diff --git a/pyproject.toml b/pyproject.toml
index e223ca9..5b7c67e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,8 @@ dependencies = [
     "structlog>=24.4.0",
     "defusedxml>=0.7.1",
     "trafilatura>=1.12.0",
+    "numpy>=1.26.0",
+    "voyageai>=0.3.0",
 ]
 
 [project.optional-dependencies]
@@ -91,6 +93,7 @@ module = [
     "googleapiclient.*",
     "youtube_transcript_api.*",
     "trafilatura.*",
+    "voyageai.*",
 ]
 ignore_missing_imports = true
 
diff --git a/src/intelstream/bot.py b/src/intelstream/bot.py
index fc23761..0030bc1 100644
--- a/src/intelstream/bot.py
+++ b/src/intelstream/bot.py
@@ -12,6 +12,7 @@
 
 if TYPE_CHECKING:
     from intelstream.database.models import Source
+    from intelstream.services.search import SearchService
 
 logger = structlog.get_logger(__name__)
 
@@ -109,6 +110,7 @@ def __init__(self, settings: Settings, repository: Repository) -> None:
 
         self.settings = settings
         self.repository = repository
+        self.search_service: SearchService | None = None
         self.start_time: datetime | None = None
         self._owner: discord.User | None = None
 
@@ -153,6 +155,10 @@ async def setup_hook(self) -> None:
         await self.add_cog(GitHubCommands(self))
         await self.add_cog(GitHubPolling(self))
 
+        from intelstream.discord.cogs.search import SearchCog
+
+        await self.add_cog(SearchCog(self))
+
         guild = discord.Object(id=self.settings.discord_guild_id)
         self.tree.copy_global_to(guild=guild)
         await self.tree.sync(guild=guild)
diff --git a/src/intelstream/config.py b/src/intelstream/config.py
index b1b9531..27d7a11 100644
--- a/src/intelstream/config.py
+++ b/src/intelstream/config.py
@@ -37,6 +37,29 @@ class Settings(BaseSettings):
         default=None, description="twitterapi.io API key (optional)"
     )
 
+    voyage_api_key: str | None = Field(
+        default=None, description="Voyage AI API key for semantic search embeddings"
+    )
+
+    search_embedding_model: str = Field(
+        default="voyage-3.5-lite",
+        description="Embedding model name for semantic search",
+    )
+
+    search_similarity_threshold: float = Field(
+        default=0.3,
+        ge=0.0,
+        le=1.0,
+        description="Minimum cosine similarity to include in search results",
+    )
+
+    search_max_results: int = Field(
+        default=5,
+        ge=1,
+        le=20,
+        description="Maximum number of search results to return",
+    )
+
     github_token: str | None = Field(
         default=None, description="GitHub Personal Access Token (optional)"
     )
diff --git a/src/intelstream/database/__init__.py b/src/intelstream/database/__init__.py
index 4fa2c29..69f93c8 100644
--- a/src/intelstream/database/__init__.py
+++ b/src/intelstream/database/__init__.py
@@ -1,4 +1,4 @@
-from intelstream.database.models import Base, ContentItem, DiscordConfig, Source
+from intelstream.database.models import Base, ContentEmbedding, ContentItem, DiscordConfig, Source
 from intelstream.database.repository import Repository
 
-__all__ = ["Base", "ContentItem", "DiscordConfig", "Repository", "Source"]
+__all__ = ["Base", "ContentEmbedding", "ContentItem", "DiscordConfig", "Repository", "Source"]
diff --git a/src/intelstream/database/models.py b/src/intelstream/database/models.py
index 13be6e0..6a4f981 100644
--- a/src/intelstream/database/models.py
+++ b/src/intelstream/database/models.py
@@ -79,11 +79,35 @@ class ContentItem(Base):
     created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
 
     source: Mapped["Source"] = relationship("Source", back_populates="content_items")
+    embedding: Mapped["ContentEmbedding | None"] = relationship(
+        "ContentEmbedding",
+        back_populates="content_item",
+        cascade="all, delete-orphan",
+        uselist=False,
+    )
 
     def __repr__(self) -> str:
         return f"<ContentItem(title={self.title!r}, source_id={self.source_id!r})>"
 
 
+class ContentEmbedding(Base):
+    __tablename__ = "content_embeddings"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid4()))
+    content_item_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("content_items.id"), nullable=False, unique=True
+    )
+    embedding_json: Mapped[str] = mapped_column(Text, nullable=False)
+    model_name: Mapped[str] = mapped_column(String(100), nullable=False)
+    text_hash: Mapped[str] = mapped_column(String(64), nullable=False)
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
+
+    content_item: Mapped["ContentItem"] = relationship("ContentItem", back_populates="embedding")
+
+    def __repr__(self) -> str:
+        return f"<ContentEmbedding(content_item_id={self.content_item_id!r}, model={self.model_name!r})>"
+
+
 class DiscordConfig(Base):
     __tablename__ = "discord_config"
 
diff --git a/src/intelstream/database/repository.py b/src/intelstream/database/repository.py
index 82beb06..8ea458b 100644
--- a/src/intelstream/database/repository.py
+++ b/src/intelstream/database/repository.py
@@ -1,7 +1,7 @@
 from datetime import UTC, datetime, timedelta
 
 import structlog
-from sqlalchemy import exists, func, select, text
+from sqlalchemy import delete, exists, func, select, text
 from sqlalchemy.exc import IntegrityError, OperationalError
 from sqlalchemy.ext.asyncio import (
     AsyncConnection,
@@ -18,6 +18,7 @@
 )
 from intelstream.database.models import (
     Base,
+    ContentEmbedding,
     ContentItem,
     DiscordConfig,
     ExtractionCache,
@@ -890,3 +891,84 @@ async def set_github_repo_active(self, repo_id: str, is_active: bool) -> bool:
                 await session.commit()
                 return True
             return False
+
+    async def add_content_embedding(
+        self, content_item_id: str, embedding_json: str, model_name: str, text_hash: str
+    ) -> ContentEmbedding:
+        async with self.session() as session:
+            result = await session.execute(
+                select(ContentEmbedding).where(ContentEmbedding.content_item_id == content_item_id)
+            )
+            existing = result.scalar_one_or_none()
+            if existing:
+                existing.embedding_json = embedding_json
+                existing.model_name = model_name
+                existing.text_hash = text_hash
+            else:
+                existing = ContentEmbedding(
+                    content_item_id=content_item_id,
+                    embedding_json=embedding_json,
+                    model_name=model_name,
+                    text_hash=text_hash,
+                )
+                session.add(existing)
+            await session.commit()
+            await session.refresh(existing)
+            return existing
+
+    async def get_all_embeddings(self) -> list[ContentEmbedding]:
+        async with self.session() as session:
+            result = await session.execute(select(ContentEmbedding))
+            return list(result.scalars().all())
+
+    async def get_items_without_embeddings(self, limit: int = 100) -> list[ContentItem]:
+        async with self.session() as session:
+            subquery = select(ContentEmbedding.content_item_id)
+            result = await session.execute(
+                select(ContentItem)
+                .where(ContentItem.id.notin_(subquery))
+                .where(ContentItem.summary.isnot(None))
+                .order_by(ContentItem.created_at.desc())
+                .limit(limit)
+            )
+            return list(result.scalars().all())
+
+    async def get_embeddings_with_items(
+        self,
+        guild_id: str | None = None,
+        source_type: str | None = None,
+        since: datetime | None = None,
+    ) -> list[tuple[ContentEmbedding, ContentItem, Source]]:
+        async with self.session() as session:
+            query = (
+                select(ContentEmbedding, ContentItem, Source)
+                .join(ContentItem, ContentEmbedding.content_item_id == ContentItem.id)
+                .join(Source, ContentItem.source_id == Source.id)
+            )
+            if guild_id:
+                query = query.where(Source.guild_id == guild_id)
+            if source_type:
+                try:
+                    st = SourceType(source_type)
+                except ValueError:
+                    return []
+                query = query.where(Source.type == st)
+            if since:
+                query = query.where(ContentItem.published_at >= since)
+            result = await session.execute(query)
+            return [(row[0], row[1], row[2]) for row in result.all()]
+
+    async def get_latest_embedding(self) -> ContentEmbedding | None:
+        async with self.session() as session:
+            result = await session.execute(
+                select(ContentEmbedding).order_by(ContentEmbedding.created_at.desc()).limit(1)
+            )
+            return result.scalar_one_or_none()
+
+    async def clear_all_embeddings(self) -> int:
+        async with self.session() as session:
+            result = await session.execute(select(func.count()).select_from(ContentEmbedding))
+            count = result.scalar_one()
+            await session.execute(delete(ContentEmbedding))
+            await session.commit()
+            return count
diff --git a/src/intelstream/discord/cogs/content_posting.py b/src/intelstream/discord/cogs/content_posting.py
index 3302d7b..5a2ac73 100644
--- a/src/intelstream/discord/cogs/content_posting.py
+++ b/src/intelstream/discord/cogs/content_posting.py
@@ -36,10 +36,23 @@ async def cog_load(self) -> None:
             max_input_length=self.bot.settings.summary_max_input_length,
         )
 
+        search_service = None
+        if self.bot.settings.voyage_api_key:
+            from intelstream.services.search import SearchService, VoyageEmbeddingProvider
+
+            provider = VoyageEmbeddingProvider(
+                self.bot.settings.voyage_api_key,
+                model=self.bot.settings.search_embedding_model,
+            )
+            search_service = SearchService(self.bot.repository, provider)
+
+        self.bot.search_service = search_service
+
         self._pipeline = ContentPipeline(
             settings=self.bot.settings,
             repository=self.bot.repository,
             summarizer=summarizer,
+            search_service=search_service,
         )
         await self._pipeline.initialize()
 
diff --git a/src/intelstream/discord/cogs/search.py b/src/intelstream/discord/cogs/search.py
new file mode 100644
index 0000000..566d607
--- /dev/null
+++ b/src/intelstream/discord/cogs/search.py
@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import discord
+import structlog
+from discord import app_commands
+from discord.ext import commands
+
+from intelstream.database.models import SourceType
+from intelstream.services.content_poster import SOURCE_TYPE_LABELS
+
+if TYPE_CHECKING:
+    from intelstream.bot import IntelStreamBot
+
+logger = structlog.get_logger()
+
+
+class SearchCog(commands.Cog):
+    def __init__(self, bot: IntelStreamBot) -> None:
+        self.bot = bot
+
+    async def cog_load(self) -> None:
+        if not self.bot.settings.voyage_api_key:
+            logger.info("Voyage API key not configured, search disabled")
+            return
+
+    @app_commands.command(name="search", description="Search across all content")
+    @app_commands.describe(
+        query="What to search for (3-200 characters)",
+        days="Limit to last N days (optional)",
+        source_type="Filter by source type (optional)",
+    )
+    @app_commands.choices(
+        source_type=[
+            app_commands.Choice(name=label, value=st.value)
+            for st, label in SOURCE_TYPE_LABELS.items()
+        ]
+    )
+    @app_commands.checks.cooldown(5, 60.0)
+    async def search(
+        self,
+        interaction: discord.Interaction,
+        query: str,
+        days: int | None = None,
+        source_type: str | None = None,
+    ) -> None:
+        if not self.bot.search_service:
+            await interaction.response.send_message(
+                "Semantic search is not configured.", ephemeral=True
+            )
+            return
+
+        if len(query) < 3 or len(query) > 200:
+            await interaction.response.send_message(
+                "Query must be between 3 and 200 characters.", ephemeral=True
+            )
+            return
+
+        if days is not None and days < 1:
+            await interaction.response.send_message(
+                "Days must be a positive number.", ephemeral=True
+            )
+            return
+
+        await interaction.response.defer(ephemeral=True)
+
+        guild_id = str(interaction.guild_id) if interaction.guild_id else None
+
+        try:
+            results = await self.bot.search_service.search(
+                query=query,
+                guild_id=guild_id,
+                source_type=source_type,
+                days=days,
+                limit=self.bot.settings.search_max_results,
+                threshold=self.bot.settings.search_similarity_threshold,
+            )
+        except Exception:
+            logger.exception("Search failed", query=query)
+            await interaction.followup.send(
+                "An error occurred while searching. Please try again.", ephemeral=True
+            )
+            return
+
+        if not results:
+            await interaction.followup.send("No relevant results found.", ephemeral=True)
+            return
+
+        embed = discord.Embed(
+            title=f'Search: "{query}"',
+            color=0x3498DB,
+            description=f"{len(results)} results found",
+        )
+        for i, result in enumerate(results, 1):
+            source_label = SOURCE_TYPE_LABELS.get(SourceType(result.source_type), "Unknown")
+            date_str = result.published_at.strftime("%b %d")
+            embed.add_field(
+                name=f"{i}. {result.title[:250]}",
+                value=f"[Link]({result.original_url}) | {source_label} | {date_str} | Score: {result.score:.2f}",
+                inline=False,
+            )
+        await interaction.followup.send(embed=embed, ephemeral=True)
+
+    @search.error
+    async def search_error(
+        self, interaction: discord.Interaction, error: app_commands.AppCommandError
+    ) -> None:
+        if isinstance(error, app_commands.CommandOnCooldown):
+            await interaction.response.send_message(
+                f"Search is on cooldown. Try again in {error.retry_after:.0f}s.",
+                ephemeral=True,
+            )
+        else:
+            raise error
diff --git a/src/intelstream/services/pipeline.py b/src/intelstream/services/pipeline.py
index 37cbcca..7001a65 100644
--- a/src/intelstream/services/pipeline.py
+++ b/src/intelstream/services/pipeline.py
@@ -2,11 +2,15 @@
 import json
 import time
 from datetime import UTC, datetime, timedelta
+from typing import TYPE_CHECKING
 
 import anthropic
 import httpx
 import structlog
 
+if TYPE_CHECKING:
+    from intelstream.services.search import SearchService
+
 from intelstream.adapters.arxiv import ArxivAdapter
 from intelstream.adapters.base import BaseAdapter, ContentData
 from intelstream.adapters.rss import RSSAdapter
@@ -29,10 +33,12 @@ def __init__(
         settings: Settings,
         repository: Repository,
         summarizer: SummarizationService | None = None,
+        search_service: "SearchService | None" = None,
     ) -> None:
         self._settings = settings
         self._repository = repository
         self._summarizer = summarizer
+        self._search_service = search_service
         self._http_client: httpx.AsyncClient | None = None
         self._adapters: dict[SourceType, BaseAdapter] = {}
 
@@ -388,4 +394,9 @@ async def _handle_first_posting_backfill(self, items: list[ContentItem]) -> None
     async def run_cycle(self) -> tuple[int, int]:
         new_items = await self.fetch_all_sources()
         summarized = await self.summarize_pending()
+        if self._search_service:
+            try:
+                await self._search_service.embed_pending()
+            except Exception:
+                logger.warning("Embedding step failed", exc_info=True)
         return new_items, summarized
diff --git a/src/intelstream/services/search.py b/src/intelstream/services/search.py
new file mode 100644
index 0000000..bf1037b
--- /dev/null
+++ b/src/intelstream/services/search.py
@@ -0,0 +1,207 @@
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import dataclass
+from datetime import UTC, datetime, timedelta
+from typing import TYPE_CHECKING, Protocol, runtime_checkable
+
+import numpy as np
+import structlog
+
+if TYPE_CHECKING:
+    from intelstream.database.models import ContentEmbedding, ContentItem, Source
+    from intelstream.database.repository import Repository
+
+logger = structlog.get_logger()
+
+
+@runtime_checkable
+class EmbeddingProvider(Protocol):
+    async def embed(self, texts: list[str]) -> list[list[float]]: ...
+
+    @property
+    def dimension(self) -> int: ...
+
+    @property
+    def model_name(self) -> str: ...
+
+
+class VoyageEmbeddingProvider:
+    def __init__(self, api_key: str, model: str = "voyage-3.5-lite") -> None:
+        import voyageai
+
+        self._client = voyageai.AsyncClient(api_key=api_key)  # type: ignore[attr-defined]
+        self._model = model
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        result = await self._client.embed(texts, model=self._model)
+        return result.embeddings  # type: ignore[return-value]
+
+    @property
+    def dimension(self) -> int:
+        return 1024
+
+    @property
+    def model_name(self) -> str:
+        return self._model
+
+
+def build_embedding_text(item: ContentItem, source: Source) -> str:
+    parts = [item.title]
+    if item.summary:
+        parts.append(item.summary)
+    parts.append(f"Source: {source.name} ({source.type.value})")
+    if item.author:
+        parts.append(f"Author: {item.author}")
+    return " ".join(parts)
+
+
+def compute_text_hash(text: str) -> str:
+    return hashlib.sha256(text.encode()).hexdigest()
+
+
+def cosine_similarity(query: np.ndarray, candidates: np.ndarray) -> np.ndarray:
+    query_len = np.linalg.norm(query)
+    if query_len == 0:
+        return np.zeros(candidates.shape[0], dtype=np.float32)
+    query_norm = query / query_len
+    candidate_norms = np.linalg.norm(candidates, axis=1, keepdims=True)
+    candidate_norms = np.where(candidate_norms == 0, 1.0, candidate_norms)
+    candidates_norm = candidates / candidate_norms
+    return candidates_norm @ query_norm  # type: ignore[no-any-return]
+
+
+@dataclass
+class SearchResult:
+    content_item_id: str
+    title: str
+    summary: str
+    original_url: str
+    source_type: str
+    source_name: str
+    published_at: datetime
+    score: float
+
+
+class SearchService:
+    def __init__(
+        self,
+        repository: Repository,
+        embedding_provider: EmbeddingProvider,
+    ) -> None:
+        self._repository = repository
+        self._provider = embedding_provider
+        self._embeddings_cache: dict[str, np.ndarray] = {}
+        self._cache_loaded = False
+
+    async def search(
+        self,
+        query: str,
+        guild_id: str | None = None,
+        source_type: str | None = None,
+        days: int | None = None,
+        limit: int = 5,
+        threshold: float = 0.3,
+    ) -> list[SearchResult]:
+        query_embedding = (await self._provider.embed([query]))[0]
+        query_array = np.array(query_embedding, dtype=np.float32)
+        candidates = await self._get_candidates(guild_id, source_type, days)
+        return self._rank(query_array, candidates, limit, threshold)
+
+    async def embed_pending(self) -> int:
+        items = await self._repository.get_items_without_embeddings(limit=100)
+        count = 0
+        for item in items:
+            try:
+                source = await self._repository.get_source_by_id(item.source_id)
+                if not source:
+                    continue
+                text = build_embedding_text(item, source)
+                text_hash = compute_text_hash(text)
+                embeddings = await self._provider.embed([text])
+                embedding_json = json.dumps(embeddings[0])
+                await self._repository.add_content_embedding(
+                    content_item_id=item.id,
+                    embedding_json=embedding_json,
+                    model_name=self._provider.model_name,
+                    text_hash=text_hash,
+                )
+                self._embeddings_cache[item.id] = np.array(embeddings[0], dtype=np.float32)
+                count += 1
+            except Exception:
+                logger.warning("Failed to embed content item", item_id=item.id, exc_info=True)
+                continue
+        return count
+
+    async def _ensure_cache_loaded(self) -> None:
+        if self._cache_loaded:
+            return
+        await self._check_model_consistency()
+        embeddings = await self._repository.get_all_embeddings()
+        for emb in embeddings:
+            self._embeddings_cache[emb.content_item_id] = np.array(
+                json.loads(emb.embedding_json), dtype=np.float32
+            )
+        self._cache_loaded = True
+
+    async def _check_model_consistency(self) -> None:
+        latest = await self._repository.get_latest_embedding()
+        if latest and latest.model_name != self._provider.model_name:
+            logger.warning(
+                "Embedding model changed, triggering full re-embed",
+                old_model=latest.model_name,
+                new_model=self._provider.model_name,
+            )
+            await self._repository.clear_all_embeddings()
+            self._embeddings_cache.clear()
+            self._cache_loaded = False
+
+    async def _get_candidates(
+        self, guild_id: str | None, source_type: str | None, days: int | None
+    ) -> list[tuple[ContentEmbedding, ContentItem, Source]]:
+        await self._ensure_cache_loaded()
+        since = datetime.now(UTC) - timedelta(days=days) if days else None
+        return await self._repository.get_embeddings_with_items(
+            guild_id=guild_id, source_type=source_type, since=since
+        )
+
+    def _rank(
+        self,
+        query_array: np.ndarray,
+        candidates: list[tuple[ContentEmbedding, ContentItem, Source]],
+        limit: int,
+        threshold: float,
+    ) -> list[SearchResult]:
+        if not candidates:
+            return []
+        embeddings = []
+        for emb, _item, _source in candidates:
+            cached = self._embeddings_cache.get(emb.content_item_id)
+            if cached is not None:
+                embeddings.append(cached)
+            else:
+                embeddings.append(np.array(json.loads(emb.embedding_json), dtype=np.float32))
+        candidates_matrix = np.stack(embeddings)
+        scores = cosine_similarity(query_array, candidates_matrix)
+        scored = [
+            (float(score), emb, item, source)
+            for score, (emb, item, source) in zip(scores, candidates, strict=True)
+            if score >= threshold
+        ]
+        scored.sort(key=lambda x: x[0], reverse=True)
+        results = []
+        for score, _emb, item, source in scored[:limit]:
+            results.append(
+                SearchResult(
+                    content_item_id=item.id,
+                    title=item.title,
+                    summary=item.summary or "",
+                    original_url=item.original_url,
+                    source_type=source.type.value,
+                    source_name=source.name,
+                    published_at=item.published_at,
+                    score=score,
+                )
+            )
+        return results
diff --git a/tests/test_database.py b/tests/test_database.py
index b79215c..fb36156 100644
--- a/tests/test_database.py
+++ b/tests/test_database.py
@@ -1082,3 +1082,185 @@ async def test_cleanup_returns_zero_when_nothing_to_remove(
 
         entry = await repository.get_extraction_cache("https://example.com/fresh")
         assert entry is not None
+
+
+class TestContentEmbeddingRepository:
+    async def _create_source_and_item(self, repository: Repository) -> tuple:
+        source = await repository.add_source(
+            source_type=SourceType.SUBSTACK,
+            name="Test Substack",
+            identifier="test-substack-emb",
+            feed_url="https://test.substack.com/feed",
+            guild_id="guild-123",
+        )
+        item = await repository.add_content_item(
+            source_id=source.id,
+            external_id="article-emb-1",
+            title="Test Article",
+            original_url="https://test.substack.com/p/test",
+            author="Author",
+            published_at=datetime.now(UTC),
+            raw_content="Content here",
+        )
+        await repository.update_content_item_summary(item.id, "A summary")
+        return source, item
+
+    async def test_add_content_embedding(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        emb = await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+        assert emb.id is not None
+        assert emb.content_item_id == item.id
+        assert emb.model_name == "test-model"
+
+    async def test_add_duplicate_embedding_updates_existing(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="model-v1",
+            text_hash="hash1",
+        )
+        updated = await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.4, 0.5, 0.6]",
+            model_name="model-v2",
+            text_hash="hash2",
+        )
+        assert updated.model_name == "model-v2"
+        assert updated.embedding_json == "[0.4, 0.5, 0.6]"
+
+        all_embs = await repository.get_all_embeddings()
+        assert len(all_embs) == 1
+
+    async def test_get_all_embeddings(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+        embs = await repository.get_all_embeddings()
+        assert len(embs) == 1
+
+    async def test_get_items_without_embeddings(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+
+        items = await repository.get_items_without_embeddings()
+        assert len(items) == 1
+        assert items[0].id == item.id
+
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+
+        items_after = await repository.get_items_without_embeddings()
+        assert len(items_after) == 0
+
+    async def test_get_embeddings_with_items_filters_by_guild(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+
+        results = await repository.get_embeddings_with_items(guild_id="guild-123")
+        assert len(results) == 1
+
+        results_other = await repository.get_embeddings_with_items(guild_id="guild-999")
+        assert len(results_other) == 0
+
+    async def test_get_embeddings_with_items_filters_by_source_type(
+        self, repository: Repository
+    ) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+
+        results = await repository.get_embeddings_with_items(source_type="substack")
+        assert len(results) == 1
+
+        results_other = await repository.get_embeddings_with_items(source_type="arxiv")
+        assert len(results_other) == 0
+
+    async def test_get_embeddings_with_items_filters_by_date(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+
+        results = await repository.get_embeddings_with_items(
+            since=datetime.now(UTC) - timedelta(days=1)
+        )
+        assert len(results) == 1
+
+        results_future = await repository.get_embeddings_with_items(
+            since=datetime.now(UTC) + timedelta(days=1)
+        )
+        assert len(results_future) == 0
+
+    async def test_cascade_delete_source_deletes_embedding(self, repository: Repository) -> None:
+        source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+
+        embs_before = await repository.get_all_embeddings()
+        assert len(embs_before) == 1
+
+        await repository.delete_source(source.identifier)
+
+        embs_after = await repository.get_all_embeddings()
+        assert len(embs_after) == 0
+
+    async def test_clear_all_embeddings(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+
+        count = await repository.clear_all_embeddings()
+        assert count == 1
+
+        embs = await repository.get_all_embeddings()
+        assert len(embs) == 0
+
+    async def test_get_latest_embedding(self, repository: Repository) -> None:
+        _source, item = await self._create_source_and_item(repository)
+        await repository.add_content_embedding(
+            content_item_id=item.id,
+            embedding_json="[0.1, 0.2, 0.3]",
+            model_name="test-model",
+            text_hash="abc123",
+        )
+
+        latest = await repository.get_latest_embedding()
+        assert latest is not None
+        assert latest.content_item_id == item.id
+
+    async def test_get_latest_embedding_empty(self, repository: Repository) -> None:
+        latest = await repository.get_latest_embedding()
+        assert latest is None
diff --git a/tests/test_discord/test_content_posting.py b/tests/test_discord/test_content_posting.py
index cecf591..3b8f0e0 100644
--- a/tests/test_discord/test_content_posting.py
+++ b/tests/test_discord/test_content_posting.py
@@ -17,6 +17,7 @@ def mock_bot():
     bot.settings.summary_max_tokens = 2048
     bot.settings.summary_max_input_length = 100000
     bot.settings.discord_max_message_length = 2000
+    bot.settings.voyage_api_key = None
     bot.guilds = []
     bot.wait_until_ready = AsyncMock()
     bot.notify_owner = AsyncMock()
diff --git a/tests/test_discord/test_search.py b/tests/test_discord/test_search.py
new file mode 100644
index 0000000..6af9541
--- /dev/null
+++ b/tests/test_discord/test_search.py
@@ -0,0 +1,168 @@
+from datetime import UTC, datetime
+from unittest.mock import AsyncMock, MagicMock
+
+import discord
+import pytest
+from discord import app_commands
+
+from intelstream.discord.cogs.search import SearchCog
+from intelstream.services.search import SearchResult
+
+
+@pytest.fixture
+def mock_bot():
+    bot = MagicMock()
+    bot.settings = MagicMock()
+    bot.settings.voyage_api_key = "test-voyage-key"
+    bot.settings.search_max_results = 5
+    bot.settings.search_similarity_threshold = 0.3
+    bot.search_service = MagicMock()
+    bot.search_service.search = AsyncMock()
+    return bot
+
+
+@pytest.fixture
+def search_cog(mock_bot):
+    return SearchCog(mock_bot)
+
+
+@pytest.fixture
+def mock_interaction():
+    interaction = MagicMock(spec=discord.Interaction)
+    interaction.response = MagicMock()
+    interaction.response.defer = AsyncMock()
+    interaction.response.send_message = AsyncMock()
+    interaction.followup = MagicMock()
+    interaction.followup.send = AsyncMock()
+    interaction.user = MagicMock()
+    interaction.user.id = 12345
+    interaction.guild_id = 123456789
+    return interaction
+
+
+@pytest.fixture
+def sample_results():
+    return [
+        SearchResult(
+            content_item_id="item-1",
+            title="Scaling Laws for Transformers",
+            summary="A paper about scaling",
+            original_url="https://arxiv.org/abs/1234",
+            source_type="arxiv",
+            source_name="ArXiv cs.CL",
+            published_at=datetime(2024, 1, 15, tzinfo=UTC),
+            score=0.94,
+        ),
+        SearchResult(
+            content_item_id="item-2",
+            title="RLHF Techniques Overview",
+            summary="An overview of RLHF",
+            original_url="https://example.substack.com/p/rlhf",
+            source_type="substack",
+            source_name="AI Newsletter",
+            published_at=datetime(2024, 1, 10, tzinfo=UTC),
+            score=0.87,
+        ),
+    ]
+
+
+class TestSearchCog:
+    async def test_search_command_returns_embed_with_results(
+        self, search_cog, mock_bot, mock_interaction, sample_results
+    ):
+        mock_bot.search_service.search.return_value = sample_results
+
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query="scaling laws", days=None, source_type=None
+        )
+
+        mock_interaction.response.defer.assert_called_once_with(ephemeral=True)
+        mock_interaction.followup.send.assert_called_once()
+        call_kwargs = mock_interaction.followup.send.call_args.kwargs
+        assert isinstance(call_kwargs["embed"], discord.Embed)
+        assert call_kwargs["ephemeral"] is True
+
+    async def test_search_command_handles_no_results(self, search_cog, mock_bot, mock_interaction):
+        mock_bot.search_service.search.return_value = []
+
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query="nonexistent topic", days=None, source_type=None
+        )
+
+        mock_interaction.followup.send.assert_called_once()
+        call_args = mock_interaction.followup.send.call_args
+        assert "No relevant results found" in call_args.args[0]
+
+    async def test_search_command_handles_service_unavailable(
+        self, search_cog, mock_bot, mock_interaction
+    ):
+        mock_bot.search_service = None
+
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query="test query", days=None, source_type=None
+        )
+
+        mock_interaction.response.send_message.assert_called_once()
+        call_args = mock_interaction.response.send_message.call_args
+        assert "not configured" in call_args.args[0]
+
+    async def test_search_command_rejects_short_query(self, search_cog, mock_interaction):
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query="ab", days=None, source_type=None
+        )
+
+        mock_interaction.response.send_message.assert_called_once()
+        call_args = mock_interaction.response.send_message.call_args
+        assert "3 and 200 characters" in call_args.args[0]
+
+    async def test_search_command_rejects_long_query(self, search_cog, mock_interaction):
+        long_query = "a" * 201
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query=long_query, days=None, source_type=None
+        )
+
+        mock_interaction.response.send_message.assert_called_once()
+        call_args = mock_interaction.response.send_message.call_args
+        assert "3 and 200 characters" in call_args.args[0]
+
+    async def test_search_command_rejects_zero_days(self, search_cog, mock_interaction):
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query="test query", days=0, source_type=None
+        )
+
+        mock_interaction.response.send_message.assert_called_once()
+        call_args = mock_interaction.response.send_message.call_args
+        assert "positive" in call_args.args[0]
+
+    async def test_search_command_rejects_negative_days(self, search_cog, mock_interaction):
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query="test query", days=-5, source_type=None
+        )
+
+        mock_interaction.response.send_message.assert_called_once()
+        call_args = mock_interaction.response.send_message.call_args
+        assert "positive" in call_args.args[0]
+
+    async def test_search_error_handler_returns_cooldown_message(
+        self, search_cog, mock_interaction
+    ):
+        error = app_commands.CommandOnCooldown(app_commands.Cooldown(5, 60.0), 30.0)
+
+        await search_cog.search_error(mock_interaction, error)
+
+        mock_interaction.response.send_message.assert_called_once()
+        call_args = mock_interaction.response.send_message.call_args
+        assert "cooldown" in call_args.args[0]
+
+    async def test_search_command_handles_search_exception(
+        self, search_cog, mock_bot, mock_interaction
+    ):
+        mock_bot.search_service.search.side_effect = Exception("API error")
+
+        await search_cog.search.callback(
+            search_cog, mock_interaction, query="test query", days=None, source_type=None
+        )
+
+        mock_interaction.followup.send.assert_called_once()
+        call_args = mock_interaction.followup.send.call_args
+        assert "error occurred" in call_args.args[0]
diff --git a/tests/test_services/test_search.py b/tests/test_services/test_search.py
new file mode 100644
index 0000000..7682733
--- /dev/null
+++ b/tests/test_services/test_search.py
@@ -0,0 +1,370 @@
+import json
+from datetime import UTC, datetime
+from unittest.mock import AsyncMock, MagicMock, PropertyMock
+
+import numpy as np
+import pytest
+
+from intelstream.database.models import ContentEmbedding, ContentItem, Source, SourceType
+from intelstream.database.repository import Repository
+from intelstream.services.search import (
+    SearchService,
+    build_embedding_text,
+    compute_text_hash,
+    cosine_similarity,
+)
+
+
+@pytest.fixture
+def mock_repository():
+    return AsyncMock(spec=Repository)
+
+
+@pytest.fixture
+def mock_provider():
+    provider = MagicMock()
+    provider.embed = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    type(provider).dimension = PropertyMock(return_value=3)
+    type(provider).model_name = PropertyMock(return_value="test-model")
+    return provider
+
+
+@pytest.fixture
+def search_service(mock_repository, mock_provider):
+    return SearchService(mock_repository, mock_provider)
+
+
+@pytest.fixture
+def sample_source():
+    source = MagicMock(spec=Source)
+    source.id = "source-1"
+    source.name = "Test Source"
+    source.type = SourceType.SUBSTACK
+    source.guild_id = "guild-1"
+    return source
+
+
+@pytest.fixture
+def sample_item():
+    item = MagicMock(spec=ContentItem)
+    item.id = "item-1"
+    item.source_id = "source-1"
+    item.title = "Test Article"
+    item.summary = "A great article about testing"
+    item.original_url = "https://example.com/article"
+    item.author = "Test Author"
+    item.published_at = datetime(2024, 1, 15, 12, 0, 0, tzinfo=UTC)
+    return item
+
+
+@pytest.fixture
+def sample_embedding():
+    emb = MagicMock(spec=ContentEmbedding)
+    emb.content_item_id = "item-1"
+    emb.embedding_json = json.dumps([0.1, 0.2, 0.3])
+    emb.model_name = "test-model"
+    emb.text_hash = "abc123"
+    return emb
+
+
+class TestSearchServiceInitialization:
+    def test_initializes_with_repository_and_provider(self, search_service):
+        assert search_service._repository is not None
+        assert search_service._provider is not None
+
+    def test_cache_not_loaded_on_init(self, search_service):
+        assert search_service._cache_loaded is False
+        assert len(search_service._embeddings_cache) == 0
+
+
+class TestBuildEmbeddingText:
+    def test_combines_title_summary_and_metadata(self, sample_item, sample_source):
+        text = build_embedding_text(sample_item, sample_source)
+        assert "Test Article" in text
+        assert "A great article about testing" in text
+        assert "Test Source" in text
+        assert "substack" in text
+        assert "Test Author" in text
+
+    def test_handles_no_summary(self, sample_item, sample_source):
+        sample_item.summary = None
+        text = build_embedding_text(sample_item, sample_source)
+        assert "Test Article" in text
+        assert "Test Source" in text
+
+    def test_handles_no_author(self, sample_item, sample_source):
+        sample_item.author = ""
+        text = build_embedding_text(sample_item, sample_source)
+        assert "Test Article" in text
+
+
+class TestComputeTextHash:
+    def test_returns_sha256_hex(self):
+        h = compute_text_hash("test")
+        assert len(h) == 64
+
+    def test_same_input_same_hash(self):
+        assert compute_text_hash("hello") == compute_text_hash("hello")
+
+    def test_different_input_different_hash(self):
+        assert compute_text_hash("hello") != compute_text_hash("world")
+
+
+class TestCosineSimilarity:
+    def test_identical_vectors_return_1(self):
+        v = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+        candidates = np.array([[1.0, 0.0, 0.0]], dtype=np.float32)
+        scores = cosine_similarity(v, candidates)
+        assert abs(scores[0] - 1.0) < 1e-6
+
+    def test_orthogonal_vectors_return_0(self):
+        v = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+        candidates = np.array([[0.0, 1.0, 0.0]], dtype=np.float32)
+        scores = cosine_similarity(v, candidates)
+        assert abs(scores[0]) < 1e-6
+
+    def test_opposite_vectors_return_negative(self):
+        v = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+        candidates = np.array([[-1.0, 0.0, 0.0]], dtype=np.float32)
+        scores = cosine_similarity(v, candidates)
+        assert scores[0] < -0.99
+
+    def test_zero_query_vector_returns_zeros(self):
+        v = np.array([0.0, 0.0, 0.0], dtype=np.float32)
+        candidates = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype=np.float32)
+        scores = cosine_similarity(v, candidates)
+        assert scores.shape == (2,)
+        np.testing.assert_array_equal(scores, np.zeros(2, dtype=np.float32))
+
+    def test_zero_candidate_vector_returns_zero_score(self):
+        v = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+        candidates = np.array([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0]], dtype=np.float32)
+        scores = cosine_similarity(v, candidates)
+        assert abs(scores[0]) < 1e-6
+        assert abs(scores[1] - 1.0) < 1e-6
+
+
+class TestEmbeddingGeneration:
+    async def test_embed_pending_processes_items_without_embeddings(
+        self, search_service, mock_repository, mock_provider, sample_item, sample_source
+    ):
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_items_without_embeddings.return_value = [sample_item]
+        mock_repository.get_source_by_id.return_value = sample_source
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        count = await search_service.embed_pending()
+
+        assert count == 1
+        mock_repository.add_content_embedding.assert_called_once()
+
+    async def test_embed_pending_handles_empty_list(self, search_service, mock_repository):
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_items_without_embeddings.return_value = []
+
+        count = await search_service.embed_pending()
+
+        assert count == 0
+
+    async def test_embed_pending_updates_cache(
+        self, search_service, mock_repository, mock_provider, sample_item, sample_source
+    ):
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_items_without_embeddings.return_value = [sample_item]
+        mock_repository.get_source_by_id.return_value = sample_source
+        mock_provider.embed.return_value = [[0.5, 0.6, 0.7]]
+
+        await search_service.embed_pending()
+
+        assert sample_item.id in search_service._embeddings_cache
+        np.testing.assert_array_almost_equal(
+            search_service._embeddings_cache[sample_item.id],
+            np.array([0.5, 0.6, 0.7], dtype=np.float32),
+        )
+
+    async def test_embed_pending_continues_on_single_item_failure(
+        self, search_service, mock_repository, mock_provider, sample_source
+    ):
+        item1 = MagicMock(spec=ContentItem)
+        item1.id = "item-1"
+        item1.source_id = "source-1"
+        item1.title = "Item 1"
+        item1.summary = "Summary 1"
+        item1.author = "Author"
+
+        item2 = MagicMock(spec=ContentItem)
+        item2.id = "item-2"
+        item2.source_id = "source-1"
+        item2.title = "Item 2"
+        item2.summary = "Summary 2"
+        item2.author = "Author"
+
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_items_without_embeddings.return_value = [item1, item2]
+        mock_repository.get_source_by_id.return_value = sample_source
+        mock_provider.embed.side_effect = [Exception("API error"), [[0.1, 0.2, 0.3]]]
+
+        count = await search_service.embed_pending()
+
+        assert count == 1
+
+    async def test_embed_pending_skips_item_without_source(
+        self, search_service, mock_repository, mock_provider, sample_item
+    ):
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_items_without_embeddings.return_value = [sample_item]
+        mock_repository.get_source_by_id.return_value = None
+
+        count = await search_service.embed_pending()
+
+        assert count == 0
+        mock_provider.embed.assert_not_called()
+
+
+class TestSearch:
+    async def test_search_returns_ranked_results(
+        self,
+        search_service,
+        mock_repository,
+        mock_provider,
+        sample_embedding,
+        sample_item,
+        sample_source,
+    ):
+        mock_repository.get_latest_embedding.return_value = MagicMock(model_name="test-model")
+        mock_repository.get_all_embeddings.return_value = [sample_embedding]
+        mock_repository.get_embeddings_with_items.return_value = [
+            (sample_embedding, sample_item, sample_source)
+        ]
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        results = await search_service.search("test query")
+
+        assert len(results) == 1
+        assert results[0].title == "Test Article"
+        assert results[0].score > 0
+
+    async def test_search_returns_empty_for_no_embeddings(
+        self, search_service, mock_repository, mock_provider
+    ):
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_all_embeddings.return_value = []
+        mock_repository.get_embeddings_with_items.return_value = []
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        results = await search_service.search("test query")
+
+        assert len(results) == 0
+
+    async def test_search_filters_below_threshold(
+        self,
+        search_service,
+        mock_repository,
+        mock_provider,
+        sample_item,
+        sample_source,
+    ):
+        emb = MagicMock(spec=ContentEmbedding)
+        emb.content_item_id = "item-1"
+        emb.embedding_json = json.dumps([1.0, 0.0, 0.0])
+
+        mock_repository.get_latest_embedding.return_value = MagicMock(model_name="test-model")
+        mock_repository.get_all_embeddings.return_value = [emb]
+        mock_repository.get_embeddings_with_items.return_value = [(emb, sample_item, sample_source)]
+        mock_provider.embed.return_value = [[0.0, 0.0, 1.0]]
+
+        results = await search_service.search("test query", threshold=0.9)
+
+        assert len(results) == 0
+
+    async def test_search_respects_limit(
+        self,
+        search_service,
+        mock_repository,
+        mock_provider,
+        sample_source,
+    ):
+        candidates = []
+        all_embeddings = []
+        for i in range(5):
+            item = MagicMock(spec=ContentItem)
+            item.id = f"item-{i}"
+            item.source_id = "source-1"
+            item.title = f"Article {i}"
+            item.summary = f"Summary {i}"
+            item.original_url = f"https://example.com/{i}"
+            item.author = "Author"
+            item.published_at = datetime(2024, 1, 15, 12, 0, 0, tzinfo=UTC)
+
+            emb = MagicMock(spec=ContentEmbedding)
+            emb.content_item_id = f"item-{i}"
+            emb.embedding_json = json.dumps([0.1 * (i + 1), 0.2, 0.3])
+            emb.model_name = "test-model"
+
+            candidates.append((emb, item, sample_source))
+            all_embeddings.append(emb)
+
+        mock_repository.get_latest_embedding.return_value = MagicMock(model_name="test-model")
+        mock_repository.get_all_embeddings.return_value = all_embeddings
+        mock_repository.get_embeddings_with_items.return_value = candidates
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        results = await search_service.search("test", limit=2, threshold=0.0)
+
+        assert len(results) == 2
+
+    async def test_search_lazy_loads_cache(self, search_service, mock_repository, mock_provider):
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_all_embeddings.return_value = []
+        mock_repository.get_embeddings_with_items.return_value = []
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        assert search_service._cache_loaded is False
+
+        await search_service.search("test")
+
+        assert search_service._cache_loaded is True
+        mock_repository.get_all_embeddings.assert_called_once()
+
+    async def test_search_passes_filters_to_repository(
+        self, search_service, mock_repository, mock_provider
+    ):
+        mock_repository.get_latest_embedding.return_value = None
+        mock_repository.get_all_embeddings.return_value = []
+        mock_repository.get_embeddings_with_items.return_value = []
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        await search_service.search("test", guild_id="guild-1", source_type="arxiv", days=30)
+
+        call_kwargs = mock_repository.get_embeddings_with_items.call_args.kwargs
+        assert call_kwargs["guild_id"] == "guild-1"
+        assert call_kwargs["source_type"] == "arxiv"
+        assert call_kwargs["since"] is not None
+
+
+class TestModelConsistency:
+    async def test_model_change_clears_all_embeddings(
+        self, search_service, mock_repository, mock_provider
+    ):
+        latest = MagicMock(spec=ContentEmbedding)
+        latest.model_name = "old-model"
+        mock_repository.get_latest_embedding.return_value = latest
+        mock_repository.get_all_embeddings.return_value = []
+        mock_repository.get_embeddings_with_items.return_value = []
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        await search_service.search("test")
+
+        mock_repository.clear_all_embeddings.assert_called_once()
+
+    async def test_same_model_does_not_clear(self, search_service, mock_repository, mock_provider):
+        latest = MagicMock(spec=ContentEmbedding)
+        latest.model_name = "test-model"
+        mock_repository.get_latest_embedding.return_value = latest
+        mock_repository.get_all_embeddings.return_value = []
+        mock_repository.get_embeddings_with_items.return_value = []
+        mock_provider.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        await search_service.search("test")
+
+        mock_repository.clear_all_embeddings.assert_not_called()
diff --git a/uv.lock b/uv.lock
index 7ebc22d..649fe4a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -100,6 +100,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b4/63/278a98c715ae467624eafe375542d8ba9b4383a016df8fdefe0ae28382a7/aiohttp-3.13.3-cp314-cp314t-win_amd64.whl", hash = "sha256:44531a36aa2264a1860089ffd4dce7baf875ee5a6079d5fb42e261c704ef7344", size = 499694, upload-time = "2026-01-03T17:32:24.546Z" },
 ]
 
+[[package]]
+name = "aiolimiter"
+version = "1.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/23/b52debf471f7a1e42e362d959a3982bdcb4fe13a5d46e63d28868807a79c/aiolimiter-1.2.1.tar.gz", hash = "sha256:e02a37ea1a855d9e832252a105420ad4d15011505512a1a1d814647451b5cca9", size = 7185, upload-time = "2024-12-08T15:31:51.496Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f3/ba/df6e8e1045aebc4778d19b8a3a9bc1808adb1619ba94ca354d9ba17d86c3/aiolimiter-1.2.1-py3-none-any.whl", hash = "sha256:d3f249e9059a20badcb56b61601a83556133655c11d1eb3dd3e04ff069e5f3c7", size = 6711, upload-time = "2024-12-08T15:31:49.874Z" },
+]
+
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 name = "aiosignal"
 version = "1.4.0"
@@ -415,6 +424,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" },
 ]
 
+[[package]]
+name = "click"
+version = "8.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -651,6 +672,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/4e/eb/c96d64137e29ae17d83ad2552470bafe3a7a915e85434d9942077d7fd011/feedparser-6.0.12-py3-none-any.whl", hash = "sha256:6bbff10f5a52662c00a2e3f86a38928c37c48f77b3c511aedcd51de933549324", size = 81480, upload-time = "2025-09-10T13:33:58.022Z" },
 ]
 
+[[package]]
+name = "ffmpeg-python"
+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "future" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/dd/5e/d5f9105d59c1325759d838af4e973695081fbbc97182baf73afc78dec266/ffmpeg-python-0.2.0.tar.gz", hash = "sha256:65225db34627c578ef0e11c8b1eb528bb35e024752f6f10b78c011f6f64c4127", size = 21543, upload-time = "2019-07-06T00:19:08.989Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d7/0c/56be52741f75bad4dc6555991fabd2e07b432d333da82c11ad701123888a/ffmpeg_python-0.2.0-py3-none-any.whl", hash = "sha256:ac441a0404e053f8b6a1113a77c0f452f1cfc62f6344a769475ffdc0f56c23c5", size = 25024, upload-time = "2019-07-06T00:19:07.215Z" },
+]
+
 [[package]]
 name = "filelock"
 version = "3.20.3"
@@ -749,6 +782,24 @@ wheels = [
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 ]
 
+[[package]]
+name = "fsspec"
+version = "2026.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/51/7c/f60c259dcbf4f0c47cc4ddb8f7720d2dcdc8888c8e5ad84c73ea4531cc5b/fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff", size = 313441, upload-time = "2026-02-05T21:50:53.743Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/ab/fb21f4c939bb440104cc2b396d3be1d9b7a9fd3c6c2a53d98c45b3d7c954/fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437", size = 202505, upload-time = "2026-02-05T21:50:51.819Z" },
+]
+
+[[package]]
+name = "future"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a7/b2/4140c69c6a66432916b26158687e821ba631a4c9273c474343badf84d3ba/future-1.0.0.tar.gz", hash = "sha256:bd2968309307861edae1458a4f8a4f3598c03be43b97521076aebf5d94c07b05", size = 1228490, upload-time = "2024-02-21T11:52:38.461Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/da/71/ae30dadffc90b9006d77af76b393cb9dfbfc9629f339fc1574a1c52e6806/future-1.0.0-py3-none-any.whl", hash = "sha256:929292d34f5872e70396626ef385ec22355a1fae8ad29e1a734c3e43f9fbc216", size = 491326, upload-time = "2024-02-21T11:52:35.956Z" },
+]
+
 [[package]]
 name = "google-api-core"
 version = "2.29.0"
@@ -872,6 +923,35 @@ wheels = [
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