The azure-search-openai-demo project can set up a full RAG chat app on Azure AI Search and OpenAI so that you can chat on custom data, like internal enterprise data or domain-specific knowledge sets. For full instructions on setting up the project, consult the main README, and then return here for detailed instructions on the data ingestion component.
The chat app provides two ways to ingest data: manual indexing and integrated vectorization. This document explains the differences between the two approaches and provides an overview of the manual indexing process.
In order to ingest a document format, we need a tool that can turn it into text. By default, the manual indexing uses Azure Document Intelligence (DI in the table below), but we also have local parsers for several formats. The local parsers are not as sophisticated as Azure Document Intelligence, but they can be used to decrease charges.
| Format | Manual indexing | Integrated Vectorization |
|---|---|---|
| Yes (DI or local with PyPDF) | Yes | |
| HTML | Yes (DI or local with BeautifulSoup) | Yes |
| DOCX, PPTX, XLSX | Yes (DI) | Yes |
| Images (JPG, PNG, BPM, TIFF, HEIFF) | Yes (DI) | Yes |
| TXT | Yes (Local) | Yes |
| JSON | Yes (Local) | Yes |
| CSV | Yes (Local) | Yes |
The Blob indexer used by the Integrated Vectorization approach also supports a few additional formats.
The prepdocs.py script is responsible for both uploading and indexing documents. The typical usage is to call it using scripts/prepdocs.sh (Mac/Linux) or scripts/prepdocs.ps1 (Windows), as these scripts will set up a Python virtual environment and pass in the required parameters based on the current azd environment. You can pass additional arguments directly to the script, for example scripts/prepdocs.ps1 --removeall. Whenever azd up or azd provision is run, the script is called automatically.
The script uses the following steps to index documents:
- If it doesn't yet exist, create a new index in Azure AI Search.
- Upload the PDFs to Azure Blob Storage.
- Split the PDFs into chunks of text.
- Upload the chunks to Azure AI Search. If using vectors (the default), also compute the embeddings and upload those alongside the text.
We're often asked why we need to break up the PDFs into chunks when Azure AI Search supports searching large documents.
Chunking allows us to limit the amount of information we send to OpenAI due to token limits. By breaking up the content, it allows us to easily find potential chunks of text that we can inject into OpenAI. The method of chunking we use leverages a sliding window of text such that sentences that end one chunk will start the next. This allows us to reduce the chance of losing the context of the text.
If needed, you can modify the chunking algorithm in app/backend/prepdocslib/textsplitter.py.
To enhance search functionality, categorize data during the ingestion process with the --category argument, for example scripts/prepdocs.ps1 --category ExampleCategoryName. This argument specifies the category to which the data belongs, enabling you to filter search results based on these categories.
After running the script with the desired category, ensure these categories are added to the 'Include Category' dropdown list. This can be found in the developer settings in Settings.tsx. The default option for this dropdown is "All". By including specific categories, you can refine your search results more effectively.
To upload more PDFs, put them in the data/ folder and run ./scripts/prepdocs.sh or ./scripts/prepdocs.ps1.
A recent change added checks to see what's been uploaded before. The prepdocs script now writes an .md5 file with an MD5 hash of each file that gets uploaded. Whenever the prepdocs script is re-run, that hash is checked against the current hash and the file is skipped if it hasn't changed.
You may want to remove documents from the index. For example, if you're using the sample data, you may want to remove the documents that are already in the index before adding your own.
To remove all documents, use scripts/prepdocs.sh --removeall or scripts/prepdocs.ps1 --removeall.
You can also remove individual documents by using the --remove flag. Open either scripts/prepdocs.sh or scripts/prepdocs.ps1 and replace /data/* with /data/YOUR-DOCUMENT-FILENAME-GOES-HERE.pdf. Then run scripts/prepdocs.sh --remove or scripts/prepdocs.ps1 --remove.
Azure AI Search includes an integrated vectorization feature, a cloud-based approach to data ingestion. Integrated vectorization takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies.
See this notebook to understand the process of setting up integrated vectorization.
We have integrated that code into our prepdocs script, so you can use it without needing to understand the details.
You must first explicitly enable integrated vectorization in the azd environment to use this feature.
This feature cannot be used on existing index. You need to create a new index or drop and recreate an existing index. In the newly created index schema, a new field 'parent_id' is added. This is used internally by the indexer to manage life cycle of chunks.
This feature is not supported in the free SKU for Azure AI Search.
To add additional documents to the index, first upload them to your data source (Blob storage, by default). Then navigate to the Azure portal, find the index, and run it. The Azure AI Search indexer will identify the new documents and ingest them into the index.
To remove documents from the index, remove them from your data source (Blob storage, by default). Then navigate to the Azure portal, find the index, and run it. The Azure AI Search indexer will take care of removing those documents from the index.
If you would like the indexer to run automatically, you can set it up to run on a schedule.
If you are not sure if a file successfully uploaded, you can query the index from the Azure Portal or from the REST API. Open the index and paste the queries below into the search bar.
To see all the filenames uploaded to the index:
{
"search": "*",
"count": true,
"top": 1,
"facets": ["sourcefile"]
}To search for specific filenames:
{
"search": "*",
"count": true,
"top": 1,
"filter": "sourcefile eq 'employee_handbook.pdf'",
"facets": ["sourcefile"]
}