Comments (1)
Source doc is still included in the metadata - If you choose to use Vector (you should) as a search strategy for RAG, then the embedding model generates 1536 vectors, regardless of content length. So, for both specificity and search optimization, it makes sense to split potential search results into things that would actually be responsive AND not immediately exceed the model's context window
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Related Issues (20)
- Escaping the wildcard in bash causes the `'` to be passed in the argument value
- More Docs Support for ingestion. HOT 2
- Deployment prompts for openAIApiKey HOT 1
- The subscription does not have QuotaId/Feature required by SKU 'S0' from kind 'OpenAI' or contains blocked QuotaId/Feature. HOT 1
- Feature: Bing Search Resource for Internet "live-data" ingestion
- Bug: OnPostChatPromptAsync doesn't create an async yield array for internal back-end exceptions
- Search service SKU HOT 1
- PDF upload will not generate or index sections if the number of characters on the page is less than 1000
- Cannot deploy to Azure - Docker step hangs on Windows 10 HOT 1
- After Running "PrepareDocs.dll" its getting stuck
- Connecting on docker doesn't work.
- Update to latest dependencies HOT 2
- Delete existing documents HOT 1
- ERROR: error executing step command 'package --all': failed packaging service 'web': failing invoking action 'package', parsing source container image: empty image URL provided HOT 2
- azd up requires openAIApiKey infra param? HOT 5
- Citation can't be found in UI/Blob storage
- Acceptance Criteria Checklist (DoD) HOT 5
- Azure Cognitive Search -> Azure AI Search branding change
- Trying the Demo - It is asking for openAIApiKey but that's not in the instructions HOT 1
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