cleanup
This commit is contained in:
Nicolò Boschi
parent
1b7e0bc380
commit
45b3a68332
6 changed files with 350 additions and 325 deletions
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@ -28,7 +28,7 @@ class EntryPoint(BaseModel):
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class WeightComponents(BaseModel):
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"""Breakdown of weight calculation components."""
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activation: float = Field(description="Activation from spreading", ge=0.0, le=1.0)
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activation: float = Field(description="Activation from spreading (can exceed 1.0 through accumulation)", ge=0.0)
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semantic_similarity: float = Field(description="Semantic similarity to query", ge=0.0, le=1.0)
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recency: float = Field(description="Recency weight", ge=0.0, le=1.0)
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frequency: float = Field(description="Normalized frequency weight", ge=0.0, le=1.0)
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@ -91,13 +91,12 @@ The system uses:
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# Store memory instance on app for route handlers to access
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app.state.memory = memory
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# Register all routes
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_register_routes(app)
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return app
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# Create default app instance with default embeddings
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app = create_app()
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class SearchRequest(BaseModel):
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"""Request model for search endpoint."""
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query: str
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@ -226,11 +225,17 @@ class ThinkRequest(BaseModel):
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}
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class OpinionItem(BaseModel):
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"""Model for an opinion with confidence score."""
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text: str
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confidence: float
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class ThinkResponse(BaseModel):
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"""Response model for think endpoint."""
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text: str
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based_on: Dict[str, List[Dict[str, Any]]] # {"world": [...], "agent": [...], "opinion": [...]}
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new_opinions: List[str] = [] # List of newly formed opinions
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new_opinions: List[OpinionItem] = [] # List of newly formed opinions with confidence
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class Config:
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json_schema_extra = {
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@ -241,7 +246,9 @@ class ThinkResponse(BaseModel):
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"agent": [{"text": "I discussed AI applications last week", "score": 0.85}],
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"opinion": [{"text": "I believe AI should be used ethically", "score": 0.8}]
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},
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"new_opinions": ["AI has great potential when used responsibly"]
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"new_opinions": [
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{"text": "AI has great potential when used responsibly", "confidence": 0.95}
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]
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}
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}
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@ -262,6 +269,8 @@ class GraphDataResponse(BaseModel):
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"""Response model for graph data endpoint."""
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nodes: List[Dict[str, Any]]
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edges: List[Dict[str, Any]]
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table_rows: List[Dict[str, Any]]
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total_units: int
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class Config:
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json_schema_extra = {
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@ -272,28 +281,35 @@ class GraphDataResponse(BaseModel):
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],
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"edges": [
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{"from": "1", "to": "2", "type": "semantic", "weight": 0.8}
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]
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],
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"table_rows": [
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{"id": "abc12345...", "text": "Alice works at Google", "context": "Work info", "date": "2024-01-15 10:30", "entities": "Alice (PERSON), Google (ORGANIZATION)"}
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],
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"total_units": 2
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}
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}
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@app.get("/", include_in_schema=False)
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async def index():
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def _register_routes(app: FastAPI):
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"""Register all API routes on the given app instance."""
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@app.get("/", include_in_schema=False)
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async def index():
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"""Serve the visualization page."""
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return FileResponse(str(Path(__file__).parent / "templates" / "index.html"))
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@app.get(
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@app.get(
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"/api/graph",
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response_model=GraphDataResponse,
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tags=["Visualization"],
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summary="Get memory graph data",
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description="Retrieve graph data for visualization, optionally filtered by agent_id and fact_type (world/agent/opinion)"
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)
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async def api_graph(
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)
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async def api_graph(
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agent_id: Optional[str] = None,
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fact_type: Optional[str] = None
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):
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):
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"""Get graph data from database, optionally filtered by agent_id and fact_type."""
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try:
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data = await app.state.memory.get_graph_data(agent_id, fact_type)
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@ -305,14 +321,14 @@ async def api_graph(
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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@app.post(
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"/api/search",
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response_model=SearchResponse,
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tags=["Search"],
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summary="Search all memory types",
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description="Search across all memory types (world, agent, opinion) using semantic similarity and spreading activation"
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)
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async def api_search(request: SearchRequest):
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)
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async def api_search(request: SearchRequest):
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"""Run a search and return results with trace."""
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try:
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# Run search with tracing
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@ -339,14 +355,14 @@ async def api_search(request: SearchRequest):
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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@app.post(
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"/api/world_search",
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response_model=SearchResponse,
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tags=["Search"],
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summary="Search world facts",
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description="Search only world facts - general knowledge about people, places, events, and things that happen"
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)
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async def api_world_search(request: SearchRequest):
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)
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async def api_world_search(request: SearchRequest):
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"""Search only world facts (general knowledge about the world)."""
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try:
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# Run search with fact_type filter for 'world'
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@ -374,14 +390,14 @@ async def api_world_search(request: SearchRequest):
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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@app.post(
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"/api/agent_search",
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response_model=SearchResponse,
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tags=["Search"],
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summary="Search agent action facts",
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description="Search only agent facts - memories about what the AI agent did, actions taken, and tasks performed"
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)
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async def api_agent_search(request: SearchRequest):
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)
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async def api_agent_search(request: SearchRequest):
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"""Search only agent facts (facts about what the agent did)."""
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try:
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# Run search with fact_type filter for 'agent'
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@ -409,14 +425,14 @@ async def api_agent_search(request: SearchRequest):
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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@app.post(
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"/api/opinion_search",
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response_model=SearchResponse,
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tags=["Search"],
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summary="Search agent opinions",
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description="Search only opinion facts - the agent's formed beliefs, perspectives, and viewpoints"
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)
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async def api_opinion_search(request: SearchRequest):
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)
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async def api_opinion_search(request: SearchRequest):
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"""Search only opinion facts (agent's formed opinions and perspectives)."""
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try:
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# Run search with fact_type filter for 'opinion'
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@ -444,24 +460,24 @@ async def api_opinion_search(request: SearchRequest):
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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@app.post(
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"/api/think",
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response_model=ThinkResponse,
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tags=["Reasoning"],
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summary="Think and generate answer",
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description="""
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Think and formulate an answer using agent identity, world facts, and opinions.
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Think and formulate an answer using agent identity, world facts, and opinions.
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This endpoint:
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1. Retrieves agent facts (agent's identity)
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2. Retrieves world facts relevant to the query
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3. Retrieves existing opinions (agent's perspectives)
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4. Uses LLM to formulate a contextual answer
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5. Extracts and stores any new opinions formed
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6. Returns plain text answer, the facts used, and new opinions
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This endpoint:
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1. Retrieves agent facts (agent's identity)
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2. Retrieves world facts relevant to the query
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3. Retrieves existing opinions (agent's perspectives)
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4. Uses LLM to formulate a contextual answer
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5. Extracts and stores any new opinions formed
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6. Returns plain text answer, the facts used, and new opinions
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"""
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)
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async def api_think(request: ThinkRequest):
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)
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async def api_think(request: ThinkRequest):
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try:
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# Use the memory system's think_async method
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result = await app.state.memory.think_async(
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@ -484,14 +500,14 @@ async def api_think(request: ThinkRequest):
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raise HTTPException(status_code=500, detail=str(e))
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@app.get(
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@app.get(
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"/api/agents",
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response_model=AgentsResponse,
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tags=["Management"],
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summary="List all agents",
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description="Get a list of all agent IDs that have stored memories in the system"
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)
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async def api_agents():
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)
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async def api_agents():
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"""Get list of available agents from database."""
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try:
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agent_list = await app.state.memory.list_agents()
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@ -503,30 +519,30 @@ async def api_agents():
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raise HTTPException(status_code=500, detail=str(e))
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@app.post(
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@app.post(
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"/api/memories/batch",
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response_model=BatchPutResponse,
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tags=["Memory Storage"],
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summary="Store multiple memories",
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description="""
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Store multiple memory items in batch with automatic fact extraction.
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Store multiple memory items in batch with automatic fact extraction.
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Features:
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- Efficient batch processing
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- Automatic fact extraction from natural language
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- Entity recognition and linking
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- Document tracking with optional upsert
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- Temporal and semantic linking
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Features:
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- Efficient batch processing
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- Automatic fact extraction from natural language
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- Entity recognition and linking
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- Document tracking with optional upsert
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- Temporal and semantic linking
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The system automatically:
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1. Extracts semantic facts from the content
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2. Generates embeddings
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3. Deduplicates similar facts
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4. Creates temporal, semantic, and entity links
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5. Tracks document metadata
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The system automatically:
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1. Extracts semantic facts from the content
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2. Generates embeddings
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3. Deduplicates similar facts
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4. Creates temporal, semantic, and entity links
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5. Tracks document metadata
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"""
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)
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async def api_batch_put(request: BatchPutRequest):
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)
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async def api_batch_put(request: BatchPutRequest):
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try:
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# Validate agent_id - prevent writing to reserved agents
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RESERVED_AGENT_IDS = {"locomo"}
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/locomo")
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async def api_locomo():
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@app.get("/api/locomo")
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async def api_locomo():
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"""Get Locomo benchmark results."""
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import json
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try:
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@ -587,6 +603,10 @@ async def api_locomo():
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raise HTTPException(status_code=500, detail=str(e))
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# Create default app instance
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app = create_app()
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if __name__ == "__main__":
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import uvicorn
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print("\n" + "=" * 80)
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@ -135,7 +135,7 @@ window.loadDataView = async function(factType) {
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try {
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// Build URL with agent filter and fact_type filter
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let url = `/api/graph?agent_id=${encodeURIComponent(currentAgentId)}`;
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let url = `api/graph?agent_id=${encodeURIComponent(currentAgentId)}`;
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if (factType !== 'all') {
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url += `&fact_type=${factType}`;
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}
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@ -361,7 +361,7 @@ async function loadGraphData() {
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}
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// Build URL with agent filter
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let url = `/api/graph?agent_id=${encodeURIComponent(currentAgentId)}`;
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let url = `api/graph?agent_id=${encodeURIComponent(currentAgentId)}`;
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const response = await fetch(url);
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@ -577,7 +577,7 @@ async function loadAgents() {
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if (agentsLoaded) return;
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try {
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const response = await fetch('/api/agents');
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const response = await fetch('api/agents');
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const data = await response.json();
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const select = document.getElementById('search-agent-id');
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@ -758,7 +758,7 @@ async function loadAgentsForPane(paneId) {
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}
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try {
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const response = await fetch('/api/agents');
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const response = await fetch('api/agents');
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if (!response.ok) {
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throw new Error(`HTTP ${response.status}: ${response.statusText}`);
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}
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@ -802,11 +802,11 @@ window.runSearchInPane = async function(paneId) {
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try {
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// Determine endpoint based on search type
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let endpoint = '/api/search';
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let endpoint = 'api/search';
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if (searchType === 'world') {
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endpoint = '/api/world_search';
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endpoint = 'api/world_search';
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} else if (searchType === 'agent') {
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endpoint = '/api/agent_search';
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endpoint = 'api/agent_search';
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}
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statusBar.innerHTML = '<span style="color: #ff9800;">🔄 Searching...</span>';
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@ -1488,8 +1488,8 @@ async function loadGlobalAgents() {
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return;
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}
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console.log('Fetching /api/agents...'); // Debug
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const response = await fetch('/api/agents');
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console.log('Fetching api/agents...'); // Debug
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const response = await fetch('api/agents');
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if (!response.ok) {
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throw new Error(`HTTP ${response.status}: ${response.statusText}`);
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@ -1617,9 +1617,9 @@ window.runThink = async function() {
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resultDiv.style.display = 'none';
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loadingDiv.style.display = 'block';
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console.log('Calling /api/think with', { query, agentId, thinkingBudget, topK }); // Debug log
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console.log('Calling api/think with', { query, agentId, thinkingBudget, topK }); // Debug log
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const response = await fetch('/api/think', {
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const response = await fetch('api/think', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json'
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@ -1701,11 +1701,16 @@ window.runThink = async function() {
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if (data.new_opinions && data.new_opinions.length > 0) {
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newOpinionsListDiv.innerHTML = data.new_opinions.map((opinion, idx) => `
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<div style="margin-bottom: 15px; padding: 15px; background: white; border-radius: 6px; border-left: 4px solid #4caf50; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
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<div style="display: flex; align-items: center; margin-bottom: 8px;">
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<div style="display: flex; align-items: center; justify-content: space-between; margin-bottom: 8px;">
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<div style="display: flex; align-items: center;">
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<span style="background: #4caf50; color: white; padding: 4px 8px; border-radius: 12px; font-size: 11px; font-weight: bold; margin-right: 10px;">NEW</span>
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<span style="color: #666; font-size: 12px;">#${idx + 1}</span>
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</div>
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<div style="font-size: 14px; color: #333; line-height: 1.5;">${opinion}</div>
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<span style="background: #e3f2fd; color: #1976d2; padding: 3px 8px; border-radius: 10px; font-size: 11px; font-weight: 600;">
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${(opinion.confidence * 100).toFixed(0)}% confidence
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</span>
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</div>
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<div style="font-size: 14px; color: #333; line-height: 1.5;">${opinion.text}</div>
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</div>
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`).join('');
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newOpinionsDiv.style.display = 'block';
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@ -4,7 +4,7 @@ let locomoData = null;
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window.loadLocomoResults = async function() {
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try {
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const response = await fetch('/api/locomo');
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const response = await fetch('api/locomo');
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locomoData = await response.json();
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console.log('Loaded locomo data:', locomoData);
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renderLocomoResults();
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@ -4,7 +4,7 @@
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<title>Memory Graph - Live Visualization</title>
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<meta charset="utf-8">
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<script src="https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.28.1/cytoscape.min.js"></script>
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<link rel="stylesheet" href="/static/css/styles.css">
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<link rel="stylesheet" href="./static/css/styles.css">
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</head>
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<body>
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<div class="breadcrumb-container">
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@ -300,7 +300,7 @@
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</div>
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</div>
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<script src="/static/js/app.js"></script>
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<script src="/static/js/locomo.js"></script>
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<script src="./static/js/app.js"></script>
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<script src="./static/js/locomo.js"></script>
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</body>
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</html>
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2
serve.sh
2
serve.sh
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@ -1,3 +1,3 @@
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#!/bin/bash
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# Start the FastAPI server with hot reload
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uv run uvicorn web.server:app --reload --host 0.0.0.0 --port 8080
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uv run uvicorn memora.web.server:app --reload --host 0.0.0.0 --port 8080
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