OpenSpec: разнести openspec по проектам

vector_db_setup.md

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+## Vector Database Setup Guide
+
+### 1. Faiss (Similarity Search)
+Install: pip install faiss-cpu
+
+```python
+import faiss
+import numpy as np
+
+# Create index
+index = faiss.IndexFlatL2(128)
+
+# Add vectors
+vectors = np.random.rand(100, 128).astype('float32')
+index.add(vectors)
+```
+
+### 2. Weaviate
+Install: docker run -d -p 8000:8000 weaviate/weaviate
+
+Connect with:
+```python
+from weaviate import Weaviate
+client = Weaviate.connect("http://localhost:8000")
+client.create_class("Vectors")
+```
+
+### 3. Milvus
+Install: pip install pymilvus
+
+Start server: milvus-standalone-server
+
+Connect:
+```python
+from pymilvus import connections, Collection
+connections.connect("default", host="localhost", port="19530")
+coll = Collection("vector_collection")
+coll.insert([vectors])
+```
+
+### 4. Neo4j (Graph Storage)
+Install: pip install neo4j
+
+Connect:
+```python
+from neo4j import GraphDatabase
+driver = GraphDatabase.driver("neo4j://localhost:7687", auth=basic_auth("neo4j", "password"))
+with driver.session() as session:
+    session.write_transaction(
+        lambda tx: tx.run("CREATE (a:Vector {vector: $vector})", vector=vectors)
+    )
+```
+
+Each system has unique features - choose based on your needs: Faiss for similarity search, Weaviate for semantic vectors, Milvus for large-scale storage, or Neo4j for graph-based relationships.