From bcb5e94435c1d15dc212ec736e581a320ece2414 Mon Sep 17 00:00:00 2001 From: = Enea_Gore Date: Fri, 4 Apr 2025 20:01:44 +0200 Subject: [PATCH 1/6] setup weviate, update pydantic, create schema, create approach, initial commit --- .../endpoints/health_endpoint.py | 4 +- athena/athena/athena/endpoints.py | 2 +- athena/athena/athena/schemas/exercise.py | 2 +- athena/athena/athena/schemas/feedback.py | 2 +- .../athena/schemas/modeling_exercise.py | 4 +- .../athena/schemas/programming_exercise.py | 4 +- athena/athena/athena/schemas/schema.py | 2 +- athena/athena/athena/schemas/submission.py | 2 +- athena/athena/athena/schemas/text_exercise.py | 4 +- athena/athena/poetry.lock | 25 +- athena/athena/pyproject.toml | 2 +- .../module_text_llm/__main__.py | 7 +- .../module_text_llm/module_text_llm/config.py | 3 +- .../in_context_learning/__init__.py | 13 + .../in_context_learning/agent.py | 68 +++ .../in_context_learning/docker-compose.yml | 32 + .../feedback_icl/generate_embeddings.py | 7 + .../feedback_icl/retrieve_rag_context_icl.py | 59 ++ .../feedback_icl/store_feedback_icl.py | 101 +++ .../feedback_icl/store_indices_icl.py | 47 ++ .../generate_suggestions.py | 164 +++++ .../in_context_learning/ollama_prompt.py | 82 +++ .../prompt_generate_suggestions.py | 54 ++ .../modules/text/module_text_llm/poetry.lock | 577 ++++++++++++++++-- .../text/module_text_llm/pyproject.toml | 3 +- 25 files changed, 1178 insertions(+), 92 deletions(-) create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/docker-compose.yml create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/ollama_prompt.py create mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py diff --git a/athena/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py b/athena/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py index effdf926..9ee9f70a 100644 --- a/athena/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py +++ b/athena/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py @@ -1,6 +1,6 @@ import httpx from pydantic import BaseModel, Field - +from typing import Literal from .modules_endpoint import get_modules from assessment_module_manager.app import app from assessment_module_manager.logger import logger @@ -29,7 +29,7 @@ class HealthResponse(BaseModel): and whether all the modules are healthy (i.e. reachable). Additional information about the modules is also provided. """ - status: str = Field(const=True, default="ok", example="ok") + status: str = Field(Literal=True, default="ok", example="ok") modules: dict = Field( example=[ { diff --git a/athena/athena/athena/endpoints.py b/athena/athena/athena/endpoints.py index 6d259a2a..a3014050 100644 --- a/athena/athena/athena/endpoints.py +++ b/athena/athena/athena/endpoints.py @@ -156,7 +156,7 @@ class SubmissionSelectorRequest(BaseModel): class Config: # Allow camelCase field names in the API (converted to snake_case) alias_generator = to_camel - allow_population_by_field_name = True + populate_by_name = True @app.post("/select_submission", responses=module_responses) @authenticated diff --git a/athena/athena/athena/schemas/exercise.py b/athena/athena/athena/schemas/exercise.py index 1f173e2e..ee85e05a 100644 --- a/athena/athena/athena/schemas/exercise.py +++ b/athena/athena/athena/schemas/exercise.py @@ -29,4 +29,4 @@ class Exercise(Schema, ABC): meta: dict = Field({}, example={"internal_id": "5"}) class Config: - orm_mode = True + from_attributes = True diff --git a/athena/athena/athena/schemas/feedback.py b/athena/athena/athena/schemas/feedback.py index 4cf9c6a2..24af4941 100644 --- a/athena/athena/athena/schemas/feedback.py +++ b/athena/athena/athena/schemas/feedback.py @@ -33,4 +33,4 @@ def to_model(self, is_suggestion: bool = False, lms_id: Optional[int] = None, lm return type(self).get_model_class()(**self.dict(), is_suggestion=is_suggestion, lms_id=lms_id, lms_url=lms_url) class Config: - orm_mode = True + from_attributes = True diff --git a/athena/athena/athena/schemas/modeling_exercise.py b/athena/athena/athena/schemas/modeling_exercise.py index 70452f83..69cdeb5f 100644 --- a/athena/athena/athena/schemas/modeling_exercise.py +++ b/athena/athena/athena/schemas/modeling_exercise.py @@ -1,4 +1,4 @@ -from typing import Optional +from typing import Optional, Literal from pydantic import Field from .exercise_type import ExerciseType @@ -8,6 +8,6 @@ class ModelingExercise(Exercise): """A modeling exercise that can be solved by students, enhanced with metadata.""" - type: ExerciseType = Field(ExerciseType.modeling, const=True) + type: ExerciseType = Field(ExerciseType.modeling, Literal=True) example_solution: Optional[str] = Field(None, description="An example solution to the exercise.") diff --git a/athena/athena/athena/schemas/programming_exercise.py b/athena/athena/athena/schemas/programming_exercise.py index ac875b05..5d554236 100644 --- a/athena/athena/athena/schemas/programming_exercise.py +++ b/athena/athena/athena/schemas/programming_exercise.py @@ -5,12 +5,12 @@ from athena.helpers.programming.code_repository import get_repository_zip, get_repository from .exercise_type import ExerciseType from .exercise import Exercise - +from typing import Literal class ProgrammingExercise(Exercise): """A programming exercise that can be solved by students, enhanced with metadata.""" - type: ExerciseType = Field(ExerciseType.programming, const=True) + type: ExerciseType = Field(ExerciseType.programming, Literal=True) programming_language: str = Field(description="The programming language that is used for this exercise.", example="java") solution_repository_uri: AnyUrl = Field(description="URL to the solution git repository, which contains the " diff --git a/athena/athena/athena/schemas/schema.py b/athena/athena/athena/schemas/schema.py index 67e146ad..b0e7e6b7 100644 --- a/athena/athena/athena/schemas/schema.py +++ b/athena/athena/athena/schemas/schema.py @@ -32,4 +32,4 @@ def to_model(self): class Config: # Allow camelCase field names in the API (converted to snake_case) alias_generator = to_camel - allow_population_by_field_name = True \ No newline at end of file + populate_by_name = True \ No newline at end of file diff --git a/athena/athena/athena/schemas/submission.py b/athena/athena/athena/schemas/submission.py index f9e39c37..0bf684bd 100644 --- a/athena/athena/athena/schemas/submission.py +++ b/athena/athena/athena/schemas/submission.py @@ -12,4 +12,4 @@ class Submission(Schema, ABC): meta: dict = Field({}, example={}) class Config: - orm_mode = True + from_attributes = True diff --git a/athena/athena/athena/schemas/text_exercise.py b/athena/athena/athena/schemas/text_exercise.py index 6c836766..09ea6512 100644 --- a/athena/athena/athena/schemas/text_exercise.py +++ b/athena/athena/athena/schemas/text_exercise.py @@ -1,4 +1,4 @@ -from typing import Optional +from typing import Optional, Literal from pydantic import Field from .exercise_type import ExerciseType @@ -8,6 +8,6 @@ class TextExercise(Exercise): """A text exercise that can be solved by students, enhanced with metadata.""" - type: ExerciseType = Field(ExerciseType.text, const=True) + type: ExerciseType = Field(ExerciseType.text, Literal=True) example_solution: Optional[str] = Field(None, description="An example solution to the exercise.") diff --git a/athena/athena/poetry.lock b/athena/athena/poetry.lock index a9d17180..181221bc 100644 --- a/athena/athena/poetry.lock +++ b/athena/athena/poetry.lock @@ -262,39 +262,40 @@ files = [ [[package]] name = "httpcore" -version = "0.17.3" +version = "1.0.7" description = "A minimal low-level HTTP client." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "httpcore-0.17.3-py3-none-any.whl", hash = "sha256:c2789b767ddddfa2a5782e3199b2b7f6894540b17b16ec26b2c4d8e103510b87"}, - {file = "httpcore-0.17.3.tar.gz", hash = "sha256:a6f30213335e34c1ade7be6ec7c47f19f50c56db36abef1a9dfa3815b1cb3888"}, + {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"}, + {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"}, ] [package.dependencies] -anyio = ">=3.0,<5.0" certifi = "*" h11 = ">=0.13,<0.15" -sniffio = "==1.*" [package.extras] +asyncio = ["anyio (>=4.0,<5.0)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] +trio = ["trio (>=0.22.0,<1.0)"] [[package]] name = "httpx" -version = "0.24.1" +version = "0.26.0" description = "The next generation HTTP client." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "httpx-0.24.1-py3-none-any.whl", hash = "sha256:06781eb9ac53cde990577af654bd990a4949de37a28bdb4a230d434f3a30b9bd"}, - {file = "httpx-0.24.1.tar.gz", hash = "sha256:5853a43053df830c20f8110c5e69fe44d035d850b2dfe795e196f00fdb774bdd"}, + {file = "httpx-0.26.0-py3-none-any.whl", hash = "sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd"}, + {file = "httpx-0.26.0.tar.gz", hash = "sha256:451b55c30d5185ea6b23c2c793abf9bb237d2a7dfb901ced6ff69ad37ec1dfaf"}, ] [package.dependencies] +anyio = "*" certifi = "*" -httpcore = ">=0.15.0,<0.18.0" +httpcore = "==1.*" idna = "*" sniffio = "*" @@ -1134,4 +1135,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "3.11.*" -content-hash = "0c115e830083d14aa96e0f88f102dfcac65ac31bc522fb8c3bc68337adbeaccc" +content-hash = "c12b8f645a94ce769948ada5c243b2bcbc2cf10b20c0323a30cb70d13eadec37" diff --git a/athena/athena/pyproject.toml b/athena/athena/pyproject.toml index 98d83e5d..d76ab52e 100644 --- a/athena/athena/pyproject.toml +++ b/athena/athena/pyproject.toml @@ -9,7 +9,7 @@ license = "MIT" python = "3.11.*" fastapi = "^0.109.1" uvicorn = "^0.23.0" -httpx = "^0.24.1" +httpx = "^0.26.0" gitpython = "^3.1.41" sqlalchemy = {extras = ["mypy"], version = "^2.0.21"} psycopg2 = "^2.9.9" diff --git a/athena/modules/text/module_text_llm/module_text_llm/__main__.py b/athena/modules/text/module_text_llm/module_text_llm/__main__.py index 0bfc6e41..647d8b7f 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/__main__.py +++ b/athena/modules/text/module_text_llm/module_text_llm/__main__.py @@ -11,7 +11,8 @@ from module_text_llm.evaluation import get_feedback_statistics, get_llm_statistics from module_text_llm.generate_evaluation import generate_evaluation from module_text_llm.approach_controller import generate_suggestions - +from module_text_llm.RandomMessage import write_random_message,query_messages,create_minimal_schema +from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import store_feedback_icl @submissions_consumer def receive_submissions(exercise: Exercise, submissions: List[Submission]): logger.info("receive_submissions: Received %d submissions for exercise %d", len(submissions), exercise.id) @@ -25,8 +26,10 @@ def select_submission(exercise: Exercise, submissions: List[Submission]) -> Subm @feedback_consumer def process_incoming_feedback(exercise: Exercise, submission: Submission, feedbacks: List[Feedback]): + logger.info("Will try to create Schema") logger.info("process_feedback: Received %d feedbacks for submission %d of exercise %d.", len(feedbacks), submission.id, exercise.id) - + store_feedback_icl(submission, exercise, feedbacks) + @feedback_provider async def suggest_feedback(exercise: Exercise, submission: Submission, is_graded: bool, module_config: Configuration) -> List[Feedback]: logger.info("suggest_feedback: %s suggestions for submission %d of exercise %d were requested, with approach: %s", diff --git a/athena/modules/text/module_text_llm/module_text_llm/config.py b/athena/modules/text/module_text_llm/module_text_llm/config.py index 7d904eae..c1b65d00 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/config.py +++ b/athena/modules/text/module_text_llm/module_text_llm/config.py @@ -1,3 +1,4 @@ +from module_text_llm.in_context_learning import InContextLearningConfig from module_text_llm.self_consistency import SelfConsistencyConfig from pydantic import BaseModel, Field from typing import Union @@ -7,7 +8,7 @@ from module_text_llm.basic_approach import BasicApproachConfig from module_text_llm.divide_and_conquer import DivideAndConquerConfig -ApproachConfigUnion = Union[BasicApproachConfig, ChainOfThoughtConfig, DivideAndConquerConfig, SelfConsistencyConfig] +ApproachConfigUnion = Union[BasicApproachConfig, ChainOfThoughtConfig, DivideAndConquerConfig, SelfConsistencyConfig, InContextLearningConfig] @config_schema_provider class Configuration(BaseModel): diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py new file mode 100644 index 00000000..b3e6d6a0 --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py @@ -0,0 +1,13 @@ +from module_text_llm.approach_config import ApproachConfig +from pydantic import Field +from typing import Literal +from athena.text import Exercise, Submission +from module_text_llm.in_context_learning.generate_suggestions import generate_suggestions +from module_text_llm.in_context_learning.prompt_generate_suggestions import GenerateSuggestionsPrompt + +class InContextLearningConfig(ApproachConfig): + type: Literal['in_context_learning'] = 'in_context_learning' + generate_suggestions_prompt: GenerateSuggestionsPrompt = Field(default=GenerateSuggestionsPrompt()) + + async def generate_suggestions(self, exercise: Exercise, submission: Submission, config,*, debug: bool, is_graded: bool): + return await generate_suggestions(exercise, submission, config, debug, is_graded) \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py new file mode 100644 index 00000000..359504a3 --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py @@ -0,0 +1,68 @@ +# from langchain_openai import ChatOpenAI +# from langchain_core.prompts import ChatPromptTemplate +# from langchain.agents import AgentExecutor, create_tool_calling_agent +# from pydantic import BaseModel, Field +# from langchain_core.tools import tool +# from module_text_llm.in_context_learning.feedback_icl.retrieve_rag_context_icl import retrieve_rag_context_icl +# from module_text_llm.in_context_learning.prompt_generate_suggestions import FeedbackModel +# from typing import List + +# # @tool +# # def retrieve_rag_context(submission_segment: str ,exercise_id: int) -> str: +# # """ +# # This method takes a segment from a submission and for a given exercise id, +# # returns feedback that has been given for similar texts. + +# # Args: +# # submission_segment: A segment of the submission. +# # exercise_id: The id of the exercise. + +# # Returns: +# # str: A formatted string of feedbacks which reference text similar to the submission_segment. +# # """ +# # return retrieve_rag_context_icl(submission_segment,exercise_id) + +# @tool +# class AssessmentModel(BaseModel): +# """Collection of feedbacks making up an assessment""" + +# feedbacks: List[FeedbackModel] = Field(description="Assessment feedbacks") + +# class AssessmentModelParse(BaseModel): +# """Collection of feedbacks making up an assessment""" + +# feedbacks: List[FeedbackModel] = Field(description="Assessment feedbacks") + +# class TutorAgent: +# def __init__(self,config): +# self.model = config.model.get_model() +# self.outputModel = ChatOpenAI(model="gpt-4o-mini") +# self.approach_config = None +# self.openai_tools = [retrieve_rag_context] +# self.setConfig(config) + + +# def setConfig(self,approach_config): +# self.approach_config = approach_config +# self.prompt = ChatPromptTemplate.from_messages( +# [ +# ("system", self.approach_config.generate_suggestions_prompt.system_message), +# ("human", "{submission}"), +# ("placeholder", "{agent_scratchpad}"), +# ]) + +# self.agent = create_tool_calling_agent(self.model, self.openai_tools , self.prompt) +# self.agent_executor = AgentExecutor(agent=self.agent, tools=self.openai_tools) +# self.config = {"configurable": {"session_id": "test-session"}} + +# def call_agent(self, prompt): +# """Calls the agent with a prompt and returns the response output.""" + +# response = self.agent_executor.invoke( +# input = prompt +# ) + +# res = self.model.with_structured_output(AssessmentModelParse).invoke(f"Format the following output {response}") +# return res + + \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/docker-compose.yml b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/docker-compose.yml new file mode 100644 index 00000000..4448f410 --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/docker-compose.yml @@ -0,0 +1,32 @@ +--- +services: + weaviate: + command: + - --host + - 0.0.0.0 + - --port + - '8080' + - --scheme + - http + image: cr.weaviate.io/semitechnologies/weaviate:1.30.0 + ports: + - 8080:8080 + - 50051:50051 + volumes: + - weaviate_data:/var/lib/weaviate + restart: on-failure:0 + environment: + CLIP_INFERENCE_API: 'http://multi2vec-clip:8080' + QUERY_DEFAULTS_LIMIT: 25 + AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true' + PERSISTENCE_DATA_PATH: '/var/lib/weaviate' + ENABLE_MODULES: 'multi2vec-clip' + ENABLE_API_BASED_MODULES: 'true' + CLUSTER_HOSTNAME: 'node1' + multi2vec-clip: + image: cr.weaviate.io/semitechnologies/multi2vec-clip:sentence-transformers-clip-ViT-B-32-multilingual-v1 + environment: + ENABLE_CUDA: '0' +volumes: + weaviate_data: +... diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py new file mode 100644 index 00000000..681e8e95 --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py @@ -0,0 +1,7 @@ +from langchain_openai import OpenAIEmbeddings + +def embed_text(text): + embeddings = OpenAIEmbeddings(model="text-embedding-3-large") + query_result = embeddings.embed_query(text) + return query_result + # return np.array(query_result, dtype=np.float32) only relevant for numpy operations diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py new file mode 100644 index 00000000..35091fca --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py @@ -0,0 +1,59 @@ +from athena.logger import logger +from module_text_llm.in_context_learning.feedback_icl.store_indices_icl import retrieve_embedding_index, retrieve_feedback +from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import query_embedding +from module_text_llm.in_context_learning.feedback_icl.generate_embeddings import embed_text + +def retrieve_rag_context_icl(submission_segment: str ,exercise_id: int) -> str: + """ + This method takes a segment from a submission and for a given exercise id, + returns feedback that has been given for similar texts. + + Args: + submission_segment: A segment of the submission. + exercise_id: The id of the exercise. + + Returns: + str: A formatted string of feedbacks which reference text similar to the submission_segment. + """ + # query_submission= embed_text(submission_segment) + + rag_context = [] + + rresult_objects = query_embedding(submission_segment,exercise_id) + print(rresult_objects) + return " " + # for result in rresult_objects: + # print("result",result) + # print(type(result)) + + # if list_of_indices is not None: + # for index in list_of_indices[0]: + # if index != -1: + # stored_feedback = retrieve_feedback(index,exercise_id) + # print("Stored feedback",stored_feedback) + # rag_context.append({"submission_chunk": submission_segment, "feedback": stored_feedback}) + # formatted_rag_context = format_rag_context(rag_context) + # else: + # formatted_rag_context = "There are no submission at the moment" + # return formatted_rag_context + +def format_rag_context(rag_context): + formatted_string = "" + + for context_item in rag_context: + submission_text = context_item["submission_chunk"] + feedback = context_item["feedback"] + if feedback is not None: + formatted_string += "**Tutor provided Feedback from previous submissions of this same exercise:**\n" + feedback["text_reference"] = get_reference(feedback, submission_text) + clean_feedback = {key: value for key, value in feedback.items() if key not in ["id","index_start","index_end","is_graded","meta"]} + + formatted_string += f"{clean_feedback}\n" + formatted_string += "\n" + "-"*40 + "\n" + + return formatted_string + +def get_reference(feedback, submission_text): + if (feedback["index_start"] is not None) and (feedback["index_end"] is not None): + return submission_text[feedback["index_start"]:feedback["index_end"]] + return submission_text diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py new file mode 100644 index 00000000..bfffccbe --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py @@ -0,0 +1,101 @@ +import numpy as np +import os +from athena.text import Submission, Exercise, Feedback +from typing import List +from athena.logger import logger +from module_text_llm.in_context_learning.feedback_icl.generate_embeddings import embed_text +import weaviate +import weaviate.classes as wvc + + +def create_schema(client): + """ + Create a schema with two classes: Question and Answer. + Each class has properties for storing relevant information. + """ + + # Define the Question class + questions = client.collections.create( + name="Feedback", + vectorizer_config=wvc.config.Configure.Vectorizer.none(), + properties=[ + wvc.config.Property(name="exercise_id", data_type=wvc.config.DataType.NUMBER), + wvc.config.Property(name="submission_id", data_type=wvc.config.DataType.NUMBER), + wvc.config.Property(name="title", data_type=wvc.config.DataType.TEXT), + wvc.config.Property(name="description", data_type=wvc.config.DataType.TEXT), + wvc.config.Property(name="credits", data_type=wvc.config.DataType.NUMBER), + wvc.config.Property(name="grading_id", data_type=wvc.config.DataType.UUID), + ] + ) + + print("Schema ICL created.") + print(questions.config.get(simple=False)) + +def store_feedback_icl(submission: Submission, exercise: Exercise, feedbacks: List[Feedback]): + client = weaviate.connect_to_local() + try: + create_schema(client) + finally: + logger.info("Storing feedback for submission %d of exercise %d.", submission.id, exercise.id) + + for feedback in feedbacks: + print("doing feedback") + chunk = get_reference(feedback, submission.text) + embedding = embed_text(chunk) + store_feedback(embedding, exercise.id, submission.id, feedback, client) + # save_embedding(embedding, exercise.id) + # store_embedding_index(exercise.id, submission.id, feedback) + client.close() +def store_feedback(embedding, exercise_id, submission_id, feedback, client): + """ + Store feedback in the Weaviate database. + """ + print("storing feedback") + questions = client.collections.get("Feedback") + uuid = questions.data.insert( + properties={ + "exercise_id": exercise_id, + "submission_id": submission_id, + "title": feedback.title, + "description": feedback.description, + "credits": feedback.credits, + "grading_id": feedback.structured_grading_instruction_id + }, + vector=embedding, + ) + print(uuid) + print("Feedback stored successfully.") + return uuid + + +# Here is the usage of the function: +# list_of_indices = query_embedding(query_submission,exercise_id) +#TODO implement a filter for the exercise id. Use the certainity limit to filter the results. +def query_embedding (query,exercise_id,results_limit=1, threshold=0.5): + logger.info("Querying") + client = weaviate.connect_to_local() + feedbacks = client.collections.get("Feedback") + embedded_query = embed_text(query) + # print("embedded query",embedded_query) + + results = feedbacks.query.near_vector( + near_vector=embedded_query, + limit = results_limit, + return_metadata=wvc.query.MetadataQuery(certainty=True) + ) + + print(results) + client.close() + return results + # return "test" + +def get_reference(feedback, submission_text): + if (feedback.index_start is not None) and (feedback.index_end is not None): + return submission_text[feedback.index_start:feedback.index_end ] + return submission_text + +def check_if_embedding_exists(exercise_id): + """ + Check if the embedding index file exists for the given exercise ID. + """ + return True \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py new file mode 100644 index 00000000..a2d5eb7a --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py @@ -0,0 +1,47 @@ +import os +import json + +def load_indices(index_file): + """ Load the indices from the file or return an empty dictionary if the file does not exist. """ + if os.path.exists(index_file): + with open(index_file, 'r', encoding="utf-8") as f: + return json.load(f) + else: + return {} + +def store_embedding_index(exercise_id, submission_id,feedback): + """ Store a new submission and exercise ID with an auto-incrementing index. """ + index_file = f"indices_{exercise_id}.json" + indices = load_indices(index_file) + next_index = len(indices) + print(feedback) + indices[next_index] = { + "exercise_id": exercise_id, + "submission_id": submission_id, + "feedback": feedback.dict() + } + + with open(index_file, 'w', encoding="utf-8") as f: + json.dump(indices, f, indent=4) + + print(f"Stored new entry: Exercise ID {exercise_id}, Submission ID {submission_id} at index {next_index}") + +def retrieve_embedding_index(index,exercise_id): + index_file = f"indices_{exercise_id}.json" + index = str(index) + indices = load_indices(index_file) + + if index in indices: + return indices[index]["exercise_id"], indices[index]["submission_id"] + + return None, None + +def retrieve_feedback(index,exercise_id): + index_file = f"indices_{exercise_id}.json" + index = str(index) + indices = load_indices(index_file) + + if index in indices: + return indices[index]["feedback"] + + return None \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py new file mode 100644 index 00000000..d6e39d56 --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py @@ -0,0 +1,164 @@ +from typing import List +from module_text_llm.approach_config import ApproachConfig +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 langchain_community.chat_models import ChatOllama # type: ignore + +from llm_core.utils.predict_and_parse import predict_and_parse +from module_text_llm.helpers.utils import add_sentence_numbers, get_index_range_from_line_range, format_grading_instructions +from module_text_llm.in_context_learning.prompt_generate_suggestions import AssessmentModel +# from module_text_llm.in_context_learning.agent import TutorAgent +from module_text_llm.in_context_learning.ollama_prompt import system_message_segment, human_message_segment, Segmentation, system_message, human_message + +from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import check_if_embedding_exists +from module_text_llm.in_context_learning.feedback_icl.retrieve_rag_context_icl import retrieve_rag_context_icl + + +async def generate_suggestions(exercise: Exercise, submission: Submission, config:ApproachConfig, debug: bool, is_graded :bool) -> List[Feedback]: + model = config.model.get_model() # type: ignore[attr-defined] + isOllama = isinstance(model, ChatOllama) + + formatted_rag_context = "" + 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, + "rag_context": formatted_rag_context, + "submission": add_sentence_numbers(submission.text), + "exercise_id": exercise.id, + } + + 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+7000, + 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 [] + + if (isOllama): + embeddings_exist = check_if_embedding_exists(exercise.id) + + if embeddings_exist: + segmentation_prompt = get_chat_prompt_with_formatting_instructions( + model=model, + system_message=system_message_segment, + human_message=human_message_segment, + pydantic_object=Segmentation + ) + segmentation_prompt_input = { + "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria), + "submission": submission.text, + "problem_statement": exercise.problem_statement or "No problem statement.", + } + chain = segmentation_prompt | model + segments = chain.invoke( segmentation_prompt_input) + + for segment in segments: + formatted_rag_context += retrieve_rag_context_icl(segment[0],exercise.id) + prompt_input["rag_context"] = formatted_rag_context + else: + prompt_input["rag_context"] = "There are no submission at the moment" + + 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}", + ], + ) + else : + embeddings_exist = check_if_embedding_exists(exercise.id) + + if embeddings_exist: + segmentation_prompt = get_chat_prompt_with_formatting_instructions( + model=model, + system_message=system_message_segment, + human_message=human_message_segment, + pydantic_object=Segmentation + ) + segmentation_prompt_input = { + "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria), + "submission": submission.text, + "problem_statement": exercise.problem_statement or "No problem statement.", + } + chain = segmentation_prompt | model + segments = chain.invoke( segmentation_prompt_input) + # print("segments ",segments) + for segment in segments: + formatted_rag_context += retrieve_rag_context_icl(segment[0],exercise.id) + prompt_input["rag_context"] = formatted_rag_context + else: + prompt_input["rag_context"] = "There are no submission at the moment" + + result = await predict_and_parse( + model=model, + chat_prompt=chat_prompt, + prompt_input=prompt_input, + pydantic_object=AssessmentModel, + use_function_calling=True, + tags=[ + f"exercise-{exercise.id}", + f"submission-{submission.id}", + ], + ) + 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, + is_graded=is_graded, + structured_grading_instruction_id=grading_instruction_id, + meta={} + )) + + return feedbacks diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/ollama_prompt.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/ollama_prompt.py new file mode 100644 index 00000000..9641a380 --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/ollama_prompt.py @@ -0,0 +1,82 @@ +from pydantic import Field, BaseModel +from typing import List, Optional + +class Segment(BaseModel): + segment: str = Field(description="Segment of the text") + title: str = Field(description="Title of the criterion that this text adresses") + + +class Segmentation(BaseModel): + segments: List[Segment] = Field(description="List of segments") + + +system_message_segment = """ +You are an AI asisstnat for text assessment at a prestigious university. +You are tasked with segmenting the following text into parts that address different parts of the problem statement. +You can use the criteria from provided grading instructions to help in regards to the semantic segmentation of the text. +# Problem Statement +{problem_statement} + +# Grading Instructions +{grading_instructions} + +Return a valid json response, which contains a list. +Each element of the list should be a Segment which contains the segment text and the criterion title which it adresses. +Keep in mind that not all criterion might be adressed in the text. +""" + +human_message_segment = """ +# Submission +# {submission} +""" +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. + +# Problem statement +{problem_statement} + +# Example solution +{example_solution} + +# Grading instructions +{grading_instructions} +Max points: {max_points}, bonus points: {bonus_points} +The exercise id is: {exercise_id} +-------------------------- + +You have access to the following tutor feedback on similar texts: +{rag_context} +Respond in json. +""" + +human_message = """Student's submission to grade (with sentence numbers : ): + +{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") \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py new file mode 100644 index 00000000..ebae5378 --- /dev/null +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py @@ -0,0 +1,54 @@ +from pydantic import Field, BaseModel +from typing import List, Optional + +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. + +# Problem statement +{problem_statement} + +# Example solution +{example_solution} + +# Grading instructions +{grading_instructions} +Max points: {max_points}, bonus points: {bonus_points} +The exercise id is: {exercise_id} +-------------------------- +You have access to a tool which gives you example feedback from professional tutors. You can use these to think about what kind of feedback would be appropriate for the student's submission. +You do not need to write the exact same feedback as the examples, but you can use them as inspiration. +When providing line references, they should never overlap. +Your response must include a title, description, credits, line_start and line end for each feedback and the grading isntruction id. Line start and end can be left out if it does not apply to a specific part of the submission but its a general feedback. +""" + +human_message = """Student's submission to grade (with sentence numbers : ): + +{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") \ No newline at end of file diff --git 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14 Apr 2025 15:52:07 +0200 Subject: [PATCH 2/6] Clean up code, improve prompt format, filter by exercise id --- athena/athena/edutelligence | 1 + .../module_text_llm/__main__.py | 1 - .../in_context_learning/__init__.py | 3 +- .../in_context_learning/agent.py | 68 ------------------ .../feedback_icl/generate_embeddings.py | 1 - .../feedback_icl/retrieve_rag_context_icl.py | 58 +++++++-------- .../feedback_icl/store_feedback_icl.py | 72 +++++++------------ .../feedback_icl/store_indices_icl.py | 47 ------------ .../generate_suggestions.py | 6 +- .../prompt_generate_suggestions.py | 8 +-- 10 files changed, 61 insertions(+), 204 deletions(-) create mode 160000 athena/athena/edutelligence delete mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py delete mode 100644 athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py diff --git a/athena/athena/edutelligence b/athena/athena/edutelligence new file mode 160000 index 00000000..30e32d6d --- /dev/null +++ b/athena/athena/edutelligence @@ -0,0 +1 @@ +Subproject commit 30e32d6d6cb5df41b8dd76ff4061f97c5f4461eb diff --git a/athena/modules/text/module_text_llm/module_text_llm/__main__.py b/athena/modules/text/module_text_llm/module_text_llm/__main__.py index 647d8b7f..e705b974 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/__main__.py +++ b/athena/modules/text/module_text_llm/module_text_llm/__main__.py @@ -11,7 +11,6 @@ from module_text_llm.evaluation import get_feedback_statistics, get_llm_statistics from module_text_llm.generate_evaluation import generate_evaluation from module_text_llm.approach_controller import generate_suggestions -from module_text_llm.RandomMessage import write_random_message,query_messages,create_minimal_schema from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import store_feedback_icl @submissions_consumer def receive_submissions(exercise: Exercise, submissions: List[Submission]): diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py index b3e6d6a0..75a81b5f 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/__init__.py @@ -10,4 +10,5 @@ class InContextLearningConfig(ApproachConfig): generate_suggestions_prompt: GenerateSuggestionsPrompt = Field(default=GenerateSuggestionsPrompt()) async def generate_suggestions(self, exercise: Exercise, submission: Submission, config,*, debug: bool, is_graded: bool): - return await generate_suggestions(exercise, submission, config, debug, is_graded) \ No newline at end of file + return await generate_suggestions(exercise, submission, config, debug, is_graded) + \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py deleted file mode 100644 index 359504a3..00000000 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/agent.py +++ /dev/null @@ -1,68 +0,0 @@ -# from langchain_openai import ChatOpenAI -# from langchain_core.prompts import ChatPromptTemplate -# from langchain.agents import AgentExecutor, create_tool_calling_agent -# from pydantic import BaseModel, Field -# from langchain_core.tools import tool -# from module_text_llm.in_context_learning.feedback_icl.retrieve_rag_context_icl import retrieve_rag_context_icl -# from module_text_llm.in_context_learning.prompt_generate_suggestions import FeedbackModel -# from typing import List - -# # @tool -# # def retrieve_rag_context(submission_segment: str ,exercise_id: int) -> str: -# # """ -# # This method takes a segment from a submission and for a given exercise id, -# # returns feedback that has been given for similar texts. - -# # Args: -# # submission_segment: A segment of the submission. -# # exercise_id: The id of the exercise. - -# # Returns: -# # str: A formatted string of feedbacks which reference text similar to the submission_segment. -# # """ -# # return retrieve_rag_context_icl(submission_segment,exercise_id) - -# @tool -# class AssessmentModel(BaseModel): -# """Collection of feedbacks making up an assessment""" - -# feedbacks: List[FeedbackModel] = Field(description="Assessment feedbacks") - -# class AssessmentModelParse(BaseModel): -# """Collection of feedbacks making up an assessment""" - -# feedbacks: List[FeedbackModel] = Field(description="Assessment feedbacks") - -# class TutorAgent: -# def __init__(self,config): -# self.model = config.model.get_model() -# self.outputModel = ChatOpenAI(model="gpt-4o-mini") -# self.approach_config = None -# self.openai_tools = [retrieve_rag_context] -# self.setConfig(config) - - -# def setConfig(self,approach_config): -# self.approach_config = approach_config -# self.prompt = ChatPromptTemplate.from_messages( -# [ -# ("system", self.approach_config.generate_suggestions_prompt.system_message), -# ("human", "{submission}"), -# ("placeholder", "{agent_scratchpad}"), -# ]) - -# self.agent = create_tool_calling_agent(self.model, self.openai_tools , self.prompt) -# self.agent_executor = AgentExecutor(agent=self.agent, tools=self.openai_tools) -# self.config = {"configurable": {"session_id": "test-session"}} - -# def call_agent(self, prompt): -# """Calls the agent with a prompt and returns the response output.""" - -# response = self.agent_executor.invoke( -# input = prompt -# ) - -# res = self.model.with_structured_output(AssessmentModelParse).invoke(f"Format the following output {response}") -# return res - - \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py index 681e8e95..c9ad3ad6 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/generate_embeddings.py @@ -4,4 +4,3 @@ def embed_text(text): embeddings = OpenAIEmbeddings(model="text-embedding-3-large") query_result = embeddings.embed_query(text) return query_result - # return np.array(query_result, dtype=np.float32) only relevant for numpy operations diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py index 35091fca..a673edae 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py @@ -1,7 +1,4 @@ -from athena.logger import logger -from module_text_llm.in_context_learning.feedback_icl.store_indices_icl import retrieve_embedding_index, retrieve_feedback from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import query_embedding -from module_text_llm.in_context_learning.feedback_icl.generate_embeddings import embed_text def retrieve_rag_context_icl(submission_segment: str ,exercise_id: int) -> str: """ @@ -15,40 +12,39 @@ def retrieve_rag_context_icl(submission_segment: str ,exercise_id: int) -> str: Returns: str: A formatted string of feedbacks which reference text similar to the submission_segment. """ - # query_submission= embed_text(submission_segment) rag_context = [] - rresult_objects = query_embedding(submission_segment,exercise_id) - print(rresult_objects) - return " " - # for result in rresult_objects: - # print("result",result) - # print(type(result)) - - # if list_of_indices is not None: - # for index in list_of_indices[0]: - # if index != -1: - # stored_feedback = retrieve_feedback(index,exercise_id) - # print("Stored feedback",stored_feedback) - # rag_context.append({"submission_chunk": submission_segment, "feedback": stored_feedback}) - # formatted_rag_context = format_rag_context(rag_context) - # else: - # formatted_rag_context = "There are no submission at the moment" - # return formatted_rag_context + result_objects = query_embedding(submission_segment,exercise_id) + for result in result_objects.objects: + title = result.properties.get("title") + description = result.properties.get("description") + credits = result.properties.get("credits") + reference = result.properties.get("reference") + rag_context.append(format_context(title,description,credits,reference)) + return format_rag_context(rag_context) -def format_rag_context(rag_context): +def format_context(title,description,credits,reference): formatted_string = "" - + formatted_string += f""" + For the reference text in the submission: {reference}**\n + The tutor provided the following feedback:\n + **Title**: {title}\n + **Description**: {description}\n + **Credits**: {credits}\n + **Reference text**: {reference}**\n + **\n""" + return formatted_string + +def format_rag_context(rag_context): + formatted_string = """ **Tutor provided Feedback from previous submissions of this same exercise. + This are possible examples that could help with the grading of the current submission. However they are not identical + so please be careful when using them. You must carefully decide whether this references are relevant.**\n + **\n""" + formatted_string += "\n" + "-"*40 + "\n" + for context_item in rag_context: - submission_text = context_item["submission_chunk"] - feedback = context_item["feedback"] - if feedback is not None: - formatted_string += "**Tutor provided Feedback from previous submissions of this same exercise:**\n" - feedback["text_reference"] = get_reference(feedback, submission_text) - clean_feedback = {key: value for key, value in feedback.items() if key not in ["id","index_start","index_end","is_graded","meta"]} - - formatted_string += f"{clean_feedback}\n" + formatted_string += f"{context_item}\n" formatted_string += "\n" + "-"*40 + "\n" return formatted_string diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py index bfffccbe..1a72a58e 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py @@ -6,30 +6,24 @@ from module_text_llm.in_context_learning.feedback_icl.generate_embeddings import embed_text import weaviate import weaviate.classes as wvc - +from weaviate.classes.query import Filter def create_schema(client): """ - Create a schema with two classes: Question and Answer. - Each class has properties for storing relevant information. + Creates a schema with the information needed for ICL. """ - - # Define the Question class - questions = client.collections.create( - name="Feedback", - vectorizer_config=wvc.config.Configure.Vectorizer.none(), - properties=[ - wvc.config.Property(name="exercise_id", data_type=wvc.config.DataType.NUMBER), - wvc.config.Property(name="submission_id", data_type=wvc.config.DataType.NUMBER), - wvc.config.Property(name="title", data_type=wvc.config.DataType.TEXT), - wvc.config.Property(name="description", data_type=wvc.config.DataType.TEXT), - wvc.config.Property(name="credits", data_type=wvc.config.DataType.NUMBER), - wvc.config.Property(name="grading_id", data_type=wvc.config.DataType.UUID), - ] - ) - - print("Schema ICL created.") - print(questions.config.get(simple=False)) + feedback_collection = client.collections.create( + name="Feedback", + vectorizer_config=wvc.config.Configure.Vectorizer.none(), + properties=[ + wvc.config.Property(name="exercise_id", data_type=wvc.config.DataType.NUMBER), + wvc.config.Property(name="submission_id", data_type=wvc.config.DataType.NUMBER), + wvc.config.Property(name="title", data_type=wvc.config.DataType.TEXT), + wvc.config.Property(name="description", data_type=wvc.config.DataType.TEXT), + wvc.config.Property(name="credits", data_type=wvc.config.DataType.NUMBER), + wvc.config.Property(name="grading_id", data_type=wvc.config.DataType.NUMBER), + ] +) def store_feedback_icl(submission: Submission, exercise: Exercise, feedbacks: List[Feedback]): client = weaviate.connect_to_local() @@ -37,29 +31,26 @@ def store_feedback_icl(submission: Submission, exercise: Exercise, feedbacks: Li create_schema(client) finally: logger.info("Storing feedback for submission %d of exercise %d.", submission.id, exercise.id) - for feedback in feedbacks: - print("doing feedback") chunk = get_reference(feedback, submission.text) embedding = embed_text(chunk) - store_feedback(embedding, exercise.id, submission.id, feedback, client) - # save_embedding(embedding, exercise.id) - # store_embedding_index(exercise.id, submission.id, feedback) + store_feedback(embedding, exercise.id, submission.id, feedback,chunk, client) client.close() -def store_feedback(embedding, exercise_id, submission_id, feedback, client): + +def store_feedback(embedding, exercise_id, submission_id, feedback,chunk, client): """ Store feedback in the Weaviate database. """ - print("storing feedback") - questions = client.collections.get("Feedback") - uuid = questions.data.insert( + feedback_collection = client.collections.get("Feedback") + uuid = feedback_collection.data.insert( properties={ "exercise_id": exercise_id, "submission_id": submission_id, "title": feedback.title, "description": feedback.description, "credits": feedback.credits, - "grading_id": feedback.structured_grading_instruction_id + "grading_id": feedback.structured_grading_instruction_id, + "reference": chunk }, vector=embedding, ) @@ -67,35 +58,22 @@ def store_feedback(embedding, exercise_id, submission_id, feedback, client): print("Feedback stored successfully.") return uuid - -# Here is the usage of the function: -# list_of_indices = query_embedding(query_submission,exercise_id) -#TODO implement a filter for the exercise id. Use the certainity limit to filter the results. -def query_embedding (query,exercise_id,results_limit=1, threshold=0.5): - logger.info("Querying") +def query_embedding (query,exercise_id,results_limit=1): + logger.info("Querying weaviate database") client = weaviate.connect_to_local() feedbacks = client.collections.get("Feedback") embedded_query = embed_text(query) - # print("embedded query",embedded_query) results = feedbacks.query.near_vector( near_vector=embedded_query, limit = results_limit, + filters=Filter.by_property("exercise_id").equal(exercise_id), return_metadata=wvc.query.MetadataQuery(certainty=True) ) - - print(results) client.close() return results - # return "test" - + def get_reference(feedback, submission_text): if (feedback.index_start is not None) and (feedback.index_end is not None): return submission_text[feedback.index_start:feedback.index_end ] return submission_text - -def check_if_embedding_exists(exercise_id): - """ - Check if the embedding index file exists for the given exercise ID. - """ - return True \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py deleted file mode 100644 index a2d5eb7a..00000000 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_indices_icl.py +++ /dev/null @@ -1,47 +0,0 @@ -import os -import json - -def load_indices(index_file): - """ Load the indices from the file or return an empty dictionary if the file does not exist. """ - if os.path.exists(index_file): - with open(index_file, 'r', encoding="utf-8") as f: - return json.load(f) - else: - return {} - -def store_embedding_index(exercise_id, submission_id,feedback): - """ Store a new submission and exercise ID with an auto-incrementing index. """ - index_file = f"indices_{exercise_id}.json" - indices = load_indices(index_file) - next_index = len(indices) - print(feedback) - indices[next_index] = { - "exercise_id": exercise_id, - "submission_id": submission_id, - "feedback": feedback.dict() - } - - with open(index_file, 'w', encoding="utf-8") as f: - json.dump(indices, f, indent=4) - - print(f"Stored new entry: Exercise ID {exercise_id}, Submission ID {submission_id} at index {next_index}") - -def retrieve_embedding_index(index,exercise_id): - index_file = f"indices_{exercise_id}.json" - index = str(index) - indices = load_indices(index_file) - - if index in indices: - return indices[index]["exercise_id"], indices[index]["submission_id"] - - return None, None - -def retrieve_feedback(index,exercise_id): - index_file = f"indices_{exercise_id}.json" - index = str(index) - indices = load_indices(index_file) - - if index in indices: - return indices[index]["feedback"] - - return None \ No newline at end of file diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py index d6e39d56..bef8811f 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py @@ -16,7 +16,6 @@ # from module_text_llm.in_context_learning.agent import TutorAgent from module_text_llm.in_context_learning.ollama_prompt import system_message_segment, human_message_segment, Segmentation, system_message, human_message -from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import check_if_embedding_exists from module_text_llm.in_context_learning.feedback_icl.retrieve_rag_context_icl import retrieve_rag_context_icl @@ -62,7 +61,7 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi return [] if (isOllama): - embeddings_exist = check_if_embedding_exists(exercise.id) + embeddings_exist = True if embeddings_exist: segmentation_prompt = get_chat_prompt_with_formatting_instructions( @@ -96,7 +95,7 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi ], ) else : - embeddings_exist = check_if_embedding_exists(exercise.id) + embeddings_exist = True if embeddings_exist: segmentation_prompt = get_chat_prompt_with_formatting_instructions( @@ -112,7 +111,6 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi } chain = segmentation_prompt | model segments = chain.invoke( segmentation_prompt_input) - # print("segments ",segments) for segment in segments: formatted_rag_context += retrieve_rag_context_icl(segment[0],exercise.id) prompt_input["rag_context"] = formatted_rag_context diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py index ebae5378..48d317f5 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/prompt_generate_suggestions.py @@ -17,10 +17,10 @@ Max points: {max_points}, bonus points: {bonus_points} The exercise id is: {exercise_id} -------------------------- -You have access to a tool which gives you example feedback from professional tutors. You can use these to think about what kind of feedback would be appropriate for the student's submission. -You do not need to write the exact same feedback as the examples, but you can use them as inspiration. -When providing line references, they should never overlap. -Your response must include a title, description, credits, line_start and line end for each feedback and the grading isntruction id. Line start and end can be left out if it does not apply to a specific part of the submission but its a general feedback. +You have access to the following tutor feedback on similar texts, Keep in mind what the reference text is +and that it may not be relevant to the current submission. You must carefully decide whether this references are relevant: +{rag_context} +Respond in json. """ human_message = """Student's submission to grade (with sentence numbers : ): From 7670363988566b9276f527b727e427ea14ca43e6 Mon Sep 17 00:00:00 2001 From: = Enea_Gore Date: Mon, 21 Apr 2025 17:57:23 +0200 Subject: [PATCH 3/6] linting and code quality --- .../feedback_icl/retrieve_rag_context_icl.py | 6 +- .../feedback_icl/store_feedback_icl.py | 8 +- .../generate_suggestions.py | 95 ++++++------------- 3 files changed, 33 insertions(+), 76 deletions(-) diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py index a673edae..0f1fa89f 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py @@ -44,9 +44,9 @@ def format_rag_context(rag_context): formatted_string += "\n" + "-"*40 + "\n" for context_item in rag_context: - formatted_string += f"{context_item}\n" - formatted_string += "\n" + "-"*40 + "\n" - + formatted_string += f"{context_item}\n" + formatted_string += "\n" + "-"*40 + "\n" + return formatted_string def get_reference(feedback, submission_text): diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py index 1a72a58e..c97af474 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py @@ -33,14 +33,14 @@ def store_feedback_icl(submission: Submission, exercise: Exercise, feedbacks: Li logger.info("Storing feedback for submission %d of exercise %d.", submission.id, exercise.id) for feedback in feedbacks: chunk = get_reference(feedback, submission.text) - embedding = embed_text(chunk) - store_feedback(embedding, exercise.id, submission.id, feedback,chunk, client) + store_feedback(exercise.id, submission.id, feedback,chunk, client) client.close() -def store_feedback(embedding, exercise_id, submission_id, feedback,chunk, client): +def store_feedback(exercise_id, submission_id, feedback, chunk, client): """ Store feedback in the Weaviate database. """ + embedding = embed_text(chunk) feedback_collection = client.collections.get("Feedback") uuid = feedback_collection.data.insert( properties={ @@ -54,8 +54,6 @@ def store_feedback(embedding, exercise_id, submission_id, feedback,chunk, client }, vector=embedding, ) - print(uuid) - print("Feedback stored successfully.") return uuid def query_embedding (query,exercise_id,results_limit=1): diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py index bef8811f..990fa931 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py @@ -21,7 +21,6 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, config:ApproachConfig, debug: bool, is_graded :bool) -> List[Feedback]: model = config.model.get_model() # type: ignore[attr-defined] - isOllama = isinstance(model, ChatOllama) formatted_rag_context = "" prompt_input = { @@ -60,74 +59,34 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi emit_meta("error", f"Input too long {num_tokens_from_prompt(chat_prompt, prompt_input)} > {config.max_input_tokens}") return [] - if (isOllama): - embeddings_exist = True - - if embeddings_exist: - segmentation_prompt = get_chat_prompt_with_formatting_instructions( - model=model, - system_message=system_message_segment, - human_message=human_message_segment, - pydantic_object=Segmentation - ) - segmentation_prompt_input = { - "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria), - "submission": submission.text, - "problem_statement": exercise.problem_statement or "No problem statement.", - } - chain = segmentation_prompt | model - segments = chain.invoke( segmentation_prompt_input) + + segmentation_prompt = get_chat_prompt_with_formatting_instructions( + model=model, + system_message=system_message_segment, + human_message=human_message_segment, + pydantic_object=Segmentation + ) + segmentation_prompt_input = { + "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria), + "submission": submission.text, + "problem_statement": exercise.problem_statement or "No problem statement.", + } + chain = segmentation_prompt | model + segments = chain.invoke( segmentation_prompt_input) + for segment in segments: + formatted_rag_context += retrieve_rag_context_icl(segment[0],exercise.id) + prompt_input["rag_context"] = formatted_rag_context - for segment in segments: - formatted_rag_context += retrieve_rag_context_icl(segment[0],exercise.id) - prompt_input["rag_context"] = formatted_rag_context - else: - prompt_input["rag_context"] = "There are no submission at the moment" - - 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}", - ], - ) - else : - embeddings_exist = True - - if embeddings_exist: - segmentation_prompt = get_chat_prompt_with_formatting_instructions( - model=model, - system_message=system_message_segment, - human_message=human_message_segment, - pydantic_object=Segmentation - ) - segmentation_prompt_input = { - "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria), - "submission": submission.text, - "problem_statement": exercise.problem_statement or "No problem statement.", - } - chain = segmentation_prompt | model - segments = chain.invoke( segmentation_prompt_input) - for segment in segments: - formatted_rag_context += retrieve_rag_context_icl(segment[0],exercise.id) - prompt_input["rag_context"] = formatted_rag_context - else: - prompt_input["rag_context"] = "There are no submission at the moment" - - result = await predict_and_parse( - model=model, - chat_prompt=chat_prompt, - prompt_input=prompt_input, - pydantic_object=AssessmentModel, - use_function_calling=True, - tags=[ - f"exercise-{exercise.id}", - f"submission-{submission.id}", - ], - ) + result = await predict_and_parse( + model=model, + chat_prompt=chat_prompt, + prompt_input=prompt_input, + pydantic_object=AssessmentModel, + use_function_calling=True, + tags=[ + f"exercise-{exercise.id}", + f"submission-{submission.id}", + ],) if debug: emit_meta("generate_suggestions", { "prompt": chat_prompt.format(**prompt_input), From 059d6c80e134ff83aaeeebcaa94428651f4581b0 Mon Sep 17 00:00:00 2001 From: = Enea_Gore Date: Thu, 24 Apr 2025 20:12:29 +0200 Subject: [PATCH 4/6] change request and fix pydantic 2.8 for all llm modules --- athena/athena/edutelligence | 1 - .../modeling/module_modeling_llm/poetry.lock | 196 +++++++++++------ .../module_modeling_llm/pyproject.toml | 2 +- .../module_programming_llm/poetry.lock | 207 ++++++++++++------ .../module_programming_llm/pyproject.toml | 2 +- .../module_text_llm/__main__.py | 5 +- ...context_icl.py => retrieve_rag_context.py} | 6 +- ...tore_feedback_icl.py => store_feedback.py} | 8 +- .../generate_suggestions.py | 17 +- 9 files changed, 295 insertions(+), 149 deletions(-) delete mode 160000 athena/athena/edutelligence rename athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/{retrieve_rag_context_icl.py => retrieve_rag_context.py} (93%) rename athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/{store_feedback_icl.py => store_feedback.py} (89%) diff --git a/athena/athena/edutelligence b/athena/athena/edutelligence deleted file mode 160000 index 30e32d6d..00000000 --- a/athena/athena/edutelligence +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 30e32d6d6cb5df41b8dd76ff4061f97c5f4461eb diff --git a/athena/modules/modeling/module_modeling_llm/poetry.lock b/athena/modules/modeling/module_modeling_llm/poetry.lock index ef71273f..5fc2d964 100644 --- a/athena/modules/modeling/module_modeling_llm/poetry.lock +++ b/athena/modules/modeling/module_modeling_llm/poetry.lock @@ -122,6 +122,17 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + [[package]] name = "anyio" version = "4.7.0" @@ -166,7 +177,7 @@ develop = true [package.dependencies] fastapi = "^0.109.1" gitpython = "^3.1.41" -httpx = "^0.24.1" +httpx = "^0.26.0" psycopg2 = 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a/athena/modules/programming/module_programming_llm/pyproject.toml +++ b/athena/modules/programming/module_programming_llm/pyproject.toml @@ -20,7 +20,7 @@ nltk = "3.9.1" [tool.poetry.group.dev.dependencies] types-requests = "^2.31.0.8" -pydantic = "1.10.17" +pydantic = "2.8.0" prospector = "^1.10.2" [tool.poetry.scripts] diff --git a/athena/modules/text/module_text_llm/module_text_llm/__main__.py b/athena/modules/text/module_text_llm/module_text_llm/__main__.py index e705b974..8fae22ba 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/__main__.py +++ b/athena/modules/text/module_text_llm/module_text_llm/__main__.py @@ -11,7 +11,8 @@ from module_text_llm.evaluation import get_feedback_statistics, get_llm_statistics from module_text_llm.generate_evaluation import generate_evaluation from module_text_llm.approach_controller import generate_suggestions -from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import store_feedback_icl +from module_text_llm.in_context_learning.feedback_icl.store_feedback import store_feedback + @submissions_consumer def receive_submissions(exercise: Exercise, submissions: List[Submission]): logger.info("receive_submissions: Received %d submissions for exercise %d", len(submissions), exercise.id) @@ -27,7 +28,7 @@ def select_submission(exercise: Exercise, submissions: List[Submission]) -> Subm def process_incoming_feedback(exercise: Exercise, submission: Submission, feedbacks: List[Feedback]): logger.info("Will try to create Schema") logger.info("process_feedback: Received %d feedbacks for submission %d of exercise %d.", len(feedbacks), submission.id, exercise.id) - store_feedback_icl(submission, exercise, feedbacks) + store_feedback(submission, exercise, feedbacks) @feedback_provider async def suggest_feedback(exercise: Exercise, submission: Submission, is_graded: bool, module_config: Configuration) -> List[Feedback]: diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context.py similarity index 93% rename from athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py rename to athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context.py index 0f1fa89f..599ff04b 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context_icl.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/retrieve_rag_context.py @@ -1,6 +1,6 @@ -from module_text_llm.in_context_learning.feedback_icl.store_feedback_icl import query_embedding +from module_text_llm.in_context_learning.feedback_icl.store_feedback import query_embedding -def retrieve_rag_context_icl(submission_segment: str ,exercise_id: int) -> str: +def retrieve_rag_context(submission_segment: str ,exercise_id: int) -> str: """ This method takes a segment from a submission and for a given exercise id, returns feedback that has been given for similar texts. @@ -39,7 +39,7 @@ def format_context(title,description,credits,reference): def format_rag_context(rag_context): formatted_string = """ **Tutor provided Feedback from previous submissions of this same exercise. This are possible examples that could help with the grading of the current submission. However they are not identical - so please be careful when using them. You must carefully decide whether this references are relevant.**\n + so please be careful when using them. You must carefully decide whether these references are relevant.**\n **\n""" formatted_string += "\n" + "-"*40 + "\n" diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback.py similarity index 89% rename from athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py rename to athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback.py index c97af474..dc6feefd 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback_icl.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/feedback_icl/store_feedback.py @@ -25,7 +25,7 @@ def create_schema(client): ] ) -def store_feedback_icl(submission: Submission, exercise: Exercise, feedbacks: List[Feedback]): +def store_feedback(submission: Submission, exercise: Exercise, feedbacks: List[Feedback]): client = weaviate.connect_to_local() try: create_schema(client) @@ -33,10 +33,10 @@ def store_feedback_icl(submission: Submission, exercise: Exercise, feedbacks: Li logger.info("Storing feedback for submission %d of exercise %d.", submission.id, exercise.id) for feedback in feedbacks: chunk = get_reference(feedback, submission.text) - store_feedback(exercise.id, submission.id, feedback,chunk, client) + store_single_feedback(exercise.id, submission.id, feedback,chunk, client) client.close() -def store_feedback(exercise_id, submission_id, feedback, chunk, client): +def store_single_feedback(exercise_id, submission_id, feedback, chunk, client): """ Store feedback in the Weaviate database. """ @@ -56,7 +56,7 @@ def store_feedback(exercise_id, submission_id, feedback, chunk, client): ) return uuid -def query_embedding (query,exercise_id,results_limit=1): +def query_embedding(query,exercise_id,results_limit=1): logger.info("Querying weaviate database") client = weaviate.connect_to_local() feedbacks = client.collections.get("Feedback") diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py index 990fa931..0af7d28a 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py @@ -13,10 +13,9 @@ from llm_core.utils.predict_and_parse import predict_and_parse from module_text_llm.helpers.utils import add_sentence_numbers, get_index_range_from_line_range, format_grading_instructions from module_text_llm.in_context_learning.prompt_generate_suggestions import AssessmentModel -# from module_text_llm.in_context_learning.agent import TutorAgent from module_text_llm.in_context_learning.ollama_prompt import system_message_segment, human_message_segment, Segmentation, system_message, human_message -from module_text_llm.in_context_learning.feedback_icl.retrieve_rag_context_icl import retrieve_rag_context_icl +from module_text_llm.in_context_learning.feedback_icl.retrieve_rag_context import retrieve_rag_context async def generate_suggestions(exercise: Exercise, submission: Submission, config:ApproachConfig, debug: bool, is_graded :bool) -> List[Feedback]: @@ -64,19 +63,22 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi model=model, system_message=system_message_segment, human_message=human_message_segment, - pydantic_object=Segmentation - ) + pydantic_object=Segmentation) + segmentation_prompt_input = { "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria), "submission": submission.text, "problem_statement": exercise.problem_statement or "No problem statement.", } + chain = segmentation_prompt | model segments = chain.invoke( segmentation_prompt_input) + for segment in segments: - formatted_rag_context += retrieve_rag_context_icl(segment[0],exercise.id) - prompt_input["rag_context"] = formatted_rag_context - + formatted_rag_context += retrieve_rag_context(segment[0],exercise.id) + + prompt_input["rag_context"] = formatted_rag_context + result = await predict_and_parse( model=model, chat_prompt=chat_prompt, @@ -87,6 +89,7 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi f"exercise-{exercise.id}", f"submission-{submission.id}", ],) + if debug: emit_meta("generate_suggestions", { "prompt": chat_prompt.format(**prompt_input), From 3ed001f89983cf4b0f0ea94548386fe9c0dcd9a0 Mon Sep 17 00:00:00 2001 From: = Enea_Gore Date: Thu, 24 Apr 2025 20:16:25 +0200 Subject: [PATCH 5/6] rename segmentation prompt --- .../in_context_learning/generate_suggestions.py | 4 +--- .../{ollama_prompt.py => segmentation_prompt.py} | 0 2 files changed, 1 insertion(+), 3 deletions(-) rename athena/modules/text/module_text_llm/module_text_llm/in_context_learning/{ollama_prompt.py => segmentation_prompt.py} (100%) diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py index 0af7d28a..6a7e5392 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/generate_suggestions.py @@ -8,13 +8,11 @@ check_prompt_length_and_omit_features_if_necessary, num_tokens_from_prompt, ) -from langchain_community.chat_models import ChatOllama # type: ignore from llm_core.utils.predict_and_parse import predict_and_parse from module_text_llm.helpers.utils import add_sentence_numbers, get_index_range_from_line_range, format_grading_instructions +from module_text_llm.in_context_learning.segmentation_prompt import system_message_segment, human_message_segment, Segmentation from module_text_llm.in_context_learning.prompt_generate_suggestions import AssessmentModel -from module_text_llm.in_context_learning.ollama_prompt import system_message_segment, human_message_segment, Segmentation, system_message, human_message - from module_text_llm.in_context_learning.feedback_icl.retrieve_rag_context import retrieve_rag_context diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/ollama_prompt.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/segmentation_prompt.py similarity index 100% rename from athena/modules/text/module_text_llm/module_text_llm/in_context_learning/ollama_prompt.py rename to athena/modules/text/module_text_llm/module_text_llm/in_context_learning/segmentation_prompt.py From fc7aea28c71a8c427bec7fa5c194906eb46b22a6 Mon Sep 17 00:00:00 2001 From: = Enea_Gore Date: Thu, 24 Apr 2025 20:20:01 +0200 Subject: [PATCH 6/6] simplify segmentation prompt --- .../segmentation_prompt.py | 53 +------------------ 1 file changed, 1 insertion(+), 52 deletions(-) diff --git a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/segmentation_prompt.py b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/segmentation_prompt.py index 9641a380..b824c297 100644 --- a/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/segmentation_prompt.py +++ b/athena/modules/text/module_text_llm/module_text_llm/in_context_learning/segmentation_prompt.py @@ -28,55 +28,4 @@ class Segmentation(BaseModel): human_message_segment = """ # Submission # {submission} -""" -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. - -# Problem statement -{problem_statement} - -# Example solution -{example_solution} - -# Grading instructions -{grading_instructions} -Max points: {max_points}, bonus points: {bonus_points} -The exercise id is: {exercise_id} --------------------------- - -You have access to the following tutor feedback on similar texts: -{rag_context} -Respond in json. -""" - -human_message = """Student's submission to grade (with sentence numbers : ): - -{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") \ No newline at end of file +""" \ No newline at end of file