diff --git a/.vscode/tasks.json b/.vscode/tasks.json index bc4a0691c9..d4fdd8a2fe 100644 --- a/.vscode/tasks.json +++ b/.vscode/tasks.json @@ -3,14 +3,6 @@ "tasks": [ { "label": "Start App", - "type": "dotenv", - "targetTasks": [ - "Start App (Script)" - ], - "file": "${input:dotEnvFilePath}" - }, - { - "label": "Start App (Script)", "type": "shell", "command": "${workspaceFolder}/app/start.sh", "windows": { @@ -24,12 +16,5 @@ }, "problemMatcher": [] } - ], - "inputs": [ - { - "id": "dotEnvFilePath", - "type": "command", - "command": "azure-dev.commands.getDotEnvFilePath" - } ] } diff --git a/README.md b/README.md index 373fcf65cd..847653232c 100644 --- a/README.md +++ b/README.md @@ -288,11 +288,11 @@ Here are the most common failure scenarios and solutions: ### Resources - [Additional documentation for this app](docs/README.md) -- [📖 Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and AI Search](https://aka.ms/entgptsearchblog) +- [📖 Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and AI Search](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w-azure-openai-and/3762087) - [📖 Azure AI 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/azure/cognitive-services/openai/overview#comparing-azure-openai-and-openai/) -- [📖 Access Control in Generative AI applications with Azure Cognitive Search](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/access-control-in-generative-ai-applications-with-azure/ba-p/3956408) +- [📖 Access Control in Generative AI applications with Azure AI Search](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/access-control-in-generative-ai-applications-with-azure-ai-search/3956408) - [📺 Quickly build and deploy OpenAI apps on Azure, infused with your own data](https://www.youtube.com/watch?v=j8i-OM5kwiY) - [📺 AI Chat App Hack series](https://www.youtube.com/playlist?list=PL5lwDBUC0ag6_dGZst5m3G72ewfwXLcXV) diff --git a/docs/customization.md b/docs/customization.md index cb1601fafc..dfe39a7e97 100644 --- a/docs/customization.md +++ b/docs/customization.md @@ -117,7 +117,7 @@ You can also try changing the ChatCompletion parameters, like temperature, to se ### Improving Azure AI Search results -If the problem is with Azure AI 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. Check out this blog post which [evaluates AI search strategies](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167) for a better understanding of the differences. +If the problem is with Azure AI 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. Check out this blog post which [evaluates AI search strategies](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid-retrieval-and-ranking-ca/3929167) for a better understanding of the differences. #### Configuring parameters in the app diff --git a/docs/data_ingestion.md b/docs/data_ingestion.md index 1837018154..db64792029 100644 --- a/docs/data_ingestion.md +++ b/docs/data_ingestion.md @@ -57,7 +57,7 @@ If needed, you can modify the chunking algorithm in `scripts/prepdocslib/textspl 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`](/app/frontend/src/components/settings/Settings.tsx). The default option for this dropdown is "All". By including specific categories, you can refine your search results more effectively. +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`](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/app/frontend/src/components/Settings/Settings.tsx). The default option for this dropdown is "All". By including specific categories, you can refine your search results more effectively. ### Indexing additional documents @@ -75,7 +75,7 @@ You can also remove individual documents by using the `--remove` flag. Open eith ## Integrated Vectorization -Azure AI Search includes an [integrated vectorization feature](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in/ba-p/3960809#:~:text=Integrated%20vectorization%20is%20a%20new%20feature%20of%20Azure,pull-indexers%2C%20and%20vectorization%20of%20text%20queries%20through%20vectorizers), 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. +Azure AI Search includes an [integrated vectorization feature](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in-azure-ai-search/3960809), 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](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb) 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. diff --git a/docs/deploy_features.md b/docs/deploy_features.md index 64ee402979..0d7a47f389 100644 --- a/docs/deploy_features.md +++ b/docs/deploy_features.md @@ -206,7 +206,7 @@ azd env set USE_SPEECH_OUTPUT_BROWSER true ## Enabling Integrated Vectorization -Azure AI search recently introduced an [integrated vectorization feature in preview mode](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in/ba-p/3960809#:~:text=Integrated%20vectorization%20is%20a%20new%20feature%20of%20Azure,pull-indexers%2C%20and%20vectorization%20of%20text%20queries%20through%20vectorizers). This feature is a cloud-based approach to data ingestion, which takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies. +Azure AI search recently introduced an [integrated vectorization feature in preview mode](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in-azure-ai-search/3960809). This feature is a cloud-based approach to data ingestion, which takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies. To enable integrated vectorization with this sample: diff --git a/docs/deploy_lowcost.md b/docs/deploy_lowcost.md index 809e87a807..fd3e33feff 100644 --- a/docs/deploy_lowcost.md +++ b/docs/deploy_lowcost.md @@ -40,7 +40,7 @@ However, if your goal is to minimize costs while prototyping your application, f If you have one already, either delete that service or follow instructions to reuse your [existing search service](../README.md#existing-azure-ai-search-resource). 2. The free tier does not support semantic ranker, so the app UI will no longer display - the option to use the semantic ranker. Note that will generally result in [decreased search relevance](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167). + the option to use the semantic ranker. Note that will generally result in [decreased search relevance](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid-retrieval-and-ranking-ca/3929167). 1. Use the free tier of Azure Document Intelligence (used in analyzing files): diff --git a/docs/productionizing.md b/docs/productionizing.md index f1b957f626..2d99dd893c 100644 --- a/docs/productionizing.md +++ b/docs/productionizing.md @@ -83,7 +83,7 @@ and scale up the maximum/minimum based on load. * **Networking**: We recommend [deploying inside a Virtual Network](./deploy_private.md). If the app is only for internal enterprise use, use a private DNS zone. Also consider using Azure API Management (APIM) for firewalls and other forms of protection. - For more details, read [Azure OpenAI Landing Zone reference architecture](https://techcommunity.microsoft.com/t5/azure-architecture-blog/azure-openai-landing-zone-reference-architecture/ba-p/3882102). + For more details, read [Azure OpenAI Landing Zone reference architecture](https://techcommunity.microsoft.com/blog/azurearchitectureblog/azure-openai-landing-zone-reference-architecture/3882102). ## Load testing