Initial commit

src/qdrant_mcp/__init__.py

@@ -0,0 +1,4 @@
+from .main import mcp
+
+def run():
+    mcp.run(transport="sse")

src/qdrant_mcp/config.py

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+from pydantic import Field
+from vault_settings import VaultSettings
+
+
+class Settings(VaultSettings):
+    url: str = Field(...)
+    api_key: str | None = None

src/qdrant_mcp/engine.py

@@ -0,0 +1,35 @@
+from collections.abc import Sequence
+from typing import Any
+
+from qdrant_client import AsyncQdrantClient, models
+
+from .config import Settings
+from .models import SearchRow
+
+
+class QdrantEngine:
+    def __init__(self) -> None:
+        self.settings = Settings()
+        self.client = AsyncQdrantClient(
+            url=self.settings.url, api_key=self.settings.api_key
+        )
+
+    async def semantic_search(
+        self,
+        embedding: Sequence[float] | models.NamedVector,
+        collection: str,
+        limit: int = 10,
+        conditions: Any | None = None,
+        threshold: float | None = None,
+    ) -> list[SearchRow]:
+        points = await self.client.search(
+            collection_name=collection,
+            query_vector=embedding,
+            query_filter=conditions,
+            limit=limit,
+            with_payload=True,
+            with_vectors=False,
+            score_threshold=threshold,
+        )
+
+        return [SearchRow(chunk_id=str(point.id), score=point.score, payload=point.payload) for point in points if point.payload is not None]

src/qdrant_mcp/main.py

@@ -0,0 +1,9 @@
+from fastmcp import FastMCP
+
+from .engine import QdrantEngine
+
+mcp = FastMCP("Qdrant MCP")
+
+engine = QdrantEngine()
+
+_ = mcp.tool(engine.semantic_search)

src/qdrant_mcp/models.py

@@ -0,0 +1,7 @@
+from typing import Any
+from pydantic import BaseModel
+
+class SearchRow(BaseModel):
+    chunk_id: str
+    score: float
+    payload: dict[str, Any]