Moving to OpenAI Embeddings API.

apps/rss_feeds/models.py

@@ -1070,7 +1070,7 @@ class Feed(models.Model):
 
         self.similar_feeds.clear()
         for result in results:
-            feed_id = result['_source']['feed_id']
+            feed_id = result["_source"]["feed_id"]
             try:
                 self.similar_feeds.add(feed_id)
             except IntegrityError:
@@ -1084,11 +1084,11 @@ class Feed(models.Model):
         combined_content_vector = SearchFeed.generate_combined_feed_content_vector(feed_ids)
         results = SearchFeed.vector_query(combined_content_vector, feed_ids_to_exclude=feed_ids)
         logging.debug(
-            f"Found {len(results)} recommendations for feeds {feed_ids}: {r['_source']['title'] for r in results}"
+            f"Found {len(results)} recommendations for feeds {feed_ids}: {[r['_source']['title'] for r in results]}"
         )
 
         return results
-    
+
     def _split_favicon_color(self, color=None):
         if not color:
             color = self.favicon_color

apps/search/models.py

@@ -12,6 +12,7 @@ import redis
 import urllib3
 from django.conf import settings
 from django.contrib.auth.models import User
+from openai import OpenAI
 
 from apps.search.tasks import (
     FinishIndexSubscriptionsForSearch,
@@ -20,6 +21,7 @@ from apps.search.tasks import (
     IndexSubscriptionsForSearch,
 )
 from utils import log as logging
+from utils.ai_functions import setup_openai_model
 from utils.feed_functions import chunks
 
 
@@ -491,7 +493,7 @@ class SearchStory:
 
 class SearchFeed:
     _es_client = None
-    name = "discover-feeds"
+    name = "discover-feeds-openai"
     model = None
 
     @classmethod
@@ -578,7 +580,7 @@ class SearchFeed:
             },
             "content_vector": {
                 "type": "dense_vector",
-                "dims": 384,  # Numbers of dims from all-MiniLM-L6-v2
+                "dims": 1536,  # Numbers of dims from text-embedding-3-small
             },
         }
         cls.ES().indices.put_mapping(
@@ -774,27 +776,11 @@ class SearchFeed:
     def generate_feed_content_vector(cls, feed_id):
         from apps.rss_feeds.models import Feed
 
-        if cls.model is None:
-            logging.debug(" ---> ~BG~FBLoading SentenceTransformer model")
-            start_time = time.time()
-            from sentence_transformers import SentenceTransformer
-
-            logging.debug(" ---> ~BG~FGDownloading SentenceTransformer model")
-
-            cls.model = SentenceTransformer("all-MiniLM-L6-v2")
-            logging.debug(
-                f" ---> ~FG~SNModel loaded, took ~SB{round(time.time() - start_time, 2)}~SN seconds"
-            )
-
         feed = Feed.objects.get(id=feed_id)
 
-        # cross_encoder = CrossEncoder("BAAI/bge-large-zh-v2", device="cpu")
-        # cross_encoder.encode([feed.feed_title, feed.feed_content], convert_to_tensors="all")
-
         stories = feed.get_stories()
         stories_text = ""
         for story in stories:
-            # stories_text += f"{story['story_title']} {story['story_authors']} {story['story_content']}"
             stories_text += f"{story['story_title']} {' '.join([tag for tag in story['story_tags']])}"
         text = f"{feed.feed_title} {feed.data.feed_tagline} {stories_text}"
 
@@ -810,12 +796,24 @@ class SearchFeed:
         # Remove extra whitespace
         text = " ".join(text.split())
 
-        encoded_text = cls.model.encode(text)
+        # Send to OpenAI
-        normalized_embedding = encoded_text / np.linalg.norm(encoded_text)
+        model_name = "text-embedding-3-small"
+        encoding = setup_openai_model(model_name)
 
-        # logging.debug(f" ---> ~FGNormalized embedding for feed {feed_id}: {normalized_embedding}")
+        # Truncate the text to the maximum number of tokens
+        max_tokens = 8191  # Maximum for text-embedding-3-small
+        encoded_text = encoding.encode(text)
+        truncated_tokens = encoded_text[:max_tokens]
+        truncated_text = encoding.decode(truncated_tokens)
 
-        return normalized_embedding
+        client = OpenAI(api_key=settings.OPENAI_API_KEY)
+
+        response = client.embeddings.create(model=model_name, input=truncated_text)
+
+        embedding = response.data[0].embedding
+        # normalized_embedding = np.array(embedding) / np.linalg.norm(embedding)
+
+        return embedding
 
     @classmethod
     def export_csv(cls):

config/requirements.txt

@@ -70,7 +70,7 @@ nose-exclude==0.5.0
 numpy==1.26.4
 oauth2==1.9.0.post1
 oauthlib==3.1.0
-openai~=0.27
+openai~=1.51.2
 paypalrestsdk==1.13.1
 pbr==5.6.0
 Pillow==8.0.1
@@ -101,7 +101,6 @@ redis>=4,<5
 requests>=2.25.0,<3
 requests-oauthlib==1.3.0
 scipy==1.12.0
-sentence_transformers==3.0.1
 sentry-sdk==1.44.1
 sgmllib3k==1.0.0
 simplejson==3.17.2

docker/monitor/Dockerfile

@@ -24,6 +24,13 @@ RUN       set -ex \
                             ' \
             && apt-get update \
             && apt-get install -y $rundDeps $buildDeps --no-install-recommends \
-            && pip install -r requirements.txt \
+            && pip install uv \
+            && uv clean || true \
+            && rm -rf /venv \
+            && uv venv /venv \
+            && PATH="/venv/bin:$PATH" \
+            && VIRTUAL_ENV="/venv" \
+            && rm -rf /root/.cache/uv \
+            && uv pip install -r requirements.txt \
             && apt-get purge -y --auto-remove ${buildDeps} \
             && rm -rf /var/lib/apt/lists/*

docker/newsblur_base_image.Dockerfile

@@ -25,8 +25,25 @@ RUN       set -ex \
             && apt-get update \
             && apt-get install -y $rundDeps $buildDeps --no-install-recommends
 COPY      config/requirements.txt /srv/newsblur/
-RUN       pip install -U pip==24
+
-RUN       pip install --no-cache-dir -r requirements.txt
+# Install Rust (required for tiktoken)
-RUN       pip cache purge
+RUN curl https://sh.rustup.rs -sSf | sh -s -- -y
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+# Install uv
+RUN pip install uv
+
+# Clean uv cache and any virtual environment from previous builds
+RUN uv clean || true && rm -rf /venv
+
+# Create and activate virtual environment in /venv
+RUN uv venv /venv
+ENV PATH="/venv/bin:$PATH"
+ENV VIRTUAL_ENV="/venv"
+
+# Install dependencies
+RUN rm -rf /root/.cache/uv && \
+    uv pip install -r requirements.txt
+
 RUN       apt-get purge -y --auto-remove ${buildDeps}
 RUN       rm -rf /var/lib/apt/lists/*

newsblur_web/docker_local_settings.py

@@ -139,6 +139,7 @@ BACKED_BY_AWS = {
     "icons_on_s3": False,
 }
 
+OPENAI_API_KEY = "sk-svcacct-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
 
 # ===========
 # = Logging =

node/package.json

@@ -4,7 +4,7 @@
   "description": "Servers used in running NewsBlur",
   "main": "favicons.js",
   "dependencies": {
-    "@postlight/mercury-parser": "^2.2.3",
+    "@postlight/parser": "^2.2.3",
     "@sentry/browser": "^6.12.0",
     "@sentry/node": "^6.12.0",
     "@sentry/tracing": "^6.12.0",