a few tweaks for better logging & old CML/crew support (#231)

* a few tweaks for better logging & old CML/crew support

* add pysqllite3 as a dep for Linux

* add pysqllite3-binary to the deps, and put the import into a try/except

* ruff

* print out the system platform for debugging purposes

* Update llm-service/app/main.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* address deprecation issues

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: jkwatson

llm-service/app/main.py

@@ -39,6 +39,7 @@
 import functools
 import logging
 import os
+import platform
 import sys
 import time
 from collections.abc import Awaitable, Callable
@@ -58,6 +59,12 @@
 logger = logging.getLogger(__name__)
 _request_received_logger = logging.getLogger(_APP_PKG_NAME + ".access")
 
+try:
+    # Manual patch required for CrewAI compatability. DO NOT REMOVE. IT BREAKS ON OLD RUNTIMES IF YOU DO
+    __import__("pysqlite3")
+    sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
+except Exception as e:
+    logger.info(f"Unable to swap out pysqlite3. Probably not installed on your platform: {platform.system()}", e)
 
 def _configure_logger() -> None:
     """Configure this module's setup/teardown logging formatting and verbosity."""

llm-service/app/services/query/agents/planner_agent.py

@@ -42,7 +42,7 @@
 from crewai import Agent, Task, Crew, Process
 from llama_index.core.base.llms.types import ChatMessage, MessageRole
 from llama_index.core.llms import LLM
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ConfigDict
 
 from app.services.models.providers import BedrockModelProvider, AzureModelProvider
 from app.services.query.agents.models import get_crewai_llm_object_direct
@@ -66,21 +66,21 @@ class PlannerTaskOutput(BaseModel):
     The output of the planner agent.
     """
 
+    model_config = ConfigDict(json_schema_extra = {
+            "example": {
+                "use_retrieval": True,
+                "explanation": "The query is related to the content described in the knowledge base summary.",
+            }
+        }
+    )
+
     use_retrieval: bool = Field(
         ..., description="Whether to use retrieval or answer directly."
     )
     explanation: str = Field(
         ..., description="Explanation for the decision made by the planner agent."
     )
 
-    class Config:
-        schema_extra = {
-            "example": {
-                "use_retrieval": True,
-                "explanation": "The query is related to the content described in the knowledge base summary.",
-            }
-        }
-
 
 class PlannerAgent:
     """

llm-service/pyproject.toml

@@ -46,6 +46,7 @@ dependencies = [
     "llama-index-callbacks-opik>=1.1.0",
     "mcp[cli]>=1.9.1",
     "crewai-tools[mcp]>=0.44.0",
+    "pysqlite3-binary==0.5.4; platform_system == 'Linux'",
 ]
 requires-python = ">=3.10,<=3.12"
 readme = "README.md"