From 5e74e8b71f1c3d41041331f37d8447a0075fa3f2 Mon Sep 17 00:00:00 2001 From: = Enea_Gore Date: Thu, 16 Jan 2025 02:52:22 +0100 Subject: [PATCH] base of icl rag --- athena/poetry.lock | 774 +++++++++--------- athena/pyproject.toml | 1 + .../module_text_llm/__main__.py | 12 +- .../module_text_llm/module_text_llm/config.py | 3 +- .../module_text_llm/generate_embeddings.py | 7 + .../icl_rag_approach/__init__.py | 13 + .../icl_rag_approach/generate_suggestions.py | 145 ++++ .../prompt_generate_suggestions.py | 56 ++ .../module_text_llm/index_storage.py | 40 + .../module_text_llm/storage_embeddings.py | 55 ++ modules/text/module_text_llm/poetry.lock | 340 ++++---- modules/text/module_text_llm/pyproject.toml | 1 + 12 files changed, 910 insertions(+), 537 deletions(-) create mode 100644 modules/text/module_text_llm/module_text_llm/generate_embeddings.py create mode 100644 modules/text/module_text_llm/module_text_llm/icl_rag_approach/__init__.py create mode 100644 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a/modules/text/module_text_llm/module_text_llm/__main__.py b/modules/text/module_text_llm/module_text_llm/__main__.py index d4e2b05dc..1e5830d7a 100644 --- a/modules/text/module_text_llm/module_text_llm/__main__.py +++ b/modules/text/module_text_llm/module_text_llm/__main__.py @@ -11,7 +11,9 @@ 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.storage_embeddings import save_embedding +from module_text_llm.generate_embeddings import embed_text +from module_text_llm.index_storage import store_embedding_index @submissions_consumer def receive_submissions(exercise: Exercise, submissions: List[Submission]): logger.info("receive_submissions: Received %d submissions for exercise %d", len(submissions), exercise.id) @@ -26,7 +28,13 @@ def select_submission(exercise: Exercise, submissions: List[Submission]) -> Subm @feedback_consumer def process_incoming_feedback(exercise: Exercise, submission: Submission, feedbacks: List[Feedback]): logger.info("process_feedback: Received %d feedbacks for submission %d of exercise %d.", len(feedbacks), submission.id, exercise.id) - + # Saving of the embeddings here. + submission_id = submission.id + exercise_id = exercise.id + embedded_submission = embed_text(submission.text) + store_embedding_index(exercise_id, submission_id) + save_embedding(embedded_submission) + @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", diff --git a/modules/text/module_text_llm/module_text_llm/config.py b/modules/text/module_text_llm/module_text_llm/config.py index c620e62e3..fde21f28f 100644 --- a/modules/text/module_text_llm/module_text_llm/config.py +++ b/modules/text/module_text_llm/module_text_llm/config.py @@ -1,3 +1,4 @@ +from module_text_llm.icl_rag_approach import IclRagApproachConfig from pydantic import BaseModel, Field from typing import Union from athena import config_schema_provider @@ -5,7 +6,7 @@ from module_text_llm.chain_of_thought_approach import ChainOfThoughtConfig from module_text_llm.basic_approach import BasicApproachConfig -ApproachConfigUnion = Union[BasicApproachConfig, ChainOfThoughtConfig] +ApproachConfigUnion = Union[IclRagApproachConfig, BasicApproachConfig, ChainOfThoughtConfig] @config_schema_provider class Configuration(BaseModel): diff --git a/modules/text/module_text_llm/module_text_llm/generate_embeddings.py b/modules/text/module_text_llm/module_text_llm/generate_embeddings.py new file mode 100644 index 000000000..ddcac3cb2 --- /dev/null +++ b/modules/text/module_text_llm/module_text_llm/generate_embeddings.py @@ -0,0 +1,7 @@ +from langchain_openai import OpenAIEmbeddings +import numpy as np +def embed_text(text): + # Load the OpenAI API key from the environment + embeddings = OpenAIEmbeddings(model="text-embedding-3-small") + query_result = embeddings.embed_query(text) + return np.array(query_result, dtype=np.float32) \ No newline at end of file diff --git a/modules/text/module_text_llm/module_text_llm/icl_rag_approach/__init__.py b/modules/text/module_text_llm/module_text_llm/icl_rag_approach/__init__.py new file mode 100644 index 000000000..3571e223c --- /dev/null +++ b/modules/text/module_text_llm/module_text_llm/icl_rag_approach/__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.icl_rag_approach.generate_suggestions import generate_suggestions +from module_text_llm.icl_rag_approach.prompt_generate_suggestions import GenerateSuggestionsPrompt + +class IclRagApproachConfig(ApproachConfig): + type: Literal['icl_rag_approach'] = 'icl_rag_approach' + generate_suggestions_prompt: GenerateSuggestionsPrompt = Field(default=GenerateSuggestionsPrompt()) + + async def generate_suggestions(self, exercise: Exercise, submission: Submission, config, debug: bool): + return await generate_suggestions(exercise, submission, config, debug) \ No newline at end of file diff --git a/modules/text/module_text_llm/module_text_llm/icl_rag_approach/generate_suggestions.py b/modules/text/module_text_llm/module_text_llm/icl_rag_approach/generate_suggestions.py new file mode 100644 index 000000000..314bf82dc --- /dev/null +++ b/modules/text/module_text_llm/module_text_llm/icl_rag_approach/generate_suggestions.py @@ -0,0 +1,145 @@ +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 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.icl_rag_approach.prompt_generate_suggestions import AssessmentModel +from module_text_llm.index_storage import retrieve_embedding_index +from module_text_llm.storage_embeddings import query_embedding +from module_text_llm.generate_embeddings import embed_text +from athena.text import get_stored_feedback,get_stored_submissions + +def format_rag_context(rag_context): + formatted_string = "" + + for context_item in rag_context: + submission_text = context_item["submission"].text + feedback_list = context_item["feedback"] + + # Format submission text + formatted_string += f"**Submission:**\n{submission_text}\n\n" + + # Format feedback list + formatted_string += "**Feedback:**\n" + for idx, feedback in enumerate(feedback_list, start=1): + formatted_string += f"{idx}. {feedback}\n" + + # Add a separator between submissions + formatted_string += "\n" + "-"*40 + "\n" + + return formatted_string +async def generate_suggestions(exercise: Exercise, submission: Submission, config:ApproachConfig, debug: bool) -> List[Feedback]: + model = config.model.get_model() # type: ignore[attr-defined] + + # We can now, retrieve the embeddings. And feed them to the prompt. + query_submission= embed_text(submission.text) + + rag_context = [] + + list_of_indices = query_embedding(query_submission) # a list of indicies in the index storage + if list_of_indices is not None: + logger.info(f"List of indices: {list_of_indices}") + for index in list_of_indices[0]: + if index != -1: + exercise_id, submission_id = retrieve_embedding_index(list_of_indices) # retrieve the embeddings from the index storage + stored_feedback = list(get_stored_feedback(exercise_id, submission_id)) + stored_submission = list(get_stored_submissions(exercise_id,only_ids=[submission_id]))[0] + # Assuming `stored_feedback` is a list and `stored_submission` has a `.text` attribute + logger.info("Stored feedback:") + for feedback_item in stored_feedback: + logger.info(f"- {feedback_item}") # Each feedback is logged on a new line + + logger.info("Stored submission:") + logger.info(f"{stored_submission.text}") # The submission text is logged + rag_context.append({"submission": stored_submission.text, "feedback": stored_feedback}) + + formatted_rag_context = format_rag_context(rag_context) + logger.info("Formatted RAG context %s: {formatted_rag_context}") + else: + format_rag_context = "There are no submission at the moment" + 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) + } + + 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 diff --git a/modules/text/module_text_llm/module_text_llm/icl_rag_approach/prompt_generate_suggestions.py b/modules/text/module_text_llm/module_text_llm/icl_rag_approach/prompt_generate_suggestions.py new file mode 100644 index 000000000..d492e7d7b --- /dev/null +++ b/modules/text/module_text_llm/module_text_llm/icl_rag_approach/prompt_generate_suggestions.py @@ -0,0 +1,56 @@ +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. + +# 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} + +# Tutor selected feedback to guide you +{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/modules/text/module_text_llm/module_text_llm/index_storage.py b/modules/text/module_text_llm/module_text_llm/index_storage.py new file mode 100644 index 000000000..11502a156 --- /dev/null +++ b/modules/text/module_text_llm/module_text_llm/index_storage.py @@ -0,0 +1,40 @@ +import json +import os + +INDEX_FILE = "indices.json" + +def load_indices(): + """ 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') as f: + return json.load(f) + else: + return {} + +def store_embedding_index(exercise_id, submission_id): + """ Store a new submission and exercise ID with an auto-incrementing index. """ + indices = load_indices() # Load existing indices from the file + next_index = len(indices) # Calculate the next available index + + # Add the new entry to the dictionary with next_index as the key + indices[next_index] = { + "exercise_id": exercise_id, + "submission_id": submission_id + } + + # Write the updated dictionary back to the file + with open(INDEX_FILE, 'w') 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): + index = str(index) # Convert index to string for dictionary lookup + """ Retrieve the exercise_id and submission_id by index. """ + indices = load_indices() + + # Find the entry by index (direct dictionary lookup) + if index in indices: + return indices[index]["exercise_id"], indices[index]["submission_id"] + + return None, None # Return None if index is not found \ No newline at end of file diff --git a/modules/text/module_text_llm/module_text_llm/storage_embeddings.py b/modules/text/module_text_llm/module_text_llm/storage_embeddings.py new file mode 100644 index 000000000..c95cae871 --- /dev/null +++ b/modules/text/module_text_llm/module_text_llm/storage_embeddings.py @@ -0,0 +1,55 @@ +import faiss +import numpy as np +import os +def save_embedding(embedding): + # Check if the index file already exists + if os.path.exists("embeddings.index"): + # If it exists, load the existing index + index = faiss.read_index("embeddings.index") + else: + # If not, create a new index + index = faiss.IndexFlatL2(embedding.shape[0]) # Use the correct dimension (embedding size) + + # Add the embedding to the index + index.add(np.array([embedding], dtype=np.float32)) # Wrap embedding in array if needed + + # Save the updated index to the disk + faiss.write_index(index, "embeddings.index") + +def query_embedding(query_embedding): + # Check if the index file exists + index_file = 'embeddings.index' + + if not os.path.exists(index_file): + print(f"Error: The index file '{index_file}' does not exist.") + return None + + try: + # Read the FAISS index + index = faiss.read_index(index_file) + except Exception as e: + print(f"Error while reading the FAISS index: {e}") + return None + + # Ensure the query_embedding is in the correct shape + try: + query_embedding = np.array([query_embedding], dtype=np.float32) # Convert to np array + if query_embedding.ndim != 2 or query_embedding.shape[1] != index.d: # Check the dimension compatibility + print(f"Error: Query embedding shape does not match the index dimensions.") + return None + except Exception as e: + print(f"Error with embedding format: {e}") + return None + + # Set the number of nearest neighbors to retrieve + k = 3 # Number of nearest neighbors + + try: + # Search the index for nearest neighbors + distances, indices = index.search(query_embedding, k) + print(f"Indices of similar texts: {indices}") + print(f"Distances: {distances}") + return indices + except Exception as e: + print(f"Error during FAISS search: {e}") + return None diff --git a/modules/text/module_text_llm/poetry.lock b/modules/text/module_text_llm/poetry.lock index 9f12b92d3..06078f3a2 100644 --- a/modules/text/module_text_llm/poetry.lock +++ b/modules/text/module_text_llm/poetry.lock @@ -124,13 +124,13 @@ frozenlist = ">=1.1.0" [[package]] name = "anyio" -version = "4.7.0" +version = "4.8.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" files = [ - {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"}, 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