Merge b4444c97e4 into 422558d06c

Pmll.py

@@ -0,0 +1,152 @@
+# Copyright 2025, Zep Software, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+PMLL memory wrapper around Graphiti – version 3.2
+-------------------------------------------------
+* Registers SpatialNode / IsNear ontology
+* Adds episodes with optional spatial anchors & distance-chaining
+* Hybrid RRF search helper
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import math
+from datetime import datetime, timezone
+from typing import Dict, Optional, Tuple
+
+from pydantic import BaseModel, Field
+
+from graphiti_core import Graphiti
+from graphiti_core.driver.neo4j_driver import Neo4jDriver
+from graphiti_core.nodes import EpisodeType
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+# --------------------------------------------------------------------------- #
+# 1. Custom ontology                                                          #
+# --------------------------------------------------------------------------- #
+class SpatialNode(BaseModel):
+    """Cartesian/geo point in 3-D space."""
+    x: float = Field(..., description="X / longitude")
+    y: float = Field(..., description="Y / latitude")
+    z: float = Field(..., description="Z / altitude (m)")
+
+
+class IsNear(BaseModel):
+    """Proximity relation between two SpatialNodes."""
+    distance_m: float = Field(..., description="Euclidean distance in metres")
+
+
+ENTITY_TYPES: Dict[str, type[BaseModel]] = {"SpatialNode": SpatialNode}
+EDGE_TYPES: Dict[str, type[BaseModel]] = {"IsNear": IsNear}
+EDGE_TYPE_MAP = {("SpatialNode", "SpatialNode"): ["IsNear"]}
+
+# --------------------------------------------------------------------------- #
+# 2. PMLL wrapper                                                             #
+# --------------------------------------------------------------------------- #
+class PMLL:
+    """Thin convenience layer that marries PMLL ideas to Graphiti."""
+
+    def __init__(self, *, neo4j_uri: str, user: str, pwd: str):
+        driver = Neo4jDriver(uri=neo4j_uri, user=user, password=pwd)
+        self.graph = Graphiti(graph_driver=driver)
+        self._last_spatial: Optional[Tuple[str, float, float, float]] = None  # uuid,x,y,z
+
+    # --------------------------- initialisation --------------------------- #
+    async def init(self) -> None:
+        """Create indices/constraints once per DB."""
+        await self.graph.build_indices_and_constraints()
+
+    # ------------------------------- ingest ------------------------------- #
+    async def add_episode(
+        self,
+        content: str | dict,
+        *,
+        spatial_origin: Tuple[float, float, float] | None = None,
+        description: str = "",
+        group_id: str | None = None,
+    ) -> None:
+        """Persist raw experience (+ optional spatial anchor)."""
+        ep_type = EpisodeType.text if isinstance(content, str) else EpisodeType.json
+        body = content if isinstance(content, str) else json.dumps(content)
+
+        await self.graph.add_episode(
+            name=f"ep@{datetime.now(timezone.utc).isoformat()}",
+            episode_body=body,
+            source=ep_type,
+            source_description=description,
+            reference_time=datetime.now(timezone.utc),
+            group_id=group_id or "",
+            entity_types=ENTITY_TYPES,
+            edge_types=EDGE_TYPES,
+            edge_type_map=EDGE_TYPE_MAP,
+        )
+
+        if spatial_origin is None:
+            return
+
+        x, y, z = spatial_origin
+
+        # Re-use node if identical to previous coords
+        if self._last_spatial and self._last_spatial[1:] == spatial_origin:
+            spatial_uuid = self._last_spatial[0]
+        else:
+            spatial_uuid = await self.graph.add_node(SpatialNode(x=x, y=y, z=z))
+
+            # Connect to previous waypoint
+            if self._last_spatial:
+                _, px, py, pz = self._last_spatial
+                await self.graph.add_edge(
+                    IsNear(distance_m=math.dist((x, y, z), (px, py, pz))),
+                    source_uuid=self._last_spatial[0],
+                    target_uuid=spatial_uuid,
+                )
+
+        self._last_spatial = (spatial_uuid, x, y, z)
+
+    # ------------------------------- query -------------------------------- #
+    async def query(
+        self, question: str, center_uuid: str | None = None, k: int = 5
+    ):
+        """Hybrid RRF search with optional centre-node re-ranking."""
+        return await self.graph.search(question, center_node_uuid=center_uuid, limit=k)
+
+    # ------------------------------ cleanup ------------------------------- #
+    async def close(self) -> None:
+        await self.graph.close()
+
+
+# --------------------------------------------------------------------------- #
+# 3. Demo                                                                     #
+# --------------------------------------------------------------------------- #
+async def _demo() -> None:
+    pmll = PMLL(neo4j_uri="bolt://localhost:7687", user="neo4j", pwd="password")
+    await pmll.init()
+
+    await pmll.add_episode("Robot entered Room A.", spatial_origin=(0, 0, 0))
+    await pmll.add_episode({"cmd": "move", "to": "Room B"}, spatial_origin=(3, 4, 0))
+
+    for hit in await pmll.query("Where is the robot now?"):
+        print("→", hit.fact)
+
+    await pmll.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(_demo())