Routing upgrade (#133)

* update

* wip

* wip

Author: truemagic-coder

poetry.lock

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "solana-agent"
-version = "31.2.5"
+version = "31.2.6"
 description = "AI Agents for Solana"
 authors = ["Bevan Hunt <bevan@bevanhunt.com>"]
 license = "MIT"
@@ -30,7 +30,7 @@ pymongo = "4.15.0"
 zep-cloud = "3.4.3"
 instructor = "1.11.3"
 pinecone = { version = "7.3.0", extras = ["asyncio"] }
-llama-index-core = "0.14.0"
+llama-index-core = "0.14.1"
 llama-index-embeddings-openai = "0.5.1"
 pypdf = "6.0.0"
 scrubadub = "2.0.1"

pyproject.toml

@@ -10,9 +10,15 @@ class QueryAnalysis(BaseModel):
         description="Name of the primary agent that should handle this query (must be one of the available agent names)",
     )
     secondary_agents: List[str] = Field(
-        ...,
+        default_factory=list,
         description="Names of secondary agents that might be helpful (must be from the available agent names)",
     )
-    complexity_level: int = Field(..., description="Complexity level (1-5)")
-    topics: List[str] = Field(..., description="Key topics in the query")
-    confidence: float = Field(..., description="Confidence in the analysis")
+    complexity_level: int = Field(
+        default=1, description="Complexity level (1-5)", ge=1, le=5
+    )
+    topics: List[str] = Field(
+        default_factory=list, description="Key topics in the query"
+    )
+    confidence: float = Field(
+        default=0.5, description="Confidence in the analysis", ge=0.0, le=1.0
+    )

solana_agent/domains/routing.py

@@ -1,11 +1,3 @@
-"""
-Routing service implementation.
-
-This service manages query routing to appropriate agents using an LLM-based
-analysis. It defaults to a small, low-cost model for routing to minimize
-overhead while maintaining quality.
-"""
-
 import logging
 from typing import Dict, List, Optional, Any
 from solana_agent.interfaces.services.routing import (
@@ -81,17 +73,18 @@ async def _analyze_query(self, query: str) -> Dict[str, Any]:
 
         USER QUERY: {query}
 
+        ROUTING RULES:
+        - Match the user query to the agent whose specialization best fits the user's intent
+        - Return the EXACT agent name that matches best
+
         INSTRUCTIONS:
-        - Look at the user query and match it to the most appropriate agent from the list above
-        - If the user mentions a specific topic or need that matches an agent's specialization, choose that agent
-        - Return the EXACT agent name (not the specialization description)
-
-        Please determine:
-        1. primary_agent: The exact name of the best matching agent (e.g., "onboarding", "support")
-        2. secondary_agents: Names of other agents that might help (empty list if none)
-        3. complexity_level: 1-5 (5 being most complex)
-        4. topics: Key topics mentioned
-        5. confidence: 0.0-1.0 (how confident you are in this routing decision)
+        - primary_agent: The exact name of the best matching agent (e.g., "onboarding", "event_feedback")
+        - secondary_agents: Other agents that might help (usually empty)
+        - complexity_level: 1-5 (5 being most complex)
+        - topics: Key topics mentioned
+        - confidence: 0.0-1.0 (how confident you are in this routing decision)
+
+        For the query "{query}", which agent should handle it?
         """
 
         try:
@@ -114,12 +107,14 @@ async def _analyze_query(self, query: str) -> Dict[str, Any]:
                 "confidence": analysis.confidence,
             }
         except Exception as e:
-            logger.error(f"Error analyzing query: {e}")  # Use logger.error
+            logger.error(f"Error analyzing query: {e}")
+            logger.debug(f"Query that failed: {query}")
+            logger.debug(f"Available agents: {list(agents.keys())}")
             # Return default analysis on error
+            first_agent = list(agents.keys())[0] if agents else "general"
+            logger.debug(f"Defaulting to first agent: {first_agent}")
             return {
-                "primary_specialization": list(agents.keys())[0]
-                if agents
-                else "general",
+                "primary_specialization": first_agent,
                 "secondary_specializations": [],
                 "complexity_level": 1,
                 "topics": [],