Add Milvus as a first-class Vektori storage backend

README.md

@@ -29,7 +29,7 @@ SENTENCE LAYER (L2)  <- raw conversation. Sequential NEXT edges. The full story.
   <img src="assets/screenshots/layers.jpeg" alt="Three-layer memory graph: Facts → Episodes → Sentences" width="680" />
 </p>
 
-Search hits Facts, graph discovers Episodes, traces back to source Sentences. SQLite by default — swap to Postgres, Neo4j, or Qdrant when you're ready to scale.
+Search hits Facts, graph discovers Episodes, traces back to source Sentences. SQLite by default — swap to Postgres, Neo4j, Qdrant, or Milvus when you're ready to scale.
 
 ---
 
@@ -49,7 +49,8 @@ Still improving. Run your own in [`/benchmarks`](benchmarks/).
 pip install vektori                      # SQLite + Postgres
 pip install 'vektori[neo4j]'             # + Neo4j support
 pip install 'vektori[qdrant]'            # + Qdrant support
-pip install 'vektori[neo4j,qdrant]'      # all backends
+pip install 'vektori[milvus]'            # + Milvus support
+pip install 'vektori[neo4j,qdrant,milvus]'  # all backends
 ```
 
 No Docker, no external services. SQLite by default.
@@ -221,6 +222,13 @@ v = Vektori(
     embedding_dimension=1024,
 )
 
+# Milvus — high-scale vector store with partition-key isolation
+v = Vektori(
+    storage_backend="milvus",
+    database_url="http://localhost:19530",
+    embedding_dimension=1024,
+)
+
 # In-memory — tests / CI
 v = Vektori(storage_backend="memory")
 ```
@@ -229,7 +237,7 @@ v = Vektori(storage_backend="memory")
 ```bash
 git clone https://github.com/vektori-ai/vektori
 cd vektori
-docker compose up -d                 # starts Postgres, Neo4j, and Qdrant
+docker compose up -d                 # starts Postgres, Neo4j, Qdrant, and Milvus
 
 # Postgres
 DATABASE_URL=postgresql://vektori:vektori@localhost:5432/vektori python examples/quickstart_postgres.py
@@ -239,11 +247,15 @@ VEKTORI_STORAGE_BACKEND=neo4j VEKTORI_DATABASE_URL=bolt://localhost:7687 vektori
 
 # Qdrant
 VEKTORI_STORAGE_BACKEND=qdrant VEKTORI_DATABASE_URL=http://localhost:6333 vektori add "I prefer dark mode" --user-id u1
+
+# Milvus
+VEKTORI_STORAGE_BACKEND=milvus VEKTORI_DATABASE_URL=http://localhost:19530 vektori add "I prefer dark mode" --user-id u1
 ```
 
 **CLI storage flags:**
 ```bash
 vektori config --storage-backend qdrant --database-url http://localhost:6333
+vektori config --storage-backend milvus --database-url http://localhost:19530
 vektori add "my note" --user-id u1
 vektori search "preferences" --user-id u1
 ```

docker-compose.yml

@@ -47,7 +47,70 @@ services:
       timeout: 5s
       retries: 10
 
+  etcd:
+    image: quay.io/coreos/etcd:v3.5.5
+    environment:
+      ETCD_AUTO_COMPACTION_MODE: revision
+      ETCD_AUTO_COMPACTION_RETENTION: "1000"
+      ETCD_QUOTA_BACKEND_BYTES: "4294967296"
+      ETCD_SNAPSHOT_COUNT: "50000"
+    command:
+      - /usr/local/bin/etcd
+      - -advertise-client-urls=http://127.0.0.1:2379
+      - -listen-client-urls=http://0.0.0.0:2379
+      - --data-dir=/etcd
+    ports:
+      - "2379:2379"
+    volumes:
+      - etcddata:/etcd
+    healthcheck:
+      test: ["CMD-SHELL", "ETCDCTL_API=3 etcdctl --endpoints=http://127.0.0.1:2379 endpoint health || exit 1"]
+      interval: 5s
+      timeout: 5s
+      retries: 20
+
+  minio:
+    image: minio/minio:RELEASE.2024-02-17T01-15-57Z
+    environment:
+      MINIO_ROOT_USER: minioadmin
+      MINIO_ROOT_PASSWORD: minioadmin
+    command: minio server /minio_data
+    ports:
+      - "9000:9000"
+    volumes:
+      - miniodata:/minio_data
+    healthcheck:
+      test: ["CMD-SHELL", "/usr/bin/mc ready local || exit 1"]
+      interval: 5s
+      timeout: 5s
+      retries: 20
+
+  milvus:
+    image: milvusdb/milvus:v2.5.3
+    command: ["milvus", "run", "standalone"]
+    environment:
+      ETCD_ENDPOINTS: etcd:2379
+      MINIO_ADDRESS: minio:9000
+    ports:
+      - "19530:19530"  # Milvus API
+      - "9091:9091"    # Health/metrics
+    volumes:
+      - milvusdata:/var/lib/milvus
+    depends_on:
+      etcd:
+        condition: service_healthy
+      minio:
+        condition: service_healthy
+    healthcheck:
+      test: ["CMD-SHELL", "curl -sf http://localhost:9091/healthz || exit 1"]
+      interval: 5s
+      timeout: 5s
+      retries: 20
+
 volumes:
   pgdata:
   neo4jdata:
   qdrantdata:
+  etcddata:
+  miniodata:
+  milvusdata:

examples/ollama_milvus_session_demo.py

@@ -0,0 +1,93 @@
+"""Local Vektori demo using Ollama + Milvus.
+
+Prerequisites:
+  1) Ollama running locally with:
+       - nomic-embed-text
+       - qwen2.5:1.5b (or adjust EXTRACTION_MODEL)
+  2) Milvus available at http://localhost:19530
+
+Run:
+  python examples/ollama_milvus_session_demo.py
+"""
+
+from __future__ import annotations
+
+import asyncio
+
+from vektori import Vektori
+
+
+EMBEDDING_MODEL = "ollama:nomic-embed-text"
+EXTRACTION_MODEL = "ollama:qwen2.5:1.5b"
+MILVUS_URL = "http://localhost:19530"
+
+
+async def main() -> None:
+    # nomic-embed-text returns 768-dim vectors; Milvus schema must match.
+    client = Vektori(
+        storage_backend="milvus",
+        database_url=MILVUS_URL,
+        embedding_model=EMBEDDING_MODEL,
+        extraction_model=EXTRACTION_MODEL,
+        embedding_dimension=768,
+        async_extraction=False,
+    )
+
+    user_id = "demo-user-ollama-milvus"
+    session_id = "session-ollama-milvus-001"
+
+    messages = [
+        {
+            "role": "user",
+            "content": (
+                "I live in Pune and work remotely as a backend engineer. "
+                "I prefer calls between 9am and 11am IST, and avoid meetings after 6pm."
+            ),
+        },
+        {
+            "role": "assistant",
+            "content": "Understood. I will schedule calls in your preferred morning window.",
+        },
+        {
+            "role": "user",
+            "content": "I also like concise weekly updates every Monday morning.",
+        },
+    ]
+
+    try:
+        ingest_result = await client.add(
+            messages=messages,
+            session_id=session_id,
+            user_id=user_id,
+            metadata={"source": "local-demo"},
+        )
+        print("Ingestion:", ingest_result)
+
+        search_result = await client.search(
+            query="When should I contact this user and what communication style do they prefer?",
+            user_id=user_id,
+            depth="l1",
+        )
+
+        facts = search_result.get("facts", [])
+        episodes = search_result.get("episodes", [])
+        sentences = search_result.get("sentences", [])
+
+        print("\nFacts:")
+        for fact in facts:
+            print(f" - {fact.get('text')}")
+
+        print("\nEpisodes:")
+        for episode in episodes:
+            print(f" - {episode.get('text')}")
+
+        print("\nSentences:")
+        for sentence in sentences[:5]:
+            print(f" - {sentence.get('text')}")
+
+    finally:
+        await client.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

pyproject.toml

@@ -8,9 +8,7 @@ dynamic = ["version"]
 description = "Open-source, self-hostable memory engine for AI agents using a three-layer sentence graph architecture."
 readme = "README.md"
 license = { file = "LICENSE" }
-authors = [
-    { name = "Laxman & Manit" }
-]
+authors = [{ name = "Laxman & Manit" }]
 requires-python = ">=3.10"
 classifiers = [
     "Development Status :: 3 - Alpha",
@@ -36,6 +34,7 @@ dependencies = [
 postgres = ["asyncpg>=0.29", "pgvector>=0.2"]
 neo4j = ["neo4j>=5.14"]
 qdrant = ["qdrant-client>=1.7"]
+milvus = ["pymilvus>=2.5.3"]
 anthropic = ["anthropic>=0.20", "voyageai>=0.2"]
 sentence-transformers = ["sentence-transformers>=2.6"]
 bge = ["FlagEmbedding>=1.2"]
@@ -52,6 +51,7 @@ all = [
     "pgvector>=0.2",
     "neo4j>=5.14",
     "qdrant-client>=1.7",
+    "pymilvus>=2.5.3",
     "anthropic>=0.20",
     "voyageai>=0.2",
     "sentence-transformers>=2.6",