Refactor into Dev package #2
Conversation
WalkthroughThis change introduces a new, modular, and well-documented implementation of a Universal Study Tutor. It adds a cleaned Python package under Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant CLI_Tool as CLI Tool (e.g., build_index, summarize)
participant Config as Config Loader
participant VectorStore as Qdrant/Chroma
participant LLM as LLM (OpenAI/Mistral)
participant Anki as Anki Deck Generator
User->>CLI_Tool: Run command (e.g., build_index)
CLI_Tool->>Config: Load config.yaml
CLI_Tool->>VectorStore: Initialize vector store
CLI_Tool->>CLI_Tool: Ingest PDFs / Extract chunks
CLI_Tool->>LLM: Summarize or process chunks (as needed)
CLI_Tool->>VectorStore: Store/retrieve vectors or summaries
CLI_Tool->>Anki: (If flashcards) Generate deck from summaries
CLI_Tool->>User: Output result (index, summary, flashcards, etc.)
Poem
✨ Finishing Touches
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Actionable comments posted: 9
🔭 Outside diff range comments (2)
Dev/pyproject.toml (1)
8-24: Pin dependencies to specific versions for reproducible, stable builds.Using wildcard (
*) specifiers can introduce breaking changes, unreproducible environments, and security risks. Please pin each package to an explicit version:
- Known latest stable (as of July 2025):
• llama-index-core = “0.12.45”
• chromadb = “1.0.15”
• qdrant-client = “1.14.2”- Other dependencies (llama-index-llms-openai, tiktoken, tenacity, genanki, tqdm, pyyaml, pytest, ruff, black): verify their current latest stable versions on PyPI and pin accordingly.
Apply something like:
[tool.poetry.dependencies] python = "^3.12" -llama-index-core = "*" -llama-index-llms-openai = "*" -chromadb = "*" -tiktoken = "*" -tenacity = "*" -qdrant-client = "*" -genanki = "*" -tqdm = "*" -pyyaml = "*" +llama-index-core = "0.12.45" +llama-index-llms-openai = "<latest version>" +chromadb = "1.0.15" +tiktoken = "<latest version>" +tenacity = "<latest version>" +qdrant-client = "1.14.2" +genanki = "<latest version>" +tqdm = "<latest version>" +pyyaml = "<latest version>" [tool.poetry.group.dev.dependencies] -pytest = "*" -ruff = "*" -black = "*" +pytest = "<latest version>" +ruff = "<latest version>" +black = "<latest version>"After pinning, run your test suite and linters to confirm compatibility.
Dev/requirements.txt (1)
1-10: Consider adding version pinning for better dependency management.The requirements file lacks version specifications, which could lead to compatibility issues when dependencies are updated. Consider pinning versions to ensure reproducible builds.
Example approach:
-llama-index-core -llama-index-llms-openai -chromadb +llama-index-core>=0.10.0,<1.0.0 +llama-index-llms-openai>=0.1.0,<1.0.0 +chromadb>=0.4.0,<1.0.0Alternatively, consider using a lock file with Poetry (
poetry.lock) for precise dependency management.
🧹 Nitpick comments (12)
messy_start/README.md (2)
53-53: Fix grammatical error in architecture description.The preposition should be "of" instead of "for" in this context.
-4. **Chat/Q&A** – retrieves relevant chunks for user questions. +4. **Chat/Q&A** – retrieves relevant chunks of user questions.
16-16: Consider using a requirements file for dependencies.The current setup instructions list dependencies in the README, which may become outdated. Consider creating a requirements.txt file to maintain consistency with the new Dev/ package structure.
Dev/tests/test_imports.py (2)
13-13: Fix PEP 8 formatting issue.Add a blank line before the function definition to comply with PEP 8 style guidelines.
] + def test_imports():
13-15: Consider improving test robustness and error reporting.The current test implementation could benefit from better error handling and more descriptive failure messages.
def test_imports(): - for m in modules: - importlib.import_module(m) + for module_name in modules: + try: + importlib.import_module(module_name) + except ImportError as e: + pytest.fail(f"Failed to import {module_name}: {e}")Also consider adding a docstring to document the test purpose:
def test_imports(): + """Test that all core study_tools modules can be imported successfully."""Dev/src/study_tools/ingest.py (1)
8-13: Consider adding error handling and removing unnecessary type conversion.The implementation is clean and uses good practices like lazy imports. However, consider these improvements:
- Add error handling for when
docs_dirdoesn't exist- Remove the unnecessary
str()conversion -SimpleDirectoryReadercan acceptPathobjects directlydef main(): from llama_index.core import SimpleDirectoryReader cfg = load_config() docs_dir = Path(cfg["paths"]["docs_dir"]) - docs = SimpleDirectoryReader(str(docs_dir)).load_data() + if not docs_dir.exists(): + print(f"Documents directory {docs_dir} does not exist") + return + docs = SimpleDirectoryReader(docs_dir).load_data() print(f"Loaded {len(docs)} docs")Dev/docs/MIGRATE_LARGE_FILES.md (1)
11-11: Fix missing comma after conjunctive adverb.Add a comma after "Otherwise" for proper grammar.
-Otherwise keep the files locally and back them up to Google Drive or GCS as needed. +Otherwise, keep the files locally and back them up to Google Drive or GCS as needed.Dev/src/study_tools/reset.py (2)
17-21: Consider using configuration for file paths and adding error handling.The hardcoded file paths could be moved to configuration for better maintainability, and error handling should be added for deletion operations.
- for f in ("summary.md", "summary.pdf", "study.apkg"): - fp = Path(f) - if fp.exists(): - fp.unlink() - print(f"Deleted {fp}") + # Consider adding output file paths to config instead of hardcoding + for f in ("summary.md", "summary.pdf", "study.apkg"): + fp = Path(f) + if fp.exists(): + try: + fp.unlink() + print(f"Deleted {fp}") + except OSError as e: + print(f"Failed to delete {fp}: {e}")
9-22: Consider adding a confirmation prompt for safety.Since this script permanently deletes files and directories, consider adding a confirmation prompt to prevent accidental data loss.
def main(): + response = input("This will permanently delete generated data. Continue? (y/N): ") + if response.lower() != 'y': + print("Operation cancelled.") + return + cfg = load_config() paths = cfg["paths"]Dev/src/study_tools/cli_chat.py (1)
4-4: Remove unused import.The
pathlib.Pathimport is not used anywhere in the code and should be removed.-from pathlib import PathDev/src/study_tools/flashcards.py (1)
4-4: Remove unused import.The
pathlib.Pathimport is not used anywhere in the code and should be removed.-from pathlib import PathDev/docs/TODO.md (1)
5-5: Use the hyphenated form "hard-coded".This is the standard spelling for this compound adjective.
-- Remove hard coded paths; read from `config.yaml`. +- Remove hard-coded paths; read from `config.yaml`.Dev/src/study_tools/build_index.py (1)
28-64: Consider refactoring to reduce function complexity.The main function has 18 local variables (exceeding the recommended 15). Consider extracting helper functions for better modularity.
def collect_pdf_documents(docs_dir: Path, chunk_config: dict) -> list: """Collect and extract pages from all PDFs in the directory.""" docs = [] for pdf in docs_dir.rglob("*.pdf"): docs.extend( extract_pages( pdf, chunk_config["pages_per_group"], chunk_config["page_overlap"], ) ) return docs def create_vector_index(docs: list, chunk_config: dict, chroma_dir: Path): """Create and persist vector index from documents.""" splitter = SentenceSplitter( chunk_size=chunk_config["chunk_size"], chunk_overlap=chunk_config["chunk_overlap"], ) nodes = splitter.get_nodes_from_documents(docs) client = QdrantClient(path=str(chroma_dir)) store = QdrantVectorStore(client, collection_name="study") storage = StorageContext.from_defaults(vector_store=store) VectorStoreIndex(nodes, storage_context=storage) storage.persist(persist_dir=str(chroma_dir)) def main(): cfg = load_config() paths = cfg["paths"] docs_dir = Path(paths["docs_dir"]) chroma_dir = Path(paths["chroma_dir"]) if chroma_dir.exists(): shutil.rmtree(chroma_dir) docs = collect_pdf_documents(docs_dir, cfg["chunking"]) create_vector_index(docs, cfg["chunking"], chroma_dir)
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📒 Files selected for processing (23)
.gitignore(1 hunks)Dev/CONTRIBUTING.md(1 hunks)Dev/README.md(1 hunks)Dev/agents.yaml(1 hunks)Dev/config.yaml(1 hunks)Dev/docs/MIGRATE_LARGE_FILES.md(1 hunks)Dev/docs/TODO.md(1 hunks)Dev/docs/changelog.md(1 hunks)Dev/docs/overview.md(1 hunks)Dev/pyproject.toml(1 hunks)Dev/requirements.txt(1 hunks)Dev/src/study_tools/__init__.py(1 hunks)Dev/src/study_tools/build_index.py(1 hunks)Dev/src/study_tools/cli_chat.py(1 hunks)Dev/src/study_tools/flashcards.py(1 hunks)Dev/src/study_tools/ingest.py(1 hunks)Dev/src/study_tools/reset.py(1 hunks)Dev/src/study_tools/summarize.py(1 hunks)Dev/src/study_tools/utils.py(1 hunks)Dev/tests/test_imports.py(1 hunks)README.md(1 hunks)messy_start/README.md(1 hunks)messy_start/schema/learning_unit.schema.json(1 hunks)
🧰 Additional context used
🧬 Code Graph Analysis (3)
Dev/src/study_tools/ingest.py (1)
Dev/src/study_tools/utils.py (1)
load_config(8-14)
Dev/src/study_tools/build_index.py (6)
Dev/src/study_tools/utils.py (1)
load_config(8-14)Dev/src/study_tools/flashcards.py (1)
main(11-35)Dev/src/study_tools/cli_chat.py (1)
main(11-39)Dev/src/study_tools/ingest.py (1)
main(8-13)Dev/src/study_tools/reset.py (1)
main(9-21)Dev/src/study_tools/summarize.py (1)
main(46-73)
Dev/src/study_tools/summarize.py (4)
Dev/src/study_tools/utils.py (1)
load_config(8-14)Dev/src/study_tools/cli_chat.py (1)
main(11-39)Dev/src/study_tools/ingest.py (1)
main(8-13)Dev/src/study_tools/build_index.py (1)
main(28-63)
🪛 LanguageTool
Dev/docs/MIGRATE_LARGE_FILES.md
[uncategorized] ~10-~10: A comma may be missing after the conjunctive/linking adverb ‘Otherwise’.
Context: ...ash git lfs migrate import '*.pdf' ``` Otherwise keep the files locally and back them up...
(SENT_START_CONJUNCTIVE_LINKING_ADVERB_COMMA)
Dev/docs/overview.md
[formatting] ~6-~6: It looks like you are using the letter ‘O’ instead of the number ‘0’. Did you mean “40”?
Context: ...es text chunks into categories. - GPT‑4o/4.1 performs heavy summarisation and ...
(ZERO_O)
Dev/README.md
[formatting] ~8-~8: It looks like you are using the letter ‘O’ instead of the number ‘0’. Did you mean “40”?
Context: ...tion using local Mistral and OpenAI GPT‑4o - CLI tools for building the index, cha...
(ZERO_O)
Dev/docs/TODO.md
[uncategorized] ~5-~5: This word is normally spelled with a hyphen.
Context: ... loader (utils.load_config). - Remove hard coded paths; read from config.yaml. - Store...
(HARD_CODE_COMPOUND)
messy_start/README.md
[uncategorized] ~53-~53: The preposition ‘of’ seems more likely in this position.
Context: ...Chat/Q&A* – retrieves relevant chunks for user questions. 5. *Flashcard Builder...
(AI_HYDRA_LEO_REPLACE_FOR_OF)
🪛 Flake8 (7.2.0)
Dev/tests/test_imports.py
[error] 13-13: expected 2 blank lines, found 1
(E302)
Dev/src/study_tools/cli_chat.py
[error] 4-4: 'pathlib.Path' imported but unused
(F401)
Dev/src/study_tools/flashcards.py
[error] 4-4: 'pathlib.Path' imported but unused
(F401)
Dev/src/study_tools/build_index.py
[error] 29-29: 'llama_index.core.Document' imported but unused
(F401)
🪛 Pylint (3.3.7)
Dev/src/study_tools/cli_chat.py
[refactor] 11-11: Too many local variables (16/15)
(R0914)
Dev/src/study_tools/flashcards.py
[refactor] 11-11: Too many local variables (19/15)
(R0914)
Dev/src/study_tools/build_index.py
[refactor] 28-28: Too many local variables (18/15)
(R0914)
Dev/src/study_tools/summarize.py
[refactor] 46-46: Too many local variables (19/15)
(R0914)
🪛 Ruff (0.11.9)
Dev/src/study_tools/cli_chat.py
4-4: pathlib.Path imported but unused
Remove unused import: pathlib.Path
(F401)
Dev/src/study_tools/flashcards.py
4-4: pathlib.Path imported but unused
Remove unused import: pathlib.Path
(F401)
Dev/src/study_tools/build_index.py
29-29: llama_index.core.Document imported but unused
Remove unused import: llama_index.core.Document
(F401)
🔇 Additional comments (15)
.gitignore (1)
1-19: Well-structured .gitignore file for Python project.The gitignore entries are comprehensive and appropriate for a Python study tools project, covering all essential exclusions including Python artifacts, virtual environments, data directories, and IDE files.
Dev/config.yaml (1)
1-24: Well-structured configuration with good separation of concerns.The YAML configuration appropriately separates different aspects of the system (paths, chunking, models, limits) with reasonable default values. The rate limiting and context window specifications demonstrate good planning for production usage.
messy_start/schema/learning_unit.schema.json (1)
1-21: Comprehensive JSON schema with clear field definitions.The schema properly defines the LearningUnit structure with appropriate required fields, clear type constraints, and helpful descriptions. The status field semantic is well-documented.
Dev/src/study_tools/utils.py (1)
1-15: Excellent implementation with good practices.The configuration loading utility demonstrates several best practices:
- Uses
pathlib.Pathfor cross-platform compatibility- Implements caching to avoid redundant file reads
- Uses
yaml.safe_loadfor security- Lazy imports the yaml module
- Robust relative path calculation
The caching logic correctly handles different paths and initial loading.
messy_start/README.md (1)
1-57: Well-structured documentation for the legacy system.The README provides comprehensive documentation of the experimental study bot with clear setup instructions and architecture overview. This serves as good historical documentation for the legacy prototype.
Dev/docs/changelog.md (1)
1-8: Clean and informative changelog entry.The changelog effectively documents the major architectural changes in the refactor, including the new package structure, configuration changes, and documentation migration.
Dev/CONTRIBUTING.md (1)
1-8: Comprehensive contributing guidelines.The guidelines cover all essential aspects of the development workflow, including dependency management, code structure, testing, quality tools, and documentation requirements.
Dev/tests/test_imports.py (1)
3-11: Clean module list definition.The module list is well-organized and includes all the core study_tools modules mentioned in the AI summary.
Dev/src/study_tools/ingest.py (1)
1-18: LGTM! Good use of lazy imports and clean structure.The module follows good practices with lazy imports for performance optimization and clean separation of concerns.
Dev/src/study_tools/__init__.py (1)
1-11: LGTM! Clean package initialization with proper API definition.The
__all__list properly defines the public interface of the package, and the implementation is clean and concise.README.md (1)
1-8: LGTM! Clean refactor approach with clear direction.The simplified README effectively serves as an index to the restructured codebase, clearly distinguishing between the original prototype and the new implementation.
Dev/docs/MIGRATE_LARGE_FILES.md (1)
1-11: LGTM! Clear and practical documentation.The documentation provides clear guidance on handling large PDF files with practical alternatives.
Dev/docs/overview.md (1)
1-13: Well-structured overview documentation.The documentation clearly explains the Hybrid-Edge architecture and system components. The LanguageTool warning about "GPT-4o" is a false positive - this is the correct model name from OpenAI.
Dev/README.md (1)
1-23: Clear and well-organized README documentation.The README provides excellent documentation with clear feature descriptions and quickstart instructions. The LanguageTool warning about "GPT-4o" is a false positive - this is the correct OpenAI model name.
Dev/agents.yaml (1)
1-14: Well-structured agent configuration.The YAML configuration clearly defines the system agents and their roles, providing good documentation for the modular architecture. The agent definitions align well with the implemented modules.
| for key in ("chroma_dir", "cache_dir"): | ||
| p = Path(paths[key]) | ||
| if p.exists(): | ||
| shutil.rmtree(p) | ||
| print(f"Deleted {p}") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for directory deletion.
Directory deletion operations should include error handling to gracefully handle permission issues or other filesystem errors.
for key in ("chroma_dir", "cache_dir"):
p = Path(paths[key])
if p.exists():
- shutil.rmtree(p)
- print(f"Deleted {p}")
+ try:
+ shutil.rmtree(p)
+ print(f"Deleted {p}")
+ except OSError as e:
+ print(f"Failed to delete {p}: {e}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| for key in ("chroma_dir", "cache_dir"): | |
| p = Path(paths[key]) | |
| if p.exists(): | |
| shutil.rmtree(p) | |
| print(f"Deleted {p}") | |
| for key in ("chroma_dir", "cache_dir"): | |
| p = Path(paths[key]) | |
| if p.exists(): | |
| try: | |
| shutil.rmtree(p) | |
| print(f"Deleted {p}") | |
| except OSError as e: | |
| print(f"Failed to delete {p}: {e}") |
🤖 Prompt for AI Agents
In Dev/src/study_tools/reset.py around lines 12 to 16, the code deletes
directories without error handling, which can cause the program to crash on
permission or filesystem errors. Wrap the shutil.rmtree call in a try-except
block to catch exceptions like PermissionError or OSError, and handle them
gracefully by logging or printing an error message instead of letting the
exception propagate.
| def main(): | ||
| from llama_index.core import StorageContext, load_index_from_storage | ||
| from llama_index.llms.openai import OpenAI | ||
| from llama_index.vector_stores.qdrant import QdrantVectorStore | ||
| from qdrant_client import QdrantClient | ||
|
|
||
| cfg = load_config() | ||
| llm = OpenAI(model=cfg["models"]["summarizer"]) | ||
| chroma_path = cfg["paths"]["chroma_dir"] | ||
| client = QdrantClient(path=chroma_path) | ||
| store = QdrantVectorStore(client, collection_name="study") | ||
| storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store) | ||
| index = load_index_from_storage(storage) | ||
| engine = index.as_chat_engine(chat_mode="condense_question", llm=llm, verbose=True) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for configuration and API operations.
The code lacks error handling for configuration loading, vector store initialization, and index loading. These operations could fail and cause the application to crash ungracefully.
Consider adding try-catch blocks around critical operations:
def main():
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.llms.openai import OpenAI
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
+ try:
cfg = load_config()
llm = OpenAI(model=cfg["models"]["summarizer"])
chroma_path = cfg["paths"]["chroma_dir"]
client = QdrantClient(path=chroma_path)
store = QdrantVectorStore(client, collection_name="study")
storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store)
index = load_index_from_storage(storage)
engine = index.as_chat_engine(chat_mode="condense_question", llm=llm, verbose=True)
+ except Exception as e:
+ print(f"Error initializing chat engine: {e}")
+ return📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def main(): | |
| from llama_index.core import StorageContext, load_index_from_storage | |
| from llama_index.llms.openai import OpenAI | |
| from llama_index.vector_stores.qdrant import QdrantVectorStore | |
| from qdrant_client import QdrantClient | |
| cfg = load_config() | |
| llm = OpenAI(model=cfg["models"]["summarizer"]) | |
| chroma_path = cfg["paths"]["chroma_dir"] | |
| client = QdrantClient(path=chroma_path) | |
| store = QdrantVectorStore(client, collection_name="study") | |
| storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store) | |
| index = load_index_from_storage(storage) | |
| engine = index.as_chat_engine(chat_mode="condense_question", llm=llm, verbose=True) | |
| def main(): | |
| from llama_index.core import StorageContext, load_index_from_storage | |
| from llama_index.llms.openai import OpenAI | |
| from llama_index.vector_stores.qdrant import QdrantVectorStore | |
| from qdrant_client import QdrantClient | |
| try: | |
| cfg = load_config() | |
| llm = OpenAI(model=cfg["models"]["summarizer"]) | |
| chroma_path = cfg["paths"]["chroma_dir"] | |
| client = QdrantClient(path=chroma_path) | |
| store = QdrantVectorStore(client, collection_name="study") | |
| storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store) | |
| index = load_index_from_storage(storage) | |
| engine = index.as_chat_engine(chat_mode="condense_question", llm=llm, verbose=True) | |
| except Exception as e: | |
| print(f"Error initializing chat engine: {e}") | |
| return |
🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 11-11: Too many local variables (16/15)
(R0914)
🤖 Prompt for AI Agents
In Dev/src/study_tools/cli_chat.py around lines 11 to 24, add try-except blocks
around the configuration loading, QdrantClient initialization, QdrantVectorStore
creation, StorageContext setup, and index loading to catch potential exceptions.
Log or print meaningful error messages and handle failures gracefully to prevent
the application from crashing unexpectedly.
| for line in qa.splitlines(): | ||
| if "?" in line: | ||
| q, a = line.split("?", 1) | ||
| note = genanki.Note(model=genanki.BASIC_MODEL, fields=[q.strip()+"?", a.strip()]) | ||
| deck.add_note(note) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve Q&A parsing logic.
The current parsing logic is fragile - it splits on any "?" character, which could fail with complex text containing multiple question marks or questions without clear answer separation.
Consider a more robust parsing approach:
- for line in qa.splitlines():
- if "?" in line:
- q, a = line.split("?", 1)
- note = genanki.Note(model=genanki.BASIC_MODEL, fields=[q.strip()+"?", a.strip()])
- deck.add_note(note)
+ # Look for Q: A: pattern or numbered questions
+ import re
+ qa_pattern = r'Q:\s*(.+?)\s*A:\s*(.+?)(?=Q:|$)'
+ matches = re.findall(qa_pattern, qa, re.DOTALL)
+ for question, answer in matches:
+ note = genanki.Note(model=genanki.BASIC_MODEL, fields=[question.strip(), answer.strip()])
+ deck.add_note(note)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| for line in qa.splitlines(): | |
| if "?" in line: | |
| q, a = line.split("?", 1) | |
| note = genanki.Note(model=genanki.BASIC_MODEL, fields=[q.strip()+"?", a.strip()]) | |
| deck.add_note(note) | |
| # Look for Q: A: pattern or numbered questions | |
| import re | |
| qa_pattern = r'Q:\s*(.+?)\s*A:\s*(.+?)(?=Q:|$)' | |
| matches = re.findall(qa_pattern, qa, re.DOTALL) | |
| for question, answer in matches: | |
| note = genanki.Note(model=genanki.BASIC_MODEL, fields=[question.strip(), answer.strip()]) | |
| deck.add_note(note) |
🤖 Prompt for AI Agents
In Dev/src/study_tools/flashcards.py around lines 28 to 32, the current logic
splits each line on the first "?" character to separate question and answer,
which is fragile for complex text with multiple question marks. To fix this,
implement a more robust parsing method such as using a regex pattern to identify
the question part ending with a question mark followed by the answer, or define
a clear delimiter between question and answer. Update the code to extract the
question and answer reliably before creating the genanki.Note and adding it to
the deck.
| def main(): | ||
| import genanki | ||
| from llama_index.core import StorageContext, load_index_from_storage | ||
| from llama_index.vector_stores.qdrant import QdrantVectorStore | ||
| from qdrant_client import QdrantClient | ||
|
|
||
| cfg = load_config() | ||
| chroma_path = cfg["paths"]["chroma_dir"] | ||
| client = QdrantClient(path=chroma_path) | ||
| store = QdrantVectorStore(client, collection_name="study") | ||
| storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store) | ||
| index = load_index_from_storage(storage) | ||
| retriever = index.as_retriever(similarity_top_k=50) | ||
|
|
||
| deck = genanki.Deck(uuid.uuid4().int >> 64, "Study-Bot Deck") | ||
| for node in index.docstore.docs.values(): | ||
| qa = retriever.query(f"Turn this into Q&A flashcards:\n\n{node.text}").response | ||
| for line in qa.splitlines(): | ||
| if "?" in line: | ||
| q, a = line.split("?", 1) | ||
| note = genanki.Note(model=genanki.BASIC_MODEL, fields=[q.strip()+"?", a.strip()]) | ||
| deck.add_note(note) | ||
|
|
||
| genanki.Package(deck).write_to_file("study.apkg") | ||
| print("study.apkg ready – import into Anki") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for file operations and API calls.
The code lacks error handling for critical operations like loading the index, querying the retriever, and writing the Anki package file.
Add comprehensive error handling:
def main():
import genanki
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
+ try:
cfg = load_config()
chroma_path = cfg["paths"]["chroma_dir"]
client = QdrantClient(path=chroma_path)
store = QdrantVectorStore(client, collection_name="study")
storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store)
index = load_index_from_storage(storage)
retriever = index.as_retriever(similarity_top_k=50)
+ except Exception as e:
+ print(f"Error initializing components: {e}")
+ return
deck = genanki.Deck(uuid.uuid4().int >> 64, "Study-Bot Deck")
+ try:
for node in index.docstore.docs.values():
qa = retriever.query(f"Turn this into Q&A flashcards:\n\n{node.text}").response
# ... parsing logic ...
genanki.Package(deck).write_to_file("study.apkg")
print("study.apkg ready – import into Anki")
+ except Exception as e:
+ print(f"Error generating flashcards: {e}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def main(): | |
| import genanki | |
| from llama_index.core import StorageContext, load_index_from_storage | |
| from llama_index.vector_stores.qdrant import QdrantVectorStore | |
| from qdrant_client import QdrantClient | |
| cfg = load_config() | |
| chroma_path = cfg["paths"]["chroma_dir"] | |
| client = QdrantClient(path=chroma_path) | |
| store = QdrantVectorStore(client, collection_name="study") | |
| storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store) | |
| index = load_index_from_storage(storage) | |
| retriever = index.as_retriever(similarity_top_k=50) | |
| deck = genanki.Deck(uuid.uuid4().int >> 64, "Study-Bot Deck") | |
| for node in index.docstore.docs.values(): | |
| qa = retriever.query(f"Turn this into Q&A flashcards:\n\n{node.text}").response | |
| for line in qa.splitlines(): | |
| if "?" in line: | |
| q, a = line.split("?", 1) | |
| note = genanki.Note(model=genanki.BASIC_MODEL, fields=[q.strip()+"?", a.strip()]) | |
| deck.add_note(note) | |
| genanki.Package(deck).write_to_file("study.apkg") | |
| print("study.apkg ready – import into Anki") | |
| def main(): | |
| import genanki | |
| from llama_index.core import StorageContext, load_index_from_storage | |
| from llama_index.vector_stores.qdrant import QdrantVectorStore | |
| from qdrant_client import QdrantClient | |
| try: | |
| cfg = load_config() | |
| chroma_path = cfg["paths"]["chroma_dir"] | |
| client = QdrantClient(path=chroma_path) | |
| store = QdrantVectorStore(client, collection_name="study") | |
| storage = StorageContext.from_defaults(persist_dir=chroma_path, vector_store=store) | |
| index = load_index_from_storage(storage) | |
| retriever = index.as_retriever(similarity_top_k=50) | |
| except Exception as e: | |
| print(f"Error initializing components: {e}") | |
| return | |
| deck = genanki.Deck(uuid.uuid4().int >> 64, "Study-Bot Deck") | |
| try: | |
| for node in index.docstore.docs.values(): | |
| qa = retriever.query(f"Turn this into Q&A flashcards:\n\n{node.text}").response | |
| for line in qa.splitlines(): | |
| if "?" in line: | |
| q, a = line.split("?", 1) | |
| note = genanki.Note(model=genanki.BASIC_MODEL, fields=[q.strip() + "?", a.strip()]) | |
| deck.add_note(note) | |
| genanki.Package(deck).write_to_file("study.apkg") | |
| print("study.apkg ready – import into Anki") | |
| except Exception as e: | |
| print(f"Error generating flashcards: {e}") |
🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 11-11: Too many local variables (19/15)
(R0914)
🤖 Prompt for AI Agents
In Dev/src/study_tools/flashcards.py around lines 11 to 35, add try-except
blocks around critical operations such as loading the index from storage,
querying the retriever for Q&A flashcards, and writing the Anki package file.
Catch exceptions to handle errors gracefully, log or print meaningful error
messages, and ensure the program does not crash unexpectedly during these file
operations and API calls.
|
|
||
| deck = genanki.Deck(uuid.uuid4().int >> 64, "Study-Bot Deck") | ||
| for node in index.docstore.docs.values(): | ||
| qa = retriever.query(f"Turn this into Q&A flashcards:\n\n{node.text}").response |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify retriever API usage.
The retriever.query() method call may be incorrect. Retrievers typically use retrieve() method and don't return objects with a response attribute.
🏁 Script executed:
#!/bin/bash
# Verify the correct API for llama_index retrievers
ast-grep --pattern 'retriever.query($$$)'Length of output: 280
🏁 Script executed:
#!/bin/bash
# Print the setup of the retriever in flashcards.py for context
sed -n '1,200p' Dev/src/study_tools/flashcards.pyLength of output: 1355
🏁 Script executed:
#!/bin/bash
# Install llama-index and inspect retriever API
pip install llama-index >/dev/null 2>&1
python3 - << 'EOF'
from llama_index import GPTVectorStoreIndex
import inspect
# Create a minimal index (empty documents list)
index = GPTVectorStoreIndex([])
retriever = index.as_retriever()
# List public methods
methods = [m for m in dir(retriever) if not m.startswith('_')]
print("PUBLIC_METHODS:", methods)
# Check for 'query' and 'retrieve'
print("HAS_QUERY:", hasattr(retriever, 'query'))
print("HAS_RETRIEVE:", hasattr(retriever, 'retrieve'))
# Inspect signature if present
if hasattr(retriever, 'retrieve'):
print("retrieve SIG:", inspect.signature(retriever.retrieve))
if hasattr(retriever, 'query'):
print("query SIG:", inspect.signature(retriever.query))
EOFLength of output: 25
🏁 Script executed:
#!/bin/bash
# Re-verify retriever API methods using available Python runtime
pip install llama-index >/dev/null 2>&1
# Use `python` instead of `python3` to accommodate the sandbox environment
python - << 'EOF'
from llama_index import GPTVectorStoreIndex
import inspect
# Create a minimal index and retriever
index = GPTVectorStoreIndex([])
retriever = index.as_retriever()
# List public methods on the retriever
methods = [m for m in dir(retriever) if not m.startswith('_')]
print("RETRIEVER_METHODS:", methods)
# Check for 'query' vs 'retrieve'
print("HAS_QUERY:", hasattr(retriever, 'query'))
print("HAS_RETRIEVE:", hasattr(retriever, 'retrieve'))
# If either exists, print its signature
if hasattr(retriever, 'query'):
print("query SIG:", inspect.signature(retriever.query))
if hasattr(retriever, 'retrieve'):
print("retrieve SIG:", inspect.signature(retriever.retrieve))
EOFLength of output: 201
🏁 Script executed:
#!/bin/bash
# Install llama-index and locate its package
pip install llama-index >/dev/null 2>&1
# Get llama-index installation directory
loc=$(pip show llama-index | grep Location | awk -F': ' '{print $2}')
echo "LLAMA_INDEX_LOCATION: $loc"
# Search for retriever class definitions
grep -R "class .*Retriever" "$loc"/llama_index
# Search for 'query' and 'retrieve' method definitions
grep -R "def query" "$loc"/llama_index
grep -R "def retrieve" "$loc"/llama_indexLength of output: 15284
Use the correct Retriever API (retrieve) or switch to a QueryEngine
The object returned by index.as_retriever() is a BaseRetriever that only implements
retrieve(str_or_query_bundle: QueryType) -> List[NodeWithScore]It does not have a query() method or a .response attribute. You have two options:
- If you want raw nodes and will drive your own Q&A logic, call
retrieve():- qa = retriever.query(f"Turn this into Q&A flashcards:\n\n{node.text}").response + nodes = retriever.retrieve(f"Turn this into Q&A flashcards:\n\n{node.text}") + # TODO: convert `nodes` into Q&A lines yourself
- If you really want the built-in “query → Response” pattern, use a QueryEngine:
query_engine = index.as_query_engine(similarity_top_k=50) qa = query_engine.query(f"Turn this into Q&A flashcards:\n\n{node.text}").response
Locations to update:
- Dev/src/study_tools/flashcards.py: line 27 (replace the
retriever.query(...).responsecall)
🤖 Prompt for AI Agents
In Dev/src/study_tools/flashcards.py at line 27, the code incorrectly calls
retriever.query(...).response, but the retriever object from
index.as_retriever() only supports the retrieve() method and does not have
query() or response attributes. To fix this, either replace the call with
retriever.retrieve(...) if you want raw nodes, or switch to using a QueryEngine
by creating it with index.as_query_engine(...) and then call
query_engine.query(...).response to get the response as intended.
| if chroma_dir.exists(): | ||
| shutil.rmtree(chroma_dir) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add safety measures for destructive directory removal.
The unconditional removal of the vector store directory could lead to accidental data loss. Consider adding a confirmation prompt or command-line flag.
+ import sys
+
if chroma_dir.exists():
- shutil.rmtree(chroma_dir)
+ response = input(f"Warning: {chroma_dir} exists. Remove it? [y/N]: ")
+ if response.lower() == 'y':
+ shutil.rmtree(chroma_dir)
+ else:
+ print("Aborting to preserve existing index.")
+ sys.exit(0)Alternatively, make this behavior configurable via command-line arguments:
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--force", action="store_true", help="Force rebuild by removing existing index")
args = parser.parse_args()
if chroma_dir.exists():
if args.force:
shutil.rmtree(chroma_dir)
else:
print(f"Error: {chroma_dir} already exists. Use --force to rebuild.")
sys.exit(1)🤖 Prompt for AI Agents
In Dev/src/study_tools/build_index.py around lines 40 to 42, the code
unconditionally removes the chroma_dir directory, which risks accidental data
loss. Modify the code to add a command-line argument like --force to control
this behavior. Parse the argument using argparse, and only remove the directory
if --force is specified; otherwise, print an error message and exit. This makes
the destructive removal explicit and safer.
|
|
||
|
|
||
| def main(): | ||
| from llama_index.core import VectorStoreIndex, StorageContext, Document |
There was a problem hiding this comment.
Remove the unused import.
Document is already imported within the extract_pages function where it's used.
- from llama_index.core import VectorStoreIndex, StorageContext, Document
+ from llama_index.core import VectorStoreIndex, StorageContext📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| from llama_index.core import VectorStoreIndex, StorageContext, Document | |
| from llama_index.core import VectorStoreIndex, StorageContext |
🧰 Tools
🪛 Flake8 (7.2.0)
[error] 29-29: 'llama_index.core.Document' imported but unused
(F401)
🪛 Ruff (0.11.9)
29-29: llama_index.core.Document imported but unused
Remove unused import: llama_index.core.Document
(F401)
🤖 Prompt for AI Agents
In Dev/src/study_tools/build_index.py at line 29, the import statement includes
Document which is not used in this scope because it is already imported inside
the extract_pages function. Remove Document from the import statement to clean
up unused imports.
| for g in tqdm(groups): | ||
| summaries.extend(summarise_group(llm, g, "English", cache_dir)) | ||
|
|
||
| Path("summary.md").write_text("\n\n".join(summaries), encoding="utf-8") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Make the output path configurable.
The hardcoded output filename conflicts with the P0 TODO item about removing hardcoded paths.
- Path("summary.md").write_text("\n\n".join(summaries), encoding="utf-8")
+ output_path = Path(cfg.get("output", {}).get("summary_file", "summary.md"))
+ output_path.write_text("\n\n".join(summaries), encoding="utf-8")Consider adding the output configuration to your config.yaml:
output:
summary_file: "Dev/data/summary.md"🤖 Prompt for AI Agents
In Dev/src/study_tools/summarize.py at line 73, the output path for the summary
file is hardcoded as "summary.md". To fix this, modify the code to read the
output filename from the configuration file (config.yaml) under the key
output.summary_file, and use that path instead of the hardcoded one. This will
make the output path configurable and align with the existing configuration
approach.
| def summarise_group(llm: Any, nodes: Sequence[Any], lang: str, cache_dir: Path) -> List[str]: | ||
| results = [] | ||
| for node in nodes: | ||
| key = _sha(node) | ||
| fp = cache_dir / f"{key}.md" | ||
| if fp.exists(): | ||
| results.append(fp.read_text(encoding="utf-8")) | ||
| continue | ||
| prompt = f"Summarise in {lang}:\n\n{node.text}" | ||
| text = asyncio.run(_complete(llm, prompt)) | ||
| fp.write_text(text, encoding="utf-8") | ||
| results.append(text) | ||
| return results | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Optimize async execution in summarise_group.
Using asyncio.run() in a loop creates a new event loop for each call, which is inefficient. Consider processing all async operations concurrently.
-def summarise_group(llm: Any, nodes: Sequence[Any], lang: str, cache_dir: Path) -> List[str]:
+async def summarise_group(llm: Any, nodes: Sequence[Any], lang: str, cache_dir: Path) -> List[str]:
results = []
+ tasks = []
for node in nodes:
key = _sha(node)
fp = cache_dir / f"{key}.md"
if fp.exists():
results.append(fp.read_text(encoding="utf-8"))
continue
prompt = f"Summarise in {lang}:\n\n{node.text}"
- text = asyncio.run(_complete(llm, prompt))
- fp.write_text(text, encoding="utf-8")
- results.append(text)
+ tasks.append((fp, _complete(llm, prompt)))
+
+ # Process all async tasks concurrently
+ for fp, task in tasks:
+ text = await task
+ fp.write_text(text, encoding="utf-8")
+ results.append(text)
+
return resultsThen update the main function to handle the async call:
summaries = []
for g in tqdm(groups):
- summaries.extend(summarise_group(llm, g, "English", cache_dir))
+ summaries.extend(asyncio.run(summarise_group(llm, g, "English", cache_dir)))📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def summarise_group(llm: Any, nodes: Sequence[Any], lang: str, cache_dir: Path) -> List[str]: | |
| results = [] | |
| for node in nodes: | |
| key = _sha(node) | |
| fp = cache_dir / f"{key}.md" | |
| if fp.exists(): | |
| results.append(fp.read_text(encoding="utf-8")) | |
| continue | |
| prompt = f"Summarise in {lang}:\n\n{node.text}" | |
| text = asyncio.run(_complete(llm, prompt)) | |
| fp.write_text(text, encoding="utf-8") | |
| results.append(text) | |
| return results | |
| # Dev/src/study_tools/summarize.py | |
| from pathlib import Path | |
| from typing import Any, Sequence, List | |
| import asyncio | |
| async def summarise_group(llm: Any, nodes: Sequence[Any], lang: str, cache_dir: Path) -> List[str]: | |
| results = [] | |
| tasks = [] | |
| for node in nodes: | |
| key = _sha(node) | |
| fp = cache_dir / f"{key}.md" | |
| if fp.exists(): | |
| results.append(fp.read_text(encoding="utf-8")) | |
| continue | |
| prompt = f"Summarise in {lang}:\n\n{node.text}" | |
| tasks.append((fp, _complete(llm, prompt))) | |
| # Process all async tasks concurrently | |
| for fp, task in tasks: | |
| text = await task | |
| fp.write_text(text, encoding="utf-8") | |
| results.append(text) | |
| return results |
| def summarise_group(llm: Any, nodes: Sequence[Any], lang: str, cache_dir: Path) -> List[str]: | |
| results = [] | |
| for node in nodes: | |
| key = _sha(node) | |
| fp = cache_dir / f"{key}.md" | |
| if fp.exists(): | |
| results.append(fp.read_text(encoding="utf-8")) | |
| continue | |
| prompt = f"Summarise in {lang}:\n\n{node.text}" | |
| text = asyncio.run(_complete(llm, prompt)) | |
| fp.write_text(text, encoding="utf-8") | |
| results.append(text) | |
| return results | |
| summaries = [] | |
| for g in tqdm(groups): | |
| summaries.extend(asyncio.run(summarise_group(llm, g, "English", cache_dir))) |
🤖 Prompt for AI Agents
In Dev/src/study_tools/summarize.py lines 31 to 44, the use of asyncio.run()
inside the loop creates a new event loop for each iteration, which is
inefficient. Refactor summarise_group to gather all _complete(llm, prompt) calls
concurrently using asyncio.gather or similar, making summarise_group an async
function. Then update the caller to await summarise_group accordingly to handle
the async execution properly.
Summary
Dev/packagemessy_start/Dev/data/Testing
PYTHONPATH=Dev/src python -m pytest Dev/tests/test_imports.py -qhttps://chatgpt.com/codex/tasks/task_e_68672e1e43408321b7ea32bb05a1bd81
Summary by CodeRabbit
New Features
Documentation
Tests
Chores