alex completed

backend/database/reset_db.py

@@ -164,7 +164,10 @@ def main():
         print("\n📝 Running migrations...")
         import subprocess
         result = subprocess.run(['uv', 'run', 'run_migrations.py'], 
-                              capture_output=True, text=True)
+                             capture_output=True, 
+                             text=True, 
+                             encoding="utf-8",    # <-- ADD THIS
+                             errors="ignore")
 
         if result.returncode != 0:
             print("❌ Migration failed!")
@@ -177,7 +180,10 @@ def main():
     print("\n🌱 Loading seed data...")
     import subprocess
     result = subprocess.run(['uv', 'run', 'seed_data.py'], 
-                          capture_output=True, text=True)
+                          capture_output=True, 
+                          text=True, 
+                          encoding="utf-8",    # <-- ADD THIS
+                          errors="ignore")
 
     if result.returncode != 0:
         print("❌ Seed data failed!")

backend/researcher/server.py

@@ -10,7 +10,7 @@
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from dotenv import load_dotenv
-from agents import Agent, Runner, trace
+from agents import Agent, Runner, trace , ModelSettings
 from agents.extensions.models.litellm_model import LitellmModel
 
 # Suppress LiteLLM warnings about optional dependencies
@@ -54,7 +54,7 @@ async def run_research_agent(topic: str = None) -> str:
     # bedrock/openai.gpt-oss-120b-1:0 for OpenAI OSS models
     # bedrock/converse/us.anthropic.claude-sonnet-4-20250514-v1:0 for Claude Sonnet 4
     # NOTE that nova-pro is needed to support tools and MCP servers; nova-lite is not enough - thank you Yuelin L.!
-    MODEL = "bedrock/us.amazon.nova-pro-v1:0"
+    MODEL = "bedrock/amazon.nova-pro-v1:0"
     model = LitellmModel(model=MODEL)
 
     # Create and run the agent with MCP server

backend/retirement/templates.py

@@ -56,7 +56,7 @@
 Market Assumptions:
 - Average equity returns: 7% annually
 - Average bond returns: 4% annually
-- Inflation rate: 3% annually
+- Inflation rate: 5% annually
 - Safe withdrawal rate: 4% initially
 
 Perform the following analyses:

backend/tagger/agent.py

@@ -6,7 +6,7 @@
 from typing import List
 import logging
 from decimal import Decimal
-
+from typing import Dict
 from pydantic import BaseModel, Field, field_validator, ConfigDict
 from agents import Agent, Runner, trace
 from agents.extensions.models.litellm_model import LitellmModel
@@ -322,3 +322,63 @@ def classification_to_db_format(classification: InstrumentClassification) -> Ins
         allocation_regions=regions_dict,
         allocation_sectors=sectors_dict,
     )
+
+class InstrumentClassificationWithRationale(BaseModel):
+    # Rationale MUST come first so LLM generates reasoning before answers
+    rationale: str = Field(
+        description="Detailed explanation of why these classifications were chosen, including specific factors considered"
+    )
+
+    asset_class: AssetClassType = Field(
+        description="Primary asset class classification"
+    )
+
+    asset_class_allocation: Dict[str, float] = Field(
+        description="Percentage breakdown by asset class",
+        example={"equity": 100.0}
+    )
+
+    region_allocation: Dict[str, float] = Field(
+        description="Percentage breakdown by geographic region",
+        example={"north_america": 70.0, "europe": 20.0, "asia_pacific": 10.0}
+    )
+
+    sector_allocation: Dict[str, float] = Field(
+        description="Percentage breakdown by sector (only for equity)",
+        example={"technology": 30.0, "healthcare": 20.0, "financial": 50.0}
+    )
+
+async def run_tagger_agent(instrument: dict) -> dict:
+    model = LitellmModel(model=f"bedrock/{bedrock_model}")
+
+    with trace("Classify instrument with explainability"):
+        agent = Agent(
+            name="Instrument Tagger with Explainability",
+            instructions=CLASSIFICATION_INSTRUCTIONS,
+            model=model,
+            response_format=InstrumentClassificationWithRationale
+        )
+
+        result = await Runner.run(
+            agent,
+            input=create_classification_task(instrument),
+            max_turns=1
+        )
+
+        classification = result.final_output_as(InstrumentClassificationWithRationale)
+
+        # Log the rationale for audit trail
+        logger.info(json.dumps({
+            "event": "CLASSIFICATION_RATIONALE",
+            "symbol": instrument["symbol"],
+            "rationale": classification.rationale,
+            "timestamp": datetime.utcnow().isoformat()
+        }))
+
+        # Return classification without rationale to planner
+        return {
+            "asset_class": classification.asset_class,
+            "asset_class_allocation": classification.asset_class_allocation,
+            "region_allocation": classification.region_allocation,
+            "sector_allocation": classification.sector_allocation
+        }

backend/tagger/templates.py

@@ -44,4 +44,19 @@
 - Each category must sum to exactly 100.0%
 - For stocks, typically 100% in one asset class, one region, one sector
 - For ETFs, distribute based on underlying holdings
-- For bonds/bond funds, use fixed_income asset class and appropriate sectors (treasury/corporate/mortgage/government_related)"""
+- For bonds/bond funds, use fixed_income asset class and appropriate sectors (treasury/corporate/mortgage/government_related)"""
+
+ANALYSIS_INSTRUCTIONS_WITH_EXPLANATION = """
+When providing recommendations, always:
+1. Start with your reasoning process
+2. List specific factors you considered
+3. Explain why certain recommendations were prioritized
+4. Include any assumptions made
+5. Note any limitations or caveats
+
+Format each recommendation as:
+**Recommendation:** [The action to take]
+**Reasoning:** [Why this recommendation was made]
+**Impact:** [Expected outcome if implemented]
+**Priority:** [High/Medium/Low based on user goals]
+"""