# Own embedding value not vector search **URL:** \ **Category:** Support\ **Tags:** python\ **Created:** [July 29, 2024, 1:09am UTC](https://forum.weaviate.io/t/own-embedding-value-not-vector-search/3201 "2024-07-29T01:09:57Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![HYK97](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/hyk97/32/2047_2.png) [@HYK97](https://forum.weaviate.io/u/HYK97)\ **Post date:** [July 29, 2024, 1:09am UTC](https://forum.weaviate.io/t/own-embedding-value-not-vector-search/3201/1 "2024-07-29T01:09:57Z")
I’m currently doing the following for my index I want to be able to embed and use it externally, so I’m using the `Configure.Vectorizer.none()` # indexing ```auto wv_client.collections.create( name=class_name, description="건강 식품 컬렉션", replication_config=Configure.replication( factor=1 ), vectorizer_config=Configure.Vectorizer.none(), vector_index_config=Configure.VectorIndex.hnsw( distance_metric=VectorDistances.COSINE ), properties=[ Property(name="product_no", description="상품 번호", data_type=DataType.NUMBER), Property(name="original_review", description="원본 리뷰", data_type=DataType.TEXT, tokenization=Tokenization.WORD), Property(name="original_title", description="원본 상품명", data_type=DataType.TEXT, tokenization=Tokenization.WORD), Property(name="original_detail", description="원본 OCR", data_type=DataType.TEXT, tokenization=Tokenization.WORD), Property(name="title", description="상품명", data_type=DataType.TEXT), Property(name="review", description="리뷰", data_type=DataType.TEXT), Property(name="detail", description="OCR", data_type=DataType.TEXT) ] ) ...... with coll.batch.dynamic() as batch: for i, item in enumerate(dumps): product_no = item['product_no'] product_name = item['prd_nm'] review_text = item['review_text'] ocr_text = item['ocr_text'] title_embedding, review_embedding, detail_embedding = embeddings[i * 3:i * 3 + 3] title_embeddings.append(title_embedding) review_embeddings.append(review_embedding) detail_embeddings.append(detail_embedding) uuid = generate_uuid5(product_no) batch.add_object( properties={ "product_no": product_no, "original_title": product_name, "original_review": review_text, "original_detail": ocr_text }, vector={ "title": title_embedding, "review": review_embedding, "detail": detail_embedding }, uuid=uuid ) ``` The problem is that I get this error when searching, how do I fix it? # search ```auto def search_weaviate(client, query_vector, class_name, feature, limit): col = client.collections.get(class_name) results = col.query.near_vector(near_vector=query_vector, limit=limit, return_properties=["product_no", "original_review"], include_vector="True", return_metadata=MetadataQuery(distance=True, creation_time=True), target_vector="detail") .... client.close() ``` `Query call with protocol GRPC search failed with message extract target vectors: class Health_food does not have named vector detail configured. Available named vectors map[].` ---
**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:** [July 29, 2024, 3:43pm UTC](https://forum.weaviate.io/t/own-embedding-value-not-vector-search/3201/2 "2024-07-29T15:43:37Z")
hi @HYK97 You never defined a named vector called `detail` in your collection. So all vectors you add to this collection, will be added to the default named vector, called `default` To fix this issue, you can either remove the `target_vectors` parameter, or set it to “default” Let me know if this helps. Thanks! ---
**Author:** ![HYK97](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/hyk97/32/2047_2.png) [@HYK97](https://forum.weaviate.io/u/HYK97)\ **Post date:** [July 30, 2024, 5:16am UTC](https://forum.weaviate.io/t/own-embedding-value-not-vector-search/3201/3 "2024-07-30T05:16:36Z")
> [@HYK97](#): > > ```auto > vector={ > "title": title_embedding, > "review": review_embedding, > "detail": detail_embedding > }, > > ``` Doesn’t this create a named vector? I thought I was creating 3 vectors like above and searching them using target\_vector. Because it saved the data properly, as shown in the photo below! ![image](https://canada1.discourse-cdn.com/flex027/uploads/weaviate/original/2X/5/5479d81c956b5138d9a9a30fa9b73d75019e4fa3.png) **If you clear the target\_vector parameter, it will not respond with any value when debugging, as shown in the photo below** ![image](https://canada1.discourse-cdn.com/flex027/uploads/weaviate/original/2X/1/197f57366e821a3ff85301b59cdb98cf6a3d3de3.png) ---
**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:** [July 30, 2024, 8:21pm UTC](https://forum.weaviate.io/t/own-embedding-value-not-vector-search/3201/4 "2024-07-30T20:21:28Z")
Hi! That would be a cool feature, hahaha. I am not sure it creates, to be honest. AFAIK, the Auto Schema should only kick in for property. I have written some code here to do what you want: > [@\[Question\] YOUR TOPIC](https://forum.weaviate.io/t/question-your-topic/3194/2): > > hi! You never defined the named vectors to begin with thinking Check here some nice academy we have about this: this is how your collection should look like: import weaviate from weaviate import classes as wvc client = weaviate.connect\_to\_local() from weaviate import classes as wvc client.collections.delete("MyCollection") client.collections.create( name="MyCollection", description="collection\_1", replication\_config=wvc.config.Configure.replication( … Let me know if this helps. Thanks!