# Vector Search

**URL:** <https://forum.weaviate.io/t/vector-search/21884>\
**Category:** Support\
**Created:** [August 21, 2025, 12:45pm UTC](https://forum.weaviate.io/t/vector-search/21884 "2025-08-21T12:45:58Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![Stephen\_Pillow](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/stephen_pillow/32/3811_2.png) [@Stephen\_Pillow](https://forum.weaviate.io/u/Stephen_Pillow)\
**Post date:** [August 21, 2025, 12:45pm UTC](https://forum.weaviate.io/t/vector-search/21884/1 "2025-08-21T12:45:58Z")

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### Description

### Server Setup Information

- Weaviate Server Version: 1.32.4-1e414fd.amd64

### Any additional Information

I have a Weaviate collection with a vector field. I have inserted the data using insert\_many or just using insert. The SigText gets vectorized. However, when I search it returns the same thing every time regardless of the query. I checked the SigTextVector vector field and it is the same for each row regardless of the SigText. So, that is why the query doesn’t work. I have called the embedding model directly and it returns different results based on the input. I check the log for my embedding model, and Weaviate is sending ‘Sig Codes’ for the prompt to embed everytime and not the value of the SigText. Can you please let me know what I am doing wrong?

Here is the vector field:  
vector\_config=[

# Set another named vector with the “text2vec-openai” vectorizer

Configure.Vectors.text2vec\_openai(  
name=“SigTextVector”,  
source\_properties=[  
“SigText”  
], # (Optional) Set the source property(ies)  
vector\_index\_config=Configure.VectorIndex.hnsw( # (Optional) Set vector index options  
ef=500, # Search list size  
ef\_construction=400,  
distance\_metric=VectorDistances.COSINE,  
filter\_strategy=VectorFilterStrategy.SWEEPING, # or ACORN TODO: What should this be set to if anything?

# Need to define M for the HNSW index, which is the number of connections per node

),  
base\_url=“[http://vllm-mxbai-embed-large-v1:8000](http://vllm-mxbai-embed-large-v1:8000)”, # Use the internal Docker container port since this will be docker to docker communication  
model=“/models/mxbai-embed-large-v1”  
)  
Here is the query:  
sigCodeCollection = client.collections.get(“SigCodes”)  
testSearch = sigCodeCollection.query.near\_text(  
query=“ONCE A WEEK”, # The model provider integration will automatically vectorize the query  
limit=5,  
target\_vector=“SigTextVector”,  
return\_metadata=MetadataQuery(  
distance=True # Include distance in the results  
)  
)  
print(f"Test Search Results: {testSearch}“)  
for obj in testSearch.objects:  
print(f"Sig Code: {obj.properties[“sigCode”]} - Sig Text: {obj.properties[“sigText”]} - Distance: {obj.metadata.distance}”)  
Here is the output:  
Sig Code: APP - Sig Text: APPLICATION - Distance: 0.5655122995376587  
Sig Code: 5T - Sig Text: TAKE 5 TABLETS - Distance: 0.5655122995376587  
Sig Code: U - Sig Text: UNITS - Distance: 0.5655122995376587  
Sig Code: 2PU - Sig Text: INHALE 2 PUFFS - Distance: 0.5655122995376587  
Sig Code: 2GTTS - Sig Text: INSTILL 2 DROPS - Distance: 0.5655122995376587!-- logs, additional setup information, anything extra you did in the setup or variables not included in any guide you followed →

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<div class="post-metadata">

**Author:** ![Stephen\_Pillow](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/stephen_pillow/32/3811_2.png) [@Stephen\_Pillow](https://forum.weaviate.io/u/Stephen_Pillow)\
**Post date:** [August 23, 2025, 3:12pm UTC](https://forum.weaviate.io/t/vector-search/21884/2 "2025-08-23T15:12:30Z")

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This issue has been solved. I just had to lower case the first letter of each field name.

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<div class="post-metadata">

**Author:** ![DudaNogueira](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/dudanogueira/32/7846_2.png) [@DudaNogueira](https://forum.weaviate.io/u/DudaNogueira)\
**Post date:** [August 27, 2025, 7:00pm UTC](https://forum.weaviate.io/t/vector-search/21884/3 "2025-08-27T19:00:59Z")

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Thanks for sharing!!!