diff --git a/Gemfile.lock b/Gemfile.lock
index 105fffd..857ccfb 100644
--- a/Gemfile.lock
+++ b/Gemfile.lock
@@ -93,6 +93,9 @@ GEM
       racc (~> 1.4)
     nokogiri (1.18.8-x86_64-linux-musl)
       racc (~> 1.4)
+    numo-linalg (0.1.7)
+      numo-narray (>= 0.9.1.4)
+    numo-narray (0.9.2.1)
     pp (0.6.2)
       prettyprint
     prettyprint (0.2.0)
@@ -156,6 +159,8 @@ DEPENDENCIES
   concurrent-ruby (= 1.3.4)
   dotenv-rails
   neighbor
+  numo-linalg
+  numo-narray
   ruby-openai
 
 BUNDLED WITH
diff --git a/app/jobs/issue_embedding_job.rb b/app/jobs/issue_embedding_job.rb
index f1527fc..d623645 100644
--- a/app/jobs/issue_embedding_job.rb
+++ b/app/jobs/issue_embedding_job.rb
@@ -30,18 +30,34 @@ def embedding_needs_update?(embedding, new_content_hash)
   def update_embedding(issue, embedding, content_hash)
     embedding_service = EmbeddingService.new
     begin
-      content = embedding_service.prepare_issue_content(issue)
-      vector = embedding_service.generate_embedding(content)
-
-      embedding.embedding_vector = vector
-      embedding.content_hash = content_hash
-      embedding.model_used = Setting.plugin_semantic_search["embedding_model"]
-      embedding.save!
-
+      _generate_and_save_embedding(issue, embedding, content_hash, embedding_service)
       Rails.logger.info("=> [SEMANTIC_SEARCH] Successfully generated embedding for Issue ##{issue.id}")
     rescue StandardError => e
       Rails.logger.error("=> [SEMANTIC_SEARCH] Failed to generate embedding for Issue ##{issue.id}: #{e.message}")
+      Rails.logger.error("=> [SEMANTIC_SEARCH] Error details: #{e.backtrace.join("\n")}")
       raise e
     end
   end
+
+  def _generate_and_save_embedding(issue, embedding, content_hash, embedding_service)
+    content = embedding_service.prepare_issue_content(issue)
+    vector, original_dimension = embedding_service.generate_embedding(content)
+
+    log_msg = "=> [SEMANTIC_SEARCH] Generated embedding with dimension: #{vector.length}, " \
+              "original: #{original_dimension}"
+    Rails.logger.info(log_msg)
+
+    vector = DimensionReductionService.validate_vector_dimension(
+      vector,
+      EmbeddingService::TARGET_DIMENSION
+    )
+
+    Rails.logger.info("=> [SEMANTIC_SEARCH] Validated embedding dimension: #{vector.length}")
+
+    embedding.embedding_vector = vector
+    embedding.content_hash = content_hash
+    embedding.model_used = Setting.plugin_semantic_search["embedding_model"]
+    embedding.original_dimension = original_dimension
+    embedding.save!
+  end
 end
diff --git a/app/services/dimension_reduction_service.rb b/app/services/dimension_reduction_service.rb
new file mode 100644
index 0000000..7c17c6c
--- /dev/null
+++ b/app/services/dimension_reduction_service.rb
@@ -0,0 +1,70 @@
+class DimensionReductionService
+  def self.reduce_dimensions(vector, source_dimension, target_dimension)
+    padded_vector = pad_vector(vector, source_dimension)
+
+    pca_like_reduction(padded_vector, target_dimension)
+  end
+
+  def self.pad_vector(vector, target_size)
+    return vector if vector.nil? || vector.length >= target_size
+
+    vector + Array.new(target_size - vector.length, 0.0)
+  end
+
+  def self.validate_vector_dimension(vector, target_dimension)
+    return nil if vector.nil?
+
+    if vector.length > target_dimension
+      vector.first(target_dimension)
+    elsif vector.length < target_dimension
+      pad_vector(vector, target_dimension)
+    else
+      vector
+    end
+  end
+
+  def self.pca_like_reduction(vector, target_dimension)
+    return vector.first(target_dimension) if vector.length <= target_dimension
+
+    top_indices = _select_top_indices(vector, target_dimension)
+    uniform_indices = _select_uniform_indices(vector, target_dimension, top_indices)
+    combined_indices = top_indices + uniform_indices
+    filled_indices = _fill_remaining_indices(vector, target_dimension, combined_indices)
+
+    selected_indices = (top_indices + uniform_indices + filled_indices).sort.uniq
+
+    selected_indices.first(target_dimension).map { |index| vector[index] }
+  end
+
+  def self._select_top_indices(vector, target_dimension)
+    importance = vector.map(&:abs)
+    top_dimensions_count = [target_dimension / 5, 1].max
+    importance.each_with_index
+              .sort_by { |val, _| -val }
+              .first(top_dimensions_count)
+              .map { |_, idx| idx }
+  end
+
+  def self._select_uniform_indices(vector, target_dimension, top_indices)
+    remaining_count = target_dimension - top_indices.length
+    return [] if remaining_count <= 0
+
+    step_size = vector.length.to_f / remaining_count
+    uniform_indices = (0...remaining_count).map { |i| (i * step_size).to_i }
+    uniform_indices.reject { |idx| top_indices.include?(idx) }
+  end
+
+  def self._fill_remaining_indices(vector, target_dimension, existing_indices)
+    filled_indices = []
+    current_idx = 0
+    needed_count = target_dimension - existing_indices.length
+
+    while filled_indices.length < needed_count && current_idx < vector.length
+      unless existing_indices.include?(current_idx) || filled_indices.include?(current_idx)
+        filled_indices << current_idx
+      end
+      current_idx += 1
+    end
+    filled_indices
+  end
+end
diff --git a/app/services/embedding_service.rb b/app/services/embedding_service.rb
index 40f7107..7c46d13 100644
--- a/app/services/embedding_service.rb
+++ b/app/services/embedding_service.rb
@@ -3,7 +3,8 @@
 class EmbeddingService
   class EmbeddingError < StandardError; end
 
-  MAX_DIMENSION = 1536
+  MAX_DIMENSION = 5500
+  TARGET_DIMENSION = 2000
 
   def initialize
     @client = OpenAI::Client.new(access_token: api_key, uri_base: base_url)
@@ -11,6 +12,18 @@ def initialize
 
   def generate_embedding(text)
     Rails.logger.info("Generating embedding for text: #{text}")
+    raw_embedding = _fetch_embedding_from_api(text)
+    original_dimension = raw_embedding.length
+    Rails.logger.info("Generated embedding with original dimension: #{original_dimension}")
+
+    processed_embedding = _process_embedding(raw_embedding)
+    [processed_embedding, original_dimension]
+  rescue Faraday::Error => e
+    Rails.logger.error("OpenAI API connection error: #{e.message}")
+    raise EmbeddingError, "Connection error while generating embedding: #{e.message}"
+  end
+
+  def _fetch_embedding_from_api(text)
     response = @client.embeddings(
       parameters: {
         model: embedding_model,
@@ -22,11 +35,24 @@ def generate_embedding(text)
       Rails.logger.error("OpenAI API error: #{response['error']}")
       raise EmbeddingError, "Failed to generate embedding: #{response['error']['message']}"
     end
+    response.dig("data", 0, "embedding")
+  end
 
-    pad_embedding(response.dig("data", 0, "embedding"))
-  rescue Faraday::Error => e
-    Rails.logger.error("OpenAI API connection error: #{e.message}")
-    raise EmbeddingError, "Connection error while generating embedding: #{e.message}"
+  def _process_embedding(embedding_vector)
+    padded_embedding = pad_embedding(embedding_vector)
+
+    reduced_embedding = DimensionReductionService.reduce_dimensions(
+      padded_embedding,
+      MAX_DIMENSION,
+      TARGET_DIMENSION
+    )
+
+    Rails.logger.info("Reduced embedding to dimension: #{TARGET_DIMENSION}")
+
+    DimensionReductionService.validate_vector_dimension(
+      reduced_embedding,
+      TARGET_DIMENSION
+    )
   end
 
   def pad_embedding(vector)
diff --git a/app/services/semantic_search_result_processor.rb b/app/services/semantic_search_result_processor.rb
new file mode 100644
index 0000000..3fea5d5
--- /dev/null
+++ b/app/services/semantic_search_result_processor.rb
@@ -0,0 +1,44 @@
+module SemanticSearchResultProcessor
+  extend self
+
+  def process_results(results)
+    results.map do |result|
+      result = process_author_info(result)
+      result = process_assignee_info(result)
+      result = calculate_similarity_score(result)
+      remove_temporary_fields(result)
+    end
+  end
+
+  private
+
+  def process_author_info(result)
+    result["author_name"] = [result["author_firstname"], result["author_lastname"]].join(" ").strip
+    result["author_name"] = result["author_login"] if result["author_name"].blank?
+    result
+  end
+
+  def process_assignee_info(result)
+    if result["assigned_to_firstname"] || result["assigned_to_lastname"] || result["assigned_to_login"]
+      result["assigned_to_name"] = [result["assigned_to_firstname"], result["assigned_to_lastname"]].join(" ").strip
+      result["assigned_to_name"] = result["assigned_to_login"] if result["assigned_to_name"].blank?
+    else
+      result["assigned_to_name"] = nil
+    end
+    result
+  end
+
+  def calculate_similarity_score(result)
+    distance = result["distance"].to_f
+    result["similarity_score"] = 1.0 / (1.0 + distance)
+    result
+  end
+
+  def remove_temporary_fields(result)
+    %w[author_firstname author_lastname author_login assigned_to_firstname assigned_to_lastname assigned_to_login
+       distance].each do |key|
+      result.delete(key)
+    end
+    result
+  end
+end
diff --git a/app/services/semantic_search_service.rb b/app/services/semantic_search_service.rb
index 1181e2c..1f77653 100644
--- a/app/services/semantic_search_service.rb
+++ b/app/services/semantic_search_service.rb
@@ -1,23 +1,72 @@
+require_relative "semantic_search_result_processor"
+
 class SemanticSearchService
+  include SemanticSearchResultProcessor
+
   def initialize
     @embedding_service = EmbeddingService.new
   end
 
-  def search(query, user, limit = 10)
-    query_embedding = @embedding_service.generate_embedding(query)
+  def search(query, user, limit = 10, _debug: false)
+    Rails.logger.info("Performing semantic search for query: #{query}")
+
+    query_embedding, original_dimension = @embedding_service.generate_embedding(query)
+
+    log_msg = "Query embedding generated with dim: #{query_embedding.length}, " \
+              "original: #{original_dimension}"
+    Rails.logger.info(log_msg)
+
+    query_embedding = DimensionReductionService.validate_vector_dimension(
+      query_embedding,
+      EmbeddingService::TARGET_DIMENSION
+    )
+
+    Rails.logger.info("Validated query embedding dimension: #{query_embedding.length}")
+
     results = fetch_raw_results(query_embedding, limit)
     processed_results = process_results(results)
-    filter_by_visibility(processed_results, user)
+
+    Rails.logger.info("Found #{processed_results.length} results before visibility filtering")
+
+    filtered_results = filter_by_visibility(processed_results, user)
+
+    Rails.logger.info("Returning #{filtered_results.length} visible results")
+
+    filtered_results
   end
 
   private
 
   def fetch_raw_results(query_embedding, limit)
     sql = build_search_sql(query_embedding, limit)
-    ActiveRecord::Base.connection.execute(sql)
+
+    sql.gsub(/ARRAY\[.*?\]::vector/, "ARRAY[...vector values...]::vector")
+
+    begin
+      ActiveRecord::Base.connection.execute(sql)
+    rescue StandardError
+      begin
+        db_dim_query = <<~SQL.squish
+          SELECT pg_catalog.format_type(atttypid, atttypmod-4)
+          FROM pg_catalog.pg_attribute
+          WHERE attrelid = 'issue_embeddings'::regclass AND attname = 'embedding_vector'
+        SQL
+        db_dimension = ActiveRecord::Base.connection.execute(db_dim_query).first["format_type"]
+
+        Rails.logger.error("Database column type: #{db_dimension}")
+        Rails.logger.error("Provided vector length: #{query_embedding.length}")
+      rescue StandardError => debug_error
+        Rails.logger.error("Error getting debug info: #{debug_error.message}")
+      end
+
+      raise
+    end
   end
 
+  # rubocop:disable Metrics/MethodLength
   def build_search_sql(query_embedding, limit)
+    vector_string = query_embedding.join(",")
+
     <<-SQL
       SELECT issue_embeddings.issue_id,
              issues.subject,
@@ -35,7 +84,7 @@ def build_search_sql(query_embedding, limit)
              assigned_users.firstname AS assigned_to_firstname,
              assigned_users.lastname AS assigned_to_lastname,
              assigned_users.login AS assigned_to_login,
-             issue_embeddings.embedding_vector <-> ARRAY[#{query_embedding.join(',')}]::vector AS distance
+             issue_embeddings.embedding_vector <-> ARRAY[#{vector_string}]::vector(#{EmbeddingService::TARGET_DIMENSION}) AS distance
       FROM issue_embeddings
       INNER JOIN issues ON issues.id = issue_embeddings.issue_id
       INNER JOIN projects ON projects.id = issues.project_id
@@ -49,45 +98,7 @@ def build_search_sql(query_embedding, limit)
       LIMIT #{limit}
     SQL
   end
-
-  def process_results(results)
-    results.map do |result|
-      result = process_author_info(result)
-      result = process_assignee_info(result)
-      result = calculate_similarity_score(result)
-      remove_temporary_fields(result)
-    end
-  end
-
-  def process_author_info(result)
-    result["author_name"] = [result["author_firstname"], result["author_lastname"]].join(" ").strip
-    result["author_name"] = result["author_login"] if result["author_name"].blank?
-    result
-  end
-
-  def process_assignee_info(result)
-    if result["assigned_to_firstname"] || result["assigned_to_lastname"] || result["assigned_to_login"]
-      result["assigned_to_name"] = [result["assigned_to_firstname"], result["assigned_to_lastname"]].join(" ").strip
-      result["assigned_to_name"] = result["assigned_to_login"] if result["assigned_to_name"].blank?
-    else
-      result["assigned_to_name"] = nil
-    end
-    result
-  end
-
-  def calculate_similarity_score(result)
-    distance = result["distance"].to_f
-    result["similarity_score"] = 1.0 / (1.0 + distance)
-    result
-  end
-
-  def remove_temporary_fields(result)
-    %w[author_firstname author_lastname author_login assigned_to_firstname assigned_to_lastname assigned_to_login
-       distance].each do |key|
-      result.delete(key)
-    end
-    result
-  end
+  # rubocop:enable Metrics/MethodLength
 
   def filter_by_visibility(processed_results, user)
     issue_ids = processed_results.map { |r| r["issue_id"] }
diff --git a/db/migrate/004_update_vector_dimension_for_pca.rb b/db/migrate/004_update_vector_dimension_for_pca.rb
new file mode 100644
index 0000000..21a8865
--- /dev/null
+++ b/db/migrate/004_update_vector_dimension_for_pca.rb
@@ -0,0 +1,7 @@
+class UpdateVectorDimensionForPca < ActiveRecord::Migration[7.2]
+  def up
+    unless column_exists?(:issue_embeddings, :original_dimension)
+      add_column :issue_embeddings, :original_dimension, :integer, default: 1536
+    end
+  end
+end
diff --git a/db/migrate/005_add_pca_support_to_issue_embeddings.rb b/db/migrate/005_add_pca_support_to_issue_embeddings.rb
new file mode 100644
index 0000000..c9b2890
--- /dev/null
+++ b/db/migrate/005_add_pca_support_to_issue_embeddings.rb
@@ -0,0 +1,15 @@
+class AddPcaSupportToIssueEmbeddings < ActiveRecord::Migration[7.2]
+  def up
+    execute "DROP INDEX IF EXISTS issue_embeddings_vector_idx"
+
+    execute "ALTER TABLE issue_embeddings ALTER COLUMN embedding_vector TYPE vector(2000)"
+
+    execute "CREATE INDEX issue_embeddings_vector_idx ON issue_embeddings USING ivfflat (embedding_vector vector_l2_ops) WITH (lists = 100)"
+  end
+
+  def down
+    execute "DROP INDEX IF EXISTS issue_embeddings_vector_idx"
+
+    execute "CREATE INDEX issue_embeddings_vector_idx ON issue_embeddings USING ivfflat (embedding_vector vector_l2_ops) WITH (lists = 100)"
+  end
+end
diff --git a/db/migrate/006_update_existing_embeddings_for_pca.rb b/db/migrate/006_update_existing_embeddings_for_pca.rb
new file mode 100644
index 0000000..cddb645
--- /dev/null
+++ b/db/migrate/006_update_existing_embeddings_for_pca.rb
@@ -0,0 +1,12 @@
+class UpdateExistingEmbeddingsForPca < ActiveRecord::Migration[7.2]
+  def up
+    return unless table_exists?(:issue_embeddings) &&
+                  column_exists?(:issue_embeddings, :embedding_vector)
+
+    unless column_exists?(:issue_embeddings, :original_dimension)
+      add_column :issue_embeddings, :original_dimension, :integer, default: 1536
+    end
+
+    execute "SELECT id, issue_id FROM issue_embeddings WHERE embedding_vector IS NOT NULL"
+  end
+end
diff --git a/scripts/embedding_viewer.py b/scripts/embedding_viewer.py
index 291ff58..5b58552 100644
--- a/scripts/embedding_viewer.py
+++ b/scripts/embedding_viewer.py
@@ -37,6 +37,7 @@ def fetch_embeddings(conn, limit=1000, project_id=None, issue_id=None):
         ie.id,
         ie.issue_id,
         ie.model_used,
+        ie.original_dimension,
         i.subject,
         p.name as project_name,
         t.name as tracker_name,
diff --git a/test/application_system_test_case.rb b/test/application_system_test_case.rb
index e7716ba..ece737b 100644
--- a/test/application_system_test_case.rb
+++ b/test/application_system_test_case.rb
@@ -14,13 +14,13 @@ def log_user(login, password)
     fill_in 'username', with: login
     fill_in 'password', with: password
     click_button 'Login', wait: 5
-    assert_selector '#loggedas'
+    assert_selector '#loggedas', wait: 5
   end
 
   def logout
     if has_link?(class: 'logout')
-      click_link(class: 'logout')
+      click_link(class: 'logout', wait: 5)
     end
-    assert_no_selector '#loggedas'
+    assert_no_selector '#loggedas', wait: 5
   end
 end
diff --git a/test/integration/semantic_search_test.rb b/test/integration/semantic_search_test.rb
index 316836c..e2609cb 100644
--- a/test/integration/semantic_search_test.rb
+++ b/test/integration/semantic_search_test.rb
@@ -13,7 +13,7 @@ def setup
     @issue = Issue.find(1)
     @embedding = IssueEmbedding.create!(
       issue: @issue,
-      embedding_vector: [0.1] * 1536,
+      embedding_vector: [0.1] * 2000,
       content_hash: 'test_hash',
       model_used: 'text-embedding-ada-002'
     )
diff --git a/test/system/semantic_search_system_test.rb b/test/system/semantic_search_system_test.rb
index 781514a..2c805a1 100644
--- a/test/system/semantic_search_system_test.rb
+++ b/test/system/semantic_search_system_test.rb
@@ -23,12 +23,12 @@ def setup
 
     @embedding = IssueEmbedding.create!(
       issue: @issue,
-      embedding_vector: [0.1] * 1536,
+      embedding_vector: [0.1] * 2000,
       content_hash: 'test_hash',
       model_used: 'text-embedding-ada-002'
     )
 
-    EmbeddingService.any_instance.stubs(:generate_embedding).returns([0.1] * 1536)
+    EmbeddingService.any_instance.stubs(:generate_embedding).returns([0.1] * 2000)
 
     mock_result = [{
       "issue_id" => @issue.id,
diff --git a/test/test_helper.rb b/test/test_helper.rb
index 39462d6..5468ea1 100644
--- a/test/test_helper.rb
+++ b/test/test_helper.rb
@@ -10,7 +10,13 @@ def initialize
   end
 
   def generate_embedding(text)
-    Array.new(1536) { 0.1 }
+    vector = Array.new(1536) { 0.1 }
+    reduced_vector = DimensionReductionService.reduce_dimensions(
+      DimensionReductionService.pad_vector(vector, 5500),
+      5500,
+      2000
+    )
+    return reduced_vector, 1536
   end
 
   def prepare_issue_content(issue)
diff --git a/test/unit/dimension_reduction_service_test.rb b/test/unit/dimension_reduction_service_test.rb
new file mode 100644
index 0000000..48e5aba
--- /dev/null
+++ b/test/unit/dimension_reduction_service_test.rb
@@ -0,0 +1,61 @@
+require File.expand_path('../../test_helper', __FILE__)
+
+class DimensionReductionServiceTest < ActiveSupport::TestCase
+  def test_reduce_dimensions
+    vector = Array.new(3000) { rand }
+    source_dimension = 5500
+    target_dimension = 2000
+
+    padded_vector = DimensionReductionService.pad_vector(vector, source_dimension)
+    assert_equal source_dimension, padded_vector.length
+
+    reduced_vector = DimensionReductionService.reduce_dimensions(padded_vector, source_dimension, target_dimension)
+    assert_equal target_dimension, reduced_vector.length
+  end
+
+  def test_pca_like_reduction
+    vector_size = 5500
+    test_vector = Array.new(vector_size, 0.1)
+    important_positions = [10, 100, 1000, 2000, 3000, 4000, 5000]
+    important_positions.each do |pos|
+      test_vector[pos] = 0.9
+    end
+
+    target_dimension = 20
+    reduced_vector = DimensionReductionService.send(:pca_like_reduction, test_vector, target_dimension)
+
+    assert_equal target_dimension, reduced_vector.length
+
+    top_dimensions_count = [target_dimension / 5, 1].max
+
+    preserved_important = reduced_vector.count { |val| val.abs > 0.8 }
+
+    assert preserved_important > 0, "Should preserve at least one important dimension"
+  end
+
+  def test_pad_vector
+    vector = Array.new(100) { 0.5 }
+    target_size = 200
+
+    padded_vector = DimensionReductionService.pad_vector(vector, target_size)
+    assert_equal target_size, padded_vector.length
+
+    vector.each_with_index do |val, i|
+      assert_equal val, padded_vector[i]
+    end
+
+    (vector.length...target_size).each do |i|
+      assert_equal 0.0, padded_vector[i]
+    end
+  end
+
+  def test_pad_vector_no_padding_needed
+    vector = Array.new(100) { 0.5 }
+
+    padded_vector = DimensionReductionService.pad_vector(vector, 100)
+    assert_equal vector, padded_vector
+
+    padded_vector = DimensionReductionService.pad_vector(vector, 50)
+    assert_equal vector, padded_vector
+  end
+end
diff --git a/test/unit/embedding_service_test.rb b/test/unit/embedding_service_test.rb
index ac0dc1a..6441c14 100644
--- a/test/unit/embedding_service_test.rb
+++ b/test/unit/embedding_service_test.rb
@@ -48,8 +48,9 @@ def test_generate_embedding
       }
     ).returns(mock_response)
 
-    result = @service.generate_embedding("Test text")
-    assert_equal mock_embedding, result
+    result, original_dimension = @service.generate_embedding("Test text")
+    assert_equal 2000, result.length
+    assert_equal 1536, original_dimension
   end
 
   def test_generate_embedding_handles_error_response
@@ -77,6 +78,16 @@ def test_generate_embedding_handles_network_error
     end
   end
 
+  def test_pad_embedding
+    vector = Array.new(1000) { 0.1 }
+    result = @service.pad_embedding(vector)
+    assert_equal EmbeddingService::MAX_DIMENSION, result.length
+
+    vector = Array.new(EmbeddingService::MAX_DIMENSION) { 0.1 }
+    result = @service.pad_embedding(vector)
+    assert_equal EmbeddingService::MAX_DIMENSION, result.length
+  end
+
   def test_prepare_issue_content
     issue = Issue.find(1)
 
diff --git a/test/unit/issue_embedding_job_test.rb b/test/unit/issue_embedding_job_test.rb
index 480e386..f7319de 100644
--- a/test/unit/issue_embedding_job_test.rb
+++ b/test/unit/issue_embedding_job_test.rb
@@ -24,6 +24,7 @@ def test_job_creates_embedding_when_enabled
     embedding = IssueEmbedding.find_by(issue_id: @issue.id)
     assert_not_nil embedding
     assert_equal @issue.id, embedding.issue_id
+    assert_equal 1536, embedding.original_dimension
   end
 
   def test_job_does_nothing_when_disabled
@@ -48,9 +49,10 @@ def test_job_does_not_update_unchanged_embedding
     content_hash = IssueEmbedding.calculate_content_hash(@issue)
     original_embedding = IssueEmbedding.create!(
       issue_id: @issue.id,
-      embedding_vector: [0.1] * 1536,
+      embedding_vector: Array.new(2000) { 0.1 },
       content_hash: content_hash,
-      model_used: 'text-embedding-ada-002'
+      model_used: 'text-embedding-ada-002',
+      original_dimension: 1536
     )
 
     job = IssueEmbeddingJob.new
diff --git a/test/unit/issue_embedding_test.rb b/test/unit/issue_embedding_test.rb
index 5c4f4e7..117ae0b 100644
--- a/test/unit/issue_embedding_test.rb
+++ b/test/unit/issue_embedding_test.rb
@@ -7,7 +7,7 @@ def setup
     @issue = Issue.find(1)
     @embedding = IssueEmbedding.new(
       issue: @issue,
-      embedding_vector: [0.1] * 1536,
+      embedding_vector: [0.1] * 2000,
       content_hash: 'test_hash',
       model_used: 'text-embedding-ada-002'
     )
@@ -29,7 +29,7 @@ def test_validations
     assert_not @embedding.valid?
     assert_includes @embedding.errors[:embedding_vector], 'cannot be blank'
 
-    @embedding.embedding_vector = [0.1] * 1536
+    @embedding.embedding_vector = [0.1] * 2000
     @embedding.content_hash = nil
     assert_not @embedding.valid?
     assert_includes @embedding.errors[:content_hash], 'cannot be blank'
@@ -93,7 +93,7 @@ def test_needs_update
     current_hash = IssueEmbedding.calculate_content_hash(issue)
     embedding = IssueEmbedding.create!(
       issue: issue,
-      embedding_vector: [0.1] * 1536,
+      embedding_vector: [0.1] * 2000,
       content_hash: current_hash,
       model_used: 'text-embedding-ada-002'
     )
diff --git a/test/unit/semantic_search_service_test.rb b/test/unit/semantic_search_service_test.rb
index 1165ace..4de261a 100644
--- a/test/unit/semantic_search_service_test.rb
+++ b/test/unit/semantic_search_service_test.rb
@@ -10,18 +10,20 @@ def setup
     @service = SemanticSearchService.new
     @user = User.find(1)
     @query = "test search query"
-    @query_embedding = Array.new(1536) { rand }
+    @query_embedding = Array.new(2000) { rand }
+    @original_dimension = 1536
   end
 
   def test_search
-    @mock_embedding_service.expects(:generate_embedding).with(@query).returns(@query_embedding)
+    @mock_embedding_service.expects(:generate_embedding).with(@query).returns([@query_embedding, @original_dimension])
 
     issue = Issue.find(1)
     embedding = IssueEmbedding.new(
       issue: issue,
-      embedding_vector: Array.new(1536) { rand },
+      embedding_vector: Array.new(2000) { rand },
       content_hash: 'test_hash',
-      model_used: 'text-embedding-ada-002'
+      model_used: 'text-embedding-ada-002',
+      original_dimension: 1536
     )
     embedding.save!
 
@@ -72,24 +74,26 @@ def test_search
   end
 
   def test_filter_by_visibility
-    @mock_embedding_service.expects(:generate_embedding).with(@query).returns(@query_embedding)
+    @mock_embedding_service.expects(:generate_embedding).with(@query).returns([@query_embedding, @original_dimension])
 
     visible_issue = Issue.find(1)
     invisible_issue = Issue.find(2)
 
     visible_embedding = IssueEmbedding.new(
       issue: visible_issue,
-      embedding_vector: Array.new(1536) { rand },
+      embedding_vector: Array.new(2000) { rand },
       content_hash: 'visible_hash',
-      model_used: 'text-embedding-ada-002'
+      model_used: 'text-embedding-ada-002',
+      original_dimension: 1536
     )
     visible_embedding.save!
 
     invisible_embedding = IssueEmbedding.new(
       issue: invisible_issue,
-      embedding_vector: Array.new(1536) { rand },
+      embedding_vector: Array.new(2000) { rand },
       content_hash: 'invisible_hash',
-      model_used: 'text-embedding-ada-002'
+      model_used: 'text-embedding-ada-002',
+      original_dimension: 1536
     )
     invisible_embedding.save!
 
@@ -149,7 +153,7 @@ def test_filter_by_visibility
   end
 
   def test_search_with_empty_results
-    @mock_embedding_service.expects(:generate_embedding).with(@query).returns(@query_embedding)
+    @mock_embedding_service.expects(:generate_embedding).with(@query).returns([@query_embedding, @original_dimension])
 
     ActiveRecord::Base.connection.stubs(:execute).returns([])
 
@@ -160,7 +164,7 @@ def test_search_with_empty_results
   end
 
   def test_search_with_limit
-    @mock_embedding_service.expects(:generate_embedding).with(@query).returns(@query_embedding)
+    @mock_embedding_service.expects(:generate_embedding).with(@query).returns([@query_embedding, @original_dimension])
 
     custom_limit = 5
     @service.expects(:build_search_sql).with(@query_embedding, custom_limit).returns("SELECT 1")
@@ -180,7 +184,7 @@ def test_search_handles_embedding_error
   end
 
   def test_search_handles_database_error
-    @mock_embedding_service.expects(:generate_embedding).with(@query).returns(@query_embedding)
+    @mock_embedding_service.expects(:generate_embedding).with(@query).returns([@query_embedding, @original_dimension])
 
     @service.expects(:build_search_sql).with(@query_embedding, 10).returns("SELECT 1")
     ActiveRecord::Base.connection.stubs(:execute)