# Not able to get more than top 4 result matches, regardless of configuration **URL:** \ **Category:** Elasticsearch\ **Tags:** vector-search\ **Created:** [May 26, 2025, 7:34pm UTC](https://discuss.elastic.co/t/not-able-to-get-more-than-top-4-result-matches-regardless-of-configuration/378546 "2025-05-26T19:34:38Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![dave\_espinosa\_qs](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/dave_espinosa_qs/32/143411_2.png) [@dave\_espinosa\_qs](https://discuss.elastic.co/u/dave_espinosa_qs)\ **Post date:** [May 26, 2025, 7:34pm UTC](https://discuss.elastic.co/t/not-able-to-get-more-than-top-4-result-matches-regardless-of-configuration/378546/1 "2025-05-26T19:34:38Z")
Hello everyone, I am using **ES Enterprise version 8.18.1** (I cannot change it for the time being BTW, as that is managed by other department). I created a Vector Database - like ES Vector Store, with the following **Mappings** (currently, it is a dummy with 1000 records in it): ```bash { "mappings": { "dynamic": "true", "dynamic_templates": [ { "all_text_fields": { "match_mapping_type": "string", "mapping": { "analyzer": "iq_text_base", "fields": { "delimiter": { "analyzer": "iq_text_delimiter", "type": "text", "index_options": "freqs" }, "joined": { "search_analyzer": "q_text_bigram", "analyzer": "i_text_bigram", "type": "text", "index_options": "freqs" }, "prefix": { "search_analyzer": "q_prefix", "analyzer": "i_prefix", "type": "text", "index_options": "docs" }, "enum": { "ignore_above": 2048, "type": "keyword" }, "stem": { "analyzer": "iq_text_stem", "type": "text" } } } } } ], "properties": { "course_id": { "type": "text", "fields": { "delimiter": { "type": "text", "index_options": "freqs", "analyzer": "iq_text_delimiter" }, "enum": { "type": "keyword", "ignore_above": 2048 }, "joined": { "type": "text", "index_options": "freqs", "analyzer": "i_text_bigram", "search_analyzer": "q_text_bigram" }, "prefix": { "type": "text", "index_options": "docs", "analyzer": "i_prefix", "search_analyzer": "q_prefix" }, "stem": { "type": "text", "analyzer": "iq_text_stem" } }, "analyzer": "iq_text_base" }, "course_name_backup": { "type": "text", "fields": { "delimiter": { "type": "text", "index_options": "freqs", "analyzer": "iq_text_delimiter" }, "enum": { "type": "keyword", "ignore_above": 2048 }, "joined": { "type": "text", "index_options": "freqs", "analyzer": "i_text_bigram", "search_analyzer": "q_text_bigram" }, "prefix": { "type": "text", "index_options": "docs", "analyzer": "i_prefix", "search_analyzer": "q_prefix" }, "stem": { "type": "text", "analyzer": "iq_text_stem" } }, "analyzer": "iq_text_base" }, "embedding": { "type": "dense_vector", "dims": 768, "index": true, "similarity": "cosine", "index_options": { "type": "int8_hnsw", "m": 16, "ef_construction": 100 } }, "id": { "type": "text", "fields": { "delimiter": { "type": "text", "index_options": "freqs", "analyzer": "iq_text_delimiter" }, "enum": { "type": "keyword", "ignore_above": 2048 }, "joined": { "type": "text", "index_options": "freqs", "analyzer": "i_text_bigram", "search_analyzer": "q_text_bigram" }, "prefix": { "type": "text", "index_options": "docs", "analyzer": "i_prefix", "search_analyzer": "q_prefix" }, "stem": { "type": "text", "analyzer": "iq_text_stem" } }, "analyzer": "iq_text_base" }, "language": { "type": "text", "fields": { "delimiter": { "type": "text", "index_options": "freqs", "analyzer": "iq_text_delimiter" }, "enum": { "type": "keyword", "ignore_above": 2048 }, "joined": { "type": "text", "index_options": "freqs", "analyzer": "i_text_bigram", "search_analyzer": "q_text_bigram" }, "prefix": { "type": "text", "index_options": "docs", "analyzer": "i_prefix", "search_analyzer": "q_prefix" }, "stem": { "type": "text", "analyzer": "iq_text_stem" } }, "analyzer": "iq_text_base" }, "main_text": { "type": "text", "fields": { "delimiter": { "type": "text", "index_options": "freqs", "analyzer": "iq_text_delimiter" }, "enum": { "type": "keyword", "ignore_above": 2048 }, "joined": { "type": "text", "index_options": "freqs", "analyzer": "i_text_bigram", "search_analyzer": "q_text_bigram" }, "prefix": { "type": "text", "index_options": "docs", "analyzer": "i_prefix", "search_analyzer": "q_prefix" }, "stem": { "type": "text", "analyzer": "iq_text_stem" } }, "analyzer": "iq_text_base" }, "provider": { "type": "text", "fields": { "delimiter": { "type": "text", "index_options": "freqs", "analyzer": "iq_text_delimiter" }, "enum": { "type": "keyword", "ignore_above": 2048 }, "joined": { "type": "text", "index_options": "freqs", "analyzer": "i_text_bigram", "search_analyzer": "q_text_bigram" }, "prefix": { "type": "text", "index_options": "docs", "analyzer": "i_prefix", "search_analyzer": "q_prefix" }, "stem": { "type": "text", "analyzer": "iq_text_stem" } }, "analyzer": "iq_text_base" } } } } ``` Plus, I created the following dummy querying Python script: ```python import time from google import genai from google.genai.types import EmbedContentConfig from elasticsearch import Elasticsearch # MODEL = "text-embedding-005" MODEL = "text-multilingual-embedding-002" INDEX_NAME = "my_index_name" client = genai.Client( vertexai=True, project="my-gcp-project", location="us-central1" ) # ------------------------------------ UDFs ------------------------------------ def get_embeddings_single(input_text: str, model:str=MODEL): response = client.models.embed_content( model=model, contents=input_text, config=EmbedContentConfig( task_type="SEMANTIC_SIMILARITY", # Optional output_dimensionality=768, # Optional ), ) return response.embeddings[0].values # ---------------------- CONNECT TO ES VECTOR STORE ---------------------------- ELASTICSEARCH_VDBS_INFO = { "course_info_eng": { "url": "https://ccdcae5f73a84360909c59bc6cc8cc0a.us-central1.gcp.cloud.es.io:443", "key": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx==" }, "course_info_spa": { "url": "https://ccdcae5f73a84360909c59bc6cc8cc0a.us-central1.gcp.cloud.es.io:443", "key": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxy==" }, } t1 = time.time() es = Elasticsearch( ELASTICSEARCH_VDBS_INFO["course_info_eng"]["url"], api_key=ELASTICSEARCH_VDBS_INFO["course_info_eng"]["key"] ) t2 = time.time() print( "Connection to ES completed on {:.2f} seconds".format(t2-t1) ) # ----------------------- QUERY VECTOR DATABASE -------------------------------- query_text = "skill: Post Sound Techniques; job title: Management Consultant" # providers = ["UVA LinkedIn Learning", "EDX"] # DO NOT USE HERE k=10 t1 = time.time() query_vector = get_embeddings_single(query_text) search_query = { "knn": { "field": "embedding", "query_vector": query_vector, "k": 3, # Number of nearest neighbors to return "num_candidates": 100, # Number of candidates to consider "similarity": 0 }, "size": 50 } response = es.search(index=INDEX_NAME, body=search_query) t2 = time.time() print("Queried {} elements in {:.2f} seconds".format(len(response), t2-t1)) ``` As far as I understood this other similar case [[link]](https://discuss.elastic.co/t/not-able-to-get-more-than-10-results-in-ann-search-using-es-8-5-0-docker-image/318429), I should already be obtaining the top k=3 results, but regardless of what I do, I only get 4 (FOUR) results every time. My only suspicion is that maybe something changed between ES version 8.18.1 & 8.5.0 regarding KNN querying, but I am not sure exactly what. Notice I have already attempted the following: - Set `"k"=3` ==\> I get 4 results - Set `"k"=10` ==\> I get 4 results - Delete `"similarity"=0` ==\> I get 4 results What is going on here? ---
**Author:** ![ltrotta](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/ltrotta/32/124003_2.png) [@ltrotta](https://discuss.elastic.co/u/ltrotta)\ **Post date:** [May 27, 2025, 8:08am UTC](https://discuss.elastic.co/t/not-able-to-get-more-than-top-4-result-matches-regardless-of-configuration/378546/2 "2025-05-27T08:08:59Z")
Hello! The `k` parameter doesn't limit the result, as the [documentation](https://www.elastic.co/docs/reference/query-languages/query-dsl/query-dsl-knn-query) says: > The number of nearest neighbors to return **from each shard**. Elasticsearch collects `k` results from each shard, then merges them to find the global top results. Meaning if you have more than one shard, which is common, there will be more than 3 neighbors! What actually limits the result is the `size` parameter, so you can try setting that to 3 and it should work. Not sure why you're not getting more than 4 results though, do you have exactly 4 documents in that index? ---
**Author:** ![dave\_espinosa\_qs](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/dave_espinosa_qs/32/143411_2.png) [@dave\_espinosa\_qs](https://discuss.elastic.co/u/dave_espinosa_qs)\ **Post date:** [May 27, 2025, 5:02pm UTC](https://discuss.elastic.co/t/not-able-to-get-more-than-top-4-result-matches-regardless-of-configuration/378546/3 "2025-05-27T17:02:52Z")
Hello and thanks for posting. I have been a bit busy today, but yeah, after troubleshooting this problem a bit more carefully, I got to realize about what you mention. In fact, the `search_query` that worked in the end was: ```python # The rest of the code... k=10 query_vector = get_embeddings_single(query_text) search_query = { "size": k, "query": { "bool": { "must": [ { "knn": { "field": "embedding", "query_vector": query_vector, "k": k*10, # At least this much to make it functional "num_candidates": k*10 #To make it as quick as possible } } ], "filter": # Whatever filter you may want to include here } } } ``` Even when now it works, I still have a couple of questions: 1. I have noticed that when I try to obtain the _top k="10% of my data"_ (i.e., I try to obtain the k=100 search results, when my whole data has, say, 1000 samples) I start getting only 77 or so results... How to force the search results to obtain 100 results, even if the bottom ones are not that optimal? 2. For a Vector Database of ~330K samples, I am getting response times of 2-3 seconds... Is this behavior normal? Can it be sped-up? ---
**Author:** ![ltrotta](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/ltrotta/32/124003_2.png) [@ltrotta](https://discuss.elastic.co/u/ltrotta)\ **Post date:** [May 28, 2025, 10:29am UTC](https://discuss.elastic.co/t/not-able-to-get-more-than-top-4-result-matches-regardless-of-configuration/378546/4 "2025-05-28T10:29:12Z")
This was originally posted in the language-client category, but these last questions are more about vector search performance, so I'm redirecting this specifically to vector-search.