Initial commit

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

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+3.13

pyproject.toml

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+[project]
+name = "qdrant-mcp"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "fastmcp>=2.12.3",
+    "qdrant-client==1.13",
+    "vault-settings>=0.1.0",
+]
+
+[project.scripts]
+qdrant-mcp = "qdrant_mcp:run"
+
+[build-system]
+requires = ["uv_build"]
+build-backend = "uv_build"
+
+[tool.vault-settings]
+secret = "qdrant-mcp"
+
+[dependency-groups]
+dev = [
+    "fastembed>=0.7.3",
+]

scripts/client.py

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+import asyncio
+from fastmcp import Client
+from fastembed import TextEmbedding
+
+
+embedding_model = TextEmbedding()
+client = Client("http://localhost:8000/sse")
+
+async def call_tool(input: str, collection: str):
+    embedding: list[float] = list(embedding_model.embed(input))[0].tolist()
+
+    async with client:
+        result = await client.call_tool("semantic_search", {"embedding": embedding, "collection": collection})
+        print(result)
+
+asyncio.run(call_tool("Dime sobre las cucarachas", "dummy_collection"))

scripts/create_dummy_collection.py

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+from qdrant_client import QdrantClient
+from fastembed import TextEmbedding
+from qdrant_client.models import Distance, VectorParams, PointStruct
+from qdrant_mcp.config import Settings
+
+settings = Settings()
+embedding_model = TextEmbedding()
+client = QdrantClient(url=settings.url, api_key=settings.api_key)
+
+documents: list[str] = [
+    "Rick es el mas guapo",
+    "Los pulpos tienen tres corazones y sangre azul",
+    "Las cucarachas pueden vivir hasta una semana sin cabeza",
+    "Los koalas tienen huellas dactilares casi idénticas a las humanas",
+    "La miel nunca se echa a perder, incluso después de miles de años"
+]
+embeddings = list(embedding_model.embed(documents))
+size = len(embeddings[0])
+
+_ = client.recreate_collection(
+    collection_name="dummy_collection",
+    vectors_config=VectorParams(
+        distance=Distance.COSINE,
+        size=size
+    )
+)
+
+for idx, (emb, document) in enumerate(zip(embeddings, documents)):
+    _ = client.upsert(
+        collection_name="dummy_collection",
+        points=[
+            PointStruct(
+                id=idx,
+                vector=emb,
+                payload={
+                    "text": document
+                }
+            )
+        ]
+    )

src/qdrant_mcp/__init__.py

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+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

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+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

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+from fastmcp import FastMCP
+
+from .engine import QdrantEngine
+
+mcp = FastMCP("Qdrant MCP")
+
+engine = QdrantEngine()
+
+_ = mcp.tool(engine.semantic_search)

src/qdrant_mcp/models.py

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