Row 63591
Content Data
This page contains data entry 63591 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
Also you can send the users query to a simpler AI prompt to use the AI to extract metadata values that you need for the Vector search.
So with "What was CAC for Product A in the year 2023?" you could make a prompt to ask the AI to determine the year, product name, etc (even ask the AI to return that as JSON data).... then once you have the metadata fields that are contained in the query, you can do a search on your Vector DB using that metadata
| Field | Value |
|---|---|
| text | Also you can send the users query to a simpler AI prompt to use the AI to extract metadata values that you need for the Vector search. So with "What was CAC for Product A in the year 2023?" you could make a prompt to ask the AI to determine the year, product name, etc (even ask the AI to return that as JSON data).... then once you have the metadata fields that are contained in the query, you can do a search on your Vector DB using that metadata |
| label | r/machinelearning |
| dataType | comment |
| communityName | r/MachineLearning |
| datetime | 2024-05-23 |
| username_encoded | Z0FBQUFBQm5Lak1hLUpDUEs2Z3NfaXYtVkNjNjVUTEtaU1gxRldsaXVsSmx4VkplcXNUMk9fVlcySFJqVHhHZVA3X1FSckxkbGRqTkFkVFQ3OHgwYmQ1S25TT2Z2akM3RHA4UzFMeUV5dW1uS0owTENRb25uSUE9 |
| url_encoded | Z0FBQUFBQm5Lak9yZFFRME1rUTZQX2xKSTA2aVZvSEcwekhFTGF4OGZtRjRmVTh3WWhNcGV4WDJDa0w4bEpCbUsxT3o0bWcxM3B0aS1LYnZTaFdFUHVPWkc2dmNLOGN5eFliblBzOWh5Q3RvbFlwc1RxWjE2QjdQUng1cU96X2xxZ2djZlNkMWVja1A3M1FYV3p5S2JWSUxSSmhjUDFnWlFIUEo0bFhtYnRVa1Z0cWFjUl9fLXdpeDNkc1VQZ2UzOHNBay1peUd3aWh2 |
Raw Record
{
"text": "Also you can send the users query to a simpler AI prompt to use the AI to extract metadata values that you need for the Vector search. \n\nSo with \"What was CAC for Product A in the year 2023?\" you could make a prompt to ask the AI to determine the year, product name, etc (even ask the AI to return that as JSON data).... then once you have the metadata fields that are contained in the query, you can do a search on your Vector DB using that metadata",
"label": "r/machinelearning",
"dataType": "comment",
"communityName": "r/MachineLearning",
"datetime": "2024-05-23",
"username_encoded": "Z0FBQUFBQm5Lak1hLUpDUEs2Z3NfaXYtVkNjNjVUTEtaU1gxRldsaXVsSmx4VkplcXNUMk9fVlcySFJqVHhHZVA3X1FSckxkbGRqTkFkVFQ3OHgwYmQ1S25TT2Z2akM3RHA4UzFMeUV5dW1uS0owTENRb25uSUE9",
"url_encoded": "Z0FBQUFBQm5Lak9yZFFRME1rUTZQX2xKSTA2aVZvSEcwekhFTGF4OGZtRjRmVTh3WWhNcGV4WDJDa0w4bEpCbUsxT3o0bWcxM3B0aS1LYnZTaFdFUHVPWkc2dmNLOGN5eFliblBzOWh5Q3RvbFlwc1RxWjE2QjdQUng1cU96X2xxZ2djZlNkMWVja1A3M1FYV3p5S2JWSUxSSmhjUDFnWlFIUEo0bFhtYnRVa1Z0cWFjUl9fLXdpeDNkc1VQZ2UzOHNBay1peUd3aWh2"
}
Entry Information
- Entry ID: 63591
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000