diff --git a/README.md b/README.md
index 489532bc32..d75be50571 100644
--- a/README.md
+++ b/README.md
@@ -266,6 +266,10 @@ By default, the deployed Azure web app will only allow requests from the same or
For the frontend code, change `BACKEND_URI` in `api.ts` to point at the deployed backend URL, so that all fetch requests will be sent to the deployed backend.
+For an alternate frontend that's written in Web Components and deployed to Static Web Apps, check out
+[azure-search-openai-javascript](https://github.com/Azure-Samples/azure-search-openai-javascript) and its guide
+on [using a different backend](https://github.com/Azure-Samples/azure-search-openai-javascript#using-a-different-backend).
+
## Running locally
You can only run locally **after** having successfully run the `azd up` command. If you haven't yet, follow the steps in [Azure deployment](#azure-deployment) above.
@@ -285,19 +289,22 @@ Once in the web app:
* Explore citations and sources
* Click on "settings" to try different options, tweak prompts, etc.
+## Customizing the UI and data
+
+Once you successfully deploy the app, you can start customizing it for your needs: changing the text, tweaking the prompts, and replacing the data. Consult the [app customization guide](docs/customization.md) as well as the [data ingestion guide](docs/data_ingestion.md) for more details.
+
## Productionizing
This sample is designed to be a starting point for your own production application,
but you should do a thorough review of the security and performance before deploying
to production. Read through our [productionizing guide](docs/productionizing.md) for more details.
-
## Resources
* [Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and Cognitive Search](https://aka.ms/entgptsearchblog)
* [Azure Cognitive Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search)
* [Azure OpenAI Service](https://learn.microsoft.com/azure/cognitive-services/openai/overview)
-* [Comparing Azure OpenAI and OpenAI](https://learn.microsoft.com/en-gb/azure/cognitive-services/openai/overview#comparing-azure-openai-and-openai/)
+* [Comparing Azure OpenAI and OpenAI](https://learn.microsoft.com/azure/cognitive-services/openai/overview#comparing-azure-openai-and-openai/)
## Clean up
@@ -315,19 +322,6 @@ The resource group and all the resources will be deleted.
### FAQ
-
-Why do we need to break up the PDFs into chunks when Azure Cognitive 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.
-
-
-
-How can we upload additional PDFs without redeploying everything?
-
-To upload more PDFs, put them in the data/ folder and run `./scripts/prepdocs.sh` or `./scripts/prepdocs.ps1`.
-A [recent change](https://github.com/Azure-Samples/azure-search-openai-demo/pull/835) 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.
-
-
How does this sample compare to other Chat with Your Data samples?
@@ -367,22 +361,6 @@ Technology comparison:
In `infra/main.bicep`, change `chatGptModelName` to 'gpt-4' instead of 'gpt-35-turbo'. You may also need to adjust the capacity above that line depending on how much TPM your account is allowed.
-
-What is the difference between the Chat and Ask tabs?
-
-The chat tab uses the approach programmed in [chatreadretrieveread.py](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/app/backend/approaches/chatreadretrieveread.py).
-
-- It uses the ChatGPT API to turn the user question into a good search query.
-- It queries Azure Cognitive Search for search results for that query (optionally using the vector embeddings for that query).
-- It then combines the search results and original user question, and asks ChatGPT API to answer the question based on the sources. It includes the last 4K of message history as well (or however many tokens are allowed by the deployed model).
-
-The ask tab uses the approach programmed in [retrievethenread.py](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/app/backend/approaches/retrievethenread.py).
-
-- It queries Azure Cognitive Search for search results for the user question (optionally using the vector embeddings for that question).
-- It then combines the search results and user question, and asks ChatGPT API to answer the question based on the sources.
-
-
-
What does the `azd up` command do?
diff --git a/docs/customization.md b/docs/customization.md
new file mode 100644
index 0000000000..15d801d717
--- /dev/null
+++ b/docs/customization.md
@@ -0,0 +1,110 @@
+# Customizing the Chat App
+
+This guide provides more details for customizing the Chat App.
+
+## Using your own data
+
+The Chat App is designed to work with any PDF documents. The sample data is provided to help you get started quickly, but you can easily replace it with your own data. You'll want to first remove all the existing data, then add your own. See the [data ingestion guide](data_ingestion.md) for more details.
+
+## Customizing the UI
+
+The frontend is built using [React](https://reactjs.org/) and [Fluent UI components](https://react.fluentui.dev/). The frontend components are stored in the `app/frontend/src` folder. The typical components you'll want to customize are:
+
+- `app/frontend/index.html`: To change the page title
+- `app/frontend/src/pages/layout/Layout.tsx`: To change the header text and logo
+- `app/frontend/src/pages/chat/Chat.tsx`: To change the large heading
+- `app/frontend/src/components/Example/ExampleList.tsx`: To change the example questions
+
+## Customizing the backend
+
+The backend is built using [Quart](https://quart.palletsprojects.com/), a Python framework for asynchronous web applications. The backend code is stored in the `app/backend` folder.
+
+### Chat/Ask approaches
+
+Typically, the primary backend code you'll want to customize is the `app/backend/approaches` folder, which contains the classes powering the Chat and Ask tabs. Each class uses a different RAG (Retrieval Augmented Generation) approach, which include system messages that should be changed to match your data
+
+#### Chat approach
+
+The chat tab uses the approach programmed in [chatreadretrieveread.py](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/app/backend/approaches/chatreadretrieveread.py).
+
+1. It uses the ChatGPT API to turn the user question into a good search query.
+2. It queries Azure Cognitive Search for search results for that query (optionally using the vector embeddings for that query).
+3. It then combines the search results and original user question, and asks ChatGPT API to answer the question based on the sources. It includes the last 4K of message history as well (or however many tokens are allowed by the deployed model).
+
+The `system_message_chat_conversation` variable is currently tailored to the sample data since it starts with "Assistant helps the company employees with their healthcare plan questions, and questions about the employee handbook." Change that to match your data.
+
+### Ask approach
+
+The ask tab uses the approach programmed in [retrievethenread.py](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/app/backend/approaches/retrievethenread.py).
+
+1. It queries Azure Cognitive Search for search results for the user question (optionally using the vector embeddings for that question).
+2. It then combines the search results and user question, and asks ChatGPT API to answer the question based on the sources.
+
+The `system_chat_template` variable is currently tailored to the sample data since it starts with "You are an intelligent assistant helping Contoso Inc employees with their healthcare plan questions and employee handbook questions." Change that to match your data.
+
+## Improving answer quality
+
+Once you are running the chat app on your own data and with your own tailored system prompt,
+the next step is to test the app with questions and note the quality of the answers.
+If you notice any answers that aren't as good as you'd like, here's a process for improving them.
+
+### Identify the problem point
+
+The first step is to identify where the problem is occurring. For example, if using the Chat tab, the problem could be:
+
+1. ChatGPT is not generating a good search query based on the user question
+2. Azure Cognitive Search is not returning good search results for the query
+3. ChatGPT is not generating a good answer based on the search results and user question
+
+You can look at the "Thought process" tab in the chat app to see each of those steps,
+and determine which one is the problem.
+
+#### Improving ChatGPT results
+
+If the problem is with ChatGPT (steps 1 or 3 above), you can try changing the relevant prompt.
+
+Once you've changed the prompt, make sure you ask the same question multiple times to see if the overall quality has improved. ChatGPT can yield different results every time, even for a temperature of 0.0, but especially for a higher temperature than that (like our default of 0.7 for step 3).
+
+You can also try changing the ChatGPT parameters, like temperature, to see if that improves results for your domain.
+
+#### Improving Azure Cognitive Search results
+
+If the problem is with Azure Cognitive Search (step 2 above), the first step is to check what search parameters you're using. Generally, the best results are found with hybrid search (text + vectors) plus the additional semantic re-ranking step, and that's what we've enabled by default. There may be some domains where that combination isn't optimal, however.
+
+##### Configuring parameters in the app
+
+You can change many of the search parameters in the "Developer settings" in the frontend and see if results improve for your queries. The most relevant options:
+
+
+
+#### Configuring parameters in the Azure Portal
+
+You may find it easier to experiment with search options with the index explorer in the Azure Portal.
+Open up the Azure Cognitive Search resource, select the Indexes tab, and select the index there.
+
+Then use the JSON view of the search explorer, and make sure you specify the same options you're using in the app. For example, this query represents a search with semantic ranker configured:
+
+```json
+{
+ "search": "eye exams",
+ "queryType": "semantic",
+ "semanticConfiguration": "default",
+ "queryLanguage": "en-us",
+ "speller": "lexicon",
+ "top": 3
+}
+```
+
+You can also use the `highlight` parameter to see what text is being matched in the `content` field in the search results.
+
+```json
+{
+ "search": "eye exams",
+ "highlight": "content"
+ ...
+}
+```
+
+
+
+The search explorer works well for testing text, but is harder to use with vectors, since you'd also need to compute the vector embedding and send it in. It is probably easier to use the app frontend for testing vectors/hybrid search.
diff --git a/docs/data_ingestion.md b/docs/data_ingestion.md
new file mode 100644
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@@ -0,0 +1,38 @@
+# Indexing documents for the Chat App
+
+This guide provides more details for using the `prepdocs` script to index documents for the Chat App.
+
+## Overview of the manual indexing process
+
+The `scripts/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. Whenever `azd up` or `azd provision` is run, the script is called automatically.
+
+
+
+The script uses the following steps to index documents:
+
+1. If it doesn't yet exist, create a new index in Azure Cognitive Search.
+2. Upload the PDFs to Azure Blob Storage.
+3. Split the PDFs into chunks of text.
+4. Upload the chunks to Azure Cognitive Search. If using vectors (the default), also compute the embeddings and upload those alongside the text.
+
+### Chunking
+
+We're often asked why we need to break up the PDFs into chunks when Azure Cognitive 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 `scripts/prepdocslib/textsplitter.py`.
+
+## Indexing additional documents
+
+To upload more PDFs, put them in the data/ folder and run `./scripts/prepdocs.sh` or `./scripts/prepdocs.ps1`.
+
+A [recent change](https://github.com/Azure-Samples/azure-search-openai-demo/pull/835) 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.
+
+## Removing documents
+
+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 the `--removeall` flag. Open either `scripts/prepdocs.sh` or `scripts/prepdocs.ps1` and add `--removeall` to the command at the bottom of the file. Then run the script as usual.
+
+You can also remove individual documents by using the `--remove` flag. Open either `scripts/prepdocs.sh` or `scripts/prepdocs.ps1`, add `--remove` to the command at the bottom of the file, and replace `/data/*` with `/data/YOUR-DOCUMENT-FILENAME-GOES-HERE.pdf`. Then run the script as usual.
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