diff --git a/.gitignore b/.gitignore
index b4fd0ef..4f2cbc1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,4 @@
 /.idea
 .env
 logs/*.log
+.venv/
\ No newline at end of file
diff --git a/index/.gitignore b/index/.gitignore
new file mode 100644
index 0000000..d01db98
--- /dev/null
+++ b/index/.gitignore
@@ -0,0 +1,3 @@
+__pycache__
+.idea
+out/
\ No newline at end of file
diff --git a/index/.idea/.gitignore b/index/.idea/.gitignore
new file mode 100644
index 0000000..b58b603
--- /dev/null
+++ b/index/.idea/.gitignore
@@ -0,0 +1,5 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
diff --git a/index/.idea/index.iml b/index/.idea/index.iml
new file mode 100644
index 0000000..0c2f57c
--- /dev/null
+++ b/index/.idea/index.iml
@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.11 (index)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/index/.idea/inspectionProfiles/profiles_settings.xml b/index/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/index/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/index/.idea/misc.xml b/index/.idea/misc.xml
new file mode 100644
index 0000000..ff8c349
--- /dev/null
+++ b/index/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11 (index)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (index)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/index/.idea/modules.xml b/index/.idea/modules.xml
new file mode 100644
index 0000000..303d7b8
--- /dev/null
+++ b/index/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/index.iml" filepath="$PROJECT_DIR$/.idea/index.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/index/.idea/vcs.xml b/index/.idea/vcs.xml
new file mode 100644
index 0000000..6c0b863
--- /dev/null
+++ b/index/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/index/.python-version b/index/.python-version
new file mode 100644
index 0000000..2c07333
--- /dev/null
+++ b/index/.python-version
@@ -0,0 +1 @@
+3.11
diff --git a/index/pyproject.toml b/index/pyproject.toml
new file mode 100644
index 0000000..82d2628
--- /dev/null
+++ b/index/pyproject.toml
@@ -0,0 +1,24 @@
+[project]
+name = "defeed-index"
+version = "0.1.0"
+description = "Add your description here"
+authors = [
+    { name = "Bart", email = "bartolomej.kozorog@gmail.com" }
+]
+requires-python = ">=3.11"
+dependencies = [
+    "bertopic>=0.17.3",
+    "numpy>=2.3.3",
+    "pandas>=2.3.2",
+    "scikit-learn>=1.7.2",
+    "psycopg2-binary>=2.9.9",
+    "python-dotenv>=1.0.0",
+    "datamapplot>=0.6.4",
+]
+
+[project.scripts]
+defeed-index = "defeed_index:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
diff --git a/index/src/defeed_index/__init__.py b/index/src/defeed_index/__init__.py
new file mode 100644
index 0000000..d99e26e
--- /dev/null
+++ b/index/src/defeed_index/__init__.py
@@ -0,0 +1,19 @@
+from .registry import Registry
+from .repository import ActivityRepository, ActivityRepositoryConfig
+from .types import (
+    Activity, DecoratedActivity, ActivitySummary,
+    SearchRequest, SearchResult, SortBy, Period
+)
+
+__all__ = [
+    "Registry",
+    "ActivityRepository",
+    "ActivityRepositoryConfig",
+    "Activity",
+    "DecoratedActivity",
+    "ActivitySummary",
+    "SearchRequest",
+    "SearchResult",
+    "SortBy",
+    "Period",
+]
diff --git a/index/src/defeed_index/registry.py b/index/src/defeed_index/registry.py
new file mode 100644
index 0000000..8097a72
--- /dev/null
+++ b/index/src/defeed_index/registry.py
@@ -0,0 +1,92 @@
+import logging
+from typing import List, Optional, Dict, Any
+from bertopic import BERTopic
+from bertopic.representation import KeyBERTInspired
+from hdbscan import HDBSCAN
+from sentence_transformers import SentenceTransformer
+from sklearn.feature_extraction.text import CountVectorizer
+import pandas as pd
+from datetime import datetime, timedelta
+
+from umap import UMAP
+
+from .repository import ActivityRepository
+from .types import DecoratedActivity, SearchRequest, SearchResult
+
+
+class Registry:
+    """
+    Registry provides an abstraction layer on top of the ActivityRepository
+    and implements activity analysis using BERTopic for topic modeling.
+    """
+    
+    def __init__(self, repository: ActivityRepository):
+        self.repository = repository
+        self.logger = logging.getLogger(__name__)
+        self.activities: List[DecoratedActivity] = []
+        self.documents: List[str] = []
+        self.topics: List[int] = []
+
+        self.vectorizer_model = CountVectorizer(
+            ngram_range=(1, 2),
+            stop_words="english",
+            min_df=2,  # Require at least 2 documents for a term
+            max_df=0.95  # Ignore terms that appear in more than 95% of documents
+        )
+
+        self.embedding_model = SentenceTransformer("thenlper/gte-small")
+        self.umap_model = UMAP(
+            n_components=5,
+            min_dist=0.0,
+            metric='cosine',
+            random_state=42 # Set for reproducibility
+        )
+
+        self.hdbscan_model = HDBSCAN(
+            min_cluster_size=5,
+            metric='euclidean',
+            cluster_selection_method='eom'
+        )
+
+        self.representation_model = KeyBERTInspired()
+
+        self.topic_model = BERTopic(
+            vectorizer_model=self.vectorizer_model,
+            embedding_model=self.embedding_model,
+            representation_model=self.representation_model,
+            umap_model=self.umap_model,
+            hdbscan_model=self.hdbscan_model,
+            verbose=True,
+            min_topic_size=3,  # Minimum 3 activities per topic
+            nr_topics="auto"  # Let BERTopic determine the optimal number of topics
+        )
+        
+    def seed(self) -> None:
+        """
+        Load existing activities from the repository and seed the BERTopic index.
+        """
+
+        self.activities = self.repository.list(
+            from_date=datetime.now() - timedelta(days=10),
+        )
+
+        if not self.activities:
+            self.logger.warning("No activities found in database")
+            return
+        
+        # Prepare documents for BERTopic
+        self.documents = []
+        for activity in self.activities:
+            # full_summary is formatted in Markdown with predefined sections.
+            # This will skew the topic modeling results, so use short_summary instead.
+            self.documents.append(activity.summary.short_summary)
+        
+        self.logger.info(f"Prepared {len(self.documents)} documents for topic modeling")
+
+        embeddings = self.embedding_model.encode(self.documents, show_progress_bar=True)
+
+        self.topics, probabilities = self.topic_model.fit_transform(self.documents, embeddings)
+
+        self.logger.info(f"BERTopic modeling completed:")
+        self.logger.info(f"  - Found {len(set(self.topics))} topics")
+        self.logger.info(f"  - Processed {len(self.documents)} documents")
\ No newline at end of file
diff --git a/index/src/defeed_index/repository.py b/index/src/defeed_index/repository.py
new file mode 100644
index 0000000..725c799
--- /dev/null
+++ b/index/src/defeed_index/repository.py
@@ -0,0 +1,134 @@
+from dataclasses import dataclass
+import psycopg2
+import psycopg2.extras
+from typing import List, Optional, Dict, Any, Union
+import logging
+import os
+from datetime import datetime
+
+from .types import Activity, ActivitySummary, DecoratedActivity, SearchRequest, SearchResult, SortBy, Period
+
+@dataclass
+class ActivityRepositoryConfig:
+    host: str
+    port: int
+    database: str
+    user: str
+    password: str
+
+class ActivityRepository:
+    def __init__(self, config: ActivityRepositoryConfig):
+        self.connection_string = _build_connection_string(config)
+        self.logger = logging.getLogger(__name__)
+
+    def list(
+        self, 
+        source_ids: Optional[List[str]] = None,
+        from_date: Optional[datetime] = None, 
+        limit: Optional[int] = None,
+    ) -> List[DecoratedActivity]:
+        """
+        Read all activities from the database.
+        This is the main function needed for seeding BERTopic.
+        """
+        query = """
+        SELECT 
+            id,
+            uid,
+            source_uid,
+            source_type,
+            title,
+            body,
+            url,
+            image_url,
+            created_at,
+            short_summary,
+            full_summary,
+            raw_json,
+            embedding
+        FROM activities
+        WHERE embedding IS NOT NULL
+        """
+        
+        params = []
+        
+        if source_ids is not None:
+            query += " AND source_uid = ANY(%s)"
+            params.append(source_ids)
+        
+        if from_date is not None:
+            query += " AND created_at >= %s"
+            params.append(from_date)
+        
+        query += " ORDER BY created_at DESC"
+
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(limit)
+        
+        try:
+            with self._get_connection() as conn:
+                with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+                    cur.execute(query, params)
+                    rows = cur.fetchall()
+                    
+                    activities = []
+                    for row in rows:
+                        activities.append(_deserialize_decorated_activity(dict(row)))
+                    
+                    self.logger.info(f"Loaded {len(activities)} activities from database")
+                    return activities
+                    
+        except Exception as e:
+            self.logger.error(f"Failed to get activities: {e}")
+            raise
+    
+    def _get_connection(self):
+        return psycopg2.connect(self.connection_string)
+    
+
+def _build_connection_string(config: ActivityRepositoryConfig) -> str:
+    conn_str = f"postgresql://{config.user}"
+    if config.password:
+        conn_str += f":{config.password}"
+    conn_str += f"@{config.host}:{config.port}/{config.database}"
+
+    return conn_str
+
+def _deserialize_decorated_activity(row: Dict[str, Any]) -> DecoratedActivity:
+    """Deserialize a database row into a DecoratedActivity object."""
+    activity = _deserialize_activity(row)
+    summary = _deserialize_activity_summary(row)
+    embedding = None
+    if row.get('embedding'):
+        embedding = row['embedding']
+    similarity = row.get('similarity', 0.0)
+    return DecoratedActivity(
+        activity=activity,
+        summary=summary,
+        embedding=embedding,
+        similarity=similarity
+    )
+
+def _deserialize_activity(row: Dict[str, Any]) -> Activity:
+    """Deserialize a database row into an Activity object."""
+    return Activity(
+        uid=row['id'],
+        source_uid=row['source_uid'],
+        source_type=row['source_type'],
+        title=row['title'],
+        body=row['body'],
+        url=row['url'],
+        image_url=row['image_url'],
+        created_at=row['created_at'],
+        raw_json=row['raw_json']
+    )
+
+def _deserialize_activity_summary(row: Dict[str, Any]) -> Optional[ActivitySummary]:
+    """Deserialize a database row into an ActivitySummary object if data is available."""
+    if row.get('short_summary') and row.get('full_summary'):
+        return ActivitySummary(
+            short_summary=row['short_summary'],
+            full_summary=row['full_summary']
+        )
+    return None
\ No newline at end of file
diff --git a/index/src/defeed_index/types.py b/index/src/defeed_index/types.py
new file mode 100644
index 0000000..b0b9ba5
--- /dev/null
+++ b/index/src/defeed_index/types.py
@@ -0,0 +1,62 @@
+from dataclasses import dataclass
+from datetime import datetime
+from enum import Enum
+from typing import List, Optional
+
+
+class SortBy(Enum):
+    SIMILARITY = "similarity"
+    DATE = "date"
+
+
+class Period(Enum):
+    ALL = "all"
+    MONTH = "month"
+    WEEK = "week"
+    DAY = "day"
+
+
+@dataclass
+class ActivitySummary:
+    short_summary: str
+    full_summary: str
+
+
+@dataclass
+class Activity:
+    uid: str
+    source_uid: str
+    source_type: str
+    title: str
+    body: str
+    url: str
+    image_url: str
+    created_at: datetime
+    raw_json: str
+
+
+@dataclass
+class DecoratedActivity:
+    activity: Activity
+    summary: Optional[ActivitySummary]
+    embedding: Optional[List[float]]
+    similarity: float = 0.0
+
+
+@dataclass
+class SearchRequest:
+    source_uids: Optional[List[str]] = None
+    activity_uids: Optional[List[str]] = None
+    min_similarity: float = 0.0
+    limit: int = 50
+    cursor: Optional[str] = None
+    sort_by: SortBy = SortBy.DATE
+    period: Period = Period.ALL
+    query_embedding: Optional[List[float]] = None
+
+
+@dataclass
+class SearchResult:
+    activities: List[DecoratedActivity]
+    next_cursor: Optional[str] = None
+    has_more: bool = False
diff --git a/index/src/main.py b/index/src/main.py
new file mode 100644
index 0000000..268ad21
--- /dev/null
+++ b/index/src/main.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+Usage example for the defeed_index module.
+
+This demonstrates how to use the ActivityRepository and Registry classes
+to load activities from PostgreSQL and analyze them with BERTopic.
+"""
+
+import logging
+import os
+from dotenv import load_dotenv
+from matplotlib import pyplot as plt
+
+from defeed_index import Registry, ActivityRepository, ActivityRepositoryConfig
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+def main():
+    load_dotenv()
+    
+    config = ActivityRepositoryConfig(
+        host=os.getenv('DB_HOST'),
+        port=int(os.getenv('DB_PORT')),
+        database=os.getenv('DB_NAME'),
+        user=os.getenv('DB_USER'),
+        password=os.getenv('DB_PASSWORD')
+    )
+
+    print("📊 Initializing ActivityRepository...")
+    repository = ActivityRepository(config)
+
+    print("🧠 Initializing Registry...")
+    registry = Registry(repository)
+
+    print("🌱 Seeding BERTopic index...")
+    registry.seed()
+
+    print("\n📋 Topic Information:")
+    topic_info = registry.topic_model.get_topic_info()
+    print(topic_info)
+
+    fig = registry.topic_model.visualize_document_datamap(
+        registry.documents,
+        topics=registry.topics,
+        width=1200,
+        height=800
+    )
+    plt.savefig("./out/datamapplot_1.png", dpi=300, bbox_inches='tight')
+    plt.show()
+
+if __name__ == "__main__":
+    main()
diff --git a/index/src/notebook.ipynb b/index/src/notebook.ipynb
new file mode 100644
index 0000000..481a706
--- /dev/null
+++ b/index/src/notebook.ipynb
@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "32480936037dba5a",
+   "metadata": {},
+   "source": [
+    "1. Initialize the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-09-23T10:28:36.094788Z",
+     "start_time": "2025-09-23T10:28:29.630709Z"
+    }
+   },
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from defeed_index import Registry, ActivityRepository, ActivityRepositoryConfig\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "repository = ActivityRepository(ActivityRepositoryConfig(\n",
+    "    host=os.getenv('DB_HOST'),\n",
+    "    port=int(os.getenv('DB_PORT')),\n",
+    "    database=os.getenv('DB_NAME'),\n",
+    "    user=os.getenv('DB_USER'),\n",
+    "    password=os.getenv('DB_PASSWORD')\n",
+    "))\n",
+    "\n",
+    "registry = Registry(repository)"
+   ],
+   "outputs": [],
+   "execution_count": 41
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77198b14a291183c",
+   "metadata": {},
+   "source": [
+    "2. Seed the registry with latest activities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ee6c68e6b386530b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-23T10:29:22.039790Z",
+     "start_time": "2025-09-23T10:28:38.647670Z"
+    }
+   },
+   "source": [
+    "registry.seed()\n",
+    "registry.topic_model.get_topic_info()"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 71/71 [00:09<00:00,  7.27it/s]\n",
+      "2025-09-23 12:29:14,058 - BERTopic - Dimensionality - Fitting the dimensionality reduction algorithm\n",
+      "2025-09-23 12:29:20,830 - BERTopic - Dimensionality - Completed ✓\n",
+      "2025-09-23 12:29:20,831 - BERTopic - Cluster - Start clustering the reduced embeddings\n",
+      "2025-09-23 12:29:20,907 - BERTopic - Cluster - Completed ✓\n",
+      "2025-09-23 12:29:20,907 - BERTopic - Representation - Extracting topics using c-TF-IDF for topic reduction.\n",
+      "2025-09-23 12:29:21,015 - BERTopic - Representation - Completed ✓\n",
+      "2025-09-23 12:29:21,016 - BERTopic - Topic reduction - Reducing number of topics\n",
+      "2025-09-23 12:29:21,025 - BERTopic - Representation - Fine-tuning topics using representation models.\n",
+      "2025-09-23 12:29:22,000 - BERTopic - Representation - Completed ✓\n",
+      "2025-09-23 12:29:22,001 - BERTopic - Topic reduction - Reduced number of topics from 3 to 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "   Topic  Count                                               Name  \\\n",
+       "0     -1   1195  -1_reinforcement learning_optimization_adaptiv...   \n",
+       "1      0    621  0_3d gaussian_gaussian splatting_multimodal_sp...   \n",
+       "2      1    450                           1_linux_os_ubuntu_nvidia   \n",
+       "\n",
+       "                                      Representation  \\\n",
+       "0  [reinforcement learning, optimization, adaptiv...   \n",
+       "1  [3d gaussian, gaussian splatting, multimodal, ...   \n",
+       "2  [linux, os, ubuntu, nvidia, windows, macos, cr...   \n",
+       "\n",
+       "                                 Representative_Docs  \n",
+       "0  [Bayesian Ego-graph inference for Networked Mu...  \n",
+       "1  [MedGS: Gaussian Splatting for Multi-Modal 3D ...  \n",
+       "2  [End of 10 event success - Both.org: End of 10...  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Topic</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Representation</th>\n",
+       "      <th>Representative_Docs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>1195</td>\n",
+       "      <td>-1_reinforcement learning_optimization_adaptiv...</td>\n",
+       "      <td>[reinforcement learning, optimization, adaptiv...</td>\n",
+       "      <td>[Bayesian Ego-graph inference for Networked Mu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>621</td>\n",
+       "      <td>0_3d gaussian_gaussian splatting_multimodal_sp...</td>\n",
+       "      <td>[3d gaussian, gaussian splatting, multimodal, ...</td>\n",
+       "      <td>[MedGS: Gaussian Splatting for Multi-Modal 3D ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>450</td>\n",
+       "      <td>1_linux_os_ubuntu_nvidia</td>\n",
+       "      <td>[linux, os, ubuntu, nvidia, windows, macos, cr...</td>\n",
+       "      <td>[End of 10 event success - Both.org: End of 10...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 42
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-23T10:32:48.827223Z",
+     "start_time": "2025-09-23T10:32:41.156563Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "embeddings = registry.embedding_model.encode(registry.documents, show_progress_bar=True)"
+   ],
+   "id": "aeed2b6551b69767",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 71/71 [00:07<00:00,  9.33it/s]\n"
+     ]
+    }
+   ],
+   "execution_count": 47
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-23T10:34:15.300272Z",
+     "start_time": "2025-09-23T10:34:06.946959Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "topics, probabilities = registry.topic_model.fit_transform(registry.documents, embeddings)\n",
+    "set(topics)"
+   ],
+   "id": "f20aaec01445a400",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-09-23 12:34:06,974 - BERTopic - Dimensionality - Fitting the dimensionality reduction algorithm\n",
+      "2025-09-23 12:34:13,668 - BERTopic - Dimensionality - Completed ✓\n",
+      "2025-09-23 12:34:13,669 - BERTopic - Cluster - Start clustering the reduced embeddings\n",
+      "2025-09-23 12:34:13,720 - BERTopic - Cluster - Completed ✓\n",
+      "2025-09-23 12:34:13,721 - BERTopic - Representation - Extracting topics using c-TF-IDF for topic reduction.\n",
+      "2025-09-23 12:34:13,795 - BERTopic - Representation - Completed ✓\n",
+      "2025-09-23 12:34:13,796 - BERTopic - Topic reduction - Reducing number of topics\n",
+      "2025-09-23 12:34:13,800 - BERTopic - Representation - Fine-tuning topics using representation models.\n",
+      "2025-09-23 12:34:15,269 - BERTopic - Representation - Completed ✓\n",
+      "2025-09-23 12:34:15,270 - BERTopic - Topic reduction - Reduced number of topics from 3 to 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{-1, 0, 1}"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 52
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-23T10:37:45.581018Z",
+     "start_time": "2025-09-23T10:37:45.563189Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "registry.topic_model.get_topic_info()",
+   "id": "d60a56059cae0e56",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   Topic  Count                                               Name  \\\n",
+       "0     -1   1195  -1_reinforcement learning_optimization_adaptiv...   \n",
+       "1      0    621  0_3d gaussian_gaussian splatting_multimodal_sp...   \n",
+       "2      1    450                           1_linux_os_ubuntu_nvidia   \n",
+       "\n",
+       "                                      Representation  \\\n",
+       "0  [reinforcement learning, optimization, adaptiv...   \n",
+       "1  [3d gaussian, gaussian splatting, multimodal, ...   \n",
+       "2  [linux, os, ubuntu, nvidia, windows, macos, cr...   \n",
+       "\n",
+       "                                 Representative_Docs  \n",
+       "0  [Bayesian Ego-graph inference for Networked Mu...  \n",
+       "1  [MedGS: Gaussian Splatting for Multi-Modal 3D ...  \n",
+       "2  [End of 10 event success - Both.org: End of 10...  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Topic</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Representation</th>\n",
+       "      <th>Representative_Docs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>1195</td>\n",
+       "      <td>-1_reinforcement learning_optimization_adaptiv...</td>\n",
+       "      <td>[reinforcement learning, optimization, adaptiv...</td>\n",
+       "      <td>[Bayesian Ego-graph inference for Networked Mu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>621</td>\n",
+       "      <td>0_3d gaussian_gaussian splatting_multimodal_sp...</td>\n",
+       "      <td>[3d gaussian, gaussian splatting, multimodal, ...</td>\n",
+       "      <td>[MedGS: Gaussian Splatting for Multi-Modal 3D ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>450</td>\n",
+       "      <td>1_linux_os_ubuntu_nvidia</td>\n",
+       "      <td>[linux, os, ubuntu, nvidia, windows, macos, cr...</td>\n",
+       "      <td>[End of 10 event success - Both.org: End of 10...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 53
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1eb651f0e1989669",
+   "metadata": {},
+   "source": [
+    "3. List topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "cce567ce4e63b4bb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-23T09:53:55.885054Z",
+     "start_time": "2025-09-23T09:53:34.696126Z"
+    }
+   },
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import datamapplot  # Required for BERTopic visualization\n",
+    "\n",
+    "# First, let's check how many topics we actually have\n",
+    "print(f\"Number of topics found: {len(set(registry.topics))}\")\n",
+    "print(f\"Topic distribution: {set(registry.topics)}\")\n",
+    "\n",
+    "# Get the actual number of topics (excluding -1 which is noise)\n",
+    "actual_topics = [t for t in set(registry.topics) if t != -1]\n",
+    "print(f\"Non-noise topics: {len(actual_topics)}\")\n",
+    "\n",
+    "# Use the actual number of topics for visualization\n",
+    "if len(actual_topics) > 0:\n",
+    "    fig = registry.topic_model.visualize_document_datamap(\n",
+    "        registry.documents,\n",
+    "        topics=actual_topics,  # Use actual topics instead of range(20)\n",
+    "        width=1200,\n",
+    "        height=800\n",
+    "    )\n",
+    "    plt.savefig(\"datamapplot.png\", dpi=300, bbox_inches='tight')\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No valid topics found for visualization\")"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of topics found: 5\n",
+      "Topic distribution: {0, 1, 2, 3, -1}\n",
+      "Non-noise topics: 4\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'datamapplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[40]\u001B[39m\u001B[32m, line 14\u001B[39m\n\u001B[32m     12\u001B[39m \u001B[38;5;66;03m# Use the actual number of topics for visualization\u001B[39;00m\n\u001B[32m     13\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(actual_topics) > \u001B[32m0\u001B[39m:\n\u001B[32m---> \u001B[39m\u001B[32m14\u001B[39m     fig = \u001B[43mregistry\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtopic_model\u001B[49m\u001B[43m.\u001B[49m\u001B[43mvisualize_document_datamap\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m     15\u001B[39m \u001B[43m        \u001B[49m\u001B[43mregistry\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdocuments\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m     16\u001B[39m \u001B[43m        \u001B[49m\u001B[43mtopics\u001B[49m\u001B[43m=\u001B[49m\u001B[43mactual_topics\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# Use actual topics instead of range(20)\u001B[39;49;00m\n\u001B[32m     17\u001B[39m \u001B[43m        \u001B[49m\u001B[43mwidth\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m1200\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m     18\u001B[39m \u001B[43m        \u001B[49m\u001B[43mheight\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m800\u001B[39;49m\n\u001B[32m     19\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     20\u001B[39m     plt.savefig(\u001B[33m\"\u001B[39m\u001B[33mdatamapplot.png\u001B[39m\u001B[33m\"\u001B[39m, dpi=\u001B[32m300\u001B[39m, bbox_inches=\u001B[33m'\u001B[39m\u001B[33mtight\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m     21\u001B[39m     plt.show()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/Projects/defeed-api/index/.venv/lib/python3.11/site-packages/bertopic/_bertopic.py:2645\u001B[39m, in \u001B[36mBERTopic.visualize_document_datamap\u001B[39m\u001B[34m(self, docs, topics, embeddings, reduced_embeddings, custom_labels, title, sub_title, width, height, interactive, enable_search, topic_prefix, datamap_kwds, int_datamap_kwds)\u001B[39m\n\u001B[32m   2569\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Visualize documents and their topics in 2D as a static plot for publication using\u001B[39;00m\n\u001B[32m   2570\u001B[39m \u001B[33;03mDataMapPlot. This works best if there are between 5 and 60 topics. It is therefore best\u001B[39;00m\n\u001B[32m   2571\u001B[39m \u001B[33;03mto use a sufficiently large `min_topic_size` or set `nr_topics` when building the model.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m   2642\u001B[39m \u001B[33;03m```\u001B[39;00m\n\u001B[32m   2643\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   2644\u001B[39m check_is_fitted(\u001B[38;5;28mself\u001B[39m)\n\u001B[32m-> \u001B[39m\u001B[32m2645\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mplotting\u001B[49m\u001B[43m.\u001B[49m\u001B[43mvisualize_document_datamap\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   2646\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   2647\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdocs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2648\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtopics\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2649\u001B[39m \u001B[43m    \u001B[49m\u001B[43membeddings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2650\u001B[39m \u001B[43m    \u001B[49m\u001B[43mreduced_embeddings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2651\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcustom_labels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2652\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtitle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2653\u001B[39m \u001B[43m    \u001B[49m\u001B[43msub_title\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2654\u001B[39m \u001B[43m    \u001B[49m\u001B[43mwidth\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2655\u001B[39m \u001B[43m    \u001B[49m\u001B[43mheight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2656\u001B[39m \u001B[43m    \u001B[49m\u001B[43minteractive\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2657\u001B[39m \u001B[43m    \u001B[49m\u001B[43menable_search\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2658\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtopic_prefix\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2659\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdatamap_kwds\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2660\u001B[39m \u001B[43m    \u001B[49m\u001B[43mint_datamap_kwds\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2661\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/Projects/defeed-api/index/.venv/lib/python3.11/site-packages/bertopic/plotting/_datamap.py:178\u001B[39m, in \u001B[36mvisualize_document_datamap\u001B[39m\u001B[34m(topic_model, docs, topics, embeddings, reduced_embeddings, custom_labels, title, sub_title, width, height, interactive, enable_search, topic_prefix, datamap_kwds, int_datamap_kwds)\u001B[39m\n\u001B[32m    168\u001B[39m     figure = datamapplot.create_interactive_plot(\n\u001B[32m    169\u001B[39m         embeddings_2d,\n\u001B[32m    170\u001B[39m         named_topic_per_doc,\n\u001B[32m   (...)\u001B[39m\u001B[32m    175\u001B[39m         **int_datamap_kwds,\n\u001B[32m    176\u001B[39m     )\n\u001B[32m    177\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m178\u001B[39m     figure, _ = \u001B[43mdatamapplot\u001B[49m.create_plot(\n\u001B[32m    179\u001B[39m         embeddings_2d,\n\u001B[32m    180\u001B[39m         named_topic_per_doc,\n\u001B[32m    181\u001B[39m         figsize=(width / \u001B[32m100\u001B[39m, height / \u001B[32m100\u001B[39m),\n\u001B[32m    182\u001B[39m         dpi=\u001B[32m100\u001B[39m,\n\u001B[32m    183\u001B[39m         title=title,\n\u001B[32m    184\u001B[39m         sub_title=sub_title,\n\u001B[32m    185\u001B[39m         **datamap_kwds,\n\u001B[32m    186\u001B[39m     )\n\u001B[32m    188\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m figure\n",
+      "\u001B[31mNameError\u001B[39m: name 'datamapplot' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 40
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/index/uv.lock b/index/uv.lock
new file mode 100644
index 0000000..b933286
--- /dev/null
+++ b/index/uv.lock
@@ -0,0 +1,2224 @@
+version = 1
+revision = 1
+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "bertopic"
+version = "0.17.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "hdbscan" },
+    { name = "llvmlite" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "plotly" },
+    { name = "scikit-learn" },
+    { name = "sentence-transformers" },
+    { name = "tqdm" },
+    { name = "umap-learn" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2c/f6/731a912f10a4c74b442a1e62dea8a492455419c7f8088e8854768b3ca59c/bertopic-0.17.3.tar.gz", hash = "sha256:3f876d2eadf2037df79f059b734935d7ed2eb6f530bcc6c7a19e1a8f5d2cbaba", size = 119948 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/05/2d6b305391efff89c2b4cf19cf847f971ca163eb5c149d0d2ffac0a9c7ed/bertopic-0.17.3-py3-none-any.whl", hash = "sha256:1ac8ccc647dd8f0b424ebcc05d95741394c2a3762d314d28ecd7bf0fdb3930a3", size = 153010 },
+]
+
+[[package]]
+name = "bokeh"
+version = "3.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "contourpy" },
+    { name = "jinja2" },
+    { name = "narwhals" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "pillow" },
+    { name = "pyyaml" },
+    { name = "tornado", marker = "sys_platform != 'emscripten'" },
+    { name = "xyzservices" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/78/bd/8455ecfaa8100dbfbb2af40061c689a7a9c808f4f8c9582f0efd0c8c9a19/bokeh-3.8.0.tar.gz", hash = "sha256:bfdf5e9df910653b097f70cd38f4c2399d91af6e54a618126e2387cc33c9ec03", size = 6529746 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/96/9a/8e641b5415e12036d8a206147b8229d917a767b7d939521458d90feddcf5/bokeh-3.8.0-py3-none-any.whl", hash = "sha256:117c5e559231ad39fef87891a1a1b62b3bfefbaa47d536023537338f46015841", size = 7205343 },
+]
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+[[package]]
+name = "certifi"
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