docs: fix broken links

[JessicaXYWang]

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

Briefly describe the changes included in this Pull Request.

How is this patch tested?

• I have written tests (not required for typo or doc fix) and confirmed the proposed feature/bug-fix/change works.

Does this PR change any dependencies?

• No. You can skip this section.
• Yes. Make sure the dependencies are resolved correctly, and list changes here.

Does this PR add a new feature? If so, have you added samples on website?

• No. You can skip this section.
• Yes. Make sure you have added samples following below steps.

1. Find the corresponding markdown file for your new feature in website/docs/documentation folder.
   Make sure you choose the correct class estimators/transformers and namespace.
2. Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
3. Make sure the DocTable points to correct API link.
4. Navigate to website folder, and run yarn run start to make sure the website renders correctly.
5. Don't forget to add <!--pytest-codeblocks:cont--> before each python code blocks to enable auto-tests for python samples.
6. Make sure the WebsiteSamplesTests job pass in the pipeline.

[JessicaXYWang]

/azp run

[github-actions]

Hey @JessicaXYWang 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

We use semantic commit messages to streamline the release process.
Before your pull request can be merged, you should make sure your first commit and PR title start with a semantic prefix.
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Examples of commit messages with semantic prefixes:

• fix: Fix LightGBM crashes with empty partitions
• feat: Make HTTP on Spark back-offs configurable
• docs: Update Spark Serving usage
• build: Add codecov support
• perf: improve LightGBM memory usage
• refactor: make python code generation rely on classes
• style: Remove nulls from CNTKModel
• test: Add test coverage for CNTKModel

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[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[github-actions]

Summary by GPT-4

In this notebook, we'll demonstrate how to develop a context-aware question answering framework for any form of a document using OpenAI models, SynapseML and Azure AI Services. In this notebook, we assume that PDF documents are the source of data, however, the same framework can be easily extended to other document formats too.

We’ll cover the following key steps:

1. Preprocessing PDF Documents: Learn how to load the PDF documents into a Spark DataFrame, read the documents using the Azure AI Document Intelligence in Azure AI Services, and use SynapseML to split the documents into chunks.
2. Embedding Generation and Storage: Learn how to generate embeddings for the chunks using SynapseML and Azure OpenAI Services, store the embeddings in a vector store using Azure Cognitive Search, and search the vector store to answer the user’s question.
3. Question Answering Pipeline: Learn how to retrieve relevant document based on the user’s question and provide the answer using Langchain.

We utilize SynapseML, an ecosystem of tools designed to enhance the distributed computing framework Apache Spark. SynapseML introduces advanced networking capabilities to the Spark ecosystem and offers user-friendly SparkML transformers for various Azure AI Services.

Additionally, we employ AnalyzeDocument from Azure AI Services to extract the complete document content and present it in designated columns called "output_content" and "paragraph."

To produce embeddings for each chunk, we utilize both SynapseML and Azure OpenAI Service. By integrating Azure OpenAI service with SynapseML, we can leverage Apache Spark's distributed computing framework power to process numerous prompts using OpenAI service. This integration enables SynapseML embedding client to generate embeddings in a distributed manner, enabling efficient processing of large volumes of data. If you're interested in applying large language models at a distributed scale using Azure OpenAI and Azure Synapse Analytics, you can refer to this approach. For more detailed information on generating embeddings with Azure OpenAI, you can look here.

Azure Cognitive Search offers a user-friendly interface for creating a vector database, as well as storing and retrieving data using vector search. If you're interested in learning more about vector search, you can look here.

After processing the document, we can proceed to pose a question. We will use SynapseML to convert users' questions into an embedding and then utilize cosine similarity to retrieve top K document chunks that closely match users' questions. It's worth mentioning that alternative similarity metrics can also be employed.

Suggestions

No suggestions are needed as the changes in this PR are clear and accurate.

[JessicaXYWang]

TODO (not this PR):

[build.sbt]

SynapseML/build.sbt

Line 501 in cde6834

"test code blocks inside markdowns under folder website/docs/documentation")

[pull_request_template]

SynapseML/.github/pull_request_template.md

Line 31 in cde6834

1. Find the corresponding markdown file for your new feature in `website/docs/documentation` folder.

[website channel manifest]

SynapseML/tools/docgen/docgen/manifest.yaml

Line 3 in cde6834

input_dir: "../../../docs/"

[codecov-commenter]

Codecov Report

Merging #2042 (268c293) into master (cde6834) will decrease coverage by 16.62%.
The diff coverage is n/a.

@@             Coverage Diff             @@
##           master    #2042       +/-   ##
===========================================
- Coverage   87.07%   70.46%   -16.62%     
===========================================
  Files         306      306               
  Lines       16063    16063               
  Branches      852      852               
===========================================
- Hits        13987    11318     -2669     
- Misses       2076     4745     +2669     

see 80 files with indirect coverage changes

[JessicaXYWang]

/azp run

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Azure Pipelines successfully started running 1 pipeline(s).