[TEST] Test beartype in cognify pipeline #193
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WalkthroughThe pull request introduces significant changes across multiple files, primarily focusing on enhancing the asynchronous processing of datasets related to code graphs. A new file Changes
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cognee/modules/engine/utils/generate_node_name.py (1)
1-2: 🛠️ Refactor suggestion
Add input validation and comprehensive documentation
The function needs additional robustness and clarity improvements:
- Add input validation
- Add docstring with examples
- Consider handling more special characters
Apply this diff:
+from typing import Optional + +def generate_node_name(name: Optional[str]) -> str: + """Generate a standardized node name by converting to lowercase and replacing special characters. + + Args: + name: Input string to be converted into a node name. + If None or empty, returns an empty string. + + Returns: + A normalized string suitable for use as a node name. + + Examples: + >>> generate_node_name("Hello World") + 'hello_world' + >>> generate_node_name("User's Data") + 'users_data' + """ + if not name: + return "" return name.lower().replace(" ", "_").replace("'", "")📝 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 typing import Optional def generate_node_name(name: Optional[str]) -> str: """Generate a standardized node name by converting to lowercase and replacing special characters. Args: name: Input string to be converted into a node name. If None or empty, returns an empty string. Returns: A normalized string suitable for use as a node name. Examples: >>> generate_node_name("Hello World") 'hello_world' >>> generate_node_name("User's Data") 'users_data' """ if not name: return "" return name.lower().replace(" ", "_").replace("'", "")cognee/modules/engine/utils/generate_node_id.py (2)
4-4:
⚠️ Potential issueFix return type and add input validation.
Several issues need attention:
uuid5()returns a UUID object, but the function name suggests returning a string- No validation for empty strings or None
def generate_node_id(node_id: str) -> str: + if not node_id: + raise ValueError("node_id cannot be empty") - return uuid5(NAMESPACE_OID, node_id.lower().replace(" ", "_").replace("'", "")) + return str(uuid5(NAMESPACE_OID, node_id.lower().replace(" ", "_").replace("'", "")))📝 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 generate_node_id(node_id: str) -> str: if not node_id: raise ValueError("node_id cannot be empty") return str(uuid5(NAMESPACE_OID, node_id.lower().replace(" ", "_").replace("'", "")))
3-3: 🛠️ Refactor suggestion
Add return type annotation and consider beartype integration.
Since this PR is focused on testing beartype, consider:
- Adding return type annotation to match the input type annotation style
- Adding beartype decorator for runtime type checking
+from beartype import beartype +@beartype -def generate_node_id(node_id: str) -> str: +def generate_node_id(node_id: str) -> str:Committable suggestion skipped: line range outside the PR's diff.
cognee/tasks/chunks/__init__.py (1)
5-5: 💡 Codebase verification
Type hints present but beartype decorator missing
The function
remove_disconnected_chunkshas proper type hints (data_chunks: list[DocumentChunk] -> list[DocumentChunk]) but is missing the@beartypedecorator for runtime type checking. To fully integrate with beartype as mentioned in the PR title, the decorator should be added.
- Add
from beartype import beartypeimport- Add
@beartypedecorator above the function definition🔗 Analysis chain
Verify beartype integration for this module
Since the PR title mentions testing beartype in the cognify pipeline, we should verify if type hints and beartype decorators are properly implemented in the imported module.
Let me try a different approach to verify the beartype integration by checking the actual content of the file.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check if the imported module uses type hints and beartype decorators # Check for type hints and beartype usage in the source module echo "Checking remove_disconnected_chunks.py for type hints and beartype usage:" rg -A 5 "from beartype" "cognee/tasks/chunks/remove_disconnected_chunks.py" rg -A 5 "@beartype" "cognee/tasks/chunks/remove_disconnected_chunks.py" # Check function signature for type hints echo -e "\nChecking function signature:" ast-grep --pattern 'def remove_disconnected_chunks($$$): $$$'Length of output: 439
Script:
#!/bin/bash # Check the complete content of remove_disconnected_chunks.py echo "Content of remove_disconnected_chunks.py:" cat cognee/tasks/chunks/remove_disconnected_chunks.py # Also check if there are any type hints in general echo -e "\nChecking for any type annotations:" rg ":" cognee/tasks/chunks/remove_disconnected_chunks.pyLength of output: 1605
cognee/modules/engine/models/EntityType.py (1)
4-11:
⚠️ Potential issueAdd beartype decorator to align with PR objective
The PR objective mentions testing beartype, but the decorator is missing from this class.
Apply this diff:
+from beartype import beartype + +@beartype class EntityType(DataPoint):Committable suggestion skipped: line range outside the PR's diff.
cognee/modules/engine/models/Entity.py (1)
5-9: 🛠️ Refactor suggestion
Add type validation using beartype
Given that this PR's objective is to test beartype in the cognify pipeline, consider decorating the class with
@beartypeto enforce runtime type checking for the attributes.+from beartype import beartype +@beartype class Entity(DataPoint): name: str is_a: EntityType description: str mentioned_in: DocumentChunk📝 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 beartype import beartype @beartype class Entity(DataPoint): name: str is_a: EntityType description: str mentioned_in: DocumentChunkcognee/modules/data/processing/document_types/AudioDocument.py (1)
1-15: 🛠️ Refactor suggestion
Add type hints to support beartype testing
Since this PR is focused on testing beartype integration, the class should include proper type hints:
from typing import Iterator, Callable from uuid import UUID class AudioDocument(Document): type: str = "audio" + raw_data_location: str - def read(self, chunk_size: int): + def read(self, chunk_size: int) -> Iterator[str]: # Transcribe the audio file result = get_llm_client().create_transcript(self.raw_data_location) text = result.text - chunker = TextChunker(self, chunk_size = chunk_size, get_text = lambda: text) + chunker = TextChunker(self, chunk_size=chunk_size, get_text=lambda: str(text)) yield from chunker.read()Committable suggestion skipped: line range outside the PR's diff.
cognee/tasks/documents/extract_chunks_from_documents.py (1)
1-2: 🛠️ Refactor suggestion
Consider using standard
typingmodule instead ofbeartype.typingWhile beartype is great for runtime type checking, it's recommended to use the standard
typingmodule for type hints. This ensures better compatibility and follows Python's standard practices.-from beartype.typing import AsyncGenerator +from typing import AsyncGenerator from beartype import beartype📝 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 typing import AsyncGenerator from beartype import beartypecognee/infrastructure/databases/vector/pgvector/serialize_data.py (1)
4-15: 💡 Codebase verification
Add type hints and beartype decorator to serialize_data function
The function
serialize_dataincognee/infrastructure/databases/vector/pgvector/serialize_data.pylacks type hints and the@beartypedecorator, while other parts of the codebase consistently use beartype for type checking. You should:
- Add type hints for the input parameter and return type
- Add the
@beartypedecorator- Create corresponding test cases
Example locations where beartype is used:
cognee/tasks/documents/classify_documents.pycognee/tasks/storage/add_data_points.pycognee/tasks/summarization/summarize_text.py🔗 Analysis chain
Verify beartype test coverage
Since this PR is focused on testing beartype, ensure this serialization function is covered in the type checking tests.
Let me check the content of the configuration files and look for any existing test files that might be related to serialization.
Let me check for the actual implementation and test files related to serialization in the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check for existing beartype tests and configurations rg -l "beartype" tests/ rg -l "serialize_data.*beartype" tests/ # Look for type checking configurations fd "py.typed|mypy.ini|pytest.ini" # Check for related test files fd "test_serialize" tests/Length of output: 148
Script:
#!/bin/bash # Check configuration files content echo "=== mypy.ini ===" cat mypy.ini echo -e "\n=== pytest.ini ===" cat pytest.ini # Search for any test files that might contain serialization tests rg -l "serialize" tests/ # Look for existing type checking patterns in test files rg "from beartype|@beartype" tests/ -A 2 # Check if the serialize_data function is imported or used in tests rg "serialize_data" tests/Length of output: 2469
Script:
#!/bin/bash # Find all Python files that might contain serialization-related code fd -e py . | xargs rg -l "serialize" # Look for test files specifically fd -e py . tests/ | xargs rg -l "test.*serialize" # Check for type hints in the serialize_data function ast-grep --pattern 'def serialize_data($$$): $$$' # Look for beartype usage in the codebase rg "from beartype" -A 2Length of output: 3123
cognee/tasks/graph/extract_graph_from_code.py (2)
13-16: 🛠️ Refactor suggestion
Consider parallel storage operations and add error handling.
The storage operations are performed sequentially and lack error handling. Consider using concurrent storage and proper error management.
- for (chunk_index, chunk) in enumerate(data_chunks): - chunk_graph = chunk_graphs[chunk_index] - await add_data_points(chunk_graph.nodes) + try: + # Process storage operations concurrently + await asyncio.gather( + *[add_data_points(chunk_graphs[i].nodes) for i in range(len(data_chunks))] + ) + except Exception as e: + raise Exception(f"Failed to store graph data points: {str(e)}") from 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.try: # Process storage operations concurrently await asyncio.gather( *[add_data_points(chunk_graphs[i].nodes) for i in range(len(data_chunks))] ) except Exception as e: raise Exception(f"Failed to store graph data points: {str(e)}") from e
9-11:
⚠️ Potential issueAdd error handling for graph extraction.
The concurrent graph extraction could fail silently. Consider adding error handling and logging.
- chunk_graphs = await asyncio.gather( - *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] - ) + try: + chunk_graphs = await asyncio.gather( + *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks], + return_exceptions=True + ) + # Handle any exceptions from gather + for result in chunk_graphs: + if isinstance(result, Exception): + raise result + except Exception as e: + # Log the error with context + raise Exception(f"Failed to extract graphs: {str(e)}") from 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.try: chunk_graphs = await asyncio.gather( *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks], return_exceptions=True ) # Handle any exceptions from gather for result in chunk_graphs: if isinstance(result, Exception): raise result except Exception as e: # Log the error with context raise Exception(f"Failed to extract graphs: {str(e)}") from ecognee/infrastructure/engine/models/DataPoint.py (2)
20-24: 🛠️ Refactor suggestion
Improve method clarity and type safety
The method could benefit from:
- Type hints for the return value
- Better error handling
- More explicit validation
- def get_embeddable_data(self): + def get_embeddable_data(self) -> Optional[str]: + """Retrieve the value of the first indexed field. + + Returns: + Optional[str]: The value of the first indexed field if it exists, None otherwise. + """ + if not self._metadata or not self._metadata["index_fields"]: + return None + + field_name = self._metadata["index_fields"][0] + return getattr(self, field_name, None)📝 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 get_embeddable_data(self) -> Optional[str]: """Retrieve the value of the first indexed field. Returns: Optional[str]: The value of the first indexed field if it exists, None otherwise. """ if not self._metadata or not self._metadata["index_fields"]: return None field_name = self._metadata["index_fields"][0] return getattr(self, field_name, None)
10-15:
⚠️ Potential issueFix mutable default value and datetime initialization
There are several issues with the current implementation:
- Using a mutable dictionary as a default value for
_metadatais dangerous as it will be shared across instances- The
updated_atdefault should be a callable to ensure a fresh timestamp for each instanceApply this fix:
class DataPoint(BaseModel): id: UUID = Field(default_factory=uuid4) - updated_at: Optional[datetime] = datetime.now(timezone.utc) + updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc)) - _metadata: Optional[MetaData] = { - "index_fields": [] - } + _metadata: Optional[MetaData] = Field( + default_factory=lambda: {"index_fields": []} + )📝 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.class DataPoint(BaseModel): id: UUID = Field(default_factory=uuid4) updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc)) _metadata: Optional[MetaData] = Field( default_factory=lambda: {"index_fields": []} )cognee/tasks/documents/classify_documents.py (2)
6-16:
⚠️ Potential issueAdd input validation and improve extension handling.
The current implementation has several potential issues:
- No validation for empty input list
- Case-sensitive extension matching
- Limited extension support (e.g., no mp3/wav for audio)
Consider adding these improvements:
@beartype def classify_documents(data_documents: list[Data]) -> list[Document]: + if not data_documents: + return [] + + DOCUMENT_TYPES = { + # PDF documents + "pdf": PdfDocument, + # Audio documents + "mp3": AudioDocument, + "wav": AudioDocument, + "audio": AudioDocument, + # Image documents + "png": ImageDocument, + "jpg": ImageDocument, + "jpeg": ImageDocument, + "image": ImageDocument, + } + + def create_document(data_item: Data) -> Document: + doc_class = DOCUMENT_TYPES.get(data_item.extension.lower(), TextDocument) + return doc_class( + id=data_item.id, + name=f"{data_item.name}.{data_item.extension}", + raw_data_location=data_item.raw_data_location + ) + + documents = [create_document(data_item) for data_item in data_documents] return documents📝 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.@beartype def classify_documents(data_documents: list[Data]) -> list[Document]: if not data_documents: return [] DOCUMENT_TYPES = { # PDF documents "pdf": PdfDocument, # Audio documents "mp3": AudioDocument, "wav": AudioDocument, "audio": AudioDocument, # Image documents "png": ImageDocument, "jpg": ImageDocument, "jpeg": ImageDocument, "image": ImageDocument, } def create_document(data_item: Data) -> Document: doc_class = DOCUMENT_TYPES.get(data_item.extension.lower(), TextDocument) return doc_class( id=data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location ) documents = [create_document(data_item) for data_item in data_documents] return documents
8-14: 🛠️ Refactor suggestion
Refactor complex list comprehension for better readability and maintainability.
The current implementation using chained conditionals in a list comprehension is hard to read and maintain. Consider using a mapping dictionary and separate function for better organization.
- documents = [ - PdfDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) if data_item.extension == "pdf" else - AudioDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) if data_item.extension == "audio" else - ImageDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) if data_item.extension == "image" else - TextDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) - for data_item in data_documents - ] + DOCUMENT_TYPES = { + "pdf": PdfDocument, + "audio": AudioDocument, + "image": ImageDocument + } + + def create_document(data_item: Data) -> Document: + doc_class = DOCUMENT_TYPES.get(data_item.extension.lower(), TextDocument) + return doc_class( + id=data_item.id, + name=f"{data_item.name}.{data_item.extension}", + raw_data_location=data_item.raw_data_location + ) + + documents = [create_document(data_item) for data_item in data_documents]📝 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.DOCUMENT_TYPES = { "pdf": PdfDocument, "audio": AudioDocument, "image": ImageDocument } def create_document(data_item: Data) -> Document: doc_class = DOCUMENT_TYPES.get(data_item.extension.lower(), TextDocument) return doc_class( id=data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location ) documents = [create_document(data_item) for data_item in data_documents]cognee/modules/graph/utils/get_model_instance_from_graph.py (2)
6-10: 🛠️ Refactor suggestion
Add input validation for robustness.
The node mapping initialization lacks crucial validation checks:
- Validation for empty input lists
- Handling of duplicate node IDs
- Verification of entity_id existence in nodes
Consider adding these validations:
def get_model_instance_from_graph(nodes: list[DataPoint], edges: list, entity_id: str): + if not nodes: + raise ValueError("Nodes list cannot be empty") + if not entity_id: + raise ValueError("Entity ID cannot be empty") + if entity_id not in {node.id for node in nodes}: + raise ValueError(f"Entity ID '{entity_id}' not found in nodes") + node_map = {} + seen_ids = set() for node in nodes: + if node.id in seen_ids: + raise ValueError(f"Duplicate node ID found: {node.id}") + seen_ids.add(node.id) node_map[node.id] = node📝 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 get_model_instance_from_graph(nodes: list[DataPoint], edges: list, entity_id: str): if not nodes: raise ValueError("Nodes list cannot be empty") if not entity_id: raise ValueError("Entity ID cannot be empty") if entity_id not in {node.id for node in nodes}: raise ValueError(f"Entity ID '{entity_id}' not found in nodes") node_map = {} seen_ids = set() for node in nodes: if node.id in seen_ids: raise ValueError(f"Duplicate node ID found: {node.id}") seen_ids.add(node.id) node_map[node.id] = node
12-28:
⚠️ Potential issueAdd edge validation and error handling.
The edge processing lacks several important safety checks:
- Edge format validation
- Safe array access
- Error handling for model creation
Consider implementing these safety improvements:
for edge in edges: + if not isinstance(edge, (list, tuple)) or len(edge) < 3: + raise ValueError(f"Invalid edge format: {edge}. Expected (source, target, label, [properties])") + + try: source_node = node_map[edge[0]] target_node = node_map[edge[1]] + except KeyError as e: + raise ValueError(f"Edge references non-existent node: {e}") + edge_label = edge[2] edge_properties = edge[3] if len(edge) == 4 else {} edge_metadata = edge_properties.get("metadata", {}) edge_type = edge_metadata.get("type") + try: if edge_type == "list": NewModel = copy_model(type(source_node), { edge_label: (list[type(target_node)], PydanticUndefined) }) node_map[edge[0]] = NewModel(**source_node.model_dump(), **{ edge_label: [target_node] }) else: NewModel = copy_model(type(source_node), { edge_label: (type(target_node), PydanticUndefined) }) node_map[edge[0]] = NewModel(**source_node.model_dump(), **{ edge_label: target_node }) + except Exception as e: + raise ValueError(f"Failed to create model for edge {edge}: {str(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 edge in edges: if not isinstance(edge, (list, tuple)) or len(edge) < 3: raise ValueError(f"Invalid edge format: {edge}. Expected (source, target, label, [properties])") try: source_node = node_map[edge[0]] target_node = node_map[edge[1]] except KeyError as e: raise ValueError(f"Edge references non-existent node: {e}") edge_label = edge[2] edge_properties = edge[3] if len(edge) == 4 else {} edge_metadata = edge_properties.get("metadata", {}) edge_type = edge_metadata.get("type") try: if edge_type == "list": NewModel = copy_model(type(source_node), { edge_label: (list[type(target_node)], PydanticUndefined) }) node_map[edge[0]] = NewModel(**source_node.model_dump(), **{ edge_label: [target_node] }) else: NewModel = copy_model(type(source_node), { edge_label: (type(target_node), PydanticUndefined) }) node_map[edge[0]] = NewModel(**source_node.model_dump(), **{ edge_label: target_node }) except Exception as e: raise ValueError(f"Failed to create model for edge {edge}: {str(e)}")cognee/infrastructure/databases/graph/graph_db_interface.py (2)
6-6:
⚠️ Potential issueAdd return type annotation for beartype testing
Since this PR is focused on testing beartype, the
querymethod should specify its return type annotation to ensure proper runtime type checking.Apply this diff to add return type annotation:
- async def query(self, query: str, params: dict): + async def query(self, query: str, params: dict[str, Any]) -> Any:Consider being more specific about the return type based on your query system's actual return type (e.g.,
dict,list, etc.).📝 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.async def query(self, query: str, params: dict[str, Any]) -> Any:
6-6: 💡 Codebase verification
Implementation issues found in query method across adapters
- Neo4j adapter has implemented the query method correctly with proper async/await and parameter handling
- NetworkX adapter has an empty implementation (just
pass)- FalkorDB adapter appears to be missing the query method implementation entirely
🔗 Analysis chain
Verify implementations of the new query method
The interface change from
graph()toquery()is significant and requires verification of all implementing classes.
Let me check the implementations to verify if they have properly implemented the new
querymethod.
Let me check the specific implementations of the
querymethod in these adapters.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Find all potential implementations of GraphDBInterface echo "Searching for implementations of GraphDBInterface..." rg -l "class.*\(.*GraphDBInterface.*\)" # Find all potential usages of the old graph() method that need updating echo "Checking for old graph() method usage..." rg "\.graph\(\)" # Find current implementations of query method echo "Checking current query method implementations..." ast-grep --pattern 'class $CLASS { $$$ async def query(query: str, params: dict) $$$ }'Length of output: 656
Script:
#!/bin/bash # Check Neo4j adapter implementation echo "=== Neo4j Adapter Implementation ===" rg -A 10 "class.*Neo4jAdapter" cognee/infrastructure/databases/graph/neo4j_driver/adapter.py # Check FalkorDB adapter implementation echo "=== FalkorDB Adapter Implementation ===" rg -A 10 "class.*FalkorDBAdapter" cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py # Check NetworkX adapter implementation echo "=== NetworkX Adapter Implementation ===" rg -A 10 "class.*NetworkXAdapter" cognee/infrastructure/databases/graph/networkx/adapter.pyLength of output: 1621
Script:
#!/bin/bash # Search for query method implementations in each adapter echo "=== Neo4j query implementation ===" rg -B 2 -A 10 "async def query" cognee/infrastructure/databases/graph/neo4j_driver/adapter.py echo "=== FalkorDB query implementation ===" rg -B 2 -A 10 "async def query" cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py echo "=== NetworkX query implementation ===" rg -B 2 -A 10 "async def query" cognee/infrastructure/databases/graph/networkx/adapter.py # Also check for any remaining graph() method implementations echo "=== Checking for graph() method implementations ===" rg -B 2 -A 5 "async def graph" cognee/infrastructure/databases/graph/Length of output: 1532
cognee/infrastructure/databases/graph/get_graph_engine.py (1)
23-33:
⚠️ Potential issueAdd error handling for FalkorDB initialization
The error handling for the FalkorDB provider has been removed. This could lead to uncaught exceptions if there are issues with:
- Database connection
- Embedding engine initialization
- Module imports
Add error handling similar to the neo4j implementation:
elif config.graph_database_provider == "falkordb": + try: from cognee.infrastructure.databases.vector.embeddings import get_embedding_engine from cognee.infrastructure.databases.hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter embedding_engine = get_embedding_engine() return FalkorDBAdapter( database_url = config.graph_database_url, database_port = config.graph_database_port, embedding_engine = embedding_engine, ) + except: + passCommittable suggestion skipped: line range outside the PR's diff.
cognee/modules/storage/utils/__init__.py (3)
1-7: 🛠️ Refactor suggestion
Move
create_modelimport to the top of the fileThe
create_modelimport should be grouped with other imports at the top of the file to maintain consistent import organization and prevent potential circular import issues.import json from uuid import UUID from datetime import datetime from pydantic_core import PydanticUndefined +from pydantic import create_model from cognee.infrastructure.engine import DataPoint -from pydantic import create_modelAlso applies to: 18-18
34-46: 🛠️ Refactor suggestion
Improve type safety and simplify conditions
The function needs better type hints and could benefit from simplified condition checks.
-def get_own_properties(data_point: DataPoint): +def get_own_properties(data_point: DataPoint) -> dict[str, any]: + """Extract non-nested properties from a DataPoint instance. + + Args: + data_point: The DataPoint instance to extract properties from + + Returns: + A dictionary containing only the primitive field values + """ properties = {} + + if not hasattr(data_point, '__iter__'): + raise TypeError("data_point must be iterable") for field_name, field_value in data_point: - if field_name == "_metadata" \ - or isinstance(field_value, dict) \ - or isinstance(field_value, DataPoint) \ - or (isinstance(field_value, list) and isinstance(field_value[0], DataPoint)): + # Skip metadata, nested structures, and lists of DataPoints + if (field_name == "_metadata" or + isinstance(field_value, (dict, DataPoint)) or + (isinstance(field_value, list) and field_value and isinstance(field_value[0], DataPoint))): continue properties[field_name] = field_value return propertiesAlso, consider adding validation for empty lists to avoid potential IndexError when checking
field_value[0].📝 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 get_own_properties(data_point: DataPoint) -> dict[str, any]: """Extract non-nested properties from a DataPoint instance. Args: data_point: The DataPoint instance to extract properties from Returns: A dictionary containing only the primitive field values """ properties = {} if not hasattr(data_point, '__iter__'): raise TypeError("data_point must be iterable") for field_name, field_value in data_point: # Skip metadata, nested structures, and lists of DataPoints if (field_name == "_metadata" or isinstance(field_value, (dict, DataPoint)) or (isinstance(field_value, list) and field_value and isinstance(field_value[0], DataPoint))): continue properties[field_name] = field_value return properties
1-46: 💡 Codebase verification
Add type hints and beartype decorators for runtime type checking
The verification reveals that:
- The file lacks
@beartypedecorators while other files in the codebase consistently use them- While basic type hints exist (
DataPoint,dict,list), they could be more specific:
include_fieldsshould specify key and value typesexclude_fieldsshould uselist[str]JSONEncoder.defaultmethod lacks type hints completelySuggested changes:
- Add
from beartype import beartype- Add
@beartypedecorators to all functions- Enhance type hints to be more specific
🔗 Analysis chain
Verify beartype compatibility
Given that this PR is focused on testing beartype in the cognify pipeline, we should verify that all functions have proper type hints for beartype validation.
The initial results show beartype is used in several files, but we need to see the actual usage patterns and compare with DataPoint to make a proper assessment about type hints in this utility file.
We need to verify the type hints in the utility functions and compare them with beartype usage patterns in other files to make a proper assessment.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check if other files in the codebase use beartype decorators rg -l "from beartype import" . # Check if there's a beartype configuration file fd "beartype.*\.toml|beartype.*\.ini|beartype.*\.json|beartype.*\.yaml|beartype.*\.yml" # Look for any existing type validation decorators rg -l "@(beartype|typing|validate|typecheck)"Length of output: 644
Script:
#!/bin/bash # Let's check how beartype is used in other files for comparison rg -A 5 "from beartype import" . # Let's also check the DataPoint class to understand its type hints ast-grep --pattern 'class DataPoint'Length of output: 2770
Script:
#!/bin/bash # Let's examine the type hints in the current file rg -A 2 "def \w+\(" cognee/modules/storage/utils/__init__.py # Let's also check how DataPoint is used in other files with beartype rg -B 2 -A 5 "DataPoint.*\:" .Length of output: 603
🧰 Tools
🪛 Ruff
18-18: Module level import not at top of file
(E402)
.github/workflows/test_python_3_9.yml (1)
11-11: 💡 Codebase verification
⚠️ Potential issueFix inconsistent concurrency group configuration
The codebase shows two different patterns for concurrency groups:
- Standard pattern (most workflows):
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
- Non-standard pattern (in labeled workflows):
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ${{ github.event.label.name == 'run-checks' }}The
|character in the group name is incorrect as it's treated as a literal character, not a logical operator. Since this workflow is triggered by labels, simply use the standard pattern as it already provides proper run isolation.- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ${{ github.event.label.name == 'run-checks' }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}🔗 Analysis chain
Fix the concurrency group configuration
The current concurrency group configuration has several issues:
- Using '|' in the group name might not work as expected for grouping runs
- The label condition syntax appears incorrect and might not evaluate properly
Consider this corrected version:
- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ${{ github.event.label.name == 'run-checks' }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ contains(github.event.pull_request.labels.*.name, 'run-checks') }}Let's verify the label usage in PRs:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check if 'run-checks' label is consistently used gh api graphql -f query=' query($owner:String!, $repo:String!) { repository(owner:$owner, name:$repo) { pullRequests(last:20, states:OPEN) { nodes { number labels(first:10) { nodes { name } } } } } }' -F owner="$(gh repo view --json owner -q .owner)" -F repo="$(gh repo view --json name -q .name)"Length of output: 774
Script:
#!/bin/bash # Let's check other workflow files to understand the concurrency pattern usage fd -e yml -e yaml . .github/workflows/ --exec cat {} \; | grep -A 2 -B 2 "concurrency:"Length of output: 1664
.github/workflows/test_python_3_10.yml (2)
11-11: 🛠️ Refactor suggestion
Simplify concurrency group configuration
The current concurrency group configuration includes a boolean expression which could lead to unexpected behavior. Consider simplifying the group name and controlling execution through job conditions instead.
- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ${{ github.event.label.name == 'run-checks' }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}Then, if needed, add a condition to the job:
jobs: run_common: if: | needs.get_docs_changes.outputs.changes_outside_docs == 'true' && (github.event_name != 'pull_request' || github.event.label.name == 'run-checks')
4-8: 🛠️ Refactor suggestion
Consider broadening the pull request trigger events
The current configuration only runs tests when a PR is labeled, which could lead to missed testing opportunities and requires manual intervention. Consider including additional trigger events to ensure automated testing on PR creation and updates.
pull_request: branches: - main - types: [labeled] + types: [opened, synchronize, reopened, labeled]📝 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.workflow_dispatch: pull_request: branches: - main types: [opened, synchronize, reopened, labeled].github/workflows/test_python_3_11.yml (1)
11-11:
⚠️ Potential issueFix concurrency group configuration
The current concurrency group configuration has potential issues:
- Using '|' for string concatenation in the group name is unconventional
- The boolean expression will be treated as a string literal
- Different label states will create different group names, potentially defeating the purpose of concurrency control
Consider this alternative implementation:
- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ${{ github.event.label.name == 'run-checks' }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}Then control execution using job-level conditions:
jobs: run_common: if: | needs.get_docs_changes.outputs.changes_outside_docs == 'true' && (github.event_name == 'workflow_dispatch' || github.event.label.name == 'run-checks')cognee/infrastructure/engine/__tests__/model_to_graph_to_model.test.py (2)
63-72:
⚠️ Potential issueReplace print statements with proper assertions
The current test uses print statements which don't validate the results.
Add proper assertions to verify the conversion:
nodes, edges = get_graph_from_model(boris) - print(nodes) - print(edges) + assert len(nodes) > 0, "Should generate at least one node" + assert len(edges) > 0, "Should generate at least one edge" - person_data = nodes[len(nodes) - 1] parsed_person = get_model_instance_from_graph(nodes, edges, 'boris') - print(parsed_person) + assert parsed_person.id == boris.id + assert parsed_person.name == boris.name + assert len(parsed_person.owns_car) == len(boris.owns_car) + assert parsed_person.owns_car[0].brand == boris.owns_car[0].brandCommittable suggestion skipped: line range outside the PR's diff.
1-5: 🛠️ Refactor suggestion
Convert main block to proper test functions using pytest
The file is structured as a test but doesn't follow testing best practices. Consider restructuring using pytest fixtures and test functions.
Here's how to improve the structure:
from enum import Enum from typing import Optional +import pytest from cognee.infrastructure.engine import DataPoint from cognee.modules.graph.utils import get_graph_from_model, get_model_instance_from_graph - -if __name__ == "__main__": + +@pytest.fixture +def sample_person(): + # Move class definitions outside + return create_sample_person() + +def test_model_to_graph_conversion(sample_person): + nodes, edges = get_graph_from_model(sample_person) + assert nodes is not None + assert edges is not None + +def test_graph_to_model_conversion(sample_person): + nodes, edges = get_graph_from_model(sample_person) + parsed_person = get_model_instance_from_graph(nodes, edges, 'boris') + assert parsed_person == sample_personCommittable suggestion skipped: line range outside the PR's diff.
cognee/infrastructure/databases/vector/create_vector_engine.py (2)
1-8: 🛠️ Refactor suggestion
Improve type hints and port handling in VectorConfig
The class definition could be enhanced for better type safety and data validation:
- Dict base class should specify type parameters
- Port should be an integer instead of string for proper validation
Consider applying these changes:
- from typing import Dict + from typing import Dict, Union - class VectorConfig(Dict): + class VectorConfig(Dict[str, Union[str, int]]): vector_db_url: str - vector_db_port: str + vector_db_port: int vector_db_key: str vector_db_provider: str📝 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 typing import Dict, Union class VectorConfig(Dict[str, Union[str, int]]): vector_db_url: str vector_db_port: int vector_db_key: str vector_db_provider: str
51-57: 🛠️ Refactor suggestion
Add validation and improve error handling for FalkorDB configuration
The FalkorDB integration needs additional validation and error handling:
- The import from
hybridpackage suggests a potential architecture concern- Missing validation for required configuration
Consider these improvements:
elif config["vector_db_provider"] == "falkordb": from ..hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter + if not config["vector_db_url"] or not config["vector_db_port"]: + raise EnvironmentError("FalkorDB URL and port must be configured") + + try: + port = int(config["vector_db_port"]) + except ValueError: + raise ValueError("FalkorDB port must be a valid integer") + return FalkorDBAdapter( database_url = config["vector_db_url"], - database_port = config["vector_db_port"], + database_port = port, embedding_engine = embedding_engine, )Consider moving FalkorDB adapter to a dedicated vector or graph database package instead of the hybrid package to maintain clear architectural boundaries.
📝 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.elif config["vector_db_provider"] == "falkordb": from ..hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter if not config["vector_db_url"] or not config["vector_db_port"]: raise EnvironmentError("FalkorDB URL and port must be configured") try: port = int(config["vector_db_port"]) except ValueError: raise ValueError("FalkorDB port must be a valid integer") return FalkorDBAdapter( database_url = config["vector_db_url"], database_port = port, embedding_engine = embedding_engine,cognee/modules/data/operations/translate_text.py (3)
5-5:
⚠️ Potential issueFix the incorrect use of
asyncandyieldwith synchronousboto3clientThe
boto3client does not support asynchronous operations out of the box. Defining the function asasyncand usingyieldmay lead to blocking behavior and unexpected results. Consider making the function synchronous or using an asynchronous client likeaioboto3.Apply this diff to make the function synchronous and replace
yieldwithreturn:-async def translate_text(text, source_language: str = "sr", target_language: str = "en", region_name = "eu-west-1"): +def translate_text(text, source_language: str = "sr", target_language: str = "en", region_name = "eu-west-1"): ... - yield result.get("TranslatedText", "No translation found.") + return result.get("TranslatedText", "No translation found.") ... - yield None + return NoneAlso applies to: 33-33, 37-37, 41-41
14-14:
⚠️ Potential issueEnsure consistent return types and update docstring accordingly
The function returns
Nonewhen an error occurs, but the docstring states it returns a string. Consider returning an error message or raising an exception for consistency.If you choose to return error messages, apply this diff:
- return None + return f"BotoCoreError occurred: {e}" ... - return None + return f"ClientError occurred: {e}"And update the docstring:
- str: Translated text or an error message. + str: Translated text or an error message if an error occurred.Alternatively, if the function may return
None, update the docstring and consider usingOptional[str]in the return type hint.Also applies to: 35-41
10-11:
⚠️ Potential issueCorrect the language code standard in the docstring
AWS Translate uses ISO 639-1 language codes, not ISO 639-2. Update the docstrings to prevent confusion.
Apply this diff to correct the docstrings:
- source_language (str): The source language code (e.g., "sr" for Serbian). ISO 639-2 Code https://www.loc.gov/standards/iso639-2/php/code_list.php + source_language (str): The source language code (e.g., "sr" for Serbian). ISO 639-1 Code https://www.iso.org/iso-639-language-codes.html - target_language (str): The target language code (e.g., "en" for English). ISO 639-2 Code https://www.loc.gov/standards/iso639-2/php/code_list.php + target_language (str): The target language code (e.g., "en" for English). ISO 639-1 Code https://www.iso.org/iso-639-language-codes.html📝 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.source_language (str): The source language code (e.g., "sr" for Serbian). ISO 639-1 Code https://www.iso.org/iso-639-language-codes.html target_language (str): The target language code (e.g., "en" for English). ISO 639-1 Code https://www.iso.org/iso-639-language-codes.htmlcognee/shared/SourceCodeGraph.py (2)
31-31: 🛠️ Refactor suggestion
Use
Listinstead oflistfor type annotationIn the
has_methodsattribute,list["Function"]should beList["Function"]to ensure proper type hinting and consistency.Apply this diff to correct it:
-has_methods: list["Function"] +has_methods: List["Function"]📝 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.has_methods: List["Function"]
7-7:
⚠️ Potential issueAvoid using 'type' as an attribute name to prevent shadowing built-in names
Using
typeas an attribute name can cause confusion and potential bugs since it shadows the built-intypefunction. Consider renaming it tovariable_typeorentity_typeto improve code clarity.Apply this diff to correct it:
-type: Literal["Variable"] = "Variable" +entity_type: Literal["Variable"] = "Variable"📝 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.entity_type: Literal["Variable"] = "Variable"cognee/modules/chunking/TextChunker.py (3)
32-38: 🛠️ Refactor suggestion
Refactor duplicated
DocumentChunkcreation into a helper method.The code for creating
DocumentChunkinstances is duplicated in multiple places. Refactoring this into a helper method will improve maintainability and reduce code duplication.Apply this diff to refactor the code:
+ def create_document_chunk(self, id, text, word_count, cut_type): + return DocumentChunk( + id=id, + text=text, + word_count=word_count, + is_part_of=self.document, + chunk_index=self.chunk_index, + cut_type=cut_type, + ) def read(self): for content_text in self.get_text(): for chunk_data in chunk_by_paragraph( content_text, self.max_chunk_size, batch_paragraphs = True, ): if self.chunk_size + chunk_data["word_count"] <= self.max_chunk_size: self.paragraph_chunks.append(chunk_data) self.chunk_size += chunk_data["word_count"] else: if len(self.paragraph_chunks) == 0: - yield DocumentChunk( - id = chunk_data["chunk_id"], - text = chunk_data["text"], - word_count = chunk_data["word_count"], - is_part_of = self.document, - chunk_index = self.chunk_index, - cut_type = chunk_data["cut_type"], - ) + yield self.create_document_chunk( + id=chunk_data["chunk_id"], + text=chunk_data["text"], + word_count=chunk_data["word_count"], + cut_type=chunk_data["cut_type"], + ) self.paragraph_chunks = [] self.chunk_size = 0 else: chunk_text = " ".join(chunk["text"] for chunk in self.paragraph_chunks) try: - yield DocumentChunk( - id = uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"), - text = chunk_text, - word_count = self.chunk_size, - is_part_of = self.document, - chunk_index = self.chunk_index, - cut_type = self.paragraph_chunks[len(self.paragraph_chunks) - 1]["cut_type"], - ) + yield self.create_document_chunk( + id=uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"), + text=chunk_text, + word_count=self.chunk_size, + cut_type=self.paragraph_chunks[-1]["cut_type"], + ) except Exception as e: print(e) self.paragraph_chunks = [chunk_data] self.chunk_size = chunk_data["word_count"] self.chunk_index += 1 if len(self.paragraph_chunks) > 0: try: - yield DocumentChunk( - id = uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"), - text = " ".join(chunk["text"] for chunk in self.paragraph_chunks), - word_count = self.chunk_size, - is_part_of = self.document, - chunk_index = self.chunk_index, - cut_type = self.paragraph_chunks[len(self.paragraph_chunks) - 1]["cut_type"], - ) + yield self.create_document_chunk( + id=uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"), + text=" ".join(chunk["text"] for chunk in self.paragraph_chunks), + word_count=self.chunk_size, + cut_type=self.paragraph_chunks[-1]["cut_type"], + ) except Exception as e: print(e)Also applies to: 43-51, 60-68
43-53: 🛠️ Refactor suggestion
Improve exception handling by avoiding broad exception capture.
Catching all exceptions with
except Exception as ecan obscure the real issues and make debugging harder. It's better to catch specific exceptions or let them propagate.Apply this diff to improve exception handling:
else: chunk_text = " ".join(chunk["text"] for chunk in self.paragraph_chunks) - try: yield self.create_document_chunk( id=uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"), text=chunk_text, word_count=self.chunk_size, cut_type=self.paragraph_chunks[-1]["cut_type"], ) - except Exception as e: - print(e) self.paragraph_chunks = [chunk_data] self.chunk_size = chunk_data["word_count"] self.chunk_index += 1 if len(self.paragraph_chunks) > 0: - try: yield self.create_document_chunk( id=uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"), text=" ".join(chunk["text"] for chunk in self.paragraph_chunks), word_count=self.chunk_size, cut_type=self.paragraph_chunks[-1]["cut_type"], ) - except Exception as e: - print(e)If specific exceptions are expected, catch them explicitly and handle appropriately. Otherwise, consider allowing exceptions to propagate to be handled upstream or add proper logging.
Also applies to: 60-70
7-12:
⚠️ Potential issueMove class attributes to instance attributes within
__init__.Defining mutable default attributes at the class level can lead to shared state between instances, which can cause unexpected behaviors when multiple instances are created. It's better to initialize these attributes within the
__init__method.Apply this diff to fix the issue:
class TextChunker(): - document = None - max_chunk_size: int - chunk_index = 0 - chunk_size = 0 - paragraph_chunks = [] def __init__(self, document, get_text: callable, chunk_size: int = 1024): self.document = document self.max_chunk_size = chunk_size self.get_text = get_text + self.chunk_index = 0 + self.chunk_size = 0 + self.paragraph_chunks = []📝 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 __init__(self, document, get_text: callable, chunk_size: int = 1024): self.document = document self.max_chunk_size = chunk_size self.get_text = get_text self.chunk_index = 0 self.chunk_size = 0 self.paragraph_chunks = []cognee/modules/graph/utils/get_graph_from_model.py (2)
27-31: 🛠️ Refactor suggestion
Use tuples as dictionary keys instead of string concatenation for robustness.
Concatenating strings to create
edge_keycan lead to collisions and is less efficient. Using tuples as dictionary keys enhances readability and avoids potential issues with string manipulation.Apply this diff to update the
edge_keyusage:# First occurrence - edge_key = str(edge[0]) + str(edge[1]) + edge[2] + edge_key = (edge[0], edge[1], edge[2]) - if str(edge_key) not in added_edges: + if edge_key not in added_edges: - added_edges[str(edge_key)] = True + added_edges[edge_key] = True # Second occurrence - edge_key = str(edge[0]) + str(edge[1]) + edge[2] + edge_key = (edge[0], edge[1], edge[2]) - if str(edge_key) not in added_edges: + if edge_key not in added_edges: - added_edges[edge_key] = True + added_edges[edge_key] = TrueAlso applies to: 58-62
5-5:
⚠️ Potential issueAvoid mutable default arguments to prevent unexpected behavior.
Using mutable default arguments like dictionaries (
added_nodes={},added_edges={}) can lead to unexpected behavior because the default mutable object is shared across all function calls. This can cause data from one call to affect another.Apply this diff to fix the issue:
def get_graph_from_model(data_point: DataPoint, include_root=True, added_nodes=None, added_edges=None): + if added_nodes is None: + added_nodes = {} + if added_edges is None: + added_edges = {}📝 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 get_graph_from_model(data_point: DataPoint, include_root=True, added_nodes=None, added_edges=None): if added_nodes is None: added_nodes = {} if added_edges is None: added_edges = {}cognee/api/v1/cognify/code_graph_pipeline.py (2)
40-40:
⚠️ Potential issueUse
isinstance()for type checking to avoid potential issuesAt line 40, using
type(datasets[0]) == stris not recommended. It's better to useisinstance()for type checking, as it handles inheritance and is more idiomatic in Python.Apply this diff to fix the issue:
-if type(datasets[0]) == str: +if isinstance(datasets[0], str):📝 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.if isinstance(datasets[0], str):🧰 Tools
🪛 Ruff
40-40: Use
isandis notfor type comparisons, orisinstance()for isinstance checks(E721)
36-42:
⚠️ Potential issuePrevent potential
IndexErrorby checking ifdatasetsis empty before accessingThe code assumes that
datasetsis not empty when accessingdatasets[0]. Ifdatasetsis empty, this will raise anIndexError. Ensure thatdatasetsis not empty before accessing the first element.Consider adding a check before line 40:
+if not datasets: + logger.warning("No datasets provided to process.") + returnAlternatively, you can adjust the existing condition to handle empty lists:
-if datasets is None or len(datasets) == 0: +if not datasets:Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff
40-40: Use
isandis notfor type comparisons, orisinstance()for isinstance checks(E721)
cognee/tasks/graph/extract_graph_from_data.py (2)
55-55:
⚠️ Potential issueUse tuples instead of string concatenation for dictionary keys to prevent collisions
The keys for
existing_edges_mapandedge_keyare created by concatenating strings (lines 55 and 102), which can lead to collisions if the combined strings are not unique. It's safer to use tuples as dictionary keys.Apply this diff to modify the key generation:
@@ -55 - existing_edges_map[edge[0] + edge[1] + edge[2]] = True + existing_edges_map[(edge[0], edge[1], edge[2])] = True @@ -102 - edge_key = str(source_node_id) + str(target_node_id) + relationship_name + edge_key = (source_node_id, target_node_id, relationship_name)Ensure that all references to these keys are updated accordingly.
Also applies to: 102-102
38-42:
⚠️ Potential issueAccumulate
graph_node_edgesacross all chunks to prevent data lossCurrently,
graph_node_edgesis re-initialized within the loop overdata_chunks(lines 38-42), so only the edges from the last chunk are preserved. This leads to loss of edges from previous chunks. To fix this, initializegraph_node_edgesbefore the loop and accumulate edges across all chunks.Apply this diff to move the initialization and adjust the accumulation:
--- a/cognee/tasks/graph/extract_graph_from_data.py +++ b/cognee/tasks/graph/extract_graph_from_data.py @@ -17,0 +18 @@ + graph_node_edges = [] @@ -23,3 +24,3 @@ for (chunk_index, chunk) in enumerate(data_chunks): chunk_graph = chunk_graphs[chunk_index] - graph_node_edges = [ + graph_node_edges.extend([ (edge.target_node_id, edge.source_node_id, edge.relationship_name) \ @@ -41,0 +42 @@ + ])Committable suggestion skipped: line range outside the PR's diff.
cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py (2)
87-90: 🛠️ Refactor suggestion
Ensure Safe Manipulation of the
propertiesDictionaryWhen modifying the
propertiesdictionary, it's important to ensure that the'id'key exists to prevent potentialKeyError. Althoughdata_point.idis likely to be present, adding a safeguard can enhance robustness.Consider updating the code to safely handle the
'id'key:properties = data_point.model_dump() +if 'id' in properties: properties["uuid"] = properties["id"] del properties["id"] +else: + properties["uuid"] = None # or handle the absence appropriately📝 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.properties = data_point.model_dump() if 'id' in properties: properties["uuid"] = properties["id"] del properties["id"] else: properties["uuid"] = None # or handle the absence appropriately
120-127:
⚠️ Potential issueSafely Access
_metadataindata_pointAccessing
data_point._metadata["index_fields"][0]assumes that_metadataexists and contains the key"index_fields". To prevent potentialAttributeErrororKeyError, ensure that these exist before accessing.Consider modifying the code to include safety checks:
- text = getattr(data_point, data_point._metadata["index_fields"][0]) +index_fields = getattr(data_point, '_metadata', {}).get("index_fields", []) +if index_fields: + text = getattr(data_point, index_fields[0]) +else: + # Handle the absence appropriately, e.g., set text to None or raise an error + text = NoneCommittable suggestion skipped: line range outside the PR's diff.
cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py (2)
129-135:
⚠️ Potential issueAvoid accessing protected member
_metadatainindex_data_pointsDirectly accessing
data_point._metadata["index_fields"][0]breaks encapsulation and may lead to errors if the internal structure changes. Provide a public method or property inDataPointto access index fields safely.First, add a getter method in
DataPoint:class DataPoint: # Existing code... def get_index_field(self) -> str: return getattr(self, self.metadata["index_fields"][0])Then, update the list comprehension in
index_data_points:await self.create_data_points( f"{index_name}_{index_property_name}", [ IndexSchema( id=data_point.id, - text=getattr(data_point, data_point._metadata["index_fields"][0]), + text=data_point.get_index_field(), ) for data_point in data_points ] )
11-17: 🛠️ Refactor suggestion
Avoid using protected member
_metadatadirectly inIndexSchemaThe
_metadataattribute is conventionally considered protected. Directly accessing or defining it may lead to maintenance issues and breaks encapsulation. Consider using a public attribute or property to store metadata.Modify the
IndexSchemaclass to use a public attribute:class IndexSchema(DataPoint): text: str - _metadata: dict = { + metadata: dict = { "index_fields": ["text"] }📝 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.class IndexSchema(DataPoint): text: str metadata: dict = { "index_fields": ["text"] }cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py (3)
76-78:
⚠️ Potential issueEnsure
data_pointsis not empty to preventIndexErrorIn
create_data_points, accessingdata_points[0]without checking if the list is empty can lead to anIndexErrorifdata_pointsis empty. Add a check to handle this case.Apply this diff to handle empty
data_points:async def create_data_points(self, collection_name: str, data_points: list[DataPoint]): connection = await self.get_connection() + if not data_points: + # Handle empty data_points list appropriately + return payload_schema = type(data_points[0]) payload_schema = self.get_data_point_schema(payload_schema) ...Committable suggestion skipped: line range outside the PR's diff.
100-108:
⚠️ Potential issueAvoid using generics at runtime when instantiating
LanceDataPointIn the
create_lance_data_pointfunction, usingLanceDataPoint[str, self.get_data_point_schema(type(data_point))]tries to parameterize the class with types at runtime, which is not valid in Python. Generics are for type hints and not meant for runtime instantiation. UseLanceDataPointwithout generic parameters.Apply this diff to correct the instantiation:
def create_lance_data_point(data_point: DataPoint, vector: list[float]) -> LanceDataPoint: properties = get_own_properties(data_point) properties["id"] = str(properties["id"]) - return LanceDataPoint[str, self.get_data_point_schema(type(data_point))]( + return LanceDataPoint( id = str(data_point.id), vector = vector, payload = properties, )📝 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 create_lance_data_point(data_point: DataPoint, vector: list[float]) -> LanceDataPoint: properties = get_own_properties(data_point) properties["id"] = str(properties["id"]) return LanceDataPoint( id = str(data_point.id), vector = vector, payload = properties, )
216-220:
⚠️ Potential issueAdd error handling for accessing
data_point._metadata["index_fields"]In
index_data_points, accessingdata_point._metadata["index_fields"][0]without validation can lead toKeyErrororIndexErrorif the metadata is missing or empty. Ensure that_metadatacontainsindex_fieldsand that the attribute exists ondata_point.Apply this diff to include error checks:
async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]): await self.create_data_points(f"{index_name}_{index_property_name}", [ - IndexSchema( - id = str(data_point.id), - text = getattr(data_point, data_point._metadata["index_fields"][0]), - ) for data_point in data_points + IndexSchema( + id = str(data_point.id), + text = getattr( + data_point, + data_point._metadata.get("index_fields", [None])[0], + None + ), + ) + for data_point in data_points if data_point._metadata.get("index_fields") and hasattr(data_point, data_point._metadata["index_fields"][0]) ])📝 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.IndexSchema( id = str(data_point.id), text = getattr( data_point, data_point._metadata.get("index_fields", [None])[0], None ), ) for data_point in data_points if data_point._metadata.get("index_fields") and hasattr(data_point, data_point._metadata["index_fields"][0]) ])cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py (1)
131-131:
⚠️ Potential issuePotential
AttributeErrorwhen accessingdata_point._metadataOn line 131, accessing
data_point._metadata["index_fields"][0]may raise anAttributeErrorifdata_pointinstances do not have a_metadataattribute or aKeyErrorif the"index_fields"key is missing. Ensure that allDataPointinstances have the_metadataattribute with the expected structure.To fix this issue, verify that
data_pointobjects include_metadatawith the necessary keys, or add error handling to manage cases where_metadatais absent or improperly structured.cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py (5)
250-251:
⚠️ Potential issueMissing
collection_nameargument when callingdelete_data_pointsIn
delete_nodes, you are callingdelete_data_pointswithout supplying thecollection_nameargument.Apply this diff:
- self.delete_data_points(data_point_ids) + await self.delete_data_points(collection_name, data_point_ids)Also, ensure you
awaitthe asynchronous call and have the correctcollection_name.📝 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.async def delete_nodes(self, collection_name: str, data_point_ids: list[str]): await self.delete_data_points(collection_name, data_point_ids)
230-237:
⚠️ Potential issueAvoid using
awaitinside list comprehensions or generator expressionsUsing
awaitinside list comprehensions can lead to unexpected behavior. It's better to useasyncio.gatherwith a regular for loop.Apply this diff:
- return await asyncio.gather( - *[self.search( - collection_name = collection_name, - query_vector = query_vector, - limit = limit, - with_vector = with_vectors, - ) for query_vector in query_vectors] - ) + tasks = [ + self.search( + collection_name=collection_name, + query_vector=query_vector, + limit=limit, + with_vector=with_vectors, + ) + for query_vector in query_vectors + ] + return await asyncio.gather(*tasks)This ensures compatibility and proper execution of asynchronous tasks.
📝 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.tasks = [ self.search( collection_name=collection_name, query_vector=query_vector, limit=limit, with_vector=with_vectors, ) for query_vector in query_vectors ] return await asyncio.gather(*tasks)
24-24:
⚠️ Potential issueInitialize
embedding_enginewith an instance, not the classIn the
__init__method, the default value forembedding_engineis set to theEmbeddingEngineclass itself rather than an instance. This will cause issues when attempting to call instance methods likeembed_text. You should instantiateEmbeddingEngineor expect an instance to be passed.Apply this diff to fix the issue:
- embedding_engine = EmbeddingEngine, + embedding_engine = None, ): if embedding_engine is None: embedding_engine = EmbeddingEngine() self.embedding_engine = embedding_engineCommittable suggestion skipped: line range outside the PR's diff.
104-109:
⚠️ Potential issueAvoid using bare
exceptclausesAt line 108, a bare
exceptis used, which can catch unexpected exceptions and make debugging difficult. It's best practice to catch specific exceptions.Apply this diff to catch general exceptions explicitly:
try: indices = graph.list_indices() return any([(index[0] == index_name and index_property_name in index[1]) for index in indices.result_set]) - except: + except Exception as e: + logging.error(f"Error checking if vector index exists: {e}") return FalseReplace
Exceptionwith a more specific exception type if possible.📝 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.try: indices = graph.list_indices() return any([(index[0] == index_name and index_property_name in index[1]) for index in indices.result_set]) except Exception as e: logging.error(f"Error checking if vector index exists: {e}") return False🧰 Tools
🪛 Ruff
108-108: Do not use bare
except(E722)
248-248:
⚠️ Potential issueMissing
collection_nameargument when callingdelete_data_pointsIn
delete_node, you are callingdelete_data_pointswithout providing the requiredcollection_nameargument. This will result in aTypeError.Apply this diff:
- return await self.delete_data_points([data_point_id]) + return await self.delete_data_points(collection_name, [data_point_id])Ensure that you have access to the appropriate
collection_namein this context.Committable suggestion skipped: line range outside the PR's diff.
cognee/infrastructure/databases/graph/networkx/adapter.py (5)
83-85:
⚠️ Potential issueAvoid using mutable default arguments
The
edge_propertiesparameter inadd_edgehas a default value of an empty dictionary{}. Using mutable default arguments can lead to unexpected behavior because the same object is shared across all function calls.Apply this diff to set the default value to
Noneand initialize it within the function:async def add_edge( self, from_node: str, to_node: str, relationship_name: str, - edge_properties: Dict[str, Any] = {}, + edge_properties: Optional[Dict[str, Any]] = None, ) -> None: + if edge_properties is None: + edge_properties = {} edge_properties["updated_at"] = datetime.now(timezone.utc) self.graph.add_edge(from_node, to_node, key=relationship_name, **edge_properties)Remember to import
Optionalfromtyping:from typing import Dict, Any, List, Optional
256-257:
⚠️ Potential issueAvoid bare
exceptclausesBare
exceptclauses catch all exceptions, which can mask other issues and make debugging difficult. It's better to catch specific exceptions.Apply this diff to catch specific exceptions:
for node in graph_data["nodes"]: try: node["id"] = UUID(node["id"]) - except: + except ValueError: pass if "updated_at" in node: node["updated_at"] = datetime.strptime(node["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z") for edge in graph_data["links"]: try: source_id = UUID(edge["source"]) target_id = UUID(edge["target"]) edge["source"] = source_id edge["target"] = target_id edge["source_node_id"] = source_id edge["target_node_id"] = target_id - except: + except ValueError: pass if "updated_at" in edge: edge["updated_at"] = datetime.strptime(edge["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z")Also applies to: 270-271
🧰 Tools
🪛 Ruff
256-256: Do not use bare
except(E722)
285-288: 🛠️ Refactor suggestion
Optimize file existence checks
In the
load_graph_from_filemethod, there's a redundant checkif file_path == self.filenamereturning early, but later the code assumesfile_pathis set correctly. Ensure that the logic correctly handles the default filename and avoids unnecessary checks.
37-38:
⚠️ Potential issueImplement the
querymethod or raiseNotImplementedErrorThe
querymethod is currently unimplemented with apassstatement. If this method is intended for future use, consider raising aNotImplementedErrorto clearly indicate that it's pending implementation.Apply this diff to raise a
NotImplementedError:async def query(self, query: str, params: dict): - pass + raise NotImplementedError("The 'query' method is not yet implemented.")📝 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.async def query(self, query: str, params: dict): raise NotImplementedError("The 'query' method is not yet implemented.")
93-97:
⚠️ Potential issueCorrect parameter unpacking and edge property handling
In the
add_edgesmethod, ensure that edge tuples are unpacked correctly and handle cases whereedge_propertiesmight be missing. Also, avoid mutable default arguments similar to theadd_edgemethod.Apply this diff to correct the implementation:
async def add_edges( self, edges: List[Tuple[str, str, str, Optional[Dict[str, Any]]]], ) -> None: - edges = [(edge[0], edge[1], edge[2], { - **(edge[3] if len(edge) == 4 else {}), - "updated_at": datetime.now(timezone.utc), - }) for edge in edges] + processed_edges = [] + for edge in edges: + from_node, to_node, relationship_name = edge[:3] + edge_props = edge[3] if len(edge) == 4 and edge[3] is not None else {} + edge_props["updated_at"] = datetime.now(timezone.utc) + processed_edges.append((from_node, to_node, relationship_name, edge_props)) - self.graph.add_edges_from(edges) + self.graph.add_edges_from(processed_edges) await self.save_graph_to_file(self.filename)This ensures that
edge_propertiesare handled correctly and thatupdated_atis added to each edge.📝 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.processed_edges = [] for edge in edges: from_node, to_node, relationship_name = edge[:3] edge_props = edge[3] if len(edge) == 4 and edge[3] is not None else {} edge_props["updated_at"] = datetime.now(timezone.utc) processed_edges.append((from_node, to_node, relationship_name, edge_props)) self.graph.add_edges_from(processed_edges)cognee/infrastructure/databases/graph/neo4j_driver/adapter.py (7)
50-53:
⚠️ Potential issueMissing 'await' when calling the asynchronous 'query' method.
In the
has_nodemethod, thequerymethod is asynchronous but is called withoutawait. This will result in a coroutine object being returned instead of the actual results.Apply this diff to fix the issue:
async def has_node(self, node_id: str) -> bool: - results = self.query( + results = await self.query( """ MATCH (n) WHERE n.id = $node_id RETURN COUNT(n) > 0 AS node_exists """, {"node_id": node_id} ) return results[0]["node_exists"] if len(results) > 0 else False📝 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.async def has_node(self, node_id: str) -> bool: results = await self.query(
79-85: 🛠️ Refactor suggestion
Set timestamps correctly when adding multiple nodes.
Similar to
add_node, in theadd_nodesmethod, ensure thatcreated_atandupdated_attimestamps are set appropriately in theON CREATEandON MATCHclauses.Apply this diff to update the query:
ON CREATE SET n += node.properties, + n.created_at = timestamp() ON MATCH SET n += node.properties, -ON MATCH SET n.updated_at = timestamp() + n.updated_at = timestamp()📝 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.async def add_nodes(self, nodes: list[DataPoint]) -> None: query = """ UNWIND $nodes AS node MERGE (n {id: node.node_id}) ON CREATE SET n += node.properties, n.created_at = timestamp() ON MATCH SET n += node.properties, n.updated_at = timestamp()
44-46:
⚠️ Potential issueAvoid closing the driver after each query to prevent connection issues.
Calling
self.close()inside thequerymethod closes the driver after each query. This will cause connection errors when subsequent queries attempt to use the closed driver. It's recommended to close the driver once, typically when the application is shutting down.Apply this diff to remove the unnecessary driver closure:
async def query( self, query: str, params: Optional[Dict[str, Any]] = None, ) -> List[Dict[str, Any]]: try: async with self.get_session() as session: result = await session.run(query, parameters=params) data = await result.data() - await self.close() return data except Neo4jError as error: logger.error("Neo4j query error: %s", error, exc_info=True) raise error📝 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.result = await session.run(query, parameters = params) data = await result.data()
92-94:
⚠️ Potential issueAvoid overwriting the 'id' property in node properties.
When serializing node properties, ensure that the
idproperty is not included in thepropertiesdictionary if it's already used as an identifier. This prevents duplication and potential conflicts.Apply this diff to exclude the 'id' property:
nodes = [{ "node_id": str(node.id), - "properties": self.serialize_properties(node.model_dump()), + "properties": self.serialize_properties({ + key: value for key, value in node.model_dump().items() if key != 'id' + }), } for node in nodes]📝 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."node_id": str(node.id), "properties": self.serialize_properties({ key: value for key, value in node.model_dump().items() if key != 'id' }), } for node in nodes]
63-69: 🛠️ Refactor suggestion
Ensure consistent use of 'ON CREATE' and 'ON MATCH' clauses.
In the
add_nodemethod, bothON CREATE SETandON MATCH SETclauses are used to update the node properties. However,updated_at = timestamp()is only set in theON MATCHclause. Consider settingcreated_aton node creation andupdated_aton updates for better tracking.Apply this diff to set timestamps appropriately:
query = """MERGE (node {id: $node_id}) ON CREATE SET node += $properties, + node.created_at = timestamp() ON MATCH SET node += $properties, - ON MATCH SET node.updated_at = timestamp() + node.updated_at = timestamp() RETURN ID(node) AS internal_id, node.id AS nodeId"""📝 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.async def add_node(self, node: DataPoint): serialized_properties = self.serialize_properties(node.model_dump()) query = """MERGE (node {id: $node_id}) ON CREATE SET node += $properties, node.created_at = timestamp() ON MATCH SET node += $properties, node.updated_at = timestamp() RETURN ID(node) AS internal_id, node.id AS nodeId"""
177-182:
⚠️ Potential issueIncorrect use of node UUIDs as labels in 'add_edge' method.
Similar to
has_edge, theadd_edgemethod incorrectly uses node UUIDs as labels in the MATCH clause. Nodes should be matched using properties, not labels derived from UUIDs.Apply this diff to fix the query and parameters:
async def add_edge( self, from_node: str, to_node: str, relationship_name: str, edge_properties: Optional[Dict[str, Any]] = {}, ): serialized_properties = self.serialize_properties(edge_properties) - query = f"""MATCH (from_node:`{str(from_node)}` {{id: $from_node}}), - (to_node:`{str(to_node)}` {{id: $to_node}}) + query = f"""MATCH (from_node {{id: $from_node_id}}), + (to_node {{id: $to_node_id}}) MERGE (from_node)-[r:`{relationship_name}`]->(to_node) ON CREATE SET r += $properties, r.updated_at = timestamp() ON MATCH SET r += $properties, r.updated_at = timestamp() RETURN r""" params = { - "from_node": str(from_node), - "to_node": str(to_node), + "from_node_id": str(from_node), + "to_node_id": str(to_node), "properties": serialized_properties, } return await self.query(query, params)📝 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.query = f"""MATCH (from_node {id: $from_node_id}), (to_node {id: $to_node_id}) MERGE (from_node)-[r:`{relationship_name}`]->(to_node) ON CREATE SET r += $properties, r.updated_at = timestamp() ON MATCH SET r += $properties, r.updated_at = timestamp()
139-142:
⚠️ Potential issueIncorrect usage of UUIDs as labels in Cypher queries.
The
from_nodeandto_nodeUUIDs are being used as labels in the MATCH clause. Labels represent node types, not specific node instances. Using UUIDs as labels can cause unexpected behavior and is not recommended.Apply this diff to correct the query and parameters:
async def has_edge(self, from_node: UUID, to_node: UUID, edge_label: str) -> bool: - query = f""" - MATCH (from_node:`{str(from_node)}`)-[relationship:`{edge_label}`]->(to_node:`{str(to_node)}`) - RETURN COUNT(relationship) > 0 AS edge_exists - """ + query = f""" + MATCH (from_node)-[relationship:`{edge_label}`]->(to_node) + WHERE from_node.id = $from_node_id AND to_node.id = $to_node_id + RETURN COUNT(relationship) > 0 AS edge_exists + """ + params = { + "from_node_id": str(from_node), + "to_node_id": str(to_node), + } - edge_exists = await self.query(query) + edge_exists_result = await self.query(query, params) + edge_exists = edge_exists_result[0]["edge_exists"] if edge_exists_result else False return edge_exists📝 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.async def has_edge(self, from_node: UUID, to_node: UUID, edge_label: str) -> bool: query = f""" MATCH (from_node)-[relationship:`{edge_label}`]->(to_node) WHERE from_node.id = $from_node_id AND to_node.id = $to_node_id RETURN COUNT(relationship) > 0 AS edge_exists """ params = { "from_node_id": str(from_node), "to_node_id": str(to_node), } edge_exists_result = await self.query(query, params) edge_exists = edge_exists_result[0]["edge_exists"] if edge_exists_result else False return edge_exists
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cognee/modules/engine/utils/generate_node_name.py (1)
1-2: 🛠️ Refactor suggestion
Add docstring with examples and parameter validation
The function would benefit from a clear docstring explaining its purpose, parameters, return value, and usage examples. Also consider adding input validation.
Here's a suggested implementation:
+from typing import Optional +from beartype import beartype + +@beartype def generate_node_name(name: str) -> str: + """Generate a normalized node name by converting to lowercase and replacing special characters. + + Args: + name: Input string to be converted into a node name. + Must be a non-empty string. + + Returns: + Normalized node name string. + + Raises: + ValueError: If name is empty or None. + + Examples: + >>> generate_node_name("Hello World") + 'hello_world' + >>> generate_node_name("User's Data") + 'users_data' + """ + if not name or not isinstance(name, str): + raise ValueError("Name must be a non-empty string") + return name.lower().replace(" ", "_").replace("'", "")📝 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 typing import Optional from beartype import beartype @beartype def generate_node_name(name: str) -> str: """Generate a normalized node name by converting to lowercase and replacing special characters. Args: name: Input string to be converted into a node name. Must be a non-empty string. Returns: Normalized node name string. Raises: ValueError: If name is empty or None. Examples: >>> generate_node_name("Hello World") 'hello_world' >>> generate_node_name("User's Data") 'users_data' """ if not name or not isinstance(name, str): raise ValueError("Name must be a non-empty string") return name.lower().replace(" ", "_").replace("'", "")cognee/modules/engine/utils/generate_node_id.py (1)
3-4:
⚠️ Potential issueAdd input validation and proper type checking.
Several improvements are needed for robustness:
- The PR mentions beartype but lacks runtime type checking
- No validation for empty or None input
- The UUID object needs explicit string conversion
Here's the suggested implementation:
+from beartype import beartype from uuid import NAMESPACE_OID, uuid5 +from typing import Optional +@beartype -def generate_node_id(node_id: str) -> str: +def generate_node_id(node_id: Optional[str]) -> str: + if not node_id: + raise ValueError("node_id cannot be empty or None") + normalized_id = node_id.lower().replace(" ", "_").replace("'", "") + if not normalized_id: + raise ValueError("normalized node_id cannot be empty") - return uuid5(NAMESPACE_OID, node_id.lower().replace(" ", "_").replace("'", "")) + return str(uuid5(NAMESPACE_OID, normalized_id))📝 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 beartype import beartype from uuid import NAMESPACE_OID, uuid5 from typing import Optional @beartype def generate_node_id(node_id: Optional[str]) -> str: if not node_id: raise ValueError("node_id cannot be empty or None") normalized_id = node_id.lower().replace(" ", "_").replace("'", "") if not normalized_id: raise ValueError("normalized node_id cannot be empty") return str(uuid5(NAMESPACE_OID, normalized_id))cognee/modules/engine/models/EntityType.py (2)
4-8: 🛠️ Refactor suggestion
Consider renaming the 'type' attribute to avoid shadowing built-in
typeThe attribute name 'type' shadows Python's built-in
typefunction. Consider using a more specific name likeentity_typeortype_name.class EntityType(DataPoint): name: str - type: str + entity_type: str description: str exists_in: DocumentChunk📝 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.class EntityType(DataPoint): name: str entity_type: str description: str exists_in: DocumentChunk
4-11: 🛠️ Refactor suggestion
Add class documentation and runtime type checking
The class lacks documentation explaining its purpose and usage. Also, given the PR title mentions "beartype", consider adding runtime type checking.
+from beartype import beartype + +@beartype class EntityType(DataPoint): + """ + Represents a type classification for entities in the system. + + Attributes: + name (str): The unique identifier for this entity type + entity_type (str): The classification category + description (str): Detailed description of this entity type + exists_in (DocumentChunk): Reference to the source document chunk + """ name: str type: str description: str exists_in: DocumentChunk📝 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 beartype import beartype @beartype class EntityType(DataPoint): """ Represents a type classification for entities in the system. Attributes: name (str): The unique identifier for this entity type entity_type (str): The classification category description (str): Detailed description of this entity type exists_in (DocumentChunk): Reference to the source document chunk """ name: str type: str description: str exists_in: DocumentChunk _metadata: dict = { "index_fields": ["name"], }cognee/modules/data/processing/document_types/AudioDocument.py (2)
8-11: 🛠️ Refactor suggestion
Consider error handling for transcript creation
The transcript creation could fail for various reasons (network issues, invalid audio file, etc.), but there's no error handling.
Consider adding error handling:
def read(self, chunk_size: int): # Transcribe the audio file - result = get_llm_client().create_transcript(self.raw_data_location) - text = result.text + try: + result = get_llm_client().create_transcript(self.raw_data_location) + text = result.text + except Exception as e: + raise ValueError(f"Failed to transcribe audio file: {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 read(self, chunk_size: int): # Transcribe the audio file try: result = get_llm_client().create_transcript(self.raw_data_location) text = result.text except Exception as e: raise ValueError(f"Failed to transcribe audio file: {e}")
1-15:
⚠️ Potential issueVerify attribute initialization and add type hints
The class is missing several key components:
- No constructor is defined, yet
self.raw_data_locationis used in thereadmethod- Missing type hints for
raw_data_locationattributeConsider adding proper initialization and type hints:
class AudioDocument(Document): type: str = "audio" + raw_data_location: str + def __init__(self, raw_data_location: str) -> None: + super().__init__() + self.raw_data_location = raw_data_location📝 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 cognee.infrastructure.llm.get_llm_client import get_llm_client from cognee.modules.chunking.TextChunker import TextChunker from .Document import Document class AudioDocument(Document): type: str = "audio" raw_data_location: str def __init__(self, raw_data_location: str) -> None: super().__init__() self.raw_data_location = raw_data_location def read(self, chunk_size: int): # Transcribe the audio file result = get_llm_client().create_transcript(self.raw_data_location) text = result.text chunker = TextChunker(self, chunk_size = chunk_size, get_text = lambda: text) yield from chunker.read()cognee/tasks/documents/extract_chunks_from_documents.py (1)
9-11:
⚠️ Potential issueAdd error handling and input validation.
The current implementation lacks several important safeguards:
- No error handling for document.read() operations
- No validation of chunk_size
- No handling of empty documents list
Consider implementing these improvements:
@beartype async def extract_chunks_from_documents(documents: list[Document], chunk_size: int = 1024) -> AsyncGenerator[DocumentChunk, None]: + if not documents: + return + + if chunk_size <= 0: + raise ValueError("chunk_size must be positive") + for document in documents: - for document_chunk in document.read(chunk_size = chunk_size): - yield document_chunk + try: + for document_chunk in document.read(chunk_size=chunk_size): + yield document_chunk + except Exception as e: + # Log the error and continue with next document + logger.error(f"Error processing document: {str(e)}") + continue📝 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.if not documents: return if chunk_size <= 0: raise ValueError("chunk_size must be positive") for document in documents: try: for document_chunk in document.read(chunk_size=chunk_size): yield document_chunk except Exception as e: # Log the error and continue with next document logger.error(f"Error processing document: {str(e)}") continuecognee/modules/data/processing/document_types/ImageDocument.py (1)
7-15: 🛠️ Refactor suggestion
Consider adding error handling for image transcription
The image transcription process could fail for various reasons (invalid image, API errors, etc.), but there's no error handling in place.
Consider adding try-catch block:
def read(self, chunk_size: int): # Transcribe the image file - result = get_llm_client().transcribe_image(self.raw_data_location) - text = result.choices[0].message.content + try: + result = get_llm_client().transcribe_image(self.raw_data_location) + text = result.choices[0].message.content + except Exception as e: + raise ValueError(f"Failed to transcribe image: {str(e)}") chunker = TextChunker(self, chunk_size = chunk_size, get_text = lambda: text) yield from chunker.read()📝 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 read(self, chunk_size: int): # Transcribe the image file try: result = get_llm_client().transcribe_image(self.raw_data_location) text = result.choices[0].message.content except Exception as e: raise ValueError(f"Failed to transcribe image: {str(e)}") chunker = TextChunker(self, chunk_size = chunk_size, get_text = lambda: text) yield from chunker.read()cognee/tasks/graph/extract_graph_from_code.py (4)
8-8: 🛠️ Refactor suggestion
Add docstring and input validation
The function lacks documentation and input validation. Consider adding a detailed docstring and validating input parameters.
async def extract_graph_from_code(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel]): + """Extract and process graphs from code chunks. + + Args: + data_chunks (list[DocumentChunk]): List of document chunks containing code + graph_model (Type[BaseModel]): Pydantic model for graph structure + + Returns: + list[DocumentChunk]: Processed document chunks + + Raises: + ValueError: If data_chunks is empty or contains invalid items + """ + if not data_chunks: + raise ValueError("data_chunks cannot be empty") + if not all(isinstance(chunk, DocumentChunk) for chunk in data_chunks): + raise ValueError("All items in data_chunks must be DocumentChunk instances")📝 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.async def extract_graph_from_code(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel]): """Extract and process graphs from code chunks. Args: data_chunks (list[DocumentChunk]): List of document chunks containing code graph_model (Type[BaseModel]): Pydantic model for graph structure Returns: list[DocumentChunk]: Processed document chunks Raises: ValueError: If data_chunks is empty or contains invalid items """ if not data_chunks: raise ValueError("data_chunks cannot be empty") if not all(isinstance(chunk, DocumentChunk) for chunk in data_chunks): raise ValueError("All items in data_chunks must be DocumentChunk instances")
13-15:
⚠️ Potential issueAdd error handling for storage operations
The storage loop lacks error handling and could fail silently. Also, there's a potential index mismatch if previous operations failed.
- for (chunk_index, chunk) in enumerate(data_chunks): - chunk_graph = chunk_graphs[chunk_index] - await add_data_points(chunk_graph.nodes) + for chunk_graph, chunk in zip(results, data_chunks): + try: + if not hasattr(chunk_graph, 'nodes'): + logging.error(f"Invalid graph structure for chunk: {chunk.id}") + continue + await add_data_points(chunk_graph.nodes) + except Exception as e: + logging.error(f"Failed to store nodes for chunk {chunk.id}: {str(e)}") + raiseCommittable suggestion skipped: line range outside the PR's diff.
9-11:
⚠️ Potential issueAdd error handling and batch processing
The concurrent extraction lacks error handling and could potentially overwhelm the system with too many simultaneous tasks.
+ # Process in batches of 10 to avoid overwhelming the system + batch_size = 10 + results = [] + for i in range(0, len(data_chunks), batch_size): + batch = data_chunks[i:i + batch_size] + try: chunk_graphs = await asyncio.gather( - *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] + *[extract_content_graph(chunk.text, graph_model) for chunk in batch], + return_exceptions=True ) + # Handle any exceptions from the batch + for graph in chunk_graphs: + if isinstance(graph, Exception): + # Log the error and continue with other chunks + logging.error(f"Failed to extract graph: {str(graph)}") + else: + results.append(graph) + except Exception as e: + logging.error(f"Batch processing failed: {str(e)}") + raise📝 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.# Process in batches of 10 to avoid overwhelming the system batch_size = 10 results = [] for i in range(0, len(data_chunks), batch_size): batch = data_chunks[i:i + batch_size] try: chunk_graphs = await asyncio.gather( *[extract_content_graph(chunk.text, graph_model) for chunk in batch], return_exceptions=True ) # Handle any exceptions from the batch for graph in chunk_graphs: if isinstance(graph, Exception): # Log the error and continue with other chunks logging.error(f"Failed to extract graph: {str(graph)}") else: results.append(graph) except Exception as e: logging.error(f"Batch processing failed: {str(e)}") raise
1-17: 💡 Codebase verification
Add @beartype decorator to ensure runtime type checking
The file
extract_graph_from_code.pyshould include the beartype decorator for consistency with other task functions in the codebase. Bothextract_graph_from_data.pyandadd_data_points.pyuse the@beartypedecorator for runtime type checking. The type hints are already present, but the decorator is missing.
- Add
from beartype import beartypeimport- Add
@beartypedecorator above theextract_graph_from_codefunction🔗 Analysis chain
Verify beartype integration and pipeline compatibility
Since this PR is about testing beartype in the cognify pipeline, we should verify the type hints are compatible with beartype's runtime type checking.
Let me gather more information about the beartype integration and type hints in the related files.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check if beartype is properly integrated and if there are any type-related issues # Look for beartype usage in the codebase echo "Checking beartype integration..." rg -l "beartype" # Check for type hint usage in related files echo "Checking type hint usage in related files..." rg -l "from typing import|import typing" "cognee/tasks/graph/" # Look for any existing beartype decorators echo "Checking for beartype decorators..." rg "@beartype" "cognee/tasks/graph/"Length of output: 772
Script:
#!/bin/bash # Check the content of extract_graph_from_data.py to see how beartype is used echo "Checking extract_graph_from_data.py implementation..." cat cognee/tasks/graph/extract_graph_from_data.py # Check pyproject.toml for beartype configuration echo -e "\nChecking pyproject.toml for beartype configuration..." cat pyproject.toml | grep -A 5 -B 5 "beartype" # Check add_data_points.py since it's imported in the file under review echo -e "\nChecking add_data_points implementation..." cat cognee/tasks/storage/add_data_points.pyLength of output: 6204
cognee/infrastructure/engine/models/DataPoint.py (3)
17-18:
⚠️ Potential issueDecide on private attribute handling
The commented Config suggests incomplete private attribute handling. Either:
- Uncomment and implement proper private attribute handling, or
- Remove the commented code and document the decision
If keeping private attributes:
- # class Config: - # underscore_attrs_are_private = True + class Config: + underscore_attrs_are_private = True + validate_assignment = True📝 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.class Config: underscore_attrs_are_private = True validate_assignment = True
10-15:
⚠️ Potential issueFix mutable default and enhance metadata validation
Several potential issues to address:
- Using a mutable default value for
_metadatais dangerous- The private attribute handling is incomplete
- Missing validation for metadata structure
Apply these changes:
class DataPoint(BaseModel): id: UUID = Field(default_factory = uuid4) updated_at: Optional[datetime] = datetime.now(timezone.utc) - _metadata: Optional[MetaData] = { - "index_fields": [] - } + _metadata: Optional[MetaData] = Field( + default_factory=lambda: {"index_fields": []}, + description="Metadata containing indexable field names" + )📝 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.class DataPoint(BaseModel): id: UUID = Field(default_factory = uuid4) updated_at: Optional[datetime] = datetime.now(timezone.utc) _metadata: Optional[MetaData] = Field( default_factory=lambda: {"index_fields": []}, description="Metadata containing indexable field names" )
20-24: 🛠️ Refactor suggestion
Improve method documentation, typing, and error handling
The method needs several improvements:
- Missing return type hint and docstring
- No error handling for invalid attribute access
- Complex condition could be simplified
- def get_embeddable_data(self): + def get_embeddable_data(self) -> Optional[any]: + """Get the value of the first index field specified in metadata. + + Returns: + Optional[any]: Value of the first index field if it exists, None otherwise + """ + if not self._metadata or not self._metadata["index_fields"]: + return None + + first_field = self._metadata["index_fields"][0] + try: + return getattr(self, first_field) + except AttributeError: + return None - if self._metadata and len(self._metadata["index_fields"]) > 0 \ - and hasattr(self, self._metadata["index_fields"][0]): - - return getattr(self, self._metadata["index_fields"][0])📝 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 get_embeddable_data(self) -> Optional[any]: """Get the value of the first index field specified in metadata. Returns: Optional[any]: Value of the first index field if it exists, None otherwise """ if not self._metadata or not self._metadata["index_fields"]: return None first_field = self._metadata["index_fields"][0] try: return getattr(self, first_field) except AttributeError: return Nonecognee/infrastructure/databases/vector/config.py (1)
11-11: 💡 Codebase verification
🛠️ Refactor suggestion
Port configuration needs alignment with FalkorDB's standard port
The current default port
1234should be changed as the codebase shows:
- FalkorDB adapter is a supported vector database provider
- QDrant adapter uses its standard port (6333)
- The port value is passed through to the FalkorDB driver's connection
Recommend changing to:
- vector_db_port: int = 1234 + vector_db_port: int = int(os.getenv("VECTOR_DB_PORT", "6334")) # FalkorDB default port + + @field_validator("vector_db_port") + def validate_port(cls, v: int) -> int: + if not 0 <= v <= 65535: + raise ValueError("Port must be between 0 and 65535") + return v🔗 Analysis chain
Consider improving port configuration and validation
The hard-coded default port
1234raises several concerns:
- It's not a standard port for any common database system
- No validation ensures the port is within valid range (0-65535)
- Hard-coded ports can cause conflicts in containerized environments
Consider these improvements:
- vector_db_port: int = 1234 + vector_db_port: int = int(os.getenv("VECTOR_DB_PORT", "6334")) # FalkorDB default port + + @field_validator("vector_db_port") + def validate_port(cls, v: int) -> int: + if not 0 <= v <= 65535: + raise ValueError("Port must be between 0 and 65535") + return vLet's check if this port is used consistently:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for port usage across the codebase rg -A 2 "(?:port.*1234|1234.*port)" --type pyLength of output: 293
Script:
#!/bin/bash # Let's check for any FalkorDB or vector database related configurations and ports rg -i "falkordb|vector.*db.*port" --type py -A 2 # Also check for any environment variable usage in configuration rg "os\.getenv|environ" cognee/infrastructure/databases/vector/config.py -A 2 # Search for any database adapter implementations to understand port usage fd "adapter|client" -e py --exec rg -l "port.*=|connect.*port"Length of output: 5991
Script:
#!/bin/bash # Let's check the specific port usage in FalkorDB adapter and other relevant files rg -A 3 "port.*=|connect.*port" ./cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py ./cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py ./cognee/api/client.py # Check if there are any default port configurations in these adapters rg "DEFAULT.*PORT|port.*=.*\d+" ./cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py ./cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py # Let's also check the vector engine creation to see how ports are handled rg -A 3 "database_port" ./cognee/infrastructure/databases/vector/create_vector_engine.pyLength of output: 2452
cognee/tasks/chunks/remove_disconnected_chunks.py (1)
1-2: 💡 Codebase verification
Update import path in
cognee/modules/data/processing/has_new_chunks.pyFound one file still using the old import path:
cognee/modules/data/processing/has_new_chunks.pyneeds to be updated to usefrom cognee.modules.chunking.models.DocumentChunk import DocumentChunk🔗 Analysis chain
Verify the new import path usage across the codebase
The import path for
DocumentChunkhas been updated. Let's ensure this change is consistent across the codebase.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining old import paths and verify new import path usage # Expected: All imports should use the new path pattern echo "Checking for old import pattern..." rg "from cognee.modules.chunking import DocumentChunk" echo "Verifying new import pattern usage..." rg "from cognee.modules.chunking.models.DocumentChunk import DocumentChunk"Length of output: 1480
cognee/tasks/documents/classify_documents.py (3)
8-14: 🛠️ Refactor suggestion
Refactor the document classification logic for better readability and maintainability
The current implementation using chained ternary operators is hard to read and maintain. Consider using a more explicit approach with a mapping or match statement.
Here's a suggested refactor:
- documents = [ - PdfDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) if data_item.extension == "pdf" else - AudioDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) if data_item.extension == "audio" else - ImageDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) if data_item.extension == "image" else - TextDocument(id = data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location) - for data_item in data_documents - ] + DOCUMENT_TYPES = { + "pdf": PdfDocument, + "audio": AudioDocument, + "image": ImageDocument + } + + def create_document(data_item: Data) -> Document: + document_class = DOCUMENT_TYPES.get(data_item.extension, TextDocument) + return document_class( + id=data_item.id, + name=f"{data_item.name}.{data_item.extension}", + raw_data_location=data_item.raw_data_location + ) + + documents = [create_document(data_item) for data_item in data_documents]📝 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.DOCUMENT_TYPES = { "pdf": PdfDocument, "audio": AudioDocument, "image": ImageDocument } def create_document(data_item: Data) -> Document: document_class = DOCUMENT_TYPES.get(data_item.extension, TextDocument) return document_class( id=data_item.id, name=f"{data_item.name}.{data_item.extension}", raw_data_location=data_item.raw_data_location ) documents = [create_document(data_item) for data_item in data_documents]
6-16:
⚠️ Potential issueAdd input validation and error handling
The function lacks validation for:
- Input data_documents being empty or None
- Required attributes (id, name, extension, raw_data_location) being None
- Unsupported file extensions
Here's a suggested implementation with proper validation:
@beartype def classify_documents(data_documents: list[Data]) -> list[Document]: + if not data_documents: + raise ValueError("data_documents cannot be empty") + + SUPPORTED_EXTENSIONS = {"pdf", "audio", "image"} + + def validate_data_item(data_item: Data) -> None: + if not all([data_item.id, data_item.name, data_item.extension, data_item.raw_data_location]): + raise ValueError(f"Missing required attributes for data_item: {data_item}") + if data_item.extension not in SUPPORTED_EXTENSIONS and not isinstance(data_item, TextDocument): + raise ValueError(f"Unsupported file extension: {data_item.extension}") + + for data_item in data_documents: + validate_data_item(data_item) # Rest of the implementation...Committable suggestion skipped: line range outside the PR's diff.
9-11: 💡 Codebase verification
Extension handling needs improvement
The review comment is correct. The current implementation has several issues:
There's a mismatch between file type detection and document classification:
- The
guess_file_type.pyonly defines custom matchers for "txt" and "pdf" files- But
classify_documents.pyassumes generic "audio" and "image" extensions which aren't properly validatedNo proper mapping exists between detected MIME types/extensions and document types
The file type detection relies on the
filetypelibrary's defaults for audio/image files, but the document classification uses overly generic extensionsRecommendations:
- Define specific file type matchers for supported audio (mp3, wav, etc.) and image (jpg, png, etc.) formats
- Create a configuration mapping between file extensions and document types
- Add validation to ensure only supported extensions are processed
🔗 Analysis chain
Review the extension matching logic
The current extension matching using "audio" and "image" is too generic. Consider:
- Using specific file extensions (e.g., mp3, wav, jpg, png)
- Maintaining a configuration of supported extensions
Let's check how these extensions are used in the codebase:
Let me check the document types implementation to see if there's any validation or configuration of supported extensions.
Let me check one more thing to see how file types are determined and if there's any mapping between extensions and document types.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for file extension handling in the codebase echo "Searching for extension-related code..." rg -l "extension.*audio|extension.*image" --type py echo "Searching for AudioDocument usage..." rg "AudioDocument" --type py -C 2 echo "Searching for ImageDocument usage..." rg "ImageDocument" --type py -C 2Length of output: 4560
Script:
#!/bin/bash # Check for extension validation or configuration echo "Checking Document base class..." rg -A 10 "class Document" --type py echo "Checking for extension configuration files..." fd -e yaml -e json -e toml -e ini echo "Checking for extension validation in data models..." rg "extension.*=|extension.*validate" --type pyLength of output: 5532
Script:
#!/bin/bash # Check file type handling echo "Checking file type implementations..." cat cognee/infrastructure/files/utils/guess_file_type.py echo "Checking file metadata extraction..." cat cognee/infrastructure/files/utils/get_file_metadata.pyLength of output: 2017
cognee/modules/data/operations/detect_language.py (1)
8-8: 💡 Codebase verification
Croatian to Serbian mapping requires documentation and configuration
The language mapping from Croatian ("hr") to Serbian ("sr") is hardcoded in the detect_language.py function, but this significant business logic decision:
- Is not documented in any configuration files
- Is not explained in the codebase beyond a simple comment
- Has implications for the translation service which explicitly uses "sr" as the default source language
Recommendations:
- Move the language mapping to a configuration file
- Document the business reasoning behind this mapping
- Add tests to verify this behavior
- Consider making the mapping configurable for different use cases
🔗 Analysis chain
Verify Croatian to Serbian language mapping requirement
The automatic mapping from Croatian ("hr") to Serbian ("sr") seems to be a significant business logic decision. Please verify if this is an intentional requirement and if it should be documented elsewhere (e.g., in configuration or requirements).
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for other instances of this mapping to ensure consistency rg -l "hr.*sr" --type py # Check if this is documented in README or similar files fd -e md -e rst -e txt | xargs rg -l "Croatian.*Serbian|hr.*sr"Length of output: 142
Script:
#!/bin/bash # Let's see the actual implementation and context of the language mapping rg -A 10 -B 10 "hr.*sr" cognee/modules/data/operations/detect_language.py # Look for any configuration files that might contain language mappings fd -e yaml -e json -e toml -e ini | xargs rg -l "language|hr|sr|Croatian|Serbian" # Check for any test files related to language detection fd -e py | grep -i "test.*language" | xargs cat # Look for any documentation strings or comments about language mapping rg -g "*.py" -l "Croatian|Serbian"Length of output: 1606
Script:
#!/bin/bash # Let's check the translate_text.py file for any related context cat cognee/modules/data/operations/translate_text.py # Let's also check the configuration files for any language-related settings cat pyproject.toml cat cognee/modules/data/extraction/texts.jsonLength of output: 7598
cognee/modules/graph/utils/get_model_instance_from_graph.py (4)
29-29:
⚠️ Potential issueAdd validation for entity_id
The return statement doesn't validate if the entity_id exists in the node_map.
Apply this improvement:
- return node_map[entity_id] + if entity_id not in node_map: + raise KeyError(f"Entity ID {entity_id} not found in node map") + return node_map[entity_id]📝 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.if entity_id not in node_map: raise KeyError(f"Entity ID {entity_id} not found in node map") return node_map[entity_id]
7-10:
⚠️ Potential issueAdd validation for node IDs
The node map population doesn't handle potential duplicate node IDs or validate node.id existence.
Consider applying this improvement:
def get_model_instance_from_graph(nodes: list[DataPoint], edges: list, entity_id: str): node_map = {} for node in nodes: + if not hasattr(node, 'id'): + raise ValueError(f"Node {node} missing required 'id' attribute") + if node.id in node_map: + raise ValueError(f"Duplicate node ID found: {node.id}") node_map[node.id] = node📝 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.node_map = {} for node in nodes: if not hasattr(node, 'id'): raise ValueError(f"Node {node} missing required 'id' attribute") if node.id in node_map: raise ValueError(f"Duplicate node ID found: {node.id}") node_map[node.id] = node
12-19:
⚠️ Potential issueAdd edge validation and safe dictionary access
The edge processing lacks proper validation and error handling.
Apply these improvements:
for edge in edges: + if len(edge) < 3: + raise ValueError(f"Edge {edge} missing required elements (source, target, label)") source_node = node_map[edge[0]] target_node = node_map[edge[1]] edge_label = edge[2] edge_properties = edge[3] if len(edge) == 4 else {} - edge_metadata = edge_properties.get("metadata", {}) - edge_type = edge_metadata.get("type") + edge_metadata = edge_properties.get("metadata", {}) if isinstance(edge_properties, dict) else {} + edge_type = edge_metadata.get("type") if isinstance(edge_metadata, dict) else None📝 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 edge in edges: if len(edge) < 3: raise ValueError(f"Edge {edge} missing required elements (source, target, label)") source_node = node_map[edge[0]] target_node = node_map[edge[1]] edge_label = edge[2] edge_properties = edge[3] if len(edge) == 4 else {} edge_metadata = edge_properties.get("metadata", {}) if isinstance(edge_properties, dict) else {} edge_type = edge_metadata.get("type") if isinstance(edge_metadata, dict) else None
20-27: 🛠️ Refactor suggestion
Improve model creation logic and add error handling
The model creation logic needs validation and optimization.
Consider these improvements:
+ if edge[0] not in node_map: + raise KeyError(f"Source node {edge[0]} not found in node map") + if edge[1] not in node_map: + raise KeyError(f"Target node {edge[1]} not found in node map") + if edge_type == "list": NewModel = copy_model(type(source_node), { edge_label: (list[type(target_node)], PydanticUndefined) }) - - node_map[edge[0]] = NewModel(**source_node.model_dump(), **{ edge_label: [target_node] }) + try: + existing_list = getattr(source_node, edge_label, []) + if not isinstance(existing_list, list): + existing_list = [] + node_map[edge[0]] = NewModel( + **source_node.model_dump(), + **{edge_label: existing_list + [target_node]} + ) + except Exception as e: + raise ValueError(f"Failed to create list model for edge {edge}: {str(e)}") else: NewModel = copy_model(type(source_node), { edge_label: (type(target_node), PydanticUndefined) }) - - node_map[edge[0]] = NewModel(**source_node.model_dump(), **{ edge_label: target_node }) + try: + node_map[edge[0]] = NewModel( + **source_node.model_dump(), + **{edge_label: target_node} + ) + except Exception as e: + raise ValueError(f"Failed to create model for edge {edge}: {str(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.if edge[0] not in node_map: raise KeyError(f"Source node {edge[0]} not found in node map") if edge[1] not in node_map: raise KeyError(f"Target node {edge[1]} not found in node map") if edge_type == "list": NewModel = copy_model(type(source_node), { edge_label: (list[type(target_node)], PydanticUndefined) }) try: existing_list = getattr(source_node, edge_label, []) if not isinstance(existing_list, list): existing_list = [] node_map[edge[0]] = NewModel( **source_node.model_dump(), **{edge_label: existing_list + [target_node]} ) except Exception as e: raise ValueError(f"Failed to create list model for edge {edge}: {str(e)}") else: NewModel = copy_model(type(source_node), { edge_label: (type(target_node), PydanticUndefined) }) try: node_map[edge[0]] = NewModel( **source_node.model_dump(), **{edge_label: target_node} ) except Exception as e: raise ValueError(f"Failed to create model for edge {edge}: {str(e)}")cognee/infrastructure/databases/graph/graph_db_interface.py (1)
6-7: 💡 Codebase verification
Interface change has been properly implemented across adapters
The verification shows that the interface change has been correctly handled:
- Found two implementations of GraphDBInterface:
- Neo4jAdapter: Fully implements the
querymethod with proper error handling and type safety- NetworkXAdapter: Has a placeholder implementation that needs to be completed
The architectural change is consistent across the codebase with no remaining references to the old
graph()method. The Neo4j implementation properly uses the new query interface, while NetworkX adapter acknowledges the interface requirement but needs implementation work.🔗 Analysis chain
Verify the impact of interface change across implementations
The change from
graph()toquery(query: str, params: dict)represents a significant architectural shift. While this makes the interface more flexible for different query types, it also requires careful updates to all implementing classes.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Find all potential implementations of GraphDBInterface echo "Searching for potential implementations of GraphDBInterface..." rg -l "class.*\(.*GraphDBInterface.*\)" # Look for any remaining references to the old graph() method echo "Checking for any remaining references to the old graph() method..." rg "\.graph\(\)"Length of output: 361
Script:
#!/bin/bash # Search for implementations using ast-grep for better accuracy ast-grep --pattern 'class $_ implements GraphDBInterface { $$$ }' ast-grep --pattern 'class $_(GraphDBInterface): $$$ ' # Search for files importing GraphDBInterface echo "Files importing GraphDBInterface:" rg "from.*GraphDBInterface.*import" --type py # Search for query method implementations echo "Query method implementations:" ast-grep --pattern 'async def query(self, query: str, params: dict): $$$'Length of output: 70634
cognee/infrastructure/databases/graph/get_graph_engine.py (1)
23-33:
⚠️ Potential issueAdd error handling for falkordb initialization
Unlike the neo4j case, there's no error handling for falkordb initialization. Both
get_embedding_engine()andFalkorDBAdapterinstantiation could fail.Consider wrapping the initialization in a try-except block similar to neo4j:
elif config.graph_database_provider == "falkordb": + try: from cognee.infrastructure.databases.vector.embeddings import get_embedding_engine from cognee.infrastructure.databases.hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter embedding_engine = get_embedding_engine() return FalkorDBAdapter( database_url = config.graph_database_url, database_port = config.graph_database_port, embedding_engine = embedding_engine, ) + except: + passCommittable suggestion skipped: line range outside the PR's diff.
cognee/modules/storage/utils/__init__.py (3)
1-7: 🛠️ Refactor suggestion
Fix import ordering according to PEP 8
The
pydanticimport should be grouped with other third-party imports at the top of the file.Apply this diff to fix the import ordering:
import json from uuid import UUID from datetime import datetime from pydantic_core import PydanticUndefined +from pydantic import create_model from cognee.infrastructure.engine import DataPoint - -from pydantic import create_modelAlso applies to: 18-18
20-33: 🛠️ Refactor suggestion
Add input validation for model parameters
While the implementation is correct, it lacks validation for input parameters. This could lead to runtime errors if invalid inputs are provided.
Consider adding these validations:
def copy_model(model: DataPoint, include_fields: dict = {}, exclude_fields: list = []): + if not isinstance(model, DataPoint): + raise TypeError("model must be an instance of DataPoint") + if not isinstance(include_fields, dict): + raise TypeError("include_fields must be a dictionary") + if not isinstance(exclude_fields, list): + raise TypeError("exclude_fields must be a list") + fields = { name: (field.annotation, field.default if field.default is not None else PydanticUndefined) for name, field in model.model_fields.items() if name not in exclude_fields }📝 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 copy_model(model: DataPoint, include_fields: dict = {}, exclude_fields: list = []): if not isinstance(model, DataPoint): raise TypeError("model must be an instance of DataPoint") if not isinstance(include_fields, dict): raise TypeError("include_fields must be a dictionary") if not isinstance(exclude_fields, list): raise TypeError("exclude_fields must be a list") fields = { name: (field.annotation, field.default if field.default is not None else PydanticUndefined) for name, field in model.model_fields.items() if name not in exclude_fields } final_fields = { **fields, **include_fields } return create_model(model.__name__, **final_fields)
34-46:
⚠️ Potential issueFix potential IndexError and simplify type checks
The current implementation has a potential bug when handling empty lists and could be simplified.
Apply these improvements:
def get_own_properties(data_point: DataPoint): properties = {} for field_name, field_value in data_point: - if field_name == "_metadata" \ - or isinstance(field_value, dict) \ - or isinstance(field_value, DataPoint) \ - or (isinstance(field_value, list) and isinstance(field_value[0], DataPoint)): + if field_name == "_metadata": + continue + + # Skip nested structures + if isinstance(field_value, (dict, DataPoint)): + continue + + # Safely check for list of DataPoints + if isinstance(field_value, list) and field_value and isinstance(field_value[0], DataPoint): continue properties[field_name] = field_value return properties📝 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 get_own_properties(data_point: DataPoint): properties = {} for field_name, field_value in data_point: if field_name == "_metadata": continue # Skip nested structures if isinstance(field_value, (dict, DataPoint)): continue # Safely check for list of DataPoints if isinstance(field_value, list) and field_value and isinstance(field_value[0], DataPoint): continue properties[field_name] = field_value return propertiescognee/infrastructure/databases/vector/vector_db_interface.py (1)
3-3: 💡 Codebase verification
Import path inconsistency detected
The import in
vector_db_interface.pyis incorrect. WhileDataPointexists, it's actually located incognee.infrastructure.engine.models.DataPoint, not directly incognee.infrastructure.engine. The current import pathfrom cognee.infrastructure.engine import DataPointworks only because of re-export in__init__.py, but it should be imported from its actual location for consistency.
- Update import in
vector_db_interface.pytofrom cognee.infrastructure.engine.models.DataPoint import DataPoint🔗 Analysis chain
Verify the DataPoint import path change
The import path change from relative to absolute suggests a structural reorganization. Let's verify the new location exists and is being used consistently across the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the DataPoint class exists in the new location and check for any remaining old imports # Check if DataPoint exists in the new location echo "Checking new DataPoint location..." ast-grep --pattern 'class DataPoint' cognee/infrastructure/engine/ # Check for any remaining old-style imports that need updating echo "Checking for remaining old-style imports..." rg "from .+models.DataPoint import" --type py # Check for any direct references to the old location echo "Checking for references to old location..." fd "models/DataPoint.py"Length of output: 493
Script:
#!/bin/bash # Let's verify the actual location and content of DataPoint class # Check the content of engine/__init__.py to understand the import chain echo "Checking engine/__init__.py content..." cat cognee/infrastructure/engine/__init__.py # Check if DataPoint exists in engine/models directory echo -e "\nChecking for DataPoint in engine/models..." fd DataPoint.py cognee/infrastructure/engine/models/ # If it exists, check its content echo -e "\nChecking DataPoint implementation..." find cognee/infrastructure/engine/models/ -name "DataPoint.py" -exec cat {} \;Length of output: 1300
cognee/modules/data/operations/translate_text.py (3)
20-24: 🛠️ Refactor suggestion
Enhance input validation for AWS Translate constraints.
AWS Translate has specific limitations that should be validated:
- Maximum text length (5,000 bytes per request)
- Supported language pairs
+from typing import Optional + +MAX_TEXT_BYTES = 5000 + async def translate_text(...): if not text: raise ValueError("No text to translate.") + + text_bytes = text.encode('utf-8') + if len(text_bytes) > MAX_TEXT_BYTES: + raise ValueError(f"Text exceeds maximum length of {MAX_TEXT_BYTES} bytes") if not source_language or not target_language: raise ValueError("Source and target language codes are required.") + + # Add supported language pair validation + supported_languages = await get_supported_languages(region_name) + if source_language not in supported_languages or target_language not in supported_languages: + raise ValueError("Unsupported language pair")Committable suggestion skipped: line range outside the PR's diff.
26-41: 🛠️ Refactor suggestion
Enhance error handling and add resilience mechanisms.
Consider adding:
- Retry mechanism for transient failures
- Timeout handling
- More specific error messages
- Proper AWS session management
+from boto3.session import Session +from botocore.config import Config + async def translate_text(...): try: - translate = boto3.client(service_name="translate", region_name=region_name, use_ssl=True) + session = Session() + config = Config( + retries=dict( + max_attempts=3, + mode='adaptive' + ), + connect_timeout=5, + read_timeout=10 + ) + translate = session.client( + service_name="translate", + region_name=region_name, + config=config + ) result = translate.translate_text( Text=text, SourceLanguageCode=source_language, TargetLanguageCode=target_language, ) - yield result.get("TranslatedText", "No translation found.") + translated_text = result.get("TranslatedText") + if not translated_text: + logger.warning("No translation returned from AWS Translate") + yield None + else: + yield translated_text except BotoCoreError as e: - logger.error(f"BotoCoreError occurred: {e}") + logger.error(f"AWS service error occurred: {str(e)}", exc_info=True) yield None except ClientError as e: - logger.error(f"ClientError occurred: {e}") + error_code = e.response['Error']['Code'] + error_message = e.response['Error']['Message'] + logger.error(f"AWS client error occurred: {error_code} - {error_message}", exc_info=True) yield None📝 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.try: session = Session() config = Config( retries=dict( max_attempts=3, mode='adaptive' ), connect_timeout=5, read_timeout=10 ) translate = session.client( service_name="translate", region_name=region_name, config=config ) result = translate.translate_text( Text=text, SourceLanguageCode=source_language, TargetLanguageCode=target_language, ) translated_text = result.get("TranslatedText") if not translated_text: logger.warning("No translation returned from AWS Translate") yield None else: yield translated_text except BotoCoreError as e: logger.error(f"AWS service error occurred: {str(e)}", exc_info=True) yield None except ClientError as e: error_code = e.response['Error']['Code'] error_message = e.response['Error']['Message'] logger.error(f"AWS client error occurred: {error_code} - {error_message}", exc_info=True) yield None
5-15: 💡 Codebase verification
🛠️ Refactor suggestion
AWS region configuration should be externalized to environment variables
The codebase shows a pattern of using environment variables and
.envfiles for configuration, but AWS region is hardcoded in bothtranslate_text.pyandfetch_secret.py. Align with the existing configuration pattern by:
- Moving AWS region to environment variables
- Using the established pydantic settings pattern for AWS configuration
- Defaulting to environment variable if provided, falling back to "eu-west-1" if not set
-async def translate_text(text, source_language: str = "sr", target_language: str = "en", region_name = "eu-west-1"): +async def translate_text( + text: str, + source_language: str = "sr", + target_language: str = "en", + region_name: str = os.getenv("AWS_REGION", "eu-west-1") +):🔗 Analysis chain
Consider externalizing AWS configuration.
The AWS region is hardcoded. Consider moving it to environment variables or configuration file for better maintainability and flexibility across different environments.
Let's check if AWS configuration exists elsewhere in the codebase:
Also, consider validating language codes against ISO 639-2 standards:
+from typing import Optional + +def is_valid_iso639_2(lang_code: str) -> bool: + # Add ISO 639-2 validation logic + pass + async def translate_text( - text, - source_language: str = "sr", - target_language: str = "en", - region_name = "eu-west-1" + text: str, + source_language: str = "sr", + target_language: str = "en", + region_name: Optional[str] = None ):🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for AWS configuration patterns rg -l "AWS_REGION|aws_region|region_name" --type pyLength of output: 125
Script:
#!/bin/bash # Check the content of fetch_secret.py for AWS configuration patterns cat cognee/fetch_secret.pyLength of output: 1806
Script:
#!/bin/bash # Check for environment variable usage in both files rg "os\.environ|getenv" cognee/fetch_secret.py cognee/modules/data/operations/translate_text.pyLength of output: 162
Script:
#!/bin/bash # Look for any config files or settings modules fd -e py "config|settings"Length of output: 720
Script:
#!/bin/bash # Check base_config.py for AWS configuration cat cognee/base_config.pyLength of output: 867
Script:
#!/bin/bash # Check infrastructure config files for AWS settings cat cognee/infrastructure/databases/vector/config.py cat cognee/infrastructure/databases/relational/config.pyLength of output: 1998
.github/workflows/test_python_3_9.yml (1)
11-11:
⚠️ Potential issueFix concurrency group syntax and improve label handling
There are several issues with the current concurrency configuration:
- The
|separator in the group name might cause parsing issues- The label check might fail for non-labeled events (like
workflow_dispatch)Apply this fix:
- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ${{ github.event.label.name == 'run-checks' }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ github.event.label.name == 'run-checks' }}Also consider:
- Adding a condition to handle non-labeled events:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ github.event.label && github.event.label.name == 'run-checks' || 'manual' }}
- Moving the label name to an environment variable for better maintainability
.github/workflows/test_python_3_10.yml (1)
4-8: 💡 Codebase verification
Workflow trigger configuration is consistent but restrictive
The verification confirms that all Python test workflows (3.9, 3.10, and 3.11) have identical trigger configurations:
- All workflows only trigger on labeled events
- Manual triggers via workflow_dispatch are available
- Consistent branch restriction to 'main'
This consistent implementation across all test workflows indicates an intentional design choice rather than an oversight. However, the original concern about delayed feedback due to requiring labels remains valid.
- All test workflows (
test_python_3.9.yml,test_python_3.10.yml,test_python_3.11.yml) require the 'labeled' event trigger- This configuration may delay test feedback as it depends on manual label application
- Consider implementing one of these alternatives:
- Add
[opened, synchronize]to thetypesarray to also run on PR creation and updates- Move the label check to job-level conditions while keeping automatic PR triggers
🔗 Analysis chain
Verify the impact of restricted PR triggers
The workflow now only triggers on labeled events, which means tests won't run automatically when PRs are created or updated. This could lead to delays in feedback if the 'run-checks' label isn't applied promptly.
Consider one of these alternatives:
- Keep automatic triggers and use the label as an additional condition in job-level
ifstatements- Document the required label in the repository's contributing guidelines
Run this script to verify other workflow files for consistency:
This script will help us verify the trigger configurations across all Python test workflows by focusing specifically on the 'on' section of each workflow file. This will give us a clearer picture of whether the trigger restrictions are consistent across all test workflows.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check if other test workflows have similar trigger restrictions # Expected: All test workflows should have consistent trigger configurations echo "Checking workflow trigger configurations:" for workflow in $(fd "test_python" .github/workflows/); do echo "=== $workflow ===" rg "on:|workflow_dispatch:|pull_request:|types:" "$workflow" -A 5 doneLength of output: 1405
Script:
#!/bin/bash # Let's check the workflow trigger configurations with a different approach echo "=== Checking workflow triggers ===" for workflow in .github/workflows/test_python_*.yml; do echo -e "\n## $workflow ##" # Extract the 'on' section and its immediate contents awk '/^on:/{p=1;print;next} p&&/^[a-z]/{p=0}p' "$workflow" doneLength of output: 1086
.github/workflows/test_python_3_11.yml (2)
11-11:
⚠️ Potential issueFix the concurrency group configuration syntax
The current concurrency group configuration has syntax issues that could cause unexpected behavior:
- The
|character in the group name might be interpreted as an OR operator- The label condition is appended as a string rather than being part of the condition
- group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} | ${{ github.event.label.name == 'run-checks' }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ github.event.label.name == 'run-checks' }}📝 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.group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ github.event.label.name == 'run-checks' }}
4-8: 💡 Codebase verification
🛠️ Refactor suggestion
Add missing label condition to match other workflow files
The workflow should include the same label condition as other test workflows in the repository. Add:
pull_request: branches: - main types: [labeled] + if: needs.get_docs_changes.outputs.changes_outside_docs == 'true' | ${{ github.event.label.name == 'run-checks' }}
- The workflow runs on the 'run-checks' label, consistent with other test workflows
- It also includes the docs-change condition to skip tests when only docs are modified
🔗 Analysis chain
Specify the required label in the workflow trigger configuration
The workflow is configured to run only on labeled events, but it doesn't specify which label triggers the workflow. This could lead to confusion or accidental triggers with wrong labels.
pull_request: branches: - main types: [labeled] + if: github.event.label.name == 'run-checks'Let's verify if other workflows use consistent label triggers:
Let me check if any of these workflows specify the required label in their configuration.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for consistency in label-based triggers across workflows # Expected: All test workflows should use the same label trigger pattern # Search for label-related configurations in all workflow files rg -U "types.*labeled" .github/workflows/Length of output: 502
Script:
#!/bin/bash # Check for label conditions in workflows rg -U "if:.*label" .github/workflows/Length of output: 809
cognee/infrastructure/engine/__tests__/model_to_graph_to_model.test.py (3)
7-8: 🛠️ Refactor suggestion
Convert to proper test framework structure
This file is located in a
__tests__directory but uses a main guard pattern instead of a proper test framework. Consider restructuring this as a proper test case using pytest or unittest.Example structure:
import pytest def test_model_to_graph_conversion(): # Setup test data boris = Person(...) # Test conversion nodes, edges = get_graph_from_model(boris) parsed_person = get_model_instance_from_graph(nodes, edges, 'boris') # Assertions assert parsed_person.id == boris.id assert parsed_person.name == boris.name # Add more assertions
65-72:
⚠️ Potential issueAdd proper assertions for conversion validation
The test only prints results without validating the conversion. Add proper assertions to verify the graph structure and parsed data.
# Verify nodes structure assert len(nodes) == 3 # Person, Car, and CarType nodes assert any(node.get('id') == 'boris' for node in nodes) # Verify edges assert any(edge['from'] == 'boris' and edge['relationship'] == 'owns_car' for edge in edges) # Verify parsed data matches original assert parsed_person.id == boris.id assert parsed_person.name == boris.name assert parsed_person.age == boris.age assert len(parsed_person.owns_car) == len(boris.owns_car) assert parsed_person.owns_car[0].brand == boris.owns_car[0].brand
68-70:
⚠️ Potential issueAvoid fragile node access pattern
Accessing the last node by index is fragile and could break if the node order changes. Consider finding the node by id or type.
- person_data = nodes[len(nodes) - 1] + person_data = next(node for node in nodes if node.get('id') == 'boris')📝 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.person_data = next(node for node in nodes if node.get('id') == 'boris') parsed_person = get_model_instance_from_graph(nodes, edges, 'boris')cognee/infrastructure/databases/vector/create_vector_engine.py (1)
51-57: 🛠️ Refactor suggestion
Multiple improvements needed for FalkorDB integration
- The deep relative import (
..hybrid.falkordb) suggests suboptimal module organization. Consider restructuring to maintain consistency with other adapters.- Unlike other providers (e.g., Weaviate), there's no validation for missing configuration.
- Parameter naming is inconsistent with other adapters (database_url vs url).
Consider applying these improvements:
elif config["vector_db_provider"] == "falkordb": - from ..hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter + from .falkordb.FalkorDBAdapter import FalkorDBAdapter + + if config["vector_db_url"] is None or config["vector_db_port"] is None: + raise EnvironmentError("FalkorDB is not configured!") return FalkorDBAdapter( - database_url = config["vector_db_url"], - database_port = config["vector_db_port"], + url = config["vector_db_url"], + port = config["vector_db_port"], embedding_engine = embedding_engine, )📝 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.elif config["vector_db_provider"] == "falkordb": from .falkordb.FalkorDBAdapter import FalkorDBAdapter if config["vector_db_url"] is None or config["vector_db_port"] is None: raise EnvironmentError("FalkorDB is not configured!") return FalkorDBAdapter( url = config["vector_db_url"], port = config["vector_db_port"], embedding_engine = embedding_engine,cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py (1)
117-119: 🛠️ Refactor suggestion
Add error handling for collection creation
The method should handle cases where the collection might already exist and validate input parameters.
Consider adding error handling:
async def create_vector_index(self, index_name: str, index_property_name: str): + if not index_name or not index_property_name: + raise ValueError("index_name and index_property_name must not be empty") + collection_name = f"{index_name}_{index_property_name}" + if await self.has_collection(collection_name): + logger.warning(f"Collection {collection_name} already exists") + return await self.create_collection(f"{index_name}_{index_property_name}")Committable suggestion skipped: line range outside the PR's diff.
cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py (2)
126-127:
⚠️ Potential issueAdd error handling and input validation.
The method should validate inputs and handle potential errors:
- Validate index_name and index_property_name length and characters
- Add error handling for collection creation failures
Example implementation:
async def create_vector_index(self, index_name: str, index_property_name: str): + if not index_name or not index_property_name: + raise ValueError("Index name and property name cannot be empty") + + collection_name = f"{index_name}_{index_property_name}" + if len(collection_name) > 255: # Adjust limit based on Qdrant's requirements + raise ValueError("Combined index name exceeds maximum length") + + try: + await self.create_collection(collection_name) + except Exception as e: + logger.error(f"Failed to create vector index: {str(e)}") + raise - await self.create_collection(f"{index_name}_{index_property_name}")Committable suggestion skipped: line range outside the PR's diff.
129-135:
⚠️ Potential issueAdd validation for index fields and error handling.
The method makes assumptions about the data structure without proper validation:
- No check if data_point._metadata exists
- No validation of "index_fields" in metadata
- No error handling if the specified field doesn't exist
Suggested implementation:
async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]): + def get_index_field(data_point: DataPoint) -> str: + if not hasattr(data_point, '_metadata'): + raise ValueError(f"Data point {data_point.id} missing _metadata") + if 'index_fields' not in data_point._metadata: + raise ValueError(f"Data point {data_point.id} missing index_fields in metadata") + field_name = data_point._metadata["index_fields"][0] + if not hasattr(data_point, field_name): + raise ValueError(f"Data point {data_point.id} missing required field {field_name}") + return getattr(data_point, field_name) + + try: await self.create_data_points(f"{index_name}_{index_property_name}", [ IndexSchema( id = data_point.id, - text = getattr(data_point, data_point._metadata["index_fields"][0]), + text = get_index_field(data_point), ) for data_point in data_points ]) + except Exception as e: + logger.error(f"Failed to index data points: {str(e)}") + raise📝 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.async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]): def get_index_field(data_point: DataPoint) -> str: if not hasattr(data_point, '_metadata'): raise ValueError(f"Data point {data_point.id} missing _metadata") if 'index_fields' not in data_point._metadata: raise ValueError(f"Data point {data_point.id} missing index_fields in metadata") field_name = data_point._metadata["index_fields"][0] if not hasattr(data_point, field_name): raise ValueError(f"Data point {data_point.id} missing required field {field_name}") return getattr(data_point, field_name) try: await self.create_data_points(f"{index_name}_{index_property_name}", [ IndexSchema( id = data_point.id, text = get_index_field(data_point), ) for data_point in data_points ]) except Exception as e: logger.error(f"Failed to index data points: {str(e)}") raisecognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py (3)
159-163:
⚠️ Potential issueAdd UUID validation for ID conversion
The code assumes all IDs can be converted to UUID. This could raise ValueError if an ID string is not a valid UUID format.
Consider adding error handling:
# In retrieve method return [ ScoredResult( - id = UUID(result.id), + id = UUID(result.id) if isinstance(result.id, str) else result.id, payload = result.payload, score = 0 ) for result in results ] # In search method return [ ScoredResult( - id = UUID(str(row.id)), + id = UUID(str(row.id)) if not isinstance(row.id, UUID) else row.id, payload = row.payload, score = row.similarity ) for row in vector_list ]Also applies to: 206-209
85-87:
⚠️ Potential issueAdd validation for empty data_points list
The code assumes data_points list is non-empty when accessing data_points[0]. This could lead to an IndexError if the list is empty.
Consider adding validation:
async def create_data_points( self, collection_name: str, data_points: List[DataPoint] ): + if not data_points: + raise ValueError("data_points list cannot be empty") async with self.get_async_session() as session: if not await self.has_collection(collection_name): await self.create_collection( collection_name=collection_name, payload_schema=type(data_points[0]), )📝 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.async def create_data_points( self, collection_name: str, data_points: List[DataPoint] ): if not data_points: raise ValueError("data_points list cannot be empty") async with self.get_async_session() as session: if not await self.has_collection(collection_name): await self.create_collection( collection_name=collection_name, payload_schema=type(data_points[0]), )
124-134:
⚠️ Potential issueAdd error handling for index field access
The index_data_points method assumes the first index field exists in data_point._metadata["index_fields"]. This could raise KeyError or IndexError if metadata is missing or malformed.
Consider adding validation:
async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]): + for data_point in data_points: + if not hasattr(data_point, '_metadata') or 'index_fields' not in data_point._metadata: + raise ValueError(f"Data point {data_point.id} missing required index metadata") + if not data_point._metadata['index_fields']: + raise ValueError(f"Data point {data_point.id} has empty index_fields") + if not hasattr(data_point, data_point._metadata['index_fields'][0]): + raise ValueError(f"Data point {data_point.id} missing required index field: {data_point._metadata['index_fields'][0]}") await self.create_data_points(f"{index_name}_{index_property_name}", [ IndexSchema( id = data_point.id, text = getattr(data_point, data_point._metadata["index_fields"][0]), ) for data_point in data_points ])📝 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.async def create_vector_index(self, index_name: str, index_property_name: str): await self.create_collection(f"{index_name}_{index_property_name}") async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]): for data_point in data_points: if not hasattr(data_point, '_metadata') or 'index_fields' not in data_point._metadata: raise ValueError(f"Data point {data_point.id} missing required index metadata") if not data_point._metadata['index_fields']: raise ValueError(f"Data point {data_point.id} has empty index_fields") if not hasattr(data_point, data_point._metadata['index_fields'][0]): raise ValueError(f"Data point {data_point.id} missing required index field: {data_point._metadata['index_fields'][0]}") await self.create_data_points(f"{index_name}_{index_property_name}", [ IndexSchema( id = data_point.id, text = getattr(data_point, data_point._metadata["index_fields"][0]), ) for data_point in data_points ])cognee/shared/SourceCodeGraph.py (1)
31-31: 🛠️ Refactor suggestion
Use
Listinstead oflistfor type annotations.In type annotations, it's recommended to use
Listfrom thetypingmodule instead of the built-inlistto support static type checking and maintain consistency.Apply this diff to update the type annotation:
-has_methods: list["Function"] +has_methods: List["Function"]Committable suggestion skipped: line range outside the PR's diff.
cognee/modules/chunking/TextChunker.py (3)
43-53:
⚠️ Potential issueImprove exception handling by logging and specifying exceptions
Catching all exceptions with
except Exception as ecan mask unexpected errors and hinder debugging. Additionally, usingprint(e)is inadequate for error reporting in production code. It's better to catch specific exceptions you anticipate and use theloggingmodule to log errors appropriately. Re-raise the exception after logging to ensure that calling code can handle it.Apply this diff to enhance error handling:
try: yield DocumentChunk( ... ) -except Exception as e: - print(e) +except SpecificException as e: + logger.exception("Error yielding DocumentChunk: %s", e) + raiseRemember to import and configure the logging module at the top of your file:
import logging logger = logging.getLogger(__name__)Replace
SpecificExceptionwith the actual exception(s) you expect could occur.
7-11:
⚠️ Potential issueAvoid using class variables for instance-specific state
Defining
document,max_chunk_size,chunk_index,chunk_size, andparagraph_chunksas class variables can lead to shared state across all instances ofTextChunker, resulting in unexpected behavior when multiple instances are used concurrently. These should be instance variables initialized within the__init__method.Apply this diff to move these variables to instance variables:
class TextChunker(): - document = None - max_chunk_size: int - chunk_index = 0 - chunk_size = 0 - paragraph_chunks = [] def __init__(self, document, get_text: callable, chunk_size: int = 1024): + self.document = document + self.max_chunk_size = chunk_size + self.get_text = get_text + self.chunk_index = 0 + self.chunk_size = 0 + self.paragraph_chunks = []📝 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 __init__(self, document, get_text: callable, chunk_size: int = 1024): self.document = document self.max_chunk_size = chunk_size self.get_text = get_text self.chunk_index = 0 self.chunk_size = 0 self.paragraph_chunks = []
60-70:
⚠️ Potential issueEnsure consistent and robust exception handling in final chunk processing
Similar to the previous comment, avoid catching all exceptions indiscriminately and improve logging in the final chunk processing. This practice ensures errors are properly logged and can be debugged effectively.
Apply this diff:
try: yield DocumentChunk( ... ) -except Exception as e: - print(e) +except SpecificException as e: + logger.exception("Error yielding final DocumentChunk: %s", e) + raiseEnsure you handle specific exceptions and log them appropriately.
Committable suggestion skipped: line range outside the PR's diff.
cognee/modules/graph/utils/get_graph_from_model.py (1)
5-5:
⚠️ Potential issueAvoid mutable default arguments in function definition
Using mutable default arguments like
{}foradded_nodesandadded_edgescan lead to unexpected behavior because the default dictionaries are shared across all function calls. This can cause unintended side effects when the function is called multiple times.Apply this diff to fix the issue:
-def get_graph_from_model(data_point: DataPoint, include_root = True, added_nodes = {}, added_edges = {}): +def get_graph_from_model(data_point: DataPoint, include_root = True, added_nodes = None, added_edges = None):Then, initialize the dictionaries inside the function:
if added_nodes is None: added_nodes = {} if added_edges is None: added_edges = {}cognee/api/v1/cognify/code_graph_pipeline.py (1)
40-40: 🛠️ Refactor suggestion
Use
isinstance()for type checking instead oftype()Using
type()for type checking is discouraged because it does not account for inheritance and can lead to issues with subclasses. It's better to useisinstance()for more reliable and Pythonic type checking.Here's the suggested change:
-if type(datasets[0]) == str: +if isinstance(datasets[0], str):📝 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.if isinstance(datasets[0], str):🧰 Tools
🪛 Ruff
40-40: Use
isandis notfor type comparisons, orisinstance()for isinstance checks(E721)
cognee/tasks/graph/extract_graph_from_data.py (4)
55-55: 🛠️ Refactor suggestion
Prevent key collisions in
existing_edges_mapUsing string concatenation for keys may lead to collisions. Use tuples as keys instead to ensure uniqueness.
Apply this diff:
- existing_edges_map[edge[0] + edge[1] + edge[2]] = True + existing_edges_map[(edge[0], edge[1], edge[2])] = True📝 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.existing_edges_map[(edge[0], edge[1], edge[2])] = True
102-105: 🛠️ Refactor suggestion
Use tuples for
edge_keyto ensure uniquenessConcatenating IDs and relationship names into a string for
edge_keycan cause collisions. Use a tuple to represent the key.Apply this diff:
- edge_key = str(source_node_id) + str(target_node_id) + relationship_name + edge_key = (source_node_id, target_node_id, relationship_name)Ensure that
existing_edges_mapchecks use this tuple key.📝 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.edge_key = (source_node_id, target_node_id, relationship_name) if edge_key not in existing_edges_map: graph_edges.append((
24-25:
⚠️ Potential issueCheck for
Nonebefore accessingchunk_graph.nodesIf
chunk_graphisNone, accessingchunk_graph.nodeswill raise anAttributeError. Add a check to ensurechunk_graphis notNonebefore iterating over its nodes.Apply this diff to fix the issue:
for (chunk_index, chunk) in enumerate(data_chunks): chunk_graph = chunk_graphs[chunk_index] + if chunk_graph is None: + continue for node in chunk_graph.nodes:Committable suggestion skipped: line range outside the PR's diff.
38-41: 💡 Codebase verification
Based on the Edge class definition in
cognee/shared/data_models.py, I can now provide a definitive response about the edge construction order.The edge construction order in the code is incorrect
The Edge class clearly defines the order as
source_node_idfollowed bytarget_node_id, but in the list comprehension, the order is reversed:(edge.target_node_id, edge.source_node_id, edge.relationship_name). This reversal changes the direction of the relationships in the graph.
- Location:
cognee/tasks/graph/extract_graph_from_data.py:38-41- Current:
(edge.target_node_id, edge.source_node_id, edge.relationship_name)- Should be:
(edge.source_node_id, edge.target_node_id, edge.relationship_name)🔗 Analysis chain
Verify the order of source and target node IDs in edge construction
In
graph_node_edges, the source and target node IDs seem to be reversed. Currently,edge.target_node_idis used as the source, andedge.source_node_idas the target. Confirm if this is intentional.Run the following script to verify the consistency of edge constructions:
Let me gather more context about the edge construction and usage pattern in the codebase.
Let me search for the Edge class definition and its usage in the codebase to understand the intended direction of edges.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of source and target node IDs in edge constructions. # Search for all instances where edges are constructed. rg -A 2 'graph_node_edges = \[' # Expected: Edge tuples should be in the format (source_node_id, target_node_id, relationship_name)Length of output: 335
Script:
#!/bin/bash # Search for the Edge class definition and its usage ast-grep --pattern 'class Edge' # Search for any methods that use these edges rg -A 5 'def.*edge' # Search for any documentation or type hints related to edges rg -A 3 '(Edge|edge).*:'Length of output: 47555
Script:
#!/bin/bash # Search for Edge class definition and its usage rg -A 10 'class Edge' # Search for where chunk_graph is created/modified rg -A 5 'chunk_graph.*='Length of output: 2520
cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py (4)
15-22: 🛠️ Refactor suggestion
Ensure consistent ID type in
IndexSchemaThe
idfield inIndexSchemais defined asstr, but elsewhere in the code, IDs are handled asUUID. To maintain consistency and prevent type-related issues, consider definingidasUUIDor ensure consistent handling of IDs as strings throughout the codebase.
100-109:
⚠️ Potential issueVerify generic type usage in
create_lance_data_pointWhen creating
LanceDataPoint, specifying generic parameters withself.get_data_point_schema(type(data_point))might lead to unexpected behavior or type issues. Ensure that the type parameters are correctly applied and compatible with theGenerictype usage inLanceDataPoint.
167-176:
⚠️ Potential issueHandle empty search results to prevent exceptions
In the
searchmethod, computingmin()andmax()on an emptyresult_valueslist will raise aValueErrorif no results are found. Add a check for emptyresult_valuesbefore calculatingmin_valueandmax_valueto avoid potential exceptions.Apply this diff to handle empty results:
result_values = list(results.to_dict("index").values()) +if not result_values: + return [] min_value = min(result["_distance"] for result in result_values) max_value = max(result["_distance"] for result in result_values)Committable suggestion skipped: line range outside the PR's diff.
214-221:
⚠️ Potential issueEnsure safe access to
data_point._metadatainindex_data_pointsAccessing
data_point._metadata["index_fields"][0]may raise aKeyErrorifindex_fieldsis not present. Confirm that allDataPointinstances have_metadatawithindex_fields, or add error handling to prevent exceptions.Consider adding a default or error handling:
IndexSchema( id = str(data_point.id), - text = getattr(data_point, data_point._metadata["index_fields"][0]), + text = getattr( + data_point, + data_point._metadata.get("index_fields", ["default_field"])[0], + "" + ), )📝 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.async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]): await self.create_data_points(f"{index_name}_{index_property_name}", [ IndexSchema( id = str(data_point.id), text = getattr( data_point, data_point._metadata.get("index_fields", ["default_field"])[0], "" ), ) for data_point in data_points ])cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py (3)
33-33:
⚠️ Potential issueAvoid using mutable default arguments
Using a mutable default argument like
params: dict = {}can lead to unexpected behavior because the same dictionary is shared across all function calls. It's better to useNoneas the default value and initialize a new dictionary inside the function.Apply this diff:
- def query(self, query: str, params: dict = {}): + def query(self, query: str, params: dict = None): + if params is None: + params = {} graph = self.driver.select_graph(self.graph_name)Committable suggestion skipped: line range outside the PR's diff.
108-109: 🛠️ Refactor suggestion
Specify exception type instead of using a bare
exceptUsing a bare
exceptclause can catch unexpected exceptions and make debugging difficult. Specify the exception type you want to handle to avoid masking other exceptions.Apply this diff:
- except: + except Exception:Or, if you know the specific exception that might occur, replace
Exceptionwith that specific exception type.Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff
108-108: Do not use bare
except(E722)
24-24:
⚠️ Potential issueFix the default value of
embedding_engineparameterThe default value for
embedding_engineis set toEmbeddingEngine, which is likely a class, not an instance. This can cause runtime errors whenembed_textis called on a class instead of an instance. Consider changing the default value toNoneand instantiatingEmbeddingEngineinside the__init__method if no instance is provided.Apply this diff:
- embedding_engine = EmbeddingEngine, + embedding_engine: EmbeddingEngine = None,Then, update the
__init__method:self.driver = FalkorDB( host = database_url, port = database_port, ) - self.embedding_engine = embedding_engine + if embedding_engine is None: + self.embedding_engine = EmbeddingEngine() + else: + self.embedding_engine = embedding_engineCommittable suggestion skipped: line range outside the PR's diff.
cognee/infrastructure/databases/graph/networkx/adapter.py (4)
37-38:
⚠️ Potential issueImplement the
querymethod or raiseNotImplementedErrorThe
querymethod is currently empty and contains only apassstatement. To clearly indicate that this method is intended to be implemented in the future, consider raising aNotImplementedError.Apply this diff to raise
NotImplementedError:async def query(self, query: str, params: dict): - pass + raise NotImplementedError("The 'query' method is not implemented yet.")📝 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.async def query(self, query: str, params: dict): raise NotImplementedError("The 'query' method is not implemented yet.")
255-257:
⚠️ Potential issueAvoid using bare
except; specify the exception typeUsing a bare
exceptclause can catch unexpected exceptions likeKeyboardInterruptorSystemExit. It's better to catch specific exceptions to prevent masking other errors.Apply this diff to specify the exception type:
try: node["id"] = UUID(node["id"]) -except: +except ValueError: passIf multiple exception types are possible, consider catching
Exceptionor the specific exceptions that can occur during UUID conversion.📝 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.node["id"] = UUID(node["id"]) except ValueError: pass🧰 Tools
🪛 Ruff
256-256: Do not use bare
except(E722)
273-275:
⚠️ Potential issueCorrect the condition to check 'updated_at' in
edgeinstead ofnodeThe condition is currently checking
if "updated_at" in nodebut then attempts to accessedge["updated_at"], which may lead to aKeyError. The condition should check for"updated_at"inedge.Apply this diff to correct the condition:
- if "updated_at" in node: + if "updated_at" in edge: edge["updated_at"] = datetime.strptime(edge["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z")📝 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.if "updated_at" in edge: edge["updated_at"] = datetime.strptime(edge["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z")
270-271:
⚠️ Potential issueAvoid using bare
except; specify the exception typeSimilar to the previous comment, replace the bare
exceptwith a specific exception to improve error handling.Apply this diff to specify the exception type:
try: # UUID conversion logic -except: +except ValueError: passEnsure that you catch the appropriate exceptions that may arise during the conversion.
Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff
270-270: Do not use bare
except(E722)
cognee/infrastructure/databases/graph/neo4j_driver/adapter.py (4)
204-210: 🛠️ Refactor suggestion
⚠️ Potential issueEnsure correct node identification and property handling in batch edge addition
In the
add_edgesmethod, verify that nodes are matched based on theiridproperties rather than labels derived from UUIDs. Also, ensure that the propertiessource_node_idandtarget_node_idare correctly assigned without duplication.Suggested modifications:
- Match nodes by
idproperty in the query.- Avoid overwriting existing properties when adding
source_node_idandtarget_node_id.Updated query:
query = """ UNWIND $edges AS edge MATCH (from_node {id: edge.from_node}) MATCH (to_node {id: edge.to_node}) CALL apoc.create.relationship(from_node, edge.relationship_name, edge.properties, to_node) YIELD rel RETURN rel """Update edge construction:
edges = [{ "from_node": str(edge[0]), "to_node": str(edge[1]), "relationship_name": edge[2], "properties": { **(edge[3] if edge[3] else {}), - "source_node_id": str(edge[0]), - "target_node_id": str(edge[1]), }, } for edge in edges]Committable suggestion skipped: line range outside the PR's diff.
63-69: 🛠️ Refactor suggestion
Set 'created_at' and 'updated_at' timestamps correctly when adding nodes
In the
add_nodemethod, theupdated_attimestamp is currently set only during theON MATCHclause. To accurately record both the creation and update times of a node, consider settingcreated_atduring theON CREATEclause andupdated_atduring bothON CREATEandON MATCHclauses.Proposed query modification:
query = """MERGE (node {id: $node_id}) ON CREATE SET node += $properties, + node.created_at = timestamp(), + node.updated_at = timestamp() ON MATCH SET node += $properties, + node.updated_at = timestamp() RETURN ID(node) AS internal_id, node.id AS nodeId"""📝 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.async def add_node(self, node: DataPoint): serialized_properties = self.serialize_properties(node.model_dump()) query = """MERGE (node {id: $node_id}) ON CREATE SET node += $properties, node.created_at = timestamp(), node.updated_at = timestamp() ON MATCH SET node += $properties, node.updated_at = timestamp() RETURN ID(node) AS internal_id, node.id AS nodeId"""
79-85: 🛠️ Refactor suggestion
Set 'created_at' and 'updated_at' timestamps correctly in batch node addition
In the
add_nodesmethod, theupdated_attimestamp is set only during theON MATCHclause. For consistent timestamping and to track node creation times, setcreated_atduringON CREATEandupdated_atduring bothON CREATEandON MATCHclauses.Proposed query modification:
query = """ UNWIND $nodes AS node MERGE (n {id: node.node_id}) ON CREATE SET n += node.properties, + n.created_at = timestamp(), + n.updated_at = timestamp() ON MATCH SET n += node.properties, + n.updated_at = timestamp() WITH n, node.node_id AS label CALL apoc.create.addLabels(n, [label]) YIELD node AS labeledNode RETURN ID(labeledNode) AS internal_id, labeledNode.id AS nodeId """Committable suggestion skipped: line range outside the PR's diff.
139-143:
⚠️ Potential issueCorrect node matching by using properties instead of labels
In the
has_edgemethod, UUIDs are incorrectly used as labels in the MATCH clause. Labels in Neo4j are meant for node categories, not unique identifiers like UUIDs. To match nodes by theirid, use property matching instead.Proposed query correction:
query = """ - MATCH (from_node:`{str(from_node)}`)-[relationship:`{edge_label}`]->(to_node:`{str(to_node)}`) + MATCH (from_node {id: $from_node_id})-[relationship:`{edge_label}`]->(to_node {id: $to_node_id}) RETURN COUNT(relationship) > 0 AS edge_exists """Update parameters:
params = { + "from_node_id": str(from_node), + "to_node_id": str(to_node) }Committable suggestion skipped: line range outside the PR's diff.
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Actionable comments posted: 10
🧹 Outside diff range and nitpick comments (16)
cognee/modules/engine/utils/generate_edge_name.py (1)
1-2: Add function documentationAdd a docstring to explain the function's purpose, parameters, return value, and provide usage examples.
Add documentation:
def generate_edge_name(name: str) -> str: + """Generate a standardized edge name by converting input to lowercase and replacing special characters. + + Args: + name (str): The input string to be converted into an edge name + + Returns: + str: A standardized edge name with spaces converted to underscores and special characters removed + + Examples: + >>> generate_edge_name("User's Profile") + "users_profile" + >>> generate_edge_name("Product Category") + "product_category" + """ return name.lower().replace(" ", "_").replace("'", "")cognee/tasks/storage/add_data_points.py (3)
1-7: Consider using absolute imports for better maintainabilityWhile relative imports work, consider using absolute imports (e.g.,
from cognee.tasks.storage.index_data_points import index_data_points) to make the code more maintainable and less prone to breakage if the module structure changes.
8-9: Add docstring to document function behaviorThe function would benefit from a docstring explaining:
- Purpose of the function
- Parameters and their expected format
- Return value description
- Any side effects (e.g., database operations)
- Potential exceptions that might be raised
Example:
@beartype async def add_data_points(data_points: list[DataPoint]) -> list[DataPoint]: """Add data points to the graph database. Args: data_points: List of DataPoint objects to be processed and stored Returns: The same list of DataPoint objects that was passed in Raises: Any exceptions from graph_engine operations """
26-26: Consider enhancing return value with operation statusThe function returns the original data_points without indicating success/failure of the graph operations. Consider returning a tuple of (data_points, success_status) or raising exceptions on failure to help callers handle errors appropriately.
Example:
from typing import Tuple async def add_data_points(data_points: list[DataPoint]) -> Tuple[list[DataPoint], bool]: success = False try: # ... existing operations ... success = True except Exception: # log error raise return data_points, successcognee/tests/test_qdrant.py (2)
40-41: Enhance test coverage for vector searchWhile the test verifies basic search functionality, consider adding test cases for:
- Edge cases (empty search terms, non-existent entities)
- Multiple search results
- Different entity types
Would you like me to provide examples of additional test cases?
40-41: Document schema changes in docstrings or commentsThe change in payload structure and search fields would benefit from documentation explaining the new schema.
Add a comment explaining the expected payload structure and search fields, for example:
# Search for entities using Entity_name field # Expected payload structure: {"text": str, ...} random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0] random_node_name = random_node.payload["text"]cognee/tests/test_weaviate.py (2)
Line range hint
1-58: Enhance test coverage and documentationWhile the test demonstrates basic functionality, it could be improved in several areas:
- The test lacks assertions about the content of the search results
- There's no error handling for failed searches
- The test data setup could be moved to fixtures
Consider adding:
- Content validation in assertions
- Error cases
- Cleanup in a teardown phase
Example improvements:
# Add content validation assert "quantum" in random_node_name, "Search result should contain the search term" # Add error handling test with pytest.raises(SomeException): await vector_engine.search("Entity_name", "") # Empty search term # Add cleanup def teardown_module(): asyncio.run(cognee.prune.prune_data()) asyncio.run(cognee.prune.prune_system(metadata=True))
38-39: Consider using test constants for search parametersThe hardcoded search parameters could be moved to test constants for better maintainability.
+# At the top of the file +ENTITY_TYPE = "Entity_name" +SEARCH_TERM = "quantum computer" + # In the test -random_node = (await vector_engine.search("Entity_name", "quantum computer"))[0] +random_node = (await vector_engine.search(ENTITY_TYPE, SEARCH_TERM))[0]cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py (1)
170-179: Add UUID validation and document score inversionTwo potential improvements for the search results handling:
- The UUID conversion could fail with invalid IDs
- The score inversion (1 - score) needs documentation explaining its purpose
Consider adding validation and documentation:
return [ ScoredResult( - id = UUID(result.id), + id = UUID(result.id) if is_valid_uuid(result.id) else result.id, payload = { **result.payload, - "id": UUID(result.id), + "id": UUID(result.id) if is_valid_uuid(result.id) else result.id, }, + # Invert score because Qdrant returns similarity scores (1=similar, 0=different) + # while our application expects distance scores (0=similar, 1=different) score = 1 - result.score, ) for result in results ] +def is_valid_uuid(uuid_str: str) -> bool: + try: + UUID(uuid_str) + return True + except ValueError: + return Falsecognee/infrastructure/databases/graph/networkx/adapter.py (5)
8-8: Remove unused importThe import
from re import Ais not used anywhere in the code.-from re import A
44-48: Add return type hints for better IDE supportThe method signatures would benefit from explicit return type hints.
- async def add_node( - self, - node: DataPoint, - ) -> None: - self.graph.add_node(node.id, **node.model_dump()) + async def add_node( + self, + node: DataPoint, + ) -> None: + """Add a single node to the graph. + + Args: + node (DataPoint): The node to add + """ + self.graph.add_node(node.id, **node.model_dump())
Line range hint
183-210: Consider optimizing connection retrievalThe
get_connectionsmethod could be optimized to reduce complexity and improve performance:
- Combine predecessor and successor loops
- Use list comprehension for better readability
async def get_connections(self, node_id: UUID) -> list: if not self.graph.has_node(node_id): return [] node = self.graph.nodes[node_id] if "id" not in node: return [] - predecessors, successors = await asyncio.gather( - self.get_predecessors(node_id), - self.get_successors(node_id), - ) - - connections = [] - - for neighbor in predecessors: - if "id" in neighbor: - edge_data = self.graph.get_edge_data(neighbor["id"], node["id"]) - for edge_properties in edge_data.values(): - connections.append((neighbor, edge_properties, node)) - - for neighbor in successors: - if "id" in neighbor: - edge_data = self.graph.get_edge_data(node["id"], neighbor["id"]) - for edge_properties in edge_data.values(): - connections.append((node, edge_properties, neighbor)) - - return connections + connections = [ + (neighbor, edge_props, node) + for neighbor in await self.get_predecessors(node_id) + if "id" in neighbor + for edge_props in self.graph.get_edge_data(neighbor["id"], node["id"]).values() + ] + [ + (node, edge_props, neighbor) + for neighbor in await self.get_successors(node_id) + if "id" in neighbor + for edge_props in self.graph.get_edge_data(node["id"], neighbor["id"]).values() + ] + return connections
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235-239: Add error handling for file operationsThe
save_graph_to_filemethod should include error handling to manage potential I/O errors.async def save_graph_to_file(self, file_path: str=None) -> None: """Asynchronously save the graph to a file in JSON format.""" if not file_path: file_path = self.filename - - graph_data = nx.readwrite.json_graph.node_link_data(self.graph) - - async with aiofiles.open(file_path, "w") as file: - await file.write(json.dumps(graph_data, cls = JSONEncoder)) + try: + graph_data = nx.readwrite.json_graph.node_link_data(self.graph) + async with aiofiles.open(file_path, "w") as file: + await file.write(json.dumps(graph_data, cls = JSONEncoder)) + except Exception as e: + logger.error(f"Failed to save graph to file {file_path}: {str(e)}") + raise
Line range hint
1-307: Consider architectural improvements for better maintainabilityWhile the implementation is solid, consider the following architectural improvements:
- Add comprehensive docstrings to all public methods
- Consider implementing connection pooling for better performance with large graphs
- Add metrics/monitoring for graph operations
- Consider implementing a caching layer for frequently accessed nodes/edges
Would you like assistance in implementing any of these architectural improvements?
cognee/tasks/summarization/summarize_text.py (1)
12-12: Consider returning the generated summaries instead of input data chunksThe function currently returns the original
data_chunksafter generating summaries and adding them to the storage. If the intention is to provide the generated summaries to the caller for further processing, consider returningsummariesinstead.Apply the following changes to return the summaries and update the return type annotation:
-async def summarize_text(data_chunks: list[DocumentChunk], summarization_model: Type[BaseModel]) -> list[DocumentChunk]: +async def summarize_text(data_chunks: list[DocumentChunk], summarization_model: Type[BaseModel]) -> list[TextSummary]: ... - return data_chunks + return summariescognee/tasks/graph/extract_graph_from_data.py (1)
14-16: Handle exceptions in asynchronous graph extraction for robustness.Using
asyncio.gatherwithout exception handling may cause the entire operation to fail if one coroutine raises an exception. To improve resilience, consider using thereturn_exceptions=Trueparameter and handle exceptions individually.Modify the
asyncio.gathercall:chunk_graphs = await asyncio.gather( *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] + , return_exceptions=True)Then, handle exceptions after gathering results:
for idx, result in enumerate(chunk_graphs): if isinstance(result, Exception): # Log the exception and handle accordingly # For example, set the result to None or remove the corresponding chunk chunk_graphs[idx] = NoneThis ensures that exceptions in one chunk do not interrupt the processing of others.
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⛔ Files ignored due to path filters (1)
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📒 Files selected for processing (16)
README.md(1 hunks)cognee/infrastructure/databases/graph/networkx/adapter.py(9 hunks)cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py(5 hunks)cognee/modules/engine/utils/__init__.py(1 hunks)cognee/modules/engine/utils/generate_edge_name.py(1 hunks)cognee/modules/engine/utils/generate_node_name.py(1 hunks)cognee/shared/utils.py(4 hunks)cognee/tasks/documents/check_permissions_on_documents.py(1 hunks)cognee/tasks/documents/classify_documents.py(1 hunks)cognee/tasks/documents/extract_chunks_from_documents.py(1 hunks)cognee/tasks/graph/extract_graph_from_data.py(1 hunks)cognee/tasks/storage/add_data_points.py(1 hunks)cognee/tasks/summarization/summarize_text.py(1 hunks)cognee/tests/test_qdrant.py(1 hunks)cognee/tests/test_weaviate.py(1 hunks)pyproject.toml(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- README.md
🚧 Files skipped from review as they are similar to previous changes (5)
- cognee/modules/engine/utils/generate_node_name.py
- cognee/shared/utils.py
- cognee/tasks/documents/check_permissions_on_documents.py
- cognee/tasks/documents/classify_documents.py
- cognee/tasks/documents/extract_chunks_from_documents.py
🧰 Additional context used
🪛 Ruff
cognee/infrastructure/databases/graph/networkx/adapter.py
256-256: Do not use bare except
(E722)
270-270: Do not use bare except
(E722)
🔇 Additional comments (14)
cognee/modules/engine/utils/generate_edge_name.py (1)
1-2: Verify usage in graph operations
Since this is a utility function for graph edge names, we should verify its usage in graph-related operations.
✅ Verification successful
Let me check the actual usage in the graph extraction file to understand how this function is being used.
Function usage is consistent and properly integrated
The verification shows that generate_edge_name is used appropriately in graph operations:
- It's used in
extract_graph_from_data.pyto normalize relationship names before creating edges - The function's behavior (lowercasing and replacing spaces/apostrophes) aligns with similar utilities like
generate_node_id - The normalized edge names are used consistently in both Neo4j and NetworkX graph adapters
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for usage of generate_edge_name in graph-related files
echo "Searching for generate_edge_name usage in graph operations..."
rg -l "generate_edge_name" | grep -i "graph"
# Check for existing edge name generation patterns that might conflict
echo "Checking for existing edge name generation patterns..."
rg -A 2 "\.lower\(\)\.replace\(['\"] ['\"], ['\"]_['\"]\)"
Length of output: 653
Script:
#!/bin/bash
# Check how generate_edge_name is used in graph extraction
echo "Checking usage in graph extraction:"
rg -A 5 "generate_edge_name" cognee/tasks/graph/extract_graph_from_data.py
# Also check if there are any other edge name manipulations in graph-related files
echo -e "\nChecking other edge name patterns in graph files:"
rg -l "edge.*name" | grep -i "graph" | xargs rg "edge.*name" -A 2
Length of output: 5676
cognee/modules/engine/utils/__init__.py (2)
1-3: LGTM! Clean and well-organized imports.
The imports are well-structured with clear separation of concerns, making the utility functions easily accessible from the package level.
1-3: Verify the module structure and dependencies.
Let's ensure all required modules are present in the new location and the imports resolve correctly.
✅ Verification successful
Let me verify the imports in the files that use these utility functions to ensure they're using the correct paths.
Module structure and dependencies are correctly configured
All utility modules are present in the new location (cognee/modules/engine/utils/), and the imports in dependent files (extract_graph_from_data.py and infer_data_ontology.py) are correctly using the new path cognee.modules.engine.utils. No references to the old location were found.
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Verify the presence and structure of the utility modules
# Check if all referenced modules exist
echo "Checking for utility modules..."
for module in generate_node_id.py generate_node_name.py generate_edge_name.py; do
if fd -p "cognee/modules/engine/utils/$module"; then
echo "✓ Found $module"
else
echo "✗ Missing $module"
fi
done
# Check for any remaining references to the old location
echo -e "\nChecking for references to old location..."
rg -l "cognee/modules/graph/utils" || echo "No references to old location found"
# Look for any imports of these functions in the codebase
echo -e "\nChecking usage of utility functions..."
rg -l "import.*generate_(node|edge)_(id|name)" || echo "No imports found"
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echo "Checking import statements in extract_graph_from_data.py:"
rg "import.*generate_(node|edge)_(id|name)" cognee/tasks/graph/extract_graph_from_data.py -B1 -A1
echo -e "\nChecking import statements in infer_data_ontology.py:"
rg "import.*generate_(node|edge)_(id|name)" cognee/tasks/graph/infer_data_ontology.py -B1 -A1
# Also check if there are any direct references to the old path
echo -e "\nChecking for any remaining old path references in these files:"
rg "graph/utils" cognee/tasks/graph/extract_graph_from_data.py cognee/tasks/graph/infer_data_ontology.py
Length of output: 1026
cognee/tasks/storage/add_data_points.py (1)
19-24:
Add error handling for async operations
The async operations lack error handling which could lead to inconsistent state if any operation fails.
Consider wrapping the operations in try-except blocks:
try:
graph_engine = await get_graph_engine()
except Exception as e:
logger.error(f"Failed to get graph engine: {e}")
raise
try:
await index_data_points(data_points)
await graph_engine.add_nodes(nodes)
await graph_engine.add_edges(edges)
except Exception as e:
logger.error(f"Failed to process graph data: {e}")
# Consider cleanup or rollback operations
raiseVerify the order of operations
Ensure that indexing must occur before adding nodes/edges to the graph.
✅ Verification successful
Let me analyze the search results to verify the order of operations and generate a final response.
Error handling is needed, but operation order is correct
The search results confirm that the order of operations (indexing before graph operations) is consistent across the codebase:
- In
add_data_points.py, indexing happens before graph operations - The
index_data_pointsfunction is implemented in vector database adapters (QDrant, PGVector, Weaviate, LanceDB) and handles vector indexing - Graph operations (
add_nodes,add_edges) are implemented in graph database adapters (NetworkX, Neo4j) and handle graph persistence
The original error handling suggestion remains valid as these are critical persistence operations.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other usages of these operations to verify the correct order
rg -A 5 "index_data_points|add_nodes|add_edges" --type py
Length of output: 13477
pyproject.toml (2)
73-73: Consider performance implications of runtime type checking
Adding beartype for runtime type checking aligns with the PR objective but could impact performance. Consider:
- Implementing selective type checking for critical paths only
- Adding performance benchmarks before full deployment
- Using it primarily in development/testing environments
✅ Verification successful
Based on the results, I can now generate a final response since I have a clear picture of how beartype is being used in the codebase.
Runtime type checking is appropriately scoped to critical data processing tasks
The current implementation already follows best practices by selectively applying @beartype decorators to key data processing functions:
- Data classification (
classify_documents) - Document chunking (
extract_chunks_from_documents) - Storage operations (
add_data_points) - Summarization (
summarize_text) - Graph extraction (
extract_graph_from_data)
These are high-level task functions where type validation is valuable for data integrity, and the performance overhead is justified given their operational scope.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check current usage of beartype decorators
# Search for beartype usage patterns
rg "@beartype" -A 2
# Look for performance-critical paths
ast-grep --pattern 'from beartype import beartype'
Length of output: 1877
61-61: Verify compatibility with LanceDB major version upgrade
The upgrade from LanceDB 0.8.0 to 0.15.0 is a significant jump that might introduce breaking changes.
✅ Verification successful
Let's analyze the actual usage patterns to understand potential breaking changes impact.
LanceDB upgrade appears safe to proceed
The codebase uses LanceDB's stable APIs that have remained consistent:
- Core functionality like
connect_async,create_table,vector_search, andmerge_insertare standard features - The implementation follows LanceDB's recommended patterns using
LanceModelandVector - No deprecated or removed features are being used
The adapter implementation is well-isolated and follows a clean interface pattern, making it easier to handle any minor compatibility issues if they arise.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for breaking changes in LanceDB upgrade
# Check changelog or release notes
curl -s https://raw.githubusercontent.com/lancedb/lancedb/main/CHANGELOG.md | grep -A 10 "0.15.0"
# Search for LanceDB usage in codebase
rg "lancedb" -l
Length of output: 585
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#!/bin/bash
# Check LanceDB usage patterns in the codebase
rg -A 5 "from lancedb|import lancedb"
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cognee/tests/test_qdrant.py (1)
40-41: Verify schema changes across the codebase
The changes to the search field ("Entity_name") and payload access ("text") suggest a schema modification. Let's ensure these changes are consistent throughout the codebase.
✅ Verification successful
The previous search was too restrictive with exact quotes. Let's broaden the search to find all payload access patterns and search method calls, along with the full test file content for better context.
Schema changes are consistent across the codebase
The search results show that the schema changes are consistently implemented across all test files and core functionality:
-
All vector engine searches for entities use "Entity_name" as the collection name:
- test_qdrant.py, test_weaviate.py, test_neo4j.py, test_library.py, test_pgvector.py
- tasks/graph/query_graph_connections.py
-
All payload access uses the "text" field consistently:
- All test files access node.payload["text"]
- No instances of the old "name" field were found
The changes in test_qdrant.py align with the established patterns across the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for any remaining uses of the old schema
echo "Checking for old payload access patterns..."
rg -l '"name".*payload' --type py
echo "Checking for old entity search patterns..."
rg -l '"entities".*search' --type py
# Verify the new schema is used consistently
echo "Checking new schema usage..."
rg -l '"Entity_name".*search' --type py
rg -l '"text".*payload' --type py
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# Let's try a broader search without exact string matches
echo "Checking payload access patterns..."
rg "payload\[" --type py -A 1 -B 1
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cat cognee/tests/test_qdrant.py
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cognee/tests/test_weaviate.py (1)
38-39: Verify schema changes across the codebase
The changes in search parameters ("Entity_name") and payload access ("text") suggest a schema modification. Let's ensure these changes are consistent across the codebase.
✅ Verification successful
Let me try another search pattern to verify the payload access consistency.
Based on the search results, I can now generate the final response:
Schema changes are consistent across the codebase
The changes in the test file align with the rest of the codebase:
- All test files consistently use "Entity_name" as the collection name for vector search
- All payload access patterns consistently use payload["text"] across different vector engine implementations (Weaviate, Qdrant, PGVector, etc.)
- The search term variation ("quantum computer" vs "AI") is just test-specific data and doesn't impact the schema
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other occurrences of vector_engine.search to verify consistent usage
rg -A 2 'vector_engine\.search\(' --type py
# Search for payload access patterns to verify schema consistency
rg -A 2 'payload\[(["'\''])(?:name|text)\1\]' --type py
# Search for class/type definitions to verify entity schema
ast-grep --pattern 'class Entity_name {
$$$
}'
Length of output: 2428
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#!/bin/bash
# Search for payload access patterns without backreference
rg -A 2 'payload\[["'\''][a-z]+["'\'']\]' --type py
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rg -A 5 'class Entity_name' --type py
Length of output: 1235
cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py (3)
13-18: LGTM: Well-structured schema definition
The IndexSchema class is well-designed with clear field definition and metadata structure.
108-109: LGTM: Good use of model_dump() and proper ID handling
The changes to use model_dump() and string IDs are appropriate and follow best practices.
128-129: Verify consistent collection naming across codebase
The new index naming convention {index_name}_{index_property_name} should be used consistently throughout the codebase.
✅ Verification successful
Let me gather more information about collection name usage with different patterns.
Let me try one more search with a modified pattern to specifically look for the collection name string formations.
Collection naming convention is consistently implemented across vector adapters
Based on the search results, the collection naming pattern f"{index_name}_{index_property_name}" is consistently used across all vector database adapters:
- QDrantAdapter.py
- WeaviateAdapter.py
- PGVectorAdapter.py
- LanceDBAdapter.py
Each adapter implements the same naming convention in both their create_vector_index and subsequent collection operations, ensuring consistency throughout the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Search for collection name usage patterns to ensure consistency
echo "Searching for collection name patterns..."
rg -g '!*.pyc' -g '!*test*' 'collection_name.*=.*_.*_' .
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ast-grep --pattern 'await client.create_collection(collection_name = $_)' .
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# Search for collection name patterns and collection operations more broadly
echo "Searching for collection name references..."
rg -g '!*.pyc' -g '!*test*' 'collection.*=|collection_name.*=' -A 2 .
echo "Searching for create_collection calls..."
rg -g '!*.pyc' -g '!*test*' 'create_collection\(' -A 2 .
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echo "Searching for collection name string formations..."
rg -g '!*.pyc' -g '!*test*' 'f".*_.*"' .
echo "Searching specifically in vector adapter implementations..."
rg -g '!*.pyc' -g '!*test*' 'create_vector_index' -A 3 .
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cognee/tasks/summarization/summarize_text.py (3)
2-3: Addition of runtime type checking with Beartype
The inclusion of beartype and Type imports enables runtime type enforcement at runtime, which can help catch type-related errors early.
24-24: Ensure correct access to the summary attribute
In text = chunk_summaries[chunk_index].summary, confirm that extract_summary returns an object with a summary attribute. If it returns a plain string, this line may raise an AttributeError.
If extract_summary returns a string, update the code as follows:
- text = chunk_summaries[chunk_index].summary,
+ text = chunk_summaries[chunk_index],11-12: Ensure all function calls are updated to match the new signature
The collection_name parameter has been removed from the summarize_text function signature. Please verify that all calls to this function have been updated accordingly to prevent any runtime errors.
Run the following script to find any calls to summarize_text that still use the collection_name parameter:
✅ Verification successful
Function calls are already compatible with the new signature
Based on the verification results, the only usage of summarize_text is in cognee/api/v1/cognify/cognify_v2.py and it's already using the correct parameter signature without the collection_name parameter. The function is called with only the required parameters: the data chunks (implicit in the context) and the summarization model.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Find all occurrences where 'summarize_text' is called with 'collection_name'.
rg -A 2 $'summarize_text\(.+, .+, collection_name='
Length of output: 52
Script:
#!/bin/bash
# Let's search for all calls to summarize_text function to verify the parameter usage
rg -A 2 "summarize_text\(" --type python
Length of output: 73
Script:
#!/bin/bash
# Let's also check for any imports of summarize_text to ensure we find all usage
rg "from.*summarize_text.*import.*summarize_text" --type python
Length of output: 96
Script:
#!/bin/bash
# Let's also use ast-grep to find function calls
ast-grep --pattern 'summarize_text($$$)'
Length of output: 42
Script:
#!/bin/bash
# Search for summarize_text function calls without file type restriction
rg -A 2 "summarize_text\("
Length of output: 341
Script:
#!/bin/bash
# Search for imports of summarize_text
rg "from.*summarize_text.*import"
Length of output: 117
Script:
#!/bin/bash
# Find all Python files that might contain the function call
fd "\.py$" -x grep -l "summarize_text"
Length of output: 164
Script:
#!/bin/bash
# Let's check the actual usage in the API file
rg -A 5 "summarize_text" ./cognee/api/v1/cognify/cognify_v2.py
Length of output: 381
| def generate_edge_name(name: str) -> str: | ||
| return name.lower().replace(" ", "_").replace("'", "") |
There was a problem hiding this comment.
Add beartype runtime type checking
Given that this PR is specifically about testing beartype in the cognify pipeline, the function should implement runtime type checking using beartype.
Apply this diff:
+from beartype import beartype
+@beartype
def generate_edge_name(name: str) -> str:
return name.lower().replace(" ", "_").replace("'", "")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def generate_edge_name(name: str) -> str: | |
| return name.lower().replace(" ", "_").replace("'", "") | |
| from beartype import beartype | |
| @beartype | |
| def generate_edge_name(name: str) -> str: | |
| return name.lower().replace(" ", "_").replace("'", "") |
🛠️ Refactor suggestion
Add input validation and handle edge cases
The function should validate input and handle additional edge cases for more robust edge name generation.
Consider this improved implementation:
def generate_edge_name(name: str) -> str:
+ if not name:
+ raise ValueError("Edge name cannot be empty")
+ # Replace multiple spaces with single underscore
+ # Handle both types of quotes and other common special characters
- return name.lower().replace(" ", "_").replace("'", "")
+ return re.sub(r'\s+', '_', name.lower()).translate(str.maketrans("'\"", " ", "!@#$%^&*()+={}[]|\\:;\"'<>,.?/"))Committable suggestion skipped: line range outside the PR's diff.
| nodes = [] | ||
| edges = [] | ||
|
|
||
| for data_point in data_points: | ||
| property_nodes, property_edges = get_graph_from_model(data_point) | ||
|
|
||
| nodes.extend(property_nodes) | ||
| edges.extend(property_edges) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling and consider memory optimization
The current implementation has two potential issues:
- No error handling for
get_graph_from_modelwhich could fail for invalid data points - For large datasets, collecting all nodes and edges in memory before processing could cause memory pressure
Consider this approach:
nodes = []
edges = []
for data_point in data_points:
try:
property_nodes, property_edges = get_graph_from_model(data_point)
nodes.extend(property_nodes)
edges.extend(property_edges)
# Optional: Process in batches to reduce memory usage
if len(nodes) >= BATCH_SIZE:
await graph_engine.add_nodes(nodes)
await graph_engine.add_edges(edges)
nodes = []
edges = []
except Exception as e:
logger.error(f"Failed to process data point: {e}")
# Decide whether to continue or raise| if not await client.collection_exists(collection_name): | ||
| await client.create_collection( | ||
| collection_name = collection_name, | ||
| vectors_config = { | ||
| "text": models.VectorParams( | ||
| size = self.embedding_engine.get_vector_size(), | ||
| distance = "Cosine" | ||
| ) | ||
| } | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling and ensure proper resource cleanup
While the collection existence check is good, the create_collection operation needs proper error handling.
Consider wrapping the operation in a try-finally block:
async def create_collection(
self,
collection_name: str,
payload_schema = None,
):
client = self.get_qdrant_client()
- if not await client.collection_exists(collection_name):
- await client.create_collection(
- collection_name = collection_name,
- vectors_config = {
- "text": models.VectorParams(
- size = self.embedding_engine.get_vector_size(),
- distance = "Cosine"
- )
- }
- )
- await client.close()
+ try:
+ if not await client.collection_exists(collection_name):
+ await client.create_collection(
+ collection_name = collection_name,
+ vectors_config = {
+ "text": models.VectorParams(
+ size = self.embedding_engine.get_vector_size(),
+ distance = "Cosine"
+ )
+ }
+ )
+ finally:
+ await client.close()📝 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.
| if not await client.collection_exists(collection_name): | |
| await client.create_collection( | |
| collection_name = collection_name, | |
| vectors_config = { | |
| "text": models.VectorParams( | |
| size = self.embedding_engine.get_vector_size(), | |
| distance = "Cosine" | |
| ) | |
| } | |
| ) | |
| async def create_collection( | |
| self, | |
| collection_name: str, | |
| payload_schema = None, | |
| ): | |
| client = self.get_qdrant_client() | |
| try: | |
| if not await client.collection_exists(collection_name): | |
| await client.create_collection( | |
| collection_name = collection_name, | |
| vectors_config = { | |
| "text": models.VectorParams( | |
| size = self.embedding_engine.get_vector_size(), | |
| distance = "Cosine" | |
| ) | |
| } | |
| ) | |
| finally: | |
| await client.close() |
| async def create_vector_index(self, index_name: str, index_property_name: str): | ||
| await self.create_collection(f"{index_name}_{index_property_name}") | ||
|
|
||
| async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]): | ||
| await self.create_data_points(f"{index_name}_{index_property_name}", [ | ||
| IndexSchema( | ||
| id = data_point.id, | ||
| text = getattr(data_point, data_point._metadata["index_fields"][0]), | ||
| ) for data_point in data_points | ||
| ]) | ||
|
|
There was a problem hiding this comment.
Add validation and error handling for index operations
The current implementation has several potential failure points:
- No validation of index_name/index_property_name for valid collection name characters
- No error handling for missing fields in getattr()
- No validation of data_points list being non-empty
Consider adding these safety checks:
async def create_vector_index(self, index_name: str, index_property_name: str):
+ if not index_name or not index_property_name:
+ raise ValueError("Index name and property name must not be empty")
+ if not all(c.isalnum() or c in '_-' for c in f"{index_name}_{index_property_name}"):
+ raise ValueError("Index name and property name must contain only alphanumeric characters, underscores, or hyphens")
await self.create_collection(f"{index_name}_{index_property_name}")
async def index_data_points(self, index_name: str, index_property_name: str, data_points: list[DataPoint]):
+ if not data_points:
+ raise ValueError("data_points list must not be empty")
+
await self.create_data_points(f"{index_name}_{index_property_name}", [
IndexSchema(
id = data_point.id,
- text = getattr(data_point, data_point._metadata["index_fields"][0]),
+ text = getattr(data_point, data_point._metadata["index_fields"][0], None) or
+ raise ValueError(f"Missing required index field {data_point._metadata['index_fields'][0]} in data point"),
) for data_point in data_points
])Committable suggestion skipped: line range outside the PR's diff.
| async def query(self, query: str, params: dict): | ||
| pass |
There was a problem hiding this comment.
Implement the query method
The query method is currently a placeholder. Since this method is part of the GraphDBInterface, it should be implemented to handle graph queries.
Would you like help implementing the query method with NetworkX's graph querying capabilities?
| try: | ||
| node["id"] = UUID(node["id"]) | ||
| except: | ||
| pass | ||
| if "updated_at" in node: | ||
| node["updated_at"] = datetime.strptime(node["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z") | ||
|
|
||
| for edge in graph_data["links"]: | ||
| try: | ||
| source_id = UUID(edge["source"]) | ||
| target_id = UUID(edge["target"]) | ||
|
|
||
| edge["source"] = source_id | ||
| edge["target"] = target_id | ||
| edge["source_node_id"] = source_id | ||
| edge["target_node_id"] = target_id | ||
| except: | ||
| pass |
There was a problem hiding this comment.
Improve error handling for UUID conversion
The bare except blocks could mask important errors. Consider catching specific exceptions and logging them for better debugging.
- try:
- node["id"] = UUID(node["id"])
- except:
- pass
+ try:
+ node["id"] = UUID(node["id"])
+ except ValueError as e:
+ logger.warning(f"Failed to convert node ID to UUID: {e}")
+ pass
- try:
- source_id = UUID(edge["source"])
- target_id = UUID(edge["target"])
-
- edge["source"] = source_id
- edge["target"] = target_id
- edge["source_node_id"] = source_id
- edge["target_node_id"] = target_id
- except:
- pass
+ try:
+ source_id = UUID(edge["source"])
+ target_id = UUID(edge["target"])
+
+ edge["source"] = source_id
+ edge["target"] = target_id
+ edge["source_node_id"] = source_id
+ edge["target_node_id"] = target_id
+ except ValueError as e:
+ logger.warning(f"Failed to convert edge IDs to UUID: {e}")
+ passCommittable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff
256-256: Do not use bare except
(E722)
270-270: Do not use bare except
(E722)
| vector_engine = get_vector_engine() | ||
| summaries = [ | ||
| TextSummary( | ||
| id = uuid5(chunk.id, "TextSummary"), |
There was a problem hiding this comment.
Verify the use of uuid5 for generating unique IDs
In the line id = uuid5(chunk.id, "TextSummary"), ensure that chunk.id is a UUID object, as uuid5 expects a UUID namespace as its first argument. If chunk.id is not a UUID, this may result in an error.
If chunk.id is not a UUID, consider using a valid UUID namespace like NAMESPACE_DNS or converting chunk.id to a UUID. For example:
from uuid import uuid5, NAMESPACE_DNS
# ...
id = uuid5(NAMESPACE_DNS, f"{chunk.id}-TextSummary")| for edge in existing_edges: | ||
| existing_edges_map[edge[0] + edge[1] + edge[2]] = True | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Prevent potential key collisions by using tuples as dictionary keys.
Currently, keys in existing_edges_map are created by concatenating strings:
existing_edges_map[edge[0] + edge[1] + edge[2]] = Trueand
edge_key = str(source_node_id) + str(target_node_id) + relationship_nameString concatenation may lead to key collisions and is less efficient. It is recommended to use tuples as dictionary keys to ensure uniqueness and improve code clarity.
Update the code as follows:
At lines 55-56:
- existing_edges_map[edge[0] + edge[1] + edge[2]] = True
+ existing_edges_map[(edge[0], edge[1], edge[2])] = TrueAt lines 102-103:
- edge_key = str(source_node_id) + str(target_node_id) + relationship_name
+ edge_key = (source_node_id, target_node_id, relationship_name)And check for the key existence accordingly:
- if edge_key not in existing_edges_map:
+ if edge_key not in existing_edges_map:
# Proceed to add the edgeAlso applies to: 102-105
| graph_node_edges = [ | ||
| (edge.target_node_id, edge.source_node_id, edge.relationship_name) \ | ||
| for edge in chunk_graph.edges | ||
| ] |
There was a problem hiding this comment.
Ensure correct ordering of source and target node IDs in graph_node_edges.
In the construction of graph_node_edges, the tuples are currently formed with edge.target_node_id before edge.source_node_id. This may lead to confusion or errors if the expected convention is (source_node_id, target_node_id, relationship_name). Please verify and adjust the order to maintain consistency throughout the codebase.
Apply this diff to correct the ordering:
- (edge.target_node_id, edge.source_node_id, edge.relationship_name) \
+ (edge.source_node_id, edge.target_node_id, edge.relationship_name) \📝 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.
| graph_node_edges = [ | |
| (edge.target_node_id, edge.source_node_id, edge.relationship_name) \ | |
| for edge in chunk_graph.edges | |
| ] | |
| graph_node_edges = [ | |
| (edge.source_node_id, edge.target_node_id, edge.relationship_name) \ | |
| for edge in chunk_graph.edges | |
| ] |
|
@0xideas Should we close this PR? |
Summary by CodeRabbit
Release Notes
New Features
FalkorDBAdapterclass for enhanced database interactions.Improvements
Bug Fixes
Documentation