duffyduck
70d1500096
OpenClaw (aria-core) ist raus, ARIA laeuft jetzt mit eigenem Agent-Framework
im aria-brain Container. Vector-DB-basiertes Gedaechtnis statt Sessions,
eigener Conversation-Loop mit Hot+Cold-Memory + Rolling Window, Tool-Use
fuer Skills, Memory-Destillat-Pipeline.
aria-brain/ (neuer Container)
- main.py FastAPI auf 8080, alle Endpoints
- agent.py Conversation-Loop mit Tool-Use (skill_create + run_<skill>)
- conversation.py Rolling Window, JSONL-Persistenz, Distill-Marker
- proxy_client.py httpx-Wrapper zum Claude-Proxy, OpenAI-Format
- prompts.py System-Prompt aus Hot+Cold+Skills
- migration.py Markdown-Parser fuer brain-import/ → atomare Memories
- skills.py Filesystem-Layer fuer /data/skills/<name>/ (Python-only,
venv pro Skill, tar.gz Export/Import, Run-Logs)
- memory/ Embedder (sentence-transformers, multilingual MiniLM)
+ VectorStore (Qdrant-Wrapper)
docker-compose.yml
- aria-core (OpenClaw) raus, openclaw-config Volume raus
- aria-brain Service (FastAPI + Memory)
- aria-qdrant Service (Vector-DB) mit Bind-Mount aria-data/brain/qdrant/
- Diagnostic teilt jetzt Netzwerk mit Bridge (vorher: aria-core)
- Brain bekommt SSH-Mount fuer aria-wohnung + /import fuer brain-import/
bridge/aria_bridge.py
- send_to_core → HTTP-Call an aria-brain:8080/chat (statt OpenClaw-WS)
- aria-core-spezifische Handler raus: doctor_fix, aria_restart,
aria_session_reset, Auto-Compact-Logik, OpenClaw-Handshake
- Generischer container_restart-Handler (Whitelist Bridge/Brain/Qdrant)
- Side-Channel-Events aus /chat-Response (z.B. skill_created) werden
als RVS-Events forwarded
- file_list_request / file_delete_request → an Diagnostic forwarded
- Tote OpenClaw-Connection-Logik bleibt im Code als Referenz (nicht aktiv)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
"""
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Lokaler Embedder fuer Memory-Texte.
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Nutzt sentence-transformers (paraphrase-multilingual-MiniLM-L12-v2):
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- Deutsch + Englisch
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- 384-dimensionale Vektoren
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- Laeuft auf CPU, ~30ms pro kurzer Text
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- Modell wird beim ersten Aufruf in /data/_models gecached
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"""
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from __future__ import annotations
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import logging
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from typing import List
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logger = logging.getLogger(__name__)
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MODEL_NAME = "paraphrase-multilingual-MiniLM-L12-v2"
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VECTOR_DIM = 384
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class Embedder:
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def __init__(self, model_name: str = MODEL_NAME):
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self.model_name = model_name
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self._model = None
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def _load(self):
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if self._model is None:
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logger.info("Lade Embedding-Modell %s ...", self.model_name)
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from sentence_transformers import SentenceTransformer
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self._model = SentenceTransformer(self.model_name)
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logger.info("Embedding-Modell geladen.")
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def embed(self, text: str) -> List[float]:
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self._load()
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vec = self._model.encode(text, convert_to_numpy=True, normalize_embeddings=True)
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return vec.tolist()
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def embed_batch(self, texts: List[str]) -> List[List[float]]:
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self._load()
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vecs = self._model.encode(texts, convert_to_numpy=True, normalize_embeddings=True)
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return vecs.tolist()
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