base for rag approach

modules/text/module_text_llm/module_text_llm/approach_config.py

@@ -6,6 +6,7 @@
 class ApproachType(str, Enum):
     basic = "BasicApproach"
     chain_of_thought = "ChainOfThought"
+    rag = "RagApproach"
 
 class ApproachConfig(BaseModel, ABC):
     max_input_tokens: int = Field(default=3000, description="Maximum number of tokens in the input prompt.")

modules/text/module_text_llm/module_text_llm/approach_controller.py

@@ -4,13 +4,15 @@
 from module_text_llm.basic_approach import BasicApproachConfig
 from module_text_llm.chain_of_thought_approach  import ChainOfThoughtConfig
 from module_text_llm.approach_config import ApproachConfig
-
+from module_text_llm.retrieval_augmented_generation import RAGApproachConfig
 from module_text_llm.basic_approach.generate_suggestions import generate_suggestions as generate_suggestions_basic
 from module_text_llm.chain_of_thought_approach.generate_suggestions import generate_suggestions as generate_cot_suggestions
-
+from module_text_llm.retrieval_augmented_generation.generate_suggestions import generate_suggestions as generate_rag_suggestions
+
 async def generate_suggestions(exercise: Exercise, submission: Submission, config: ApproachConfig, debug: bool) -> List[Feedback]:
     if(isinstance(config, BasicApproachConfig)):
         return await generate_suggestions_basic(exercise, submission, config, debug)
     elif(isinstance(config, ChainOfThoughtConfig)):
-        return await generate_cot_suggestions(exercise, submission, config, debug)
-
+        return await generate_cot_suggestions(exercise, submission, config, debug) #RAGApproachConfig
+    elif(isinstance(config, RAGApproachConfig)):
+        return await generate_rag_suggestions(exercise, submission, config, debug) 

modules/text/module_text_llm/module_text_llm/config.py

@@ -4,8 +4,9 @@
 
 from module_text_llm.chain_of_thought_approach import ChainOfThoughtConfig
 from module_text_llm.basic_approach import BasicApproachConfig
+from module_text_llm.retrieval_augmented_generation import RAGApproachConfig
 
-ApproachConfigUnion = Union[BasicApproachConfig, ChainOfThoughtConfig]
+ApproachConfigUnion = Union[BasicApproachConfig, ChainOfThoughtConfig,RAGApproachConfig]
 
 @config_schema_provider
 class Configuration(BaseModel):

modules/text/module_text_llm/module_text_llm/retrieval_augmented_generation/__init__.py

@@ -0,0 +1,11 @@
+from module_text_llm.approach_config import ApproachConfig
+from pydantic import Field
+from typing import Literal
+
+
+from module_text_llm.retrieval_augmented_generation.prompt_generate_suggestions import GenerateSuggestionsPrompt
+
+class RAGApproachConfig(ApproachConfig):
+    type: Literal['rag'] = 'rag'
+    generate_suggestions_prompt: GenerateSuggestionsPrompt = Field(default=GenerateSuggestionsPrompt())
+

modules/text/module_text_llm/module_text_llm/retrieval_augmented_generation/generate_suggestions.py

@@ -0,0 +1,97 @@
+from typing import List
+
+from athena import emit_meta
+from athena.text import Exercise, Submission, Feedback
+from athena.logger import logger
+from llm_core.utils.llm_utils import (
+    get_chat_prompt_with_formatting_instructions, 
+    check_prompt_length_and_omit_features_if_necessary, 
+    num_tokens_from_prompt,
+)
+from athena.text import Exercise, Submission, Feedback
+from llm_core.utils.predict_and_parse import predict_and_parse
+
+from module_text_llm.config import BasicApproachConfig
+from module_text_llm.helpers.utils import add_sentence_numbers, get_index_range_from_line_range, format_grading_instructions
+from module_text_llm.basic_approach.prompt_generate_suggestions import AssessmentModel
+
+async def generate_suggestions(exercise: Exercise, submission: Submission, config: BasicApproachConfig, debug: bool) -> List[Feedback]:
+    model = config.model.get_model()  # type: ignore[attr-defined]
+    prompt_input = {
+        "max_points": exercise.max_points,
+        "bonus_points": exercise.bonus_points,
+        "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria),
+        "problem_statement": exercise.problem_statement or "No problem statement.",
+        "example_solution": exercise.example_solution,
+        "submission": add_sentence_numbers(submission.text)
+    }
+
+    chat_prompt = get_chat_prompt_with_formatting_instructions(
+        model=model, 
+        system_message=config.generate_suggestions_prompt.system_message, 
+        human_message=config.generate_suggestions_prompt.human_message, 
+        pydantic_object=AssessmentModel
+    )
+
+    # Check if the prompt is too long and omit features if necessary (in order of importance)
+    omittable_features = ["example_solution", "problem_statement", "grading_instructions"]
+    prompt_input, should_run = check_prompt_length_and_omit_features_if_necessary(
+        prompt=chat_prompt,
+        prompt_input= prompt_input,
+        max_input_tokens=config.max_input_tokens,
+        omittable_features=omittable_features,
+        debug=debug
+    )
+
+    # Skip if the prompt is too long
+    if not should_run:
+        logger.warning("Input too long. Skipping.")
+        if debug:
+            emit_meta("prompt", chat_prompt.format(**prompt_input))
+            emit_meta("error", f"Input too long {num_tokens_from_prompt(chat_prompt, prompt_input)} > {config.max_input_tokens}")
+        return []
+
+    result = await predict_and_parse(
+        model=model, 
+        chat_prompt=chat_prompt, 
+        prompt_input=prompt_input, 
+        pydantic_object=AssessmentModel,
+        tags=[
+            f"exercise-{exercise.id}",
+            f"submission-{submission.id}",
+        ],
+        use_function_calling=True
+    )
+
+    if debug:
+        emit_meta("generate_suggestions", {
+            "prompt": chat_prompt.format(**prompt_input),
+            "result": result.dict() if result is not None else None
+        })
+
+    if result is None:
+        return []
+
+    grading_instruction_ids = set(
+        grading_instruction.id 
+        for criterion in exercise.grading_criteria or [] 
+        for grading_instruction in criterion.structured_grading_instructions
+    )
+
+    feedbacks = []
+    for feedback in result.feedbacks:
+        index_start, index_end = get_index_range_from_line_range(feedback.line_start, feedback.line_end, submission.text)
+        grading_instruction_id = feedback.grading_instruction_id if feedback.grading_instruction_id in grading_instruction_ids else None
+        feedbacks.append(Feedback(
+            exercise_id=exercise.id,
+            submission_id=submission.id,
+            title=feedback.title,
+            description=feedback.description,
+            index_start=index_start,
+            index_end=index_end,
+            credits=feedback.credits,
+            structured_grading_instruction_id=grading_instruction_id,
+            meta={}
+        ))
+
+    return feedbacks

modules/text/module_text_llm/module_text_llm/retrieval_augmented_generation/prompt_generate_suggestions.py

@@ -0,0 +1,65 @@
+from pydantic import Field, BaseModel
+from typing import List, Optional
+from pydantic import BaseModel, Field
+
+system_message = """\
+You are an AI tutor for text assessment at a prestigious university.
+
+# Task
+Create graded feedback suggestions for a student\'s text submission that a human tutor would accept. \
+Meaning, the feedback you provide should be applicable to the submission with little to no modification.
+
+# Style
+1. Constructive, 2. Specific, 3. Balanced, 4. Clear and Concise, 5. Actionable, 6. Educational, 7. Contextual
+
+# Problem statement
+{problem_statement}
+
+# Example solution
+{example_solution}
+
+# Grading instructions
+{grading_instructions}
+Max points: {max_points}, bonus points: {bonus_points}\
+    
+Respond in json.
+"""
+
+human_message = """\
+Student\'s submission to grade (with sentence numbers <number>: <sentence>):
+
+Respond in json.
+
+\"\"\"
+{submission}
+\"\"\"\
+"""
+
+# Input Prompt
+class GenerateSuggestionsPrompt(BaseModel):
+    """\
+Features available: **{problem_statement}**, **{example_solution}**, **{grading_instructions}**, **{max_points}**, **{bonus_points}**, **{submission}**
+
+_Note: **{problem_statement}**, **{example_solution}**, or **{grading_instructions}** might be omitted if the input is too long._\
+"""
+    system_message: str = Field(default=system_message,
+                                description="Message for priming AI behavior and instructing it what to do.")
+    human_message: str = Field(default=human_message,
+                               description="Message from a human. The input on which the AI is supposed to act.")
+# Output Object
+class FeedbackModel(BaseModel):
+    title: str = Field(description="Very short title, i.e. feedback category or similar", example="Logic Error")
+    description: str = Field(description="Feedback description")
+    line_start: Optional[int] = Field(description="Referenced line number start, or empty if unreferenced")
+    line_end: Optional[int] = Field(description="Referenced line number end, or empty if unreferenced")
+    credits: float = Field(0.0, description="Number of points received/deducted")
+    grading_instruction_id: Optional[int] = Field(
+        description="ID of the grading instruction that was used to generate this feedback, or empty if no grading instruction was used"
+    )
+
+
+class AssessmentModel(BaseModel):
+    """Collection of feedbacks making up an assessment"""
+
+    feedbacks: List[FeedbackModel] = Field(description="Assessment feedbacks")
+