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Axiovorax/axiovorax/genesis.py

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+"""
+Genesis module for mathematical universe generation.
+"""
+import logging
+from typing import List, Any
+
+class MathematicalGenesis:
+    """
+    Handles the creation and evolution of mathematical universes from axioms.
+    """
+    def __init__(self, axioms: List[str]):
+        """
+        Initialize with a set of axioms.
+        Args:
+            axioms (List[str]): List of initial axioms.
+        """
+        self.axioms = axioms
+        self.universe = None
+        logging.info("MathematicalGenesis initialized with axioms: %s", axioms)
+
+    def generate_universe(self) -> Any:
+        """
+        Generate a new mathematical universe from the current axioms.
+        Returns:
+            Any: A representation of the generated universe.
+        """
+        logging.info("Generating mathematical universe from axioms.")
+        # Minimal working logic: create a graph of axioms
+        import networkx as nx
+        G = nx.Graph()
+        for ax in self.axioms:
+            G.add_node(ax)  # type: ignore
+
+        self.universe = G
+        logging.info("Universe generated with %d nodes.", len(G.nodes))
+        return G
+
+    def evolve_axioms(self):
+        """
+        Evolve the set of axioms (mock logic).
+        """
+        logging.info("Evolving axioms.")
+        # Example: add a new axiom
+        new_axiom = f"Axiom {len(self.axioms)+1}"
+        self.axioms.append(new_axiom)
+        logging.info("Added new axiom: %s", new_axiom)

archive/Axiovorax/notebooks/exploration.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "25cda4ee",
+   "metadata": {},
+   "source": [
+    "# Axiovorax System Exploration\n",
+    "An AI-driven, quantum-inspired engine for evolving mathematical universes and generating new theorems.\n",
+    "\n",
+    "This notebook demonstrates the core features of the Axiovorax research platform."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a58dd39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import core Axiovorax classes and set up logging\n",
+    "from axiovorax.genesis import MathematicalGenesis\n",
+    "from axiovorax.meta_cognitive_core import MetaCognitiveCore\n",
+    "from axiovorax.quantum_layer import QuantumOrchestrator\n",
+    "from modules.utils import setup_logging\n",
+    "setup_logging()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb1d87e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a MathematicalGenesis instance and generate a universe\n",
+    "axioms = [\"Axiom 1: Existence\", \"Axiom 2: Uniqueness\"]\n",
+    "mg = MathematicalGenesis(axioms)\n",
+    "universe = mg.generate_universe()\n",
+    "print(f\"Generated universe nodes: {list(universe.nodes)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "352408e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evolve axioms and show updated universe\n",
+    "mg.evolve_axioms()\n",
+    "universe2 = mg.generate_universe()\n",
+    "print(f\"Evolved universe nodes: {list(universe2.nodes)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9969aaab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run a hybrid quantum-classical algorithm\n",
+    "qo = QuantumOrchestrator()\n",
+    "result = qo.run_hybrid_algorithm({\"universe\": list(universe2.nodes)})\n",
+    "print(f\"Hybrid algorithm result: {result}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c13e2583",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use MetaCognitiveCore for proof storage and retrieval\n",
+    "mcc = MetaCognitiveCore()\n",
+    "mcc.store_proof(\"Proof of Axiom 1 implies Axiom 2\")\n",
+    "print(\"Past proofs:\", mcc.retrieve_past_results())"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

archive/Axiovorax/tests/test_genesis.py

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+"""
+Test for MathematicalGenesis universe generation.
+"""
+import pytest
+from axiovorax.genesis import MathematicalGenesis
+
+def test_generate_universe():
+    axioms = ["Axiom 1", "Axiom 2"]
+    mg = MathematicalGenesis(axioms)
+    universe = mg.generate_universe()
+    assert universe is not None
+    assert len(universe.nodes) > 0
+

archive/Axiovorax/tests/test_quantum.py

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+
+"""
+Test for QuantumOrchestrator output format.
+"""
+from axiovorax.quantum_layer import QuantumOrchestrator
+
+def test_run_hybrid_algorithm():
+    qo = QuantumOrchestrator()
+    data = {"input": 1}
+    result = qo.run_search(data)  # type: ignore
+    assert isinstance(result, dict)
+    assert "score" in result
+    assert "info" in result
+

archive/modules/__init__.py

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+\"\"\"Utility modules for Axiovorax.\"\"\"

archive/modules/open_problems.py

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+OPEN_PROBLEMS = [
+    "Riemann Hypothesis",
+    "P vs NP",
+    "Goldbach Conjecture",
+    "Hodge Conjecture"
+]
+
+class OpenProblemSolver:
+    def __init__(self, genesis_engine):
+        self.genesis = genesis_engine
+
+    def attempt_proof(self, problem):
+        axioms = self.genesis.axioms
+        return {"problem": problem, "attempt": f"Using axioms {axioms} to attempt solving {problem}...", "status": "In Progress"}

archive/modules/storage.py

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+import json
+
+def save_to_file(filename, data):
+    with open(filename, "w") as f:
+        json.dump(data, f)
+
+def load_from_file(filename):
+    with open(filename, "r") as f:
+        return json.load(f)

archive/modules/utils.py

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+def format_response(data):
+    return {"status": "success", "data": data}

archive/notebooks/exploration.ipynb

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+{
+ \"cells\": [
+  {
+   \"cell_type\": \"code\",
+   \"execution_count\": null,
+   \"metadata\": {},
+   \"outputs\": [],
+   \"source\": [
+    \"from axiovorax.genesis import MathematicalGenesis\\n\",
+    \"mg = MathematicalGenesis(axioms=['A1','A2'], seed=1)\\n\",
+    \"g = mg.generate_universe()\\n\",
+    \"mg.summary()\\n\"
+   ]
+  }
+ ],
+ \"metadata\": {
+  \"kernelspec\": {\"name\": \"python3\", \"display_name\": \"Python 3\"},
+  \"language_info\": {\"name\": \"python\"}
+ },
+ \"nbformat\": 4,
+ \"nbformat_minor\": 2
+}

archive/tests/test_api.py

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+from fastapi.testclient import TestClient
+from app import app
+
+client = TestClient(app)
+
+def test_generate_universe():
+    response = client.get("/generate_universe")
+    assert response.status_code == 200

archive/tests/test_genesis.py

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+from genesis import GenesisEngine
+
+def test_generate_universe():
+    g = GenesisEngine()
+    universe = g.generate_universe()
+    assert "axioms" in universe

archive/tests/test_quantum.py

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+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from axiovorax.quantum_layer import QuantumOrchestrator
+def test_quantum_run():     q = QuantumOrchestrator()     out = q.run_search({'statement': 'example'})     assert 'score' in out
+

backend/.env.example

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+# Example environment variables
+
+# Database URL
+DB_URL=sqlite:///:memory:
+
+# Other environment variables can be added here as needed.# Example environment variables for the backend
+# Copy this file to .env and fill in secrets before running in development
+
+# Database URL - use sqlite for local development or a Postgres URL in production
+# Examples:
+#   sqlite:///./dev.db
+#   postgresql+psycopg2://user:password@localhost:5432/dbname
+DB_URL=sqlite:///:memory:
+
+# Application secret key (used for signing tokens)
+SECRET_KEY=replace-me-with-a-secret
+
+# Sentry DSN (optional)
+SENTRY_DSN=
+
+# Other optional flags
+#LOG_LEVEL=INFODB_URL=sqlite:///./backend/db/projectv1.db
+SECRET_KEY=your_secret_key_here

backend/.gitignore

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+# Ignore Python cache and environment
+__pycache__/
+*.pyc
+venv/
+.env
+
+# Ignore SQLite DB
+backend/db/*.db
+
+# Ignore test results
+*.log
+.coverage

backend/Dockerfile

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+# Project V1 Backend Dockerfile
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt ./
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+COPY backend ./backend
+COPY .env ./backend/.env
+
+EXPOSE 8000
+
+CMD ["uvicorn", "backend.app:app", "--host", "0.0.0.0", "--port", "8000"]

backend/api.py

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+from pydantic import BaseModel
+from .universe import UniverseManager
+
+
+class AxiomsRequest(BaseModel):
+    axioms: list[str]
+
+
+class ProveRequest(BaseModel):
+    theorem: str
+    universe_id: str
+
+
+class PipelineRequest(BaseModel):
+    axioms: list[str]
+    theorem: str
+
+
+def create_app():
+    """Create and return the FastAPI app. This is lazy to avoid import-time
+    collisions with a repo-local `fastapi/` directory when running tests in
+    this workspace.
+    """
+    from fastapi import FastAPI, HTTPException
+    from .auth import require_token
+    from .pipeline import Pipeline
+
+    app = FastAPI(title="AlphaGeometry Demo API")
+    manager = UniverseManager()
+    pipeline = Pipeline(manager)
+
+    @app.post("/generateUniverse")
+    def generate_universe(req: AxiomsRequest):
+        uid = manager.create_universe(req.axioms)
+        return {"universe_id": uid}
+
+    @app.post("/prove")
+    def prove(req: ProveRequest):
+        try:
+            result = manager.prove(req.universe_id, req.theorem)
+        except KeyError:
+            raise HTTPException(status_code=404, detail="Universe not found")
+        return {"result": result}
+
+    @app.post("/pipeline/generate_and_prove")
+    def pipeline_generate_and_prove(req: PipelineRequest, x_api_key: str | None = None):
+        # basic auth header check
+        require_token(x_api_key)
+        res = pipeline.run_generate_and_prove(req.axioms, req.theorem)
+        return res
+
+    @app.get("/compare/{a}/{b}")
+    def compare(a: str, b: str):
+        try:
+            score = manager.compare_universes(a, b)
+        except KeyError:
+            raise HTTPException(status_code=404, detail="Universe not found")
+        return {"similarity": score}
+
+    @app.get("/health")
+    def health():
+        return {"status": "ok"}
+
+    return app
+
+
+__all__ = ["create_app"]

backend/app.py

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+from fastapi import FastAPI
+from backend.db.session import engine
+from backend.db.models import Base
+from backend.api.routes import router
+from backend.api.neuro_symbolic_routes import router as neuro_router
+from backend.api.quantum_routes import router as quantum_router
+from backend.api.analysis_routes import router as analysis_router
+from backend.api.visualization_routes import router as visualization_router
+from backend.api.query_routes import router as query_router
+from backend.api.vector_routes import router as vector_router
+from backend.api.vector_routes import router as vector_router
+
+app = FastAPI()
+
+@app.on_event("startup")
+def startup():
+    Base.metadata.create_all(bind=engine)
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+app.include_router(router)
+app.include_router(neuro_router)
+app.include_router(quantum_router)
+app.include_router(analysis_router)
+app.include_router(visualization_router)
+app.include_router(query_router)
+app.include_router(vector_router)
+app.include_router(vector_router)

backend/auth.py

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+from .config import get_env
+
+
+def require_token(x_api_key: str | None = None):
+    """Check the API token. Does not import FastAPI at module import time so
+    tests can run when a repo-local `fastapi/` folder is present. If FastAPI is
+    available at runtime, raise an HTTPException to produce a proper 401 for
+    API endpoints; otherwise raise PermissionError for programmatic callers.
+    """
+    expected = get_env("DEMO_API_TOKEN", "demo-token")
+    if x_api_key != expected:
+        try:
+            # import lazily so tests without FastAPI don't fail at import time
+            from fastapi import HTTPException
+            raise HTTPException(status_code=401, detail="Invalid API token")
+        except Exception:
+            raise PermissionError("Invalid API token")

backend/config.py

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+"""Configuration helpers."""
+import os
+from typing import Optional
+
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except Exception:
+    pass
+
+
+def get_env(key: str, default: Optional[str] = None) -> Optional[str]:
+    return os.environ.get(key, default)
+import os
+try:
+	from dotenv import load_dotenv
+	load_dotenv()
+except Exception:
+	# If python-dotenv isn't installed, continue with environment variables or defaults
+	try:
+		# If there's a local shim, call it
+		from dotenv import load_dotenv as _ld
+		_ld()
+	except Exception:
+		pass
+
+# Default to an in-memory SQLite DB for tests/local development when DB_URL is not set
+DB_URL = os.getenv('DB_URL') or 'sqlite:///:memory:'
+SECRET_KEY = os.getenv('SECRET_KEY') or 'dev-secret'

backend/embeddings.py

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+"""Pluggable embedding interface. Provides simple char-histogram fallback and
+an optional sentence-transformers adapter if available.
+"""
+from typing import List
+import math
+
+try:
+    from sentence_transformers import SentenceTransformer
+    SBER_AVAILABLE = True
+except Exception:
+    SBER_AVAILABLE = False
+
+
+class EmbeddingBackend:
+    def embed(self, texts: List[str]) -> List[List[float]]:
+        raise NotImplementedError()
+
+
+class CharHistogramEmbedding(EmbeddingBackend):
+    def __init__(self, dim: int = 32):
+        self.dim = dim
+
+    def embed(self, texts: List[str]) -> List[List[float]]:
+        def _embed(text: str):
+            vec = [0.0] * self.dim
+            for ch in text[:4096]:
+                vec[ord(ch) % self.dim] += 1.0
+            norm = math.sqrt(sum(v * v for v in vec)) or 1.0
+            return [v / norm for v in vec]
+
+        return [_embed(t) for t in texts]
+
+
+class SBERTEmbedding(EmbeddingBackend):
+    def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
+        if not SBER_AVAILABLE:
+            raise RuntimeError("sentence-transformers not installed")
+        self.model = SentenceTransformer(model_name)
+
+    def embed(self, texts: List[str]) -> List[List[float]]:
+        arr = self.model.encode(texts)
+        return [list(map(float, vec)) for vec in arr]
+
+
+def make_default_backend() -> EmbeddingBackend:
+    if SBER_AVAILABLE:
+        try:
+            return SBERTEmbedding()
+        except Exception:
+            pass
+    return CharHistogramEmbedding()

backend/evolution.py

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+"""Evolutionary algorithms for evolving axioms.
+
+Provides a small GeneticAlgorithm class that mutates and recombines axioms (strings)
+to produce candidate new axioms. Pure-Python fallback implementation so the
+project can run without external evolutionary frameworks.
+"""
+import random
+from typing import List, Callable
+
+
+class GeneticAlgorithm:
+    def __init__(self, population: List[str], fitness_fn: Callable[[str], float],
+                 mutate_rate: float = 0.2, crossover_rate: float = 0.5):
+        self.population = list(population)
+        self.fitness_fn = fitness_fn
+        self.mutate_rate = mutate_rate
+        self.crossover_rate = crossover_rate
+
+    def _mutate(self, axiom: str) -> str:
+        if random.random() > self.mutate_rate:
+            return axiom
+        # simple mutation: insert/delete/replace a character or word
+        ops = [self._insert_char, self._delete_char, self._replace_char, self._swap_words]
+        op = random.choice(ops)
+        return op(axiom)
+
+    def _insert_char(self, s: str) -> str:
+        pos = random.randint(0, len(s))
+        ch = random.choice('abcdefghijklmnopqrstuvwxyz ')
+        return s[:pos] + ch + s[pos:]
+
+    def _delete_char(self, s: str) -> str:
+        if not s:
+            return s
+        pos = random.randint(0, len(s)-1)
+        return s[:pos] + s[pos+1:]
+
+    def _replace_char(self, s: str) -> str:
+        if not s:
+            return s
+        pos = random.randint(0, len(s)-1)
+        ch = random.choice('abcdefghijklmnopqrstuvwxyz ')
+        return s[:pos] + ch + s[pos+1:]
+
+    def _swap_words(self, s: str) -> str:
+        parts = s.split()
+        if len(parts) < 2:
+            return s
+        i, j = random.sample(range(len(parts)), 2)
+        parts[i], parts[j] = parts[j], parts[i]
+        return ' '.join(parts)
+
+    def _crossover(self, a: str, b: str) -> str:
+        # single-point crossover at word boundary
+        wa = a.split()
+        wb = b.split()
+        if not wa or not wb:
+            return a
+        pa = random.randint(0, len(wa))
+        pb = random.randint(0, len(wb))
+        child = ' '.join(wa[:pa] + wb[pb:])
+        return child
+
+    def step(self, k: int = 10) -> List[str]:
+        """Run one generation, return top-k candidates."""
+        scored = [(p, self.fitness_fn(p)) for p in self.population]
+        scored.sort(key=lambda x: x[1], reverse=True)
+        survivors = [p for p, _ in scored[: max(2, len(scored)//2)]]
+
+        # produce new population
+        new_pop = list(survivors)
+        while len(new_pop) < len(self.population):
+            if random.random() < self.crossover_rate:
+                a, b = random.sample(survivors, 2)
+                child = self._crossover(a, b)
+            else:
+                child = random.choice(survivors)
+            child = self._mutate(child)
+            new_pop.append(child)
+
+        self.population = new_pop
+        result = [(p, self.fitness_fn(p)) for p in self.population]
+        result.sort(key=lambda x: x[1], reverse=True)
+        return [p for p, _ in result[:k]]

backend/geometry.py

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+"""Lightweight placeholder for geometry module used by legacy tests.
+
+This file provides minimal stubs so unit tests that import `geometry` can run
+in the demo environment. Replace with the project's real geometry module as needed.
+"""
+def distance(a, b):
+    return 0.0
+
+def area_of_triangle(a, b, c):
+    return 0.0

backend/grpc_server.py

@@ -0,0 +1,34 @@
+"""gRPC server skeleton for AlphaGeometry using proto/alphageometry.proto.
+
+This is a lightweight skeleton; to run it install grpcio and grpcio-tools and
+generate Python stubs from the proto. The server handlers delegate to the
+existing Pipeline and UniverseManager implementations.
+"""
+import os
+try:
+    import grpc
+    # import generated modules when available
+    from proto import alphageometry_pb2_grpc as pb2_grpc
+    from proto import alphageometry_pb2 as pb2
+except Exception:
+    grpc = None
+
+
+class AlphaGeometryServicer:
+    # placeholder methods if proto generated modules available
+    pass
+
+
+def serve(port: int = 50051):
+    if grpc is None:
+        raise RuntimeError("grpc not installed or generated stubs are missing")
+    server = grpc.server(
+        grpc.thread_pool_executor(max_workers=10)
+    )
+    pb2_grpc.add_AlphaGeometryServicer_to_server(AlphaGeometryServicer(), server)
+    server.add_insecure_port(f"[::]:{port}")
+    server.start()
+    try:
+        server.wait_for_termination()
+    except KeyboardInterrupt:
+        server.stop(0)

backend/integration_test.py

@@ -0,0 +1,45 @@
+"""Integration test script: attempts to connect to Neo4j and Faiss index.
+This script is safe: it catches missing libraries and reports status.
+"""
+import sys
+import traceback
+
+def try_neo4j():
+    try:
+        from backend.adapters.graph_adapter import Neo4jAdapter
+        print("Neo4jAdapter loaded")
+        a = Neo4jAdapter()
+        if not a.is_available():
+            print("Neo4j driver not available or connection not established")
+            return
+        # create test node
+        res = a.run("CREATE (n:Test {name:$name}) RETURN id(n) as id", name="integration_test")
+        print("Neo4j create node result:", res)
+        a.close()
+    except Exception as e:
+        print("Neo4j test failed:")
+        traceback.print_exc()
+
+
+def try_faiss():
+    try:
+        from backend.adapters.vector_adapter_full import FaissIndex
+        print("FaissIndex available")
+        dim = 32
+        idx = FaissIndex(dim)
+        vec = [float(i) for i in range(dim)]
+        idx.upsert("u1", vec)
+        results = idx.search(vec, top_k=1)
+        print("Faiss search results:", results)
+    except Exception as e:
+        print("Faiss test failed or faiss not installed:")
+        traceback.print_exc()
+
+
+def main():
+    print("Running integration tests...")
+    try_neo4j()
+    try_faiss()
+
+if __name__ == '__main__':
+    main()

backend/memory.py

@@ -0,0 +1,57 @@
+"""Cross-universe memory layer: embeddings, vector DB, knowledge graph, object store stubs."""
+from typing import Dict, Any, List
+from .adapters.vector_adapter import InMemoryVectorIndex
+from .adapters.graph_adapter import NetworkXGraph, Neo4jAdapter
+try:
+    from .adapters.vector_adapter_full import FaissIndex
+except Exception:
+    FaissIndex = None
+import json
+import os
+import os as _os
+
+
+class MemoryManager:
+    def __init__(self, storage_dir: str = "./memory_store"):
+        # optionally use FAISS if requested
+        if _os.environ.get("USE_FAISS", "0") == "1" and FaissIndex is not None:
+            try:
+                dim = int(_os.environ.get("FAISS_DIM", "32"))
+                self.vindex = FaissIndex(dim)
+            except Exception:
+                self.vindex = InMemoryVectorIndex()
+        else:
+            self.vindex = InMemoryVectorIndex()
+        self.kg = NetworkXGraph()
+        self.storage_dir = storage_dir
+        os.makedirs(self.storage_dir, exist_ok=True)
+        # optionally try to connect to Neo4j if requested via env
+        self.neo4j = None
+        try:
+            if _os.environ.get("USE_NEO4J", "0") == "1":
+                self.neo4j = Neo4jAdapter(uri=_os.environ.get("NEO4J_URI"), user=_os.environ.get("NEO4J_USER"), password=_os.environ.get("NEO4J_PASSWORD"))
+                if not self.neo4j.is_available():
+                    self.neo4j = None
+        except Exception:
+            self.neo4j = None
+
+    def index_universe(self, universe_id: str, embedding: List[float], metadata: Dict[str, Any] = None):
+        self.vindex.upsert(universe_id, embedding)
+        self.kg.add_node(universe_id, **(metadata or {}))
+        if self.neo4j:
+            try:
+                props = {"id": universe_id}
+                if metadata:
+                    props.update(metadata)
+                self.neo4j.create_node(labels=["Universe"], props=props)
+            except Exception:
+                pass
+
+    def snapshot_universe(self, universe_id: str, universe_obj: Any):
+        path = os.path.join(self.storage_dir, f"{universe_id}.json")
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(universe_obj, f, default=str)
+        return path
+
+    def query_similar(self, embedding: List[float], top_k: int = 10):
+        return self.vindex.search(embedding, top_k=top_k)