Add testing

tests/conftest.py

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+import subprocess
+import time
+
+import pytest
+from fastembed import TextEmbedding
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, PointStruct, VectorParams
+
+
+@pytest.fixture(scope="session")
+def embedding_model():
+    return TextEmbedding()
+
+
+@pytest.fixture(scope="session")
+def qdrant_client(embedding_model: TextEmbedding):
+    client = QdrantClient(":memory:")
+
+    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.tolist(), payload={"text": document})
+            ],
+        )
+
+    yield client
+
+
+@pytest.fixture(scope="session", autouse=True)
+def run_mcp():
+    # Start the MCP server in the background
+    process = subprocess.Popen(
+        ["uv", "run", "vector-search-mcp"],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+
+    # Give the server a moment to start up
+    time.sleep(2)
+
+    try:
+        yield "http://localhost:8000/sse"
+    finally:
+        # Clean up the process when tests are done
+        process.terminate()
+        try:
+            _ = process.wait(timeout=5)
+        except subprocess.TimeoutExpired:
+            process.kill()
+            _ = process.wait()

tests/test_mcp.py

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+import json
+
+from fastembed import TextEmbedding
+from fastmcp import Client
+from mcp.types import TextContent
+
+
+async def test_call_tool(embedding_model: TextEmbedding, run_mcp: str):
+    input = "Quien es el mas guapo?"
+    collection = "dummy_collection"
+
+    embedding: list[float] = list(embedding_model.embed(input))[0].tolist()
+
+    client = Client(run_mcp)
+
+    async with client:
+        name = "semantic_search"
+        body = {"embedding": embedding, "collection": collection}
+        result = await client.call_tool(name, body)
+
+    content_block = result.content[0]
+
+    assert isinstance(content_block, TextContent)
+
+    deserialized_result = json.loads(content_block.text)
+
+    top_result = deserialized_result[0]
+
+    assert top_result["chunk_id"] == "0"
+    assert top_result["score"] > 0.7
+    assert top_result["payload"] == {"text": "Rick es el mas guapo"}