Merge pull request #81 from multimindlab/low_issues_solved

Fix Streaming, Memory, SQLAlchemy Cleanup and Reliability Issues Acro…

Author: niralbhalodia

multimind/agents/agent_loader.py

@@ -93,7 +93,7 @@ def load_agent(
             max_history=memory_config.get("max_history", 100)
         )
 
-        # Create agen
+        # Create agent
         agent = Agent(
             model=model,
             memory=memory,

multimind/agents/prompt_correction.py

@@ -1,6 +1,9 @@
 from typing import Callable, Any, Dict, List
 import logging
 
+logger = logging.getLogger(__name__)
+
+
 class PromptCorrectionLayer:
     """
     Observability and self-healing layer for LLM/agent pipelines.
@@ -19,23 +22,61 @@ def add_correction_hook(self, hook: Callable[[str, Dict], str]):
     def add_adapter_update_hook(self, hook: Callable[[str, str], None]):
         self.adapter_update_hooks.append(hook)
 
+    def _compute_issue_score(self, prompt: str, output: str, trace: Dict) -> float:
+        """
+        Heuristic scoring function for potential issues / hallucinations.
+        Returns a score in [0, 1], where higher means more suspicious.
+        """
+        text = output.lower()
+        score = 0.0
+
+        # Strong indicators
+        strong_markers = [
+            "[error]", "hallucination", "not based on real data",
+            "fabricated answer", "made this up"
+        ]
+        if any(marker in text for marker in strong_markers):
+            score += 0.7
+
+        # Weaker indicators based on uncertainty phrases
+        weak_markers = [
+            "i am not sure", "i'm not sure", "i do not know",
+            "i don't know", "cannot verify", "not certain"
+        ]
+        if any(marker in text for marker in weak_markers):
+            score += 0.2
+
+        # If trace provides an explicit model_score / confidence, incorporate it.
+        # Expecting trace.get("confidence") in [0, 1] where low is suspicious.
+        confidence = trace.get("confidence")
+        if isinstance(confidence, (int, float)):
+            confidence_clamped = max(0.0, min(1.0, float(confidence)))
+            score += (1.0 - confidence_clamped) * 0.3
+
+        return min(score, 1.0)
+
     def monitor(self, prompt: str, output: str, trace: Dict = None) -> str:
         """
         Monitor output for errors/hallucinations and apply corrections if needed.
+        Uses a heuristic score instead of a single string check.
         """
         trace = trace or {}
         try:
-            # Example: simple hallucination check (can be replaced with real logic)
-            if "[error]" in output or "hallucination" in output.lower():
-                self.logger.warning(f"Detected issue in output: {output}")
+            issue_score = self._compute_issue_score(prompt, output, trace)
+            threshold = trace.get("hallucination_threshold", 0.6)
+            if issue_score >= threshold:
+                self.logger.warning(
+                    "Detected potential hallucination (score=%.2f, threshold=%.2f): %s",
+                    issue_score,
+                    threshold,
+                    output,
+                )
                 for hook in self.error_hooks:
                     hook(prompt, Exception("Detected hallucination"), trace)
-                # Apply correction hooks to the *output* and return corrected output.
-                # (Correction hooks are expected to take a string and trace, and return a string.)
                 corrected_output = output
                 for hook in self.correction_hooks:
                     corrected_output = hook(corrected_output, trace)
-                self.logger.info(f"Corrected output: {corrected_output}")
+                self.logger.info("Corrected output: %s", corrected_output)
                 return corrected_output
             return output
         except Exception as e:
@@ -54,15 +95,15 @@ def update_adapter(self, adapter_key: str, new_adapter_path: str):
 if __name__ == "__main__":
     pcl = PromptCorrectionLayer()
     def error_logger(prompt, exc, trace):
-        print(f"Error detected for prompt '{prompt}': {exc}")
+        logger.error("Error detected for prompt '%s': %s", prompt, exc)
     def simple_correction(prompt, trace):
         return prompt + " [CORRECTED]"
     def adapter_updater(adapter_key, new_path):
-        print(f"Adapter {adapter_key} updated to {new_path}")
+        logger.info("Adapter %s updated to %s", adapter_key, new_path)
     pcl.add_error_hook(error_logger)
     pcl.add_correction_hook(simple_correction)
     pcl.add_adapter_update_hook(adapter_updater)
     # Simulate monitoring
     corrected_output = pcl.monitor("What is the capital of France?", "[error] hallucination detected", {"step": 1})
-    print("Corrected output after correction:", corrected_output)
+    logger.info("Corrected output after correction: %s", corrected_output)
     pcl.update_adapter("user123", "lora_adapter_v2") 

multimind/api/multi_model_api.py

@@ -3,7 +3,8 @@
 """
 
 import logging
-from fastapi import FastAPI, HTTPException
+import os
+from fastapi import FastAPI, HTTPException, Depends, Header
 from pydantic import BaseModel, Field
 from typing import List, Dict, Optional, Union
 import asyncio
@@ -16,6 +17,19 @@
 app = FastAPI(title="Multi-Model API")
 logger = logging.getLogger(__name__)
 
+API_KEYS = os.getenv("API_KEYS", "").split(",") if os.getenv("API_KEYS") else []
+
+
+def verify_api_key(api_key: Optional[str] = Header(None, alias="X-API-Key")) -> bool:
+    """Verify the API key from request header."""
+    if not API_KEYS:
+        return True
+    if not api_key:
+        raise HTTPException(status_code=401, detail="API key required")
+    if api_key not in API_KEYS:
+        raise HTTPException(status_code=401, detail="Invalid API key")
+    return True
+
 # Reuse a single factory across requests to avoid re-loading env / re-allocating caches.
 _MODEL_FACTORY = ModelFactory()
 
@@ -79,7 +93,7 @@ class EmbeddingsRequest(BaseModel):
     model_weights: Optional[Dict[str, float]] = None
 
 @app.post("/generate")
-async def generate(request: GenerateRequest):
+async def generate(request: GenerateRequest, authenticated: bool = Depends(verify_api_key)):
     """Generate text using the multi-model wrapper."""
     try:
         multi_model = await _get_multi_model(
@@ -99,7 +113,7 @@ async def generate(request: GenerateRequest):
         raise HTTPException(status_code=500, detail="Internal server error")
 
 @app.post("/chat")
-async def chat(request: ChatRequest):
+async def chat(request: ChatRequest, authenticated: bool = Depends(verify_api_key)):
     """Generate chat completion using the multi-model wrapper."""
     try:
         multi_model = await _get_multi_model(
@@ -119,7 +133,7 @@ async def chat(request: ChatRequest):
         raise HTTPException(status_code=500, detail="Internal server error")
 
 @app.post("/embeddings")
-async def embeddings(request: EmbeddingsRequest):
+async def embeddings(request: EmbeddingsRequest, authenticated: bool = Depends(verify_api_key)):
     """Generate embeddings using the multi-model wrapper."""
     try:
         multi_model = await _get_multi_model(

multimind/api/unified_api.py

@@ -2,7 +2,7 @@
 Unified API endpoint for multi-modal processing with MoE support.
 """
 
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Depends, Header
 from pydantic import BaseModel, Field
 from typing import Dict, List, Any, Optional, Union
 import asyncio
@@ -19,9 +19,41 @@
 
 app = FastAPI(title="Unified Multi-Modal API")
 
+API_KEYS = os.getenv("API_KEYS", "").split(",") if os.getenv("API_KEYS") else []
+
+
+def verify_api_key(api_key: Optional[str] = Header(None, alias="X-API-Key")) -> bool:
+    """Verify the API key from request header."""
+    if not API_KEYS:
+        return True
+    if not api_key:
+        raise HTTPException(status_code=401, detail="API key required")
+    if api_key not in API_KEYS:
+        raise HTTPException(status_code=401, detail="Invalid API key")
+    return True
+
 # Reuse a single factory across requests to avoid re-creating model caches.
 _MODEL_FACTORY = ModelFactory()
 
+_ROUTER = None
+_WORKFLOW_REGISTRY = None
+
+
+def _get_router():
+    global _ROUTER
+    if _ROUTER is None:
+        from ..router.multi_modal_router import MultiModalRouter
+        _ROUTER = MultiModalRouter()
+    return _ROUTER
+
+
+def _get_workflow_registry():
+    global _WORKFLOW_REGISTRY
+    if _WORKFLOW_REGISTRY is None:
+        from .mcp.registry import WorkflowRegistry
+        _WORKFLOW_REGISTRY = WorkflowRegistry()
+    return _WORKFLOW_REGISTRY
+
 
 class _TextExpertAdapter(Expert):
     """Expert wrapper around a model instance for text."""
@@ -169,16 +201,13 @@ def _build_experts(modalities: List[str], router: Any) -> Dict[str, Expert]:
     return experts
 
 @app.post("/v1/process", response_model=UnifiedResponse)
-async def process_request(request: UnifiedRequest):
+async def process_request(request: UnifiedRequest, authenticated: bool = Depends(verify_api_key)):
     """Process multi-modal request using either MoE or router."""
     try:
-        # Import here to avoid circular imports
-        from ..router.multi_modal_router import MultiModalRouter, MultiModalRequest
-        from .mcp.registry import WorkflowRegistry
-        
-        # Initialize components
-        router = MultiModalRouter()
-        workflow_registry = WorkflowRegistry()
+        from ..router.multi_modal_router import MultiModalRequest
+
+        router = _get_router()
+        workflow_registry = _get_workflow_registry()
 
         # Convert inputs to router format (support multiple inputs per modality)
         content: Dict[str, Any] = {}
@@ -288,32 +317,26 @@ async def process_request(request: UnifiedRequest):
         raise HTTPException(status_code=500, detail="Internal server error")
 
 @app.get("/v1/models")
-async def list_models():
+async def list_models(authenticated: bool = Depends(verify_api_key)):
     """List available models and their capabilities."""
-    # Import here to avoid circular imports
-    from ..router.multi_modal_router import MultiModalRouter
-    router = MultiModalRouter()
-    
+    router = _get_router()
+
     models = {}
     for modality, model_dict in router.modality_registry.items():
         models[modality] = list(model_dict.keys())
     return {"models": models}
 
 @app.get("/v1/workflows")
-async def list_workflows():
+async def list_workflows(authenticated: bool = Depends(verify_api_key)):
     """List available MCP workflows."""
-    # Import here to avoid circular imports
-    from .mcp.registry import WorkflowRegistry
-    workflow_registry = WorkflowRegistry()
+    workflow_registry = _get_workflow_registry()
     return {"workflows": workflow_registry.list_workflows()}
 
 @app.get("/v1/metrics")
-async def get_metrics():
+async def get_metrics(authenticated: bool = Depends(verify_api_key)):
     """Get performance metrics for models."""
-    # Import here to avoid circular imports
-    from ..router.multi_modal_router import MultiModalRouter
-    router = MultiModalRouter()
-    
+    router = _get_router()
+
     return {
         "costs": router.cost_tracker.costs,
         "performance": router.performance_metrics.metrics

multimind/client/federated_router.py

@@ -1,6 +1,10 @@
 from typing import Callable, Dict, Any
+import logging
 import time
 
+logger = logging.getLogger(__name__)
+
+
 class FederatedRouter:
     """
     Routes between local (on-device) and cloud model clients based on context (input size, latency, privacy, etc.).
@@ -41,5 +45,5 @@ def generate(self, prompt, **kwargs):
     local = DummyClient()
     cloud = DummyClient()
     router = FederatedRouter(local, cloud)
-    print(router.generate("short prompt"))
-    print(router.generate("This is a very long prompt that should go to the cloud..." * 20)) 
+    logger.info("%s", router.generate("short prompt"))
+    logger.info("%s", router.generate("This is a very long prompt that should go to the cloud..." * 20)) 

multimind/compliance/advanced.py

@@ -42,6 +42,7 @@ def update_epsilon(self, epsilon: float):
 from datetime import datetime
 import json
 import asyncio
+import hashlib
 from pathlib import Path
 from dataclasses import dataclass
 from enum import Enum
@@ -291,11 +292,12 @@ async def check_and_heal(self, compliance_state: Dict[str, Any]) -> Dict[str, An
 
     def _get_state_metadata(self, state: Dict[str, Any]) -> Dict[str, Any]:
         """Get metadata for a compliance state."""
+        state_bytes = json.dumps(state, sort_keys=True, default=str).encode("utf-8")
         return {
             "status": state.get("status", "unknown"),
             "timestamp": datetime.now().isoformat(),
             "version": state.get("version", "1.0"),
-            "checksum": hash(str(state))
+            "checksum": hashlib.sha256(state_bytes).hexdigest()
         }
 
     def _create_rollback_point(self, state: Dict[str, Any]):
@@ -497,8 +499,13 @@ async def _extract_watermark(self, model: Any) -> str:
 
     async def _generate_fingerprint(self, model: Any) -> str:
         """Generate fingerprint for the model."""
-        # Placeholder implementation: Replace with actual fingerprint generation logic
-        return f"fingerprint_{hash(str(model))}"
+        # Deterministic cryptographic fingerprint for model identity.
+        model_payload = {
+            "type": type(model).__name__,
+            "repr": repr(model),
+        }
+        model_bytes = json.dumps(model_payload, sort_keys=True, default=str).encode("utf-8")
+        return f"fingerprint_{hashlib.sha256(model_bytes).hexdigest()}"
 
     async def verify_watermark(self, model) -> Dict[str, Any]:
         """Enhanced watermark verification with tamper detection."""
@@ -533,10 +540,17 @@ async def track_fingerprint(self, model: Any) -> Dict[str, Any]:
         """Track and return fingerprint information for a model."""
         fingerprint = await self._generate_fingerprint(model)
         await self.fingerprint_tracker.track(fingerprint)
+        model_id = hashlib.sha256(
+            json.dumps(
+                {"type": type(model).__name__, "repr": repr(model)},
+                sort_keys=True,
+                default=str
+            ).encode("utf-8")
+        ).hexdigest()[:16]
         return {
             "fingerprint": fingerprint,
             "timestamp": datetime.now().isoformat(),
-            "model_id": str(hash(str(model)))
+            "model_id": model_id
         }
 
 class AdaptivePrivacy:

multimind/compliance/advanced_config.py

@@ -40,7 +40,7 @@ class ComplianceShardConfig(BaseModel):
     jurisdiction: str
     epsilon: float = 1.0
     rules: List[Dict[str, Any]]
-    metadata: Dict[str, Any] = {}
+    metadata: Dict[str, Any] = Field(default_factory=dict)
     compliance_level: ComplianceLevel = ComplianceLevel.STANDARD
     encryption_enabled: bool = True
     metrics_tracking: bool = True

multimind/compliance/audit.py

@@ -129,16 +129,16 @@ async def cleanup_old_events(self) -> int:
 
     async def export_events(
         self,
-        format: str = "json",
+        export_format: str = "json",
         start_time: Optional[datetime] = None,
         end_time: Optional[datetime] = None
     ) -> str:
         """Export audit events in specified format."""
         events = await self.get_events(start_time=start_time, end_time=end_time)
 
-        if format == "json":
+        if export_format == "json":
             return json.dumps([e.dict() for e in events], default=str)
-        elif format == "csv":
+        elif export_format == "csv":
             import csv
             import io
             if not events:
@@ -156,7 +156,7 @@ async def export_events(
                 writer.writerow(row)
             return output.getvalue()
         else:
-            raise ValueError(f"Unsupported export format: {format}")
+            raise ValueError(f"Unsupported export format: {export_format}")
 
     async def get_compliance_report(
         self,