# Storing data in Blob Storage based on

1. Go to the storage resources created.
2. Left Menu bar in Data Storage field click Containers
3. Create Container by clicking +Container
4. New Pop up appears give appropriate name to the container
5. Click Create

6. Click and open created container

7. Click Upload, Select the file you want to upload to blobstorage on your PC.
8. Check if it matches the selected file

Containers with file

Note: Requires Azure AI Search resources to continue

9. Home > XXX-search-service-XXX

## Import Data

10. Click Import data
11. Data Source to Azure Blob Storage
12. Click an existing connection
13. Select the Storage accounts that you have created
15. Select the container & Next: Add Cognitive skills

16. Import data (may occur problem so recommended to use import and vectorize data)

Note: its under free version so limited Cognitive skills

17. Next: Customize target index

18. Next: Create an indexer
Finally, create the indexer. Indexers provide an automated workflow for transferring documents and content from external data sources to a search service's search index. Here you can schedule the frequency of data pulls and configure various settings for processing the files you search.

19. Submit

## Create Semantic configurations
1. Navigate to Your Azure Cognitive Search Service:
Open the Azure Portal and navigate to your Azure Cognitive Search service.

2. Go to "Indexes" Section:
In the left-hand menu, click on "Indexes" under your Azure Cognitive Search service.

3. Select the Index:
Choose the index for which you want to create a semantic configuration.

4. Select Semantic configurations tab:
Click Add semantic configuration


new semantic configurations is created & Don't forget to save

5. Save

Note forget to set semantic_configuration_name in your script

## Vector profiles
1. Add profile
2. Create Algorithm (default)
3. Add Vecorizer (note use embedding)


4. Save
Note: Dont forget to add vector profile in your script
5. click save top left as well

Skip the following if you use above approach
(Recommended approach )
## Note If you want to add vector search, you need to vectorize the data so Import and vectorize

It will create all required profiles. (index, indexer,vector-profile)

1. Click Import and vectorize data

2. Setup your data connection

3. Vectorize your data with Azure OpenAI

4. Create

5. Create succeede

6. Click start testing to search
it will open the indexer
vectorSea1536

## CORs setting(optional -> Not required)
Browsers disallow all cross-origin requests, so client-side JavaScript can't call APIs by default. To allow cross-origin queries on your index, enable CORS (cross-origin resource sharing) by setting the corsOptions attribute.

1. Navigate to Your Azure Cognitive Search Service:
Open the Azure Portal and navigate to your Azure Cognitive Search service.

2. Go to "Indexes" Section:
In the left-hand menu, click on "Indexes" under your Azure Cognitive Search service.

3. Select the Index:
Choose the index for which you want to create a semantic configuration.

4. Select CORS
5. From the CORS tab in the index, set Allowed Origin Types to All and click Save.