# No Result when it comes to numeric data

**URL:** <https://community.pinecone.io/t/no-result-when-it-comes-to-numeric-data/7858>\
**Category:** General\
**Tags:** semantic-search, vector-database, embeddings\
**Created:** [April 16, 2025, 11:13am UTC](https://community.pinecone.io/t/no-result-when-it-comes-to-numeric-data/7858 "2025-04-16T11:13:26Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![manjot14122002](https://sea2.discourse-cdn.com/flex020/user_avatar/community.pinecone.io/manjot14122002/32/4141_2.png) [@manjot14122002](https://community.pinecone.io/u/manjot14122002)\
**Post date:** [April 16, 2025, 11:13am UTC](https://community.pinecone.io/t/no-result-when-it-comes-to-numeric-data/7858/1 "2025-04-16T11:13:26Z")

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User Query : tell me about order number 10779

I have used index.query , index.query\_namespaces with all the metric (“cosine”, “euclidean”, “dotproduct”) but did not get any chunks related to order number 10779 .  
chunks related to order number 10779 are present in pinecone database.

If i add some more content related to that query then it will retrieve that chunk  
example: tell me about order number 10779 of white sand or i just type order detail of white sand it will then fetch the chunks.

it is not working with numeric data and i am using openai embeddings (model = text-embedding-3-large) for making chunks and for similarity search also

Any solutions

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**Author:** ![jenna](https://sea2.discourse-cdn.com/flex020/user_avatar/community.pinecone.io/jenna/32/3998_2.png) [@jenna](https://community.pinecone.io/u/jenna)\
**Post date:** [April 17, 2025, 6:42pm UTC](https://community.pinecone.io/t/no-result-when-it-comes-to-numeric-data/7858/2 "2025-04-17T18:42:29Z")

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Hi @manjot14122002 - Thanks for reaching out!

As you noticed, adding more context to your query returns results, because they are semantically related. Since you are expecting queries to contain a specific keywords, in this case an order ID, it sounds like what you are doing may be a better fit for a lexical/keyword search with a sparse index. You can read more about how to implement that [here](https://docs.pinecone.io/guides/indexes/create-an-index#create-a-sparse-index).

Another alternative would be to parse the order IDs into a metadata field and use metadata filtering as described [here](https://docs.pinecone.io/guides/data/query-data#filter-by-metadata). This would give you records that are most semantically similar to a query vector AND that have an order ID metadata field with the value `10779`.