Re-normalizing dense vectors from 1584 dimensions to 256 dimensions, applying bbq_hnsw index to save 96% memory usage on vector embeddings.

ansible/roles/elasticsearch/tasks/main.yml

@@ -35,16 +35,16 @@
     name: vm.max_map_count
     value: "262144"
     state: present
-    
-- name: Start Elasticsearch Docker container
+
+- name: Start Elasticsearch v7 Docker container
   docker_container:
     name: elasticsearch
     image: elasticsearch:7.14.0
     state: started
     hostname: "{{ inventory_hostname }}"
     ports:
-      - '9200:9200'
-      - '9300:9300'
+      - "9200:9200"
+      - "9300:9300"
     restart_policy: unless-stopped
     container_default_behavior: no_defaults
     networks_cli_compatible: yes
@@ -52,7 +52,7 @@
     network_mode: default
     networks:
       - name: newsblurnet
-        aliases: 
+        aliases:
           - elasticsearch
     user: "{{ ansible_effective_user_id|int }}:{{ ansible_effective_group_id|int }}"
     volumes:
@@ -64,8 +64,49 @@
   tags: consul
   become: yes
   template:
-    src: consul_service.json
+    src: consul_service.json.j2
     dest: /etc/consul.d/elasticsearch.json
+  vars:
+    consul_service_name: elasticsearch
+    consul_service_port: 9200
+    elasticsearch_version: 7
+  notify:
+    - reload consul
+
+- name: Start Elasticsearch v8 Docker container
+  docker_container:
+    name: elasticsearch
+    image: elasticsearch:8.17.0
+    state: started
+    hostname: "{{ inventory_hostname }}"
+    ports:
+      - "9208:9200"
+      - "9308:9300"
+    restart_policy: unless-stopped
+    container_default_behavior: no_defaults
+    networks_cli_compatible: yes
+    # network_mode: host
+    network_mode: default
+    networks:
+      - name: newsblurnet
+        aliases:
+          - elasticsearch
+    user: "{{ ansible_effective_user_id|int }}:{{ ansible_effective_group_id|int }}"
+    volumes:
+      - /srv/newsblur/docker/volumes/elasticsearch8:/usr/share/elasticsearch/data
+      - /var/log/elasticsearch8/:/var/log/elasticsearch/
+      - /srv/newsblur/config/elasticsearch/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml
+
+- name: Register elasticsearch in consul
+  tags: consul
+  become: yes
+  template:
+    src: consul_service.json.j2
+    dest: /etc/consul.d/elasticsearch.json
+  vars:
+    consul_service_name: elasticsearch
+    consul_service_port: 9208
+    elasticsearch_version: 8
   notify:
     - reload consul
 

ansible/roles/elasticsearch/templates/consul_service.json

@@ -1,14 +1,18 @@
 {
     "service": {
-        {% if not elasticsearch_secondary %}
-        "name": "db-elasticsearch",
-        {% else %}
-        "name": "db-elasticsearch-staging",
+        {% if elasticsearch_version == 7 %}
+            {% if not elasticsearch_secondary %}
+            "name": "db-elasticsearch",
+            {% else %}
+            "name": "db-elasticsearch-staging",
+            {% endif %}
+        {% elif elasticsearch_version == 8 %}
+            "name": "db-elasticsearch-v8",
         {% endif %}
         "tags": [
             "db"
         ],
-        "port": 9200,
+        "port": {{ consul_service_port }},
         "checks": [{
             "id": "es-ping",
             "http": "http://{{ ansible_host }}:5579/db_check/elasticsearch?consul=1",

apps/rss_feeds/tasks.py

@@ -8,7 +8,6 @@ from celery.exceptions import SoftTimeLimitExceeded
 from django.conf import settings
 
 from apps.profile.middleware import DBProfilerMiddleware
-from apps.search.models import DiscoverStory
 from newsblur_web.celeryapp import app
 from utils import log as logging
 from utils.redis_raw_log_middleware import RedisDumpMiddleware

apps/search/models.py

@@ -15,6 +15,7 @@ from django.conf import settings
 from django.contrib.auth.models import User
 from openai import APITimeoutError, OpenAI
 
+from apps.search.projection_matrix import project_vector
 from apps.search.tasks import (
     FinishIndexSubscriptionsForDiscover,
     FinishIndexSubscriptionsForSearch,
@@ -276,7 +277,7 @@ class MUserSearch(mongo.Document):
             feed.index_stories_for_search()
 
     @classmethod
-    def remove_all(cls, drop_index=False):
+    def remove_all(cls, drop_index=False, search=True, discover=True):
         # You only need to drop the index if there is data you want to clear.
         # A new search server won't need this, as there isn't anything to drop.
         if drop_index:
@@ -287,11 +288,11 @@ class MUserSearch(mongo.Document):
         logging.info(" ---> ~SN~FRRemoving ~SB%s~SN user searches..." % user_searches.count())
         for user_search in user_searches:
             try:
-                user_search.remove()
+                user_search.remove(search=search, discover=discover)
             except Exception as e:
                 print(" ****> Error on search removal: %s" % e)
 
-    def remove(self):
+    def remove(self, search=True, discover=True):
         from apps.reader.models import UserSubscription
         from apps.rss_feeds.models import Feed
 
@@ -305,16 +306,24 @@ class MUserSearch(mongo.Document):
                 feed = sub.feed
             except Feed.DoesNotExist:
                 continue
-            if not feed.search_indexed:
+            if search and not discover and not feed.search_indexed:
                 continue
-            feed.search_indexed = False
+            if discover and not search and not feed.discover_indexed:
+                continue
+            if search and discover and not feed.search_indexed and not feed.discover_indexed:
+                continue
+            if search:
+                feed.search_indexed = False
+                feed.search_indexing = False
+            if discover:
+                feed.discover_indexed = False
+                feed.discover_indexing = False
             feed.save()
             removed += 1
 
         logging.user(
             user,
-            "~FCRemoved ~SB%s/%s feed's search indexes~SN for ~SB~FB%s~FC~SN."
-            % (removed, total, user.username),
+            f"~FCRemoved ~SB{removed}/{total} feed's {'search' if search and not discover else 'discover' if discover and not search else 'search+discover' if search and discover else 'neither'} indexes~SN for ~SB~FB{user.username}~FC~SN.",
         )
         self.delete()
 
@@ -705,8 +714,9 @@ class DiscoverStory:
             },
             "content_vector": {
                 "type": "dense_vector",
-                "dims": 1536,  # Numbers of dims from text-embedding-3-small
-                # "store": True,  # Keep stored since we need to retrieve it # No need to be explicit
+                "dims": 256,  # Reduced from openai embedding size of 1536 to 256
+                "index": True,
+                "index_options": {"type": "bbq_hnsw"},  # Use bbq_hnsw index options for faster search
             },
         }
 
@@ -947,7 +957,10 @@ class DiscoverStory:
             return []
         story_embedding = response.data[0].embedding
 
-        return story_embedding
+        # Project the embedding down to 256 dimensions
+        projected_embedding = project_vector(story_embedding)
+
+        return projected_embedding.tolist()
 
     @classmethod
     def debug_index(cls, show_data=True, show_source=False):

apps/search/projection_matrix.py

@@ -0,0 +1,37 @@
+import os
+
+import numpy as np
+
+PROJECTION_MATRIX_PATH = os.path.join(os.path.dirname(__file__), "random_projection_matrix.npy")
+INPUT_DIMS = 1536
+OUTPUT_DIMS = 256
+
+
+def generate_projection_matrix():
+    """Generate a random projection matrix for dimensionality reduction."""
+    # Use a fixed random seed for reproducibility
+    np.random.seed(42)
+
+    # Generate random matrix
+    projection = np.random.normal(0, 1 / np.sqrt(OUTPUT_DIMS), (OUTPUT_DIMS, INPUT_DIMS))
+
+    # Normalize the matrix
+    projection = projection / np.linalg.norm(projection, axis=1)[:, np.newaxis]
+
+    return projection
+
+
+def get_projection_matrix():
+    """Get the projection matrix, generating it if it doesn't exist."""
+    if not os.path.exists(PROJECTION_MATRIX_PATH):
+        projection = generate_projection_matrix()
+        np.save(PROJECTION_MATRIX_PATH, projection)
+    return np.load(PROJECTION_MATRIX_PATH)
+
+
+def project_vector(vector):
+    """Project a vector from 1536 dimensions to 256 dimensions."""
+    projection = get_projection_matrix()
+    projected = np.dot(projection, vector)
+    # Normalize the projected vector
+    return projected / np.linalg.norm(projected)

newsblur_web/docker_local_settings.py

@@ -129,10 +129,11 @@ SESSION_REDIS_DB = 5
 
 ELASTICSEARCH_FEED_HOSTS = ["db_elasticsearch:9200"]
 ELASTICSEARCH_STORY_HOSTS = ["db_elasticsearch:9200"]
+ELASTICSEARCH_DISCOVER_HOSTS = ["db_elasticsearch:9200"]
 
 ELASTICSEARCH_FEED_HOST = "http://db_elasticsearch:9200"
 ELASTICSEARCH_STORY_HOST = "http://db_elasticsearch:9200"
-
+ELASTICSEARCH_DISCOVER_HOST = "http://db_elasticsearch:9200"
 BACKED_BY_AWS = {
     "pages_on_node": False,
     "pages_on_s3": False,

newsblur_web/settings.py

@@ -487,6 +487,7 @@ CELERY_BEAT_SCHEDULE = {
 # =========
 # = Mongo =
 # =========
+
 if DOCKERBUILD:
     MONGO_PORT = 29019
 else:
@@ -500,6 +501,14 @@ MONGO_ANALYTICS_DB = {
     "name": "nbanalytics",
 }
 
+# =================
+# = Elasticsearch =
+# =================
+
+ELASTICSEARCH_FEED_HOST = "http://db-elasticsearch.service.nyc1.consul:9200"
+ELASTICSEARCH_STORY_HOST = "http://db-elasticsearch.service.nyc1.consul:9200"
+ELASTICSEARCH_DISCOVER_HOST = "http://db-elasticsearch-v8.service.nyc1.consul:9208"
+
 # ====================
 # = Database Routers =
 # ====================