Kernel Memory with Azure OpenAI,
Blob storage and AI Search.
Azure Open AI
On AzureOpenAI resource, deploy gpt-4 chat completion model and text-embedding-ada-002 embedding model
var builder = new KernelMemoryBuilder()
.WithAzureOpenAITextGeneration(
new AzureOpenAIConfig
{
Auth = AzureOpenAIConfig.AuthTypes.APIKey,
APIKey = "Your AzureOpenAI api key",
Endpoint = "https://your-azure-open-ai-resource-name.openai.azure.com",
Deployment = "gpt-4"
})
.WithAzureOpenAITextEmbeddingGeneration(
new AzureOpenAIConfig
{
Auth = AzureOpenAIConfig.AuthTypes.APIKey,
APIKey = "Your AzureOpenAI api key",
Endpoint = "https://your-azure-open-ai-resource-name.openai.azure.com",
Deployment = "text-embedding-ada-002"
});
Azure storage account
Azure blob storage to store kenerl memory pipeline artifacts
var builder = new KernelMemoryBuilder()
.WithAzureBlobsDocumentStorage(
new AzureBlobsConfig
{
Account = "your-blob-storage-account",
Auth = AzureBlobsConfig.AuthTypes.AccountKey,
AccountKey = "your-blob-account-key",
Container = "document-ingestion"
})
Azure AI Search service
Azure AI search service as vector databases
var builder = new KernelMemoryBuilder()
.WithAzureAISearchMemoryDb(
new AzureAISearchConfig
{
Endpoint = "https://your-search-service-resource-name.search.windows.net",
Auth = AzureAISearchConfig.AuthTypes.APIKey,
APIKey = "your search service api key"
})
Import some document and ask questions
await kernelMemory.ImportDocumentAsync(
filePath: "resources/earth_book_2019_tagged.pdf",
documentId: "earth_book_2019",
index: "books");
var response =
await kernelMemory.AskAsync(
"Where is Amazon rainforest on earth?",
index: "books");
Note the index name "books", kernel memory automatically creates Azure AI Search index name "books" if it does not exist and "books" folder in the blob container.
Please feel free to reach out on twitter @roamingcode