feat: Restore mood system and implement comprehensive memory editor UI
MOOD SYSTEM FIX: - Mount bot/moods directory in docker-compose.yml for Cat container access - Update miku_personality plugin to load mood descriptions from .txt files - Add Cat logger for debugging mood loading (replaces print statements) - Moods now dynamically loaded from working_memory instead of hardcoded neutral
This commit is contained in:
53
bot/api.py
53
bot/api.py
@@ -2777,6 +2777,17 @@ def set_voice_debug_mode(enabled: bool = Form(...)):
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class MemoryDeleteRequest(BaseModel):
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confirmation: str
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class MemoryEditRequest(BaseModel):
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content: str
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metadata: Optional[dict] = None
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class MemoryCreateRequest(BaseModel):
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content: str
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collection: str # 'declarative' or 'episodic'
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user_id: Optional[str] = None
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source: Optional[str] = None
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metadata: Optional[dict] = None
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@app.get("/memory/status")
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async def get_cat_memory_status():
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"""Get Cheshire Cat connection status and feature flag."""
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@@ -2899,6 +2910,48 @@ async def delete_single_memory_point(collection: str, point_id: str):
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else:
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return {"success": False, "error": f"Failed to delete point {point_id}"}
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@app.put("/memory/point/{collection}/{point_id}")
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async def edit_memory_point(collection: str, point_id: str, request: MemoryEditRequest):
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"""Edit an existing memory point's content and/or metadata."""
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from utils.cat_client import cat_adapter
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success = await cat_adapter.update_memory_point(
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collection=collection,
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point_id=point_id,
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content=request.content,
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metadata=request.metadata
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)
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if success:
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return {"success": True, "updated": point_id}
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else:
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return {"success": False, "error": f"Failed to update point {point_id}"}
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@app.post("/memory/create")
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async def create_memory_point(request: MemoryCreateRequest):
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"""
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Manually create a new memory (declarative fact or episodic memory).
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For declarative facts, this allows you to teach Miku new knowledge.
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For episodic memories, this allows you to inject conversation context.
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"""
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from utils.cat_client import cat_adapter
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if request.collection not in ['declarative', 'episodic']:
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return {"success": False, "error": "Collection must be 'declarative' or 'episodic'"}
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# Create the memory point
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result = await cat_adapter.create_memory_point(
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collection=request.collection,
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content=request.content,
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user_id=request.user_id or "manual_admin",
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source=request.source or "manual_web_ui",
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metadata=request.metadata or {}
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)
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if result:
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return {"success": True, "point_id": result, "collection": request.collection}
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else:
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return {"success": False, "error": "Failed to create memory point"}
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def start_api():
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import uvicorn
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@@ -381,6 +381,147 @@ class CatAdapter:
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logger.error(f"Error deleting memory point: {e}")
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return False
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async def update_memory_point(self, collection: str, point_id: str, content: str, metadata: dict = None) -> bool:
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"""Update an existing memory point's content and/or metadata."""
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try:
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# First, get the existing point to retrieve its vector
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qdrant_host = self._base_url.replace("http://cheshire-cat:80", "http://cheshire-cat-vector-memory:6333")
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async with aiohttp.ClientSession() as session:
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# Get existing point
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async with session.post(
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f"{qdrant_host}/collections/{collection}/points",
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json={"ids": [point_id], "with_vector": True, "with_payload": True},
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timeout=aiohttp.ClientTimeout(total=15)
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) as response:
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if response.status != 200:
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logger.error(f"Failed to fetch point {point_id}: {response.status}")
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return False
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data = await response.json()
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points = data.get("result", [])
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if not points:
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logger.error(f"Point {point_id} not found")
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return False
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existing_point = points[0]
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existing_vector = existing_point.get("vector")
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existing_payload = existing_point.get("payload", {})
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# If content changed, we need to re-embed it
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if content != existing_payload.get("page_content"):
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# Call Cat's embedder to get new vector
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embed_response = await session.post(
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f"{self._base_url}/embedder",
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json={"text": content},
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headers=self._get_headers(),
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timeout=aiohttp.ClientTimeout(total=30)
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)
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if embed_response.status == 200:
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embed_data = await embed_response.json()
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new_vector = embed_data.get("embedding")
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else:
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logger.warning(f"Failed to re-embed content, keeping old vector")
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new_vector = existing_vector
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else:
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new_vector = existing_vector
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# Build updated payload
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updated_payload = {
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"page_content": content,
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"metadata": metadata if metadata is not None else existing_payload.get("metadata", {})
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}
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# Update the point
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async with session.put(
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f"{qdrant_host}/collections/{collection}/points",
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json={
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"points": [{
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"id": point_id,
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"vector": new_vector,
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"payload": updated_payload
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}]
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},
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timeout=aiohttp.ClientTimeout(total=15)
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) as update_response:
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if update_response.status == 200:
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logger.info(f"✏️ Updated memory point {point_id} in {collection}")
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return True
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else:
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logger.error(f"Failed to update point: {update_response.status}")
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return False
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except Exception as e:
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logger.error(f"Error updating memory point: {e}")
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return False
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async def create_memory_point(self, collection: str, content: str, user_id: str, source: str, metadata: dict = None) -> Optional[str]:
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"""Create a new memory point manually."""
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try:
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import uuid
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import time
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# Generate a unique ID
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point_id = str(uuid.uuid4())
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# Get vector embedding from Cat
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async with aiohttp.ClientSession() as session:
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async with session.post(
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f"{self._base_url}/embedder",
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json={"text": content},
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headers=self._get_headers(),
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timeout=aiohttp.ClientTimeout(total=30)
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) as response:
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if response.status != 200:
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logger.error(f"Failed to embed content: {response.status}")
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return None
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data = await response.json()
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vector = data.get("embedding")
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if not vector:
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logger.error("No embedding returned from Cat")
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return None
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# Build payload
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payload = {
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"page_content": content,
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"metadata": metadata or {}
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}
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payload["metadata"]["source"] = source
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payload["metadata"]["when"] = time.time()
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# For declarative memories, add user_id to metadata
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# For episodic, it's in the source field
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if collection == "declarative":
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payload["metadata"]["user_id"] = user_id
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elif collection == "episodic":
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payload["metadata"]["source"] = user_id
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# Insert into Qdrant
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qdrant_host = self._base_url.replace("http://cheshire-cat:80", "http://cheshire-cat-vector-memory:6333")
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async with session.put(
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f"{qdrant_host}/collections/{collection}/points",
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json={
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"points": [{
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"id": point_id,
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"vector": vector,
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"payload": payload
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}]
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},
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timeout=aiohttp.ClientTimeout(total=15)
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) as insert_response:
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if insert_response.status == 200:
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logger.info(f"✨ Created new {collection} memory point: {point_id}")
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return point_id
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else:
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logger.error(f"Failed to insert point: {insert_response.status}")
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return None
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except Exception as e:
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logger.error(f"Error creating memory point: {e}")
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return None
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async def wipe_all_memories(self) -> bool:
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"""
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Delete ALL memory collections (episodic + declarative).
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