Feat/cog 553 graph memory projection #196
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WalkthroughThis pull request introduces several changes across multiple files within the Changes
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (4)
examples/python/dynamic_steps_example.py (4)
5-184: Consider moving sample data to separate files.Large string literals in the code make maintenance difficult. Consider:
- Moving the job position and CVs to separate text/JSON files
- Creating a data loading utility function
This would improve:
- Maintainability: Easier to update sample data
- Readability: Main file focuses on logic rather than data
- Reusability: Sample data could be used by other examples
Example implementation:
# utils/load_samples.py import json from pathlib import Path def load_sample_data(): samples_dir = Path(__file__).parent / 'samples' with open(samples_dir / 'job_position.txt') as f: job_position = f.read() cvs = [] for cv_file in sorted(samples_dir.glob('cv_*.txt')): with open(cv_file) as f: cvs.append(f.read()) return job_position, cvs
197-202: Add progress tracking for batch operations.When adding multiple texts, it would be helpful to track progress and handle failures:
if enable_steps.get("add_text"): text_list = [job_position, job_1, job_2, job_3, job_4, job_5] - for text in text_list: - await cognee.add(text) - print(f"Added text: {text[:35]}...") + for i, text in enumerate(text_list, 1): + try: + await cognee.add(text) + print(f"[{i}/{len(text_list)}] Added text: {text[:35]}...") + except Exception as e: + print(f"Failed to add text {i}: {e}") + raise
209-217: Improve search results handling and presentation.The current implementation could be enhanced:
if enable_steps.get("search_insights"): - search_results = await cognee.search( - SearchType.INSIGHTS, - {'query': 'Which applicant has the most relevant experience?'} - ) - print("Search results:") - for result_text in search_results: - print(result_text) + try: + search_results = await cognee.search( + SearchType.INSIGHTS, + {'query': 'Which applicant has the most relevant experience?'} + ) + if not search_results: + print("No insights found.") + return + + print("\nSearch Results:") + print("-" * 40) + for i, result_text in enumerate(search_results, 1): + print(f"{i}. {result_text}") + print("-" * 40) + except Exception as e: + print(f"Failed to search insights: {e}") + raise
219-229: Consider adding command-line argument support.The current hardcoded configuration could be made more flexible using argparse:
import argparse def parse_args(): parser = argparse.ArgumentParser(description='Process job applications using cognee.') parser.add_argument('--skip-prune-data', action='store_false', dest='prune_data', help='Skip pruning data step') parser.add_argument('--skip-prune-system', action='store_false', dest='prune_system', help='Skip pruning system step') parser.add_argument('--skip-add-text', action='store_false', dest='add_text', help='Skip adding text step') parser.add_argument('--skip-cognify', action='store_false', dest='cognify', help='Skip cognify step') parser.add_argument('--skip-search', action='store_false', dest='search_insights', help='Skip search step') return vars(parser.parse_args()) if __name__ == '__main__': steps_to_enable = parse_args() asyncio.run(main(steps_to_enable))
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examples/python/dynamic_steps_example.py(1 hunks)
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examples/python/dynamic_steps_example.py (2)
1-3: LGTM! Imports are clean and appropriate.
All imports are used and correctly support the async operations and search functionality.
1-229: Verify example coverage with library documentation.
As this is an example file, we should ensure it demonstrates all key features of the library.
✅ Verification successful
Let me analyze the example file against the library's features based on the provided context.
Example coverage verification complete - all key features demonstrated
The example in examples/python/dynamic_steps_example.py effectively demonstrates the core functionality of the library:
-
Data management:
- Data pruning with
prune_data()andprune_system() - Data addition with
cognee.add() - Batch processing of multiple text inputs
- Data pruning with
-
Knowledge graph:
- Graph creation with
cognee.cognify() - Proper initialization and cleanup
- Graph creation with
-
Search capabilities:
- Demonstrates search functionality with
SearchType.INSIGHTS - Shows query parameter handling
- Demonstrates search functionality with
-
Flow control:
- Step-by-step execution control via dictionary flags
- Proper async/await usage
- Error handling structure
-
Real-world use case:
- Practical example with job position and CV matching
- Shows both relevant and non-relevant document handling
The example provides a comprehensive demonstration of the library's key features with proper error handling and async patterns.
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cognee/infrastructure/databases/graph/neo4j_driver/adapter.py(0 hunks)cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py(1 hunks)
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- cognee/infrastructure/databases/graph/neo4j_driver/adapter.py
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cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py (1)
124-126: Verify Correct Addition of Data Points
The condition if new_size <= original_size: may not accurately detect if data points were added successfully, especially if original_size is zero after deleting all data points. This check might not catch insertion failures.
Consider enhancing the verification process. You can compare the expected number of new data points with the actual new_size:
if new_size <= original_size:
raise ValueError(
"LanceDB create_datapoints error: data points did not get added.")
+ elif new_size - original_size != len(lance_data_points):
+ raise ValueError(
+ "Mismatch in the number of data points added to the collection.")Alternatively, you can verify that each data point was added successfully by checking their presence in the collection.
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (4)
cognee/modules/chunking/TextChunker.py (4)
20-20: LGTM, but consider resetting all state variables.While initializing
paragraph_chunksis good, consider resetting all state variables (chunk_index,chunk_size) at the start ofread()for consistent behavior across multiple calls.def read(self): self.paragraph_chunks = [] + self.chunk_index = 0 + self.chunk_size = 0
Line range hint
44-45: Improve error handling for document chunk creation.The current error handling only prints exceptions, which could lead to silent failures and data loss. Consider:
- Using proper logging
- Adding context to the errors
- Potentially propagating critical errors
- except Exception as e: - print(e) + except Exception as e: + logging.error(f"Failed to create document chunk at index {self.chunk_index}: {str(e)}") + raise DocumentChunkError(f"Failed to process chunk: {str(e)}") from eAlso applies to: 65-66
Line range hint
22-53: Consider refactoring the complex chunking logic.The
readmethod is complex with nested conditionals and multiple responsibilities. Consider breaking it down into smaller, focused methods for better maintainability:
- Chunk accumulation logic
- Document chunk creation
- State management
Here's a suggested refactoring approach:
def _create_document_chunk(self, text, word_count, cut_type): return DocumentChunk( id=uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"), text=text, word_count=word_count, is_part_of=self.document, chunk_index=self.chunk_index, cut_type=cut_type ) def _process_accumulated_chunks(self): if not self.paragraph_chunks: return None chunk_text = " ".join(chunk["text"] for chunk in self.paragraph_chunks) cut_type = self.paragraph_chunks[-1]["cut_type"] return self._create_document_chunk(chunk_text, self.chunk_size, cut_type) def read(self): self._reset_state() for content_text in self.get_text(): for chunk_data in chunk_by_paragraph(content_text, self.max_chunk_size, batch_paragraphs=True): yield from self._process_chunk(chunk_data)
Line range hint
1-71: Consider adding memory safeguards.The class accumulates chunks in memory without bounds. Consider:
- Adding a maximum number of accumulated paragraphs
- Implementing a memory usage threshold
- Adding documentation about memory usage characteristics
Example implementation:
class TextChunker: MAX_ACCUMULATED_PARAGRAPHS = 1000 # Example threshold 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 def _check_memory_limits(self): if len(self.paragraph_chunks) > self.MAX_ACCUMULATED_PARAGRAPHS: logging.warning("Maximum paragraph accumulation reached, forcing chunk creation") return True return False
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🧹 Outside diff range and nitpick comments (13)
cognee/modules/graph/cognee_graph/CogneeAbstractGraph.py (5)
3-3: Standardize module naming conventionsThe module name
CogneeGraphElementsshould follow Python's naming conventions by using lowercase letters and underscores. This helps maintain consistency and readability across the codebase.Apply this diff to rename the import:
-from CogneeGraphElements import Node, Edge +from cognee_graph_elements import Node, Edge
18-18: Clarify the need for edge indexingThe TODO comment suggests uncertainty about the necessity of indexing edges using a hash table. Resolving this will help optimize data retrieval and graph performance.
Would you like assistance in evaluating the need for indexing edges or implementing an indexed data structure?
25-25: Implement dimension handling for edgesThere's a TODO comment indicating the need to add dimension to edges. Incorporating dimensions may enhance the graph's capabilities, such as supporting weighted edges or multi-dimensional relationships.
Consider adding parameters or methods to handle edge dimensions where appropriate.
37-38: Clarify edge directionality inget_edgesmethodThe
get_edgesmethod's docstring doesn't specify whether it retrieves incoming edges, outgoing edges, or both for a given node. Providing this clarification will improve understanding for future implementers.Consider updating the docstring:
-"""Retrieve edges connected to a specific node.""" +"""Retrieve edges connected to a specific node. Parameters: node_id (str): The ID of the node. Returns: List[Edge]: A list of edges connected to the node. Specify whether these are incoming, outgoing, or all edges. """
42-44: Enhance flexibility with a graph adapter interfaceThe
project_graph_from_dbmethod currently accepts aUnion[Neo4jAdapter, NetworkXAdapter]. This approach can limit extensibility to other database adapters.Consider defining an abstract base class or interface for adapters (e.g.,
AbstractGraphAdapter) and have all specific adapters inherit from it. Then, update the method signature to accept any adapter implementing this interface:-from cognee.infrastructure.databases.graph.neo4j_driver.adapter import Neo4jAdapter -from cognee.infrastructure.databases.graph.networkx.adapter import NetworkXAdapter +from cognee.infrastructure.databases.graph.adapters import AbstractGraphAdapter ... -async def project_graph_from_db(self, adapter: Union[Neo4jAdapter, NetworkXAdapter]) -> None: +async def project_graph_from_db(self, adapter: AbstractGraphAdapter) -> None:cognee/modules/graph/cognee_graph/CogneeGraph.py (4)
41-47: Implement theproject_graph_from_dbMethodThe
project_graph_from_dbmethod is currently incomplete and raises aNotImplementedError. Implementing this method is crucial for projecting the graph from the database using the provided adapter. This will enable the retrieval and construction of the graph's nodes and edges from the database.Would you like assistance in implementing the
project_graph_from_dbmethod? I can help draft a basic implementation to get started.
53-53: Moveimport asyncioto the Top of the FileThe
import asynciostatement is located after other code at line 53. Per PEP 8 style guidelines, all module-level imports should be placed at the top of the file, below any module comments and docstrings, and before module globals and constants.Apply this diff to move the import to the top of the file:
1 from typing import List, Dict, Union 2 from CogneeGraphElements import Node, Edge 3 from CogneeAbstractGraph import CogneeAbstractGraph 4 from cognee.infrastructure.databases.graph import get_graph_engine 5 from cognee.infrastructure.databases.graph.neo4j_driver.adapter import Neo4jAdapter 6 from cognee.infrastructure.databases.graph.networkx.adapter import NetworkXAdapter 7 import os +8 import asyncio ... -53 import asyncio🧰 Tools
🪛 Ruff
53-53: Module level import not at top of file
(E402)
55-71: Wrap Test Code in anif __name__ == "__main__":BlockTo prevent the test code from executing unintentionally when the module is imported elsewhere, consider wrapping the test code within an
if __name__ == "__main__":block. This is a common best practice in Python to ensure that code is executed only when the script is run directly.Apply this diff to wrap the test code:
55 async def main(): # Choose the adapter (Neo4j or NetworkX) adapter = await get_graph_engine() # ... 71 asyncio.run(main()) +if __name__ == "__main__": + # Run the main function + asyncio.run(main())
22-22: Address the Pending TODO CommentsThere are several TODO comments indicating missing implementations or enhancements:
- Line 22:
# :TODO ADD dimensionin theadd_edgemethod.- Line 41:
# :TODO This should take also the list of entity types and connection types to keep.- Line 44:
# :TODO: Handle networkx and Neo4j separatelyConsider addressing these TODOs to enhance the functionality and completeness of the
CogneeGraphclass.Would you like assistance in implementing these features? I can help draft code for adding dimensions, refining the graph projection parameters, and handling different adapters separately.
Also applies to: 41-41, 44-44
cognee/modules/graph/cognee_graph/CogneeGraphElements.py (4)
5-13: Correct the indentation of the class docstringThe docstring for the
Nodeclass is over-indented. Adjusting the indentation enhances readability and adheres to PEP 257 standards.Apply this diff to fix the indentation:
class Node: - """ - Represents a node in a graph. - Attributes: - id (str): A unique identifier for the node. - attributes (Dict[str, Any]): A dictionary of attributes associated with the node. - neighbors (List[Node]): Represents the original nodes - skeleton_edges (List[Edge]): Represents the original edges - """ + """ + Represents a node in a graph. + + Attributes: + id (str): A unique identifier for the node. + attributes (Dict[str, Any]): A dictionary of attributes associated with the node. + skeleton_neighbours (List["Node"]): Represents the original nodes. + skeleton_edges (List["Edge"]): Represents the original edges. + """
60-67: Adjust the indentation of the class docstringThe docstring for the
Edgeclass is over-indented. Proper indentation improves readability and conforms to PEP 257 guidelines.Apply this diff to fix the indentation:
class Edge: - """ - Represents an edge in a graph, connecting two nodes. - Attributes: - node1 (Node): The starting node of the edge. - node2 (Node): The ending node of the edge. - attributes (Dict[str, Any]): A dictionary of attributes associated with the edge. - directed (bool): A flag indicating whether the edge is directed or undirected. - """ + """ + Represents an edge in a graph, connecting two nodes. + + Attributes: + node1 (Node): The starting node of the edge. + node2 (Node): The ending node of the edge. + attributes (Dict[str, Any]): A dictionary of attributes associated with the edge. + directed (bool): A flag indicating whether the edge is directed or undirected. + """
10-16: Standardize the spelling of 'neighbor'There is inconsistency in the use of 'neighbour' and 'neighbor' throughout the code and docstrings. For clarity and consistency, please choose one spelling and use it consistently across the codebase.
Apply this diff to standardize to 'neighbor':
class Node: """ Represents a node in a graph. Attributes: id (str): A unique identifier for the node. attributes (Dict[str, Any]): A dictionary of attributes associated with the node. - neighbors (List[Node]): Represents the original nodes + skeleton_neighbors (List["Node"]): Represents the original nodes. skeleton_edges (List[Edge]): Represents the original edges. """ id: str attributes: Dict[str, Any] - skeleton_neighbours: List["Node"] + skeleton_neighbors: List["Node"] skeleton_edges: List["Edge"] def __init__(self, node_id: str, attributes: Optional[Dict[str, Any]] = None): self.id = node_id self.attributes = attributes if attributes is not None else {} - self.skeleton_neighbours = [] # TODO do we need it in hashtable? Do we want to index? + self.skeleton_neighbors = [] # TODO do we need it in hashtable? Do we want to index? self.skeleton_edges = [] # TODO do we need it in hashtable? Do we want to index? def add_skeleton_neighbor(self, neighbor: "Node") -> None: - if neighbor not in self.skeleton_neighbours: - self.skeleton_neighbours.append(neighbor) + if neighbor not in self.skeleton_neighbors: + self.skeleton_neighbors.append(neighbor) def remove_skeleton_neighbor(self, neighbor: "Node") -> None: - if neighbor in self.skeleton_neighbours: - self.skeleton_neighbours.remove(neighbor) + if neighbor in self.skeleton_neighbors: + self.skeleton_neighbors.remove(neighbor)Please update all other occurrences accordingly.
Also applies to: 21-22, 24-30, 36-39, 45-47
21-22: Consider addressing the TODO comments regarding data structuresThe TODO comments suggest considering the use of a hashtable or indexing for
skeleton_neighborsandskeleton_edges. Evaluating and implementing efficient data structures can enhance performance, especially for large graphs.Would you like assistance in optimizing these data structures or analyzing their impact on performance?
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cognee/modules/graph/cognee_graph/CogneeAbstractGraph.py(1 hunks)cognee/modules/graph/cognee_graph/CogneeGraph.py(1 hunks)cognee/modules/graph/cognee_graph/CogneeGraphElements.py(1 hunks)
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cognee/modules/graph/cognee_graph/CogneeGraph.py
53-53: Module level import not at top of file
(E402)
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Actionable comments posted: 6
🧹 Outside diff range and nitpick comments (7)
cognee/modules/graph/cognee_graph/CogneeAbstractGraph.py (3)
6-10: Enhance class documentation with comprehensive docstring.The current docstring lacks essential information about the class's purpose, usage, and implementation requirements.
Consider expanding it like this:
- """ - Abstract base class for representing a graph structure. - - """ + """ + Abstract base class for representing a graph structure. + + This class defines the interface for graph operations that must be implemented + by concrete graph implementations. It provides a contract for basic graph + operations such as adding nodes and edges, retrieving graph elements, and + projecting graph data from a database. + + Attributes: + None + + Implementation Requirements: + Concrete classes must implement all abstract methods defined in this interface. + Each implementation should ensure thread-safety and proper error handling. + """
12-35: Enhance method documentation and error handling specifications.While the abstract methods provide a solid foundation, they could benefit from more detailed documentation and error handling specifications.
Consider these improvements:
- Add detailed docstrings for each method:
@abstractmethod async def project_graph_from_db(self, adapter: GraphDBInterface, directed: bool, dimension: int) -> None: """Project the graph structure from a database using the provided adapter. Args: adapter (GraphDBInterface): The database adapter to use for projection. directed (bool): Whether to create a directed graph. dimension (int): The dimension of the graph projection. Raises: ConnectionError: If database connection fails. ValueError: If invalid dimension or adapter configuration. GraphProjectionError: If projection fails. Note: This is an async operation that may take time for large graphs. Implementations should consider pagination for large datasets. """ pass
- Consider adding these methods to handle common graph operations:
@abstractmethod def remove_node(self, node_id: str) -> None: """Remove a node and its associated edges from the graph.""" pass @abstractmethod def get_subgraph(self, node_ids: List[str]) -> 'CogneeAbstractGraph': """Extract a subgraph containing specified nodes and their edges.""" pass
- Consider adding validation methods:
@abstractmethod def validate_node(self, node: Node) -> bool: """Validate node properties before addition.""" pass @abstractmethod def validate_edge(self, edge: Edge) -> bool: """Validate edge properties and node existence before addition.""" pass
1-35: Consider adding memory management and scalability guidelines.As this abstract class will serve as the foundation for graph implementations that might handle large datasets, consider addressing these architectural concerns:
Memory Management:
- Add documentation about expected memory patterns
- Consider adding methods for partial graph loading
- Document expectations for handling large graphs
Scalability:
- Add methods for batch operations
- Consider adding support for graph partitioning
- Document concurrent access patterns
Example additions:
@abstractmethod async def load_partial_graph(self, criteria: Dict[str, Any]) -> None: """Load a subset of the graph based on given criteria.""" pass @abstractmethod async def batch_add_nodes(self, nodes: List[Node]) -> None: """Add multiple nodes efficiently in a single operation.""" pass @abstractmethod def get_partition_info(self) -> Dict[str, Any]: """Get information about how the graph is or can be partitioned.""" passcognee/modules/graph/cognee_graph/CogneeGraph.py (2)
7-7: Remove unused importThe
osmodule is imported but not used in the code.-import os
50-56: Return success status from projection methodThe method modifies the graph's state but doesn't provide a way to track if the projection was successful.
Consider returning a status:
async def project_graph_from_db(self, adapter: Union[NetworkXAdapter, Neo4jAdapter], node_properties_to_project: List[str], edge_properties_to_project: List[str], directed = True, node_dimension = 1, - edge_dimension = 1) -> None: + edge_dimension = 1) -> bool:Then return
Trueafter successful projection andFalseor raise an exception on failure.cognee/modules/graph/cognee_graph/CogneeGraphElements.py (2)
1-18: Documentation needs clarification for the status attributeWhile the class structure and type hints are well-defined, the
statusattribute's purpose and the meaning of its dimensions are not documented in the class docstring.Add the following to the class docstring:
""" Represents a node in a graph. Attributes: id (str): A unique identifier for the node. attributes (Dict[str, Any]): A dictionary of attributes associated with the node. neighbors (List[Node]): Represents the original nodes skeleton_edges (List[Edge]): Represents the original edges + status (np.ndarray): Binary array tracking node's state across dimensions """
43-50: Optimize edge removal logicThe current implementation iterates through all edges to check for remaining connections. This could be optimized by maintaining a connection count.
Consider maintaining a counter:
def __init__(self, node_id: str, attributes: Optional[Dict[str, Any]] = None, dimension: int = 1): # ... existing init code ... + self._neighbor_counts = {} # Track connections per neighbor def add_skeleton_edge(self, edge: "Edge") -> None: if edge not in self.skeleton_edges: self.skeleton_edges.append(edge) neighbor = edge.node2 if edge.node1 == self else edge.node1 + self._neighbor_counts[neighbor] = self._neighbor_counts.get(neighbor, 0) + 1 def remove_skeleton_edge(self, edge: "Edge") -> None: if edge in self.skeleton_edges: self.skeleton_edges.remove(edge) neighbor = edge.node2 if edge.node1 == self else edge.node1 + self._neighbor_counts[neighbor] -= 1 + if self._neighbor_counts[neighbor] == 0: + del self._neighbor_counts[neighbor] self.remove_skeleton_neighbor(neighbor)
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cognee/modules/graph/cognee_graph/CogneeAbstractGraph.py(1 hunks)cognee/modules/graph/cognee_graph/CogneeGraph.py(1 hunks)cognee/modules/graph/cognee_graph/CogneeGraphElements.py(1 hunks)
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cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
17-25: LGTM! Proper initialization implemented
The initialization of self.nodes and self.edges has been properly implemented, addressing the previous review comment. The class attributes are well-typed and the directed parameter is handled correctly.
cognee/modules/graph/cognee_graph/CogneeGraphElements.py (1)
1-110: Verify graph projection functionality
The implementation supports multi-dimensional state tracking through status arrays, which appears to be the foundation for graph memory projection mentioned in the PR title. Let's verify this functionality is being used correctly in the broader context.
✅ Verification successful
Graph projection implementation is correctly structured
The implementation provides a solid foundation for graph memory projection through:
- Multi-dimensional state tracking via status arrays in both Node and Edge classes
- Proper dimension initialization during graph projection (
project_graph_from_db) - Configurable node and edge dimensions as parameters
- Status tracking methods (
is_node_alive_in_dimension,is_edge_alive_in_dimension)
The code structure allows for:
- Creating graph projections with different dimensions
- Independent state tracking for nodes and edges
- Flexible attribute projection from the database
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🧹 Outside diff range and nitpick comments (1)
cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
50-56: Add type hints and docstring for the projection method.The method parameters should include proper type hints, and a docstring would help document the method's purpose and parameters.
async def project_graph_from_db(self, adapter: Union[NetworkXAdapter, Neo4jAdapter], node_properties_to_project: List[str], edge_properties_to_project: List[str], - directed = True, - node_dimension = 1, - edge_dimension = 1) -> None: + directed: bool = True, + node_dimension: int = 1, + edge_dimension: int = 1) -> None: + """ + Project a graph from a database using the specified adapter. + + Args: + adapter: The database adapter to use for retrieving graph data + node_properties_to_project: List of node properties to include + edge_properties_to_project: List of edge properties to include + directed: Whether the graph is directed + node_dimension: Dimension of node embeddings (must be positive) + edge_dimension: Dimension of edge embeddings (must be positive) + + Raises: + ValueError: If dimensions are invalid or data retrieval fails + """
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cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
Learnt from: hajdul88
PR: topoteretes/cognee#196
File: cognee/modules/graph/cognee_graph/CogneeGraph.py:32-38
Timestamp: 2024-11-13T16:06:32.576Z
Learning: In `CogneeGraph.py`, within the `CogneeGraph` class, it's intentional to add skeleton edges in both the `add_edge` method and the `project_graph_from_db` method to ensure that edges are added to the graph and to the nodes.
🔇 Additional comments (3)
cognee/modules/graph/cognee_graph/CogneeGraph.py (3)
1-20: LGTM! Well-structured class definition with proper type hints.
The imports are well-organized, and the class is properly documented with clear type hints for its attributes.
21-49: LGTM! Basic graph operations are well-implemented.
The constructor properly initializes attributes, and all operations include appropriate validation. The skeleton edge additions in add_edge are intentionally designed to maintain both graph and node-level edge references.
83-84: LGTM! Skeleton edge additions are intentional.
The duplicate skeleton edge additions are intentionally designed to maintain both graph and node-level edge references, as confirmed in previous discussions.
This reverts commit 0d27371.
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🧹 Outside diff range and nitpick comments (1)
cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py (1)
36-36: Review the timeout duration and add error handlingWhile adding a timeout is good practice, 100 seconds is quite long and might mask underlying performance issues. Consider:
- Reducing the timeout to a more reasonable duration (e.g., 30 seconds)
- Adding error handling for timeout scenarios
- Implementing retry logic for temporary failures
- Adding monitoring/logging for operations that approach the timeout limit
Example implementation:
from tenacity import retry, stop_after_attempt, wait_exponential from asyncio.exceptions import TimeoutError class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface): def __init__(self, connection_string: str, api_key: Optional[str], embedding_engine: EmbeddingEngine): self.db_uri: str = connection_string self.engine = create_async_engine( self.db_uri, connect_args={"timeout": 30}, # Reduced timeout pool_pre_ping=True # Verify connections before use ) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10), retry_error_cls=(TimeoutError,) ) async def _execute_with_retry(self, operation): try: return await operation() except TimeoutError as e: logger.warning(f"Database operation timed out: {str(e)}") raise
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cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py (1)
Line range hint 1-250: Consider architectural improvements for security, performance, and reliability
Several areas of the implementation could benefit from improvements:
-
Security:
- The dynamic table reflection could be vulnerable to SQL injection
- Consider using parameterized queries and input validation
-
Performance:
- Implement batch processing for vector operations
- Consider connection pooling optimizations
- Cache frequently accessed metadata
-
Error Handling:
- Add comprehensive error handling for database operations
- Implement proper cleanup in failure scenarios
-
Type Safety:
- Strengthen type hints, especially for vector operations
- Consider using Pydantic for input validation
Let's verify the security concerns:
Would you like me to provide specific implementation examples for any of these improvements?
-No there are no tests yet, I wanted to discuss this with you first. |
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Actionable comments posted: 9
🧹 Outside diff range and nitpick comments (12)
cognee/tests/unit/modules/graph/cognee_graph_test.py (4)
7-11: Enhance fixture documentationThe fixture's docstring could be more descriptive by including information about the state of the returned graph and its intended use across tests.
@pytest.fixture def setup_graph(): - """Fixture to initialize a CogneeGraph instance.""" + """Fixture to initialize a fresh, empty CogneeGraph instance. + + Returns: + CogneeGraph: A new instance with no nodes or edges, ready for testing. + """ return CogneeGraph()
12-26: Add more node validation test casesThe current tests cover basic node addition and duplication, but consider adding tests for:
- Adding a node with empty/null ID
- Adding a node with invalid ID format (if there are any restrictions)
- Adding a node with special characters in ID
Example test case:
def test_add_node_with_empty_id(setup_graph): """Test adding a node with empty ID raises an exception.""" graph = setup_graph node = Node("") with pytest.raises(ValueError, match="Node ID cannot be empty."): graph.add_node(node)
59-63: Reconsider returning None for non-existent nodesReturning
Nonefor non-existent nodes might lead to silent failures in production code. Consider:
- Raising a
ValueErrorconsistently across the API- Adding tests for case sensitivity in node IDs
Example improvement:
def test_get_node_case_sensitivity(setup_graph): """Test if node IDs are case-sensitive.""" graph = setup_graph node = Node("Node1") graph.add_node(node) # This should clarify if "node1" and "Node1" are treated as same or different assert graph.get_node("node1") is None # or is not None, depending on requirements
64-79: Add comprehensive edge retrieval testsConsider adding tests for:
- Retrieving edges by direction (incoming vs outgoing)
- Filtering edges by properties or types
- Getting edges between specific node pairs
Example test:
def test_get_edges_by_direction(setup_graph): """Test retrieving incoming and outgoing edges separately.""" graph = setup_graph node1, node2 = Node("node1"), Node("node2") graph.add_node(node1) graph.add_node(node2) edge = Edge(node1, node2) graph.add_edge(edge) # Assuming these methods exist or suggesting their addition assert edge in graph.get_outgoing_edges("node1") assert edge in graph.get_incoming_edges("node2")cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
40-42: Consider consistent error handling across getter methods
get_nodereturns None whileget_edgesraises an exception for non-existent nodes. Consider making the error handling consistent:def get_node(self, node_id: str) -> Node: - return self.nodes.get(node_id, None) + node = self.nodes.get(node_id) + if not node: + raise ValueError(f"Node with id {node_id} does not exist.") + return nodecognee/tests/unit/modules/graph/cognee_graph_elements_test.py (3)
7-14: Add test cases for default values.While the basic initialization test is good, consider adding test cases for:
- Default empty attributes
- Default dimension value
- Empty neighbor and edge lists
67-77: Enhance equality and hash test coverage.Consider adding test cases for:
- Nodes with same ID but different attributes
- Nodes with same ID but different dimensions
- Hash collisions with different node types
116-144: Add edge cases for Edge equality and hashing.While the basic directed/undirected cases are covered, consider adding tests for:
- Edges with same nodes but different attributes
- Edges with same nodes but different dimensions
- Mixed directed/undirected edge comparisons
examples/python/dynamic_steps_example.py (4)
5-184: Consider moving test data to separate fixture files.The example contains a large amount of hard-coded test data. Consider moving job descriptions and CVs to separate fixture files to:
- Improve maintainability
- Make the example more focused on demonstrating the API usage
- Enable reuse of test data across different examples
Create a new directory
examples/fixtures/and move the test data there:# examples/fixtures/job_positions.py SENIOR_DATA_SCIENTIST = """...""" # examples/fixtures/cvs.py RELEVANT_CVS = [ """...""", # Emily Carter """...""", # Michael Rodriguez """...""" # Sarah Nguyen ] IRRELEVANT_CVS = [ """...""", # David Thompson """...""" # Jessica Miller ]
186-186: Add docstring to explain function parameters and steps.Add a comprehensive docstring to explain the function's purpose, parameters, and processing steps.
async def main(enable_steps): + """Process job applications through a series of steps. + + Args: + enable_steps (dict[str, bool]): Dictionary controlling which steps to execute: + - prune_data: Clear existing data + - prune_system: Reset system state + - add_text: Add job descriptions and CVs + - cognify: Create knowledge graph + - search_insights: Query for relevant candidates + + Raises: + KeyError: If enable_steps dictionary is missing required keys + """
210-213: Parameterize the search query.The search query is hardcoded. Consider making it a parameter to improve reusability.
- if enable_steps.get("search_insights"): + if enable_steps.get("search_insights"): + query = enable_steps.get("search_query", + 'Which applicant has the most relevant experience in data science?') search_results = await cognee.search( SearchType.INSIGHTS, - {'query': 'Which applicant has the most relevant experience in data science?'} + {'query': query} )
221-227: Consider more flexible step configuration.All steps are enabled by default, which might not be ideal for demonstrating specific functionality. Consider:
- Using environment variables or command-line arguments
- Adding example configurations for different scenarios
# Example using argparse: import argparse def parse_args(): parser = argparse.ArgumentParser(description='Process job applications.') parser.add_argument('--prune-data', action='store_true') parser.add_argument('--prune-system', action='store_true') parser.add_argument('--add-text', action='store_true') parser.add_argument('--cognify', action='store_true') parser.add_argument('--search-insights', action='store_true') parser.add_argument('--all', action='store_true', help='Enable all steps') return parser.parse_args() if __name__ == '__main__': args = parse_args() steps_to_enable = { "prune_data": args.all or args.prune_data, "prune_system": args.all or args.prune_system, "add_text": args.all or args.add_text, "cognify": args.all or args.cognify, "search_insights": args.all or args.search_insights } asyncio.run(main(steps_to_enable))
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cognee/modules/graph/cognee_graph/CogneeAbstractGraph.py(1 hunks)cognee/modules/graph/cognee_graph/CogneeGraph.py(1 hunks)cognee/tests/unit/modules/graph/cognee_graph_elements_test.py(1 hunks)cognee/tests/unit/modules/graph/cognee_graph_test.py(1 hunks)examples/python/dynamic_steps_example.py(1 hunks)
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📓 Learnings (1)
cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
Learnt from: hajdul88
PR: topoteretes/cognee#196
File: cognee/modules/graph/cognee_graph/CogneeGraph.py:32-38
Timestamp: 2024-11-13T16:06:32.576Z
Learning: In `CogneeGraph.py`, within the `CogneeGraph` class, it's intentional to add skeleton edges in both the `add_edge` method and the `project_graph_from_db` method to ensure that edges are added to the graph and to the nodes.
🪛 Ruff
cognee/modules/graph/cognee_graph/CogneeGraph.py
98-98: Module level import not at top of file
(E402)
🔇 Additional comments (2)
cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
1-20: LGTM! Well-structured class definition with clear documentation.
The imports are comprehensive and the class is well-documented with proper type hints.
cognee/tests/unit/modules/graph/cognee_graph_elements_test.py (1)
1-5: LGTM! Imports are clean and appropriate.
The imports are minimal and correctly include all necessary dependencies for the tests.
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🧹 Outside diff range and nitpick comments (1)
cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
49-91: Consider breaking down the projection method into smaller functions.The
project_graph_from_dbmethod is quite complex and handles multiple responsibilities. Consider extracting the node and edge creation logic into separate methods for better maintainability:async def project_graph_from_db(self, adapter: Union[GraphDBInterface], node_properties_to_project: List[str], edge_properties_to_project: List[str], directed = True, node_dimension = 1, edge_dimension = 1) -> None: if node_dimension < 1 or edge_dimension < 1: raise ValueError("Dimensions must be positive integers") try: nodes_data, edges_data = await adapter.get_graph_data() if not nodes_data: raise ValueError("No node data retrieved from the database.") if not edges_data: raise ValueError("No edge data retrieved from the database.") - for node_id, properties in nodes_data: - node_attributes = {key: properties.get(key) for key in node_properties_to_project} - self.add_node(Node(str(node_id), node_attributes, dimension=node_dimension)) + await self._create_nodes(nodes_data, node_properties_to_project, node_dimension) - for source_id, target_id, relationship_type, properties in edges_data: - source_node = self.get_node(str(source_id)) - target_node = self.get_node(str(target_id)) - if source_node and target_node: - edge_attributes = {key: properties.get(key) for key in edge_properties_to_project} - edge_attributes['relationship_type'] = relationship_type - - edge = Edge(source_node, target_node, attributes=edge_attributes, directed=directed, dimension=edge_dimension) - self.add_edge(edge) - - source_node.add_skeleton_edge(edge) - target_node.add_skeleton_edge(edge) - else: - raise ValueError(f"Edge references nonexistent nodes: {source_id} -> {target_id}") + await self._create_edges(edges_data, edge_properties_to_project, directed, edge_dimension) except (ValueError, TypeError) as e: print(f"Error projecting graph: {e}") except Exception as ex: print(f"Unexpected error: {ex}") +async def _create_nodes(self, nodes_data: List, node_properties_to_project: List[str], node_dimension: int) -> None: + for node_id, properties in nodes_data: + node_attributes = {key: properties.get(key) for key in node_properties_to_project} + self.add_node(Node(str(node_id), node_attributes, dimension=node_dimension)) + +async def _create_edges(self, edges_data: List, edge_properties_to_project: List[str], directed: bool, edge_dimension: int) -> None: + for source_id, target_id, relationship_type, properties in edges_data: + source_node = self.get_node(str(source_id)) + target_node = self.get_node(str(target_id)) + if source_node and target_node: + edge_attributes = {key: properties.get(key) for key in edge_properties_to_project} + edge_attributes['relationship_type'] = relationship_type + + edge = Edge(source_node, target_node, attributes=edge_attributes, directed=directed, dimension=edge_dimension) + self.add_edge(edge) + + source_node.add_skeleton_edge(edge) + target_node.add_skeleton_edge(edge) + else: + raise ValueError(f"Edge references nonexistent nodes: {source_id} -> {target_id}")
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cognee/modules/graph/cognee_graph/CogneeGraph.py (1)
Learnt from: hajdul88
PR: topoteretes/cognee#196
File: cognee/modules/graph/cognee_graph/CogneeGraph.py:32-38
Timestamp: 2024-11-13T16:06:32.576Z
Learning: In `CogneeGraph.py`, within the `CogneeGraph` class, it's intentional to add skeleton edges in both the `add_edge` method and the `project_graph_from_db` method to ensure that edges are added to the graph and to the nodes.
🔇 Additional comments (5)
cognee/infrastructure/databases/graph/graph_db_interface.py (1)
66-69: Verify consistent method naming across implementations.
The new method get_graph_data is part of the graph projection feature. Let's verify its usage and naming consistency.
✅ Verification successful
Let me check the actual implementations to verify naming consistency.
Based on the results, I can now generate the final response:
Method naming is consistent across implementations
The get_graph_data method is consistently implemented in both NetworkX and Neo4j adapters with the same name and signature. The FalkorDB adapter doesn't implement this method yet, but it inherits from GraphDBInterface which defines the contract. The method is also consistently used across the codebase in utils and graph modules.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check implementations and usages of get_graph_data
# Find all database adapter implementations
echo "=== Database Adapter Implementations ==="
rg -l "class.*\(.*GraphDBInterface.*\)"
# Find all usages of get_graph_data
echo -e "\n=== Method Usages ==="
rg "get_graph_data"
# Find related graph projection code
echo -e "\n=== Related Graph Projection Code ==="
rg "project_graph_from_db"
Length of output: 1257
Script:
#!/bin/bash
# Check the implementation details of get_graph_data in each adapter
echo "=== NetworkX Adapter Implementation ==="
rg -A 10 "async def get_graph_data" cognee/infrastructure/databases/graph/networkx/adapter.py
echo -e "\n=== Neo4j Adapter Implementation ==="
rg -A 10 "async def get_graph_data" cognee/infrastructure/databases/graph/neo4j_driver/adapter.py
echo -e "\n=== FalkorDB Adapter Implementation ==="
rg -A 10 "async def get_graph_data" cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py
Length of output: 1265
cognee/modules/graph/cognee_graph/CogneeGraph.py (4)
1-19: LGTM! Well-structured imports and class definition.
The imports are properly organized, and the class documentation clearly describes its purpose and functionality.
16-24: LGTM! Clean initialization with proper type hints.
The class attributes are well-defined with type hints, and the initialization properly sets up the required data structures.
25-48: LGTM! Well-implemented node and edge management.
The methods provide proper error handling and maintain graph consistency. The duplicate skeleton edge additions are intentional as per previous discussions.
88-91:
Improve error handling in graph projection.
The current error handling only prints errors, which could lead to silent failures. Consider:
- Creating a custom exception class for graph-specific errors
- Properly propagating errors to callers
+class GraphProjectionError(Exception):
+ """Custom exception for graph projection errors."""
+ pass
- except (ValueError, TypeError) as e:
- print(f"Error projecting graph: {e}")
+ except (ValueError, TypeError) as e:
+ raise GraphProjectionError(f"Error projecting graph: {e}") from e
- except Exception as ex:
- print(f"Unexpected error: {ex}")
+ except Exception as ex:
+ raise GraphProjectionError(f"Unexpected error during projection: {ex}") from exLikely invalid or redundant comment.
Summary by CodeRabbit
Release Notes
New Features
NodeandEdge, enhancing graph representation.GraphDBInterfacefor retrieving graph data.Bug Fixes
Tests
Node,Edge, andCogneeGraphclasses to ensure functionality and error management.