Initial commit

scripts/client.py

@@ -0,0 +1,16 @@
+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

@@ -0,0 +1,40 @@
+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
+                }
+            )
+        ]
+    )