked university.
+
+
+
+**Scenario 2** - We asked `Which university located in the UK is ranked at the top of the list?`,
+and the model gave us the top-ranked university that is located in the UK.
+
+
+
+Wow! It's really amazing that the **HuggingFace Text Generation Filter** can answer your question
+based on the given dataset!
+
+## Conclusion
+
+With more and more great machine learning models coming out, it's great that we can utilize their power to make our daily work easier!
+We hope this blog post can give you a glimpse on how VulcanSQL can be involved in this revolutionary event in human history!
+
+Imaging a world that you can deliver APIs that users only need to query questions they have, the model handles SQL logic for you,
+and VulcanSQL takes care of the [data privacy](../docs/data-privacy/overview) and [API things](../docs/api-plugin/overview). It sounds exciting, isn't it?
+
+In the near future, we'll publish detailed step-by-step guides to help you write your own AI-enabled filter extensions! Stay tuned!
+
+
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diff --git a/packages/doc/docs/extensions/api.mdx b/packages/doc/docs/extensions/api.mdx
new file mode 100644
index 00000000..e4b2a262
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@@ -0,0 +1,60 @@
+# The API Extension
+
+This extension allows you to call RESTful APIs to other sources, and you can either request, update, or even delete resources to other sources!
+
+## Installation
+
+We need to install an additional package in order to call RESTful APIs:
+
+1. Install package(If you have VulcanSQL in the binary version, you can skip this step)
+
+ ```bash
+ npm i @vulcan-sql/extension-api-caller
+ ```
+2. Setup `vulcan.yaml`
+
+ ```yaml
+ extensions:
+ api: '@vulcan-sql/extension-api-caller' # add this line
+ ```
+
+## Using the API extension
+
+The minimum example
+
+```sql
+SELECT {{ {} | rest_api(url='https://dummyjson.com/products/1') }}
+```
+
+To pass the path parameters
+
+```sql
+{% set a_variable_you_can_define = { "path": { "id": 1 } } %}
+SELECT {{ a_variable_you_can_define | rest_api(url='https://dummyjson.com/products/:id') }}
+```
+
+To pass the query parameters
+
+```sql
+{% set a_variable_you_can_define = { "query": { "q": "phone" } } %}
+SELECT {{ a_variable_you_can_define | rest_api(url='https://dummyjson.com/products/search') }}
+```
+
+To issue the POST request
+
+```sql
+{% set a_variable_you_can_define = { "body": { "title": "BMW Pencil" } } %}
+SELECT {{ a_variable_you_can_define | rest_api(url='https://dummyjson.com/products/add', method='POST') }}
+```
+
+To pass the headers and multiple fields
+
+```sql
+{% set a_variable_you_can_define = { "headers": { "Content-Type": "application/json" }, "body": { "title": "BMW Pencil" } } %}
+SELECT {{ a_variable_you_can_define | rest_api(url='https://dummyjson.com/products/add', method='POST') }}
+```
+
+## Examples
+
+You can check out this [restapi-caller](https://github.com/Canner/vulcan-sql-examples/tree/main/restapi-caller) example for further details!
+
diff --git a/packages/doc/sidebars.js b/packages/doc/sidebars.js
index ece855ec..d5325dbc 100644
--- a/packages/doc/sidebars.js
+++ b/packages/doc/sidebars.js
@@ -182,6 +182,11 @@ const sidebars = {
},
]
},
+ {
+ type: 'doc',
+ label: 'API',
+ id: 'extensions/api',
+ },
// {
// type: 'category',
// label: 'Overview',