feat: add Pydantic classes to validate assignment structure and busin…

…ess rules

grader/grader.py

@@ -4,7 +4,7 @@
 import json
 import os
 
-from pydantic import BaseModel, Field, field_validator, model_validator
+from pydantic import BaseModel, Field, ValidationError, field_validator, model_validator
 
 from .exceptions import (
     AGException,
@@ -13,6 +13,7 @@
     InvalidResponseStructureError,
     ResponseFailedError,
 )
+from .langchain import LCResponse
 
 
 VALID_MESSAGE_TYPES = [
@@ -38,7 +39,7 @@ class Grade(BaseModel):
     Subclasses should override the necessary methods to provide the grading logic.
     """
 
-    potential_points: float = Field(..., description="The maximum number of points that can be awarded.", gt=0)
+    potential_points: float = Field(100, description="The maximum number of points that can be awarded.", ge=0)
     grade: float = Field(..., description="The number of points awarded.", ge=0)
     message: str = Field(..., description="A result message to the student.")
     message_type: str = Field(..., description="The type of result message.")
@@ -227,4 +228,13 @@ def grade(self):
             return self.grade_response(e)
         except IncorrectResponseTypeError as e:
             return self.grade_response(e)
+
+        # this is an experimental usage of pydantic to validate the assignment
+        try:
+            LCResponse(**self.assignment)
+        except ValidationError as e:
+            error_text = str(e)
+            e = InvalidResponseStructureError(f"The assignment is not a valid LCResponse. error: {error_text}")
+            return self.grade_response(e)
+
         return self.grade_response()

grader/langchain.py

@@ -0,0 +1,59 @@
+# -*- coding: utf-8 -*-
+"""LangChain  integration models"""
+
+from typing import List, Optional
+
+from pydantic import BaseModel, Field
+
+
+class LCRequestMetaData(BaseModel):
+    """LangChain request meta data"""
+
+    lambda_name: str = Field(..., alias="lambda")
+    model: str
+    end_point: str
+    temperature: float = Field(..., ge=0, le=1)
+    max_tokens: int = Field(..., gt=0)
+
+
+class LCMessage(BaseModel):
+    """LangChain message"""
+
+    content: str
+    additional_kwargs: dict
+    type: str
+    example: bool
+
+
+class LCChatMemory(BaseModel):
+    """LangChain chat memory"""
+
+    messages: List[LCMessage]
+
+    # @model_validator(mode="after")
+    # def validate_messages(self) -> "LCChatMemory":
+    #     """Validate that chat memory contains at least 2 dicts"""
+    #     if len(self.messages) < 2:
+    #         raise ValueError("messages must contain at least 2 objects")
+    #     return self
+
+
+class LCBody(BaseModel):
+    """LangChain body"""
+
+    chat_memory: LCChatMemory
+    output_key: Optional[str] = Field(None)
+    input_key: Optional[str] = Field(None)
+    return_messages: bool
+    human_prefix: str = Field("Human")
+    ai_prefix: str = Field("AI")
+    memory_key: str = Field("chat_history")
+    request_meta_data: LCRequestMetaData
+
+
+class LCResponse(BaseModel):
+    """LangChain Response Dict"""
+
+    is_base64_encoded: bool = Field(..., alias="isBase64Encoded")
+    status_code: int = Field(..., alias="statusCode", ge=200, le=599)
+    body: LCBody