Row 63451

Row ID: 63451 | Dataset Entry | Axioma AXP Content Repository

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A simpler (and easier) approach is to add metadata to the data when storing it as a vector.

In your example "What was CAC for Product A in the year 2023?" add year:2023 + month:3 + day:27 + product:A + data\_type:CAC

Exactly how you add + search on this metadata depends on the Vector database you are using, but all of them have the option to add other data than just the embedding.

For example with Postgres/Supabase you can store the vector embedding in a table that has rows for the year, month, day, product, data\_type, etc.. and when doing a Vector search you can specify these in the SQL: SELECT \* FROM my\_data WHERE year=2023 AND product='A' AND data\_type='CAC' AND embedding <=> query\_embedding < 1 - match\_threshold.... the real SQL query is longer and more complex but you get the idea. [https://supabase.com/docs/guides/ai/semantic-search](https://supabase.com/docs/guides/ai/semantic-search)

For CloudFlare Vector search it is simple to add + search on specific metadata values, however it's not as flexible or powerful as Postgres since you can only do simple matches and only specify a tag name once.

filter: { year: 2023, product: "A", data\_type: "CAC" } from: [https://developers.cloudflare.com/vectorize/reference/metadata-filtering/](https://developers.cloudflare.com/vectorize/reference/metadata-filtering/)

If you wanted to search for data between 2020 and 2023 CloudFlare has no feature to do that, and you would have perform multiple searches and change the year value (year:2020 + year:2021 + year:2022 + year:2023) then combine all the results. With other SQL based Vector DBs you can do something like: WHERE year >= 2020 AND year <=2023

Some of your real-world metadata/tables could have multiple values (example favorite\_brands: Sony, Microsoft, Phillips, Toshiba, Mercedes) and again using an SQL solution its easy to narrow the search but with CloudFlare complicated.

FieldValue
text A simpler (and easier) approach is to add metadata to the data when storing it as a vector. In your example "What was CAC for Product A in the year 2023?" add year:2023 + month:3 + day:27 + product:A + data\_type:CAC Exactly how you add + search on this metadata depends on the Vector database you are using, but all of them have the option to add other data than just the embedding. For example with Postgres/Supabase you can store the vector embedding in a table that has rows for the year, mo…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
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Raw Record

{
  "text": "A simpler (and easier) approach is to add metadata to the data when storing it as a vector. \n\nIn your example \"What was CAC for Product A in the year 2023?\" add year:2023 + month:3 + day:27 + product:A + data\\_type:CAC\n\nExactly how you add + search on this metadata depends on the Vector database you are using, but all of them have the option to add other data than just the embedding. \n\nFor example with Postgres/Supabase you can store the vector embedding in a table that has rows for the year, month, day, product, data\\_type, etc.. and when doing a Vector search you can specify these in the SQL: SELECT \\* FROM my\\_data WHERE year=2023 AND product='A' AND data\\_type='CAC' AND embedding <=> query\\_embedding < 1 - match\\_threshold.... the real SQL query is longer and more complex but you get the idea. [https://supabase.com/docs/guides/ai/semantic-search](https://supabase.com/docs/guides/ai/semantic-search)\n\nFor CloudFlare Vector search it is simple to add + search on specific metadata values, however it's not as flexible or powerful as Postgres since you can only do simple matches and only specify a tag name once.\n\nfilter: { year: 2023, product: \"A\", data\\_type: \"CAC\" } from: [https://developers.cloudflare.com/vectorize/reference/metadata-filtering/](https://developers.cloudflare.com/vectorize/reference/metadata-filtering/)\n\nIf you wanted to search for data between 2020 and 2023 CloudFlare has no feature to do that, and you would have perform multiple searches and change the year value (year:2020 + year:2021 + year:2022 + year:2023) then combine all the results. With other SQL based Vector DBs you can do something like: WHERE year >= 2020 AND year <=2023\n\nSome of your real-world metadata/tables could have multiple values (example favorite\\_brands: Sony, Microsoft, Phillips, Toshiba, Mercedes) and again using an SQL solution its easy to narrow the search but with CloudFlare complicated.",
  "label": "r/machinelearning",
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