Ontology creation from Knowledge Graph #65
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Actionable comments posted: 1
🧹 Nitpick comments (4)
graphrag_sdk/relation.py (1)
Line range hint
208-219: Update method docstring to document the include_all parameter.The implementation looks good, but the docstring should be updated to include the new parameter.
def to_json(self, include_all: bool = True) -> dict: """ Converts the Relation object to a JSON dictionary. + Args: + include_all (bool, optional): Whether to include all attributes in the JSON output. Defaults to True. + Returns: dict: The JSON dictionary representing the Relation object. """graphrag_sdk/ontology.py (3)
7-7: Remove unused import.The
FalkorDBimport is not used in this file.-from falkordb import FalkorDB🧰 Tools
🪛 Ruff (0.8.2)
7-7:
falkordb.FalkorDBimported but unusedRemove unused import:
falkordb.FalkorDB(F401)
109-118: Complete the docstring.The
node_limitparameter is missing from the docstring. Also, add an explanation of what happens when the limit is reached.Args: graph (Graph): The graph object representing the ontology. + node_limit (int, optional): Maximum number of nodes to analyze per label. + Defaults to 100. When reached, remaining nodes + are ignored for attribute type inference.
121-134: Consider performance optimizations.The current implementation has potential performance issues:
- Multiple queries executed in nested loops
- Complex nested queries with UNWIND operations
- Separate queries for each label combination
Consider:
- Combining queries to reduce database roundtrips
- Using APOC procedures if available
- Implementing batch processing for large graphs
Would you like me to propose a more optimized implementation?
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graphrag_sdk/attribute.py(1 hunks)graphrag_sdk/chat_session.py(1 hunks)graphrag_sdk/entity.py(2 hunks)graphrag_sdk/kg.py(1 hunks)graphrag_sdk/ontology.py(12 hunks)graphrag_sdk/relation.py(2 hunks)
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graphrag_sdk/ontology.py
7-7: falkordb.FalkorDB imported but unused
Remove unused import: falkordb.FalkorDB
(F401)
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🔇 Additional comments (6)
graphrag_sdk/chat_session.py (1)
48-48: LGTM! Verify the impact on system instruction.The addition of
include_all=Falseis a good optimization that likely reduces the size of the system instruction by excluding non-essential ontology information.Let's verify the impact on the system instruction size:
✅ Verification successful
Optimization verified: include_all=False reduces instruction size appropriately
The implementation confirms that
include_all=Falseoptimizes the ontology serialization by excluding non-essential metadata while preserving the core structure needed for system instructions. The change helps reduce the instruction size without compromising functionality.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Compare the size of system instructions with and without include_all # Note: This helps verify if the optimization actually reduces the instruction size # Search for any test files that might contain system instruction examples rg -l "cypher_system_instruction" --type pyLength of output: 91
Script:
#!/bin/bash # Check the implementation of to_json method and its usage ast-grep --pattern 'def to_json($$$)' # Also look for any test files related to ontology fd test.*ontology --type fLength of output: 105
Script:
#!/bin/bash # Check test file content for examples cat tests/test_auto_create_ontology.py # Look for ontology implementation fd ontology --type f --exclude testLength of output: 1235
Script:
#!/bin/bash # Check ontology implementation cat graphrag_sdk/ontology.py # Look at an example ontology file cat examples/movies/ontology.jsonLength of output: 16915
graphrag_sdk/entity.py (1)
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85-98: LGTM! Well-structured JSON serialization changes.The changes improve type safety with return type hints and maintain consistency in the JSON serialization pattern across the codebase.
graphrag_sdk/kg.py (1)
69-69: Verify the impact of the method rename.The change from
from_graphtofrom_schema_graphimproves clarity but might be a breaking change.Let's check for any existing usage that might be affected:
✅ Verification successful
No breaking changes detected - new method addition
The change introduces a new specific method
from_schema_graphalongside the existingfrom_kg_graph, following a clear API design pattern where ontology creation methods are specialized based on the graph type. This is not a rename but a new addition.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Search for existing usage of the old method name # This helps identify potential breaking changes # Search for direct usage of from_graph rg "Ontology\.from_graph\(" --type py # Search for potential variable assignments that might use from_graph rg "ontology\s*=\s*.*\.from_graph\(" --type pyLength of output: 88
Script:
#!/bin/bash # Check git history for the method rename git log -p --all | rg "from_graph|from_schema_graph" # Search for the new method name rg "from_schema_graph" --type py -A 2 # Search with more flexible patterns rg "from_.*graph" --type py # Check test files specifically fd "test.*\.py$" --exec rg "from_.*graph" {}Length of output: 1454
graphrag_sdk/ontology.py (2)
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85-106: LGTM! Good rename for clarity.The rename from
from_graphtofrom_schema_graphbetter reflects the method's purpose of creating an ontology from a schema graph.
154-163: LGTM! Consistent implementation.The
include_allparameter is correctly propagated to both entities and relations, maintaining consistency across the codebase.graphrag_sdk/attribute.py (1)
133-157: LGTM! Well-documented and clean implementation.The updated
to_jsonmethod:
- Clearly documents the new parameter and its effect
- Implements conditional field inclusion correctly
- Maintains backward compatibility with the default value
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Actionable comments posted: 1
♻️ Duplicate comments (1)
graphrag_sdk/ontology.py (1)
109-148:⚠️ Potential issueFix SQL injection vulnerabilities in graph queries.
The direct string interpolation of labels and limits in queries poses a security risk.
Apply this fix to parameterize the queries:
- attributes = graph.query( - f"""MATCH (a:{label[0]}) call {{ with a return [k in keys(a) | [k, typeof(a[k])]] as types }} - WITH types limit {node_limit} unwind types as kt RETURN kt, count(1)""").result_set + attributes = graph.query( + """MATCH (a:$label) call { with a return [k in keys(a) | [k, typeof(a[k])]] as types } + WITH types limit $limit unwind types as kt RETURN kt, count(1)""", + {"label": label[0], "limit": node_limit} + ).result_setSimilar changes should be applied to other queries in the method.
🧹 Nitpick comments (1)
graphrag_sdk/ontology.py (1)
7-7: Remove unused import.The
FalkorDBimport is not used in this file.-from falkordb import FalkorDB🧰 Tools
🪛 Ruff (0.8.2)
7-7:
falkordb.FalkorDBimported but unusedRemove unused import:
falkordb.FalkorDB(F401)
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graphrag_sdk/ontology.py(12 hunks)
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graphrag_sdk/ontology.py
7-7: falkordb.FalkorDB imported but unused
Remove unused import: falkordb.FalkorDB
(F401)
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🔇 Additional comments (3)
graphrag_sdk/ontology.py (3)
Line range hint
85-106: LGTM! Clear method renaming.The rename from
from_graphtofrom_schema_graphbetter reflects the method's purpose of creating an ontology from a schema graph.
168-177: LGTM! Well-implemented JSON serialization enhancement.The addition of the
include_allparameter provides good flexibility for JSON serialization, and it's correctly propagated to entities and relations.
29-32: LGTM! Documentation improvements.The docstring updates are well-formatted and provide clear descriptions of parameters and return values.
Also applies to: 46-53, 69-77, 305-310, 317-322, 329-334, 341-346, 367-369
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Actionable comments posted: 1
🧹 Nitpick comments (1)
graphrag_sdk/chat_session.py (1)
117-142: Add error handling and type hints.The method implementation is clear, but could be improved with:
- Type hints for the ontology parameter
- Error handling for missing dictionary keys
- Dictionary validation
Consider this implementation:
- def clean_ontology_for_prompt(self, ontology: dict) -> str: + def clean_ontology_for_prompt(self, ontology: Ontology) -> str: """ Cleans the ontology by removing 'unique' and 'required' keys and prepares it for use in a prompt. Args: - ontology (dict): The ontology to clean and transform. + ontology (Ontology): The ontology to clean and transform. Returns: str: The cleaned ontology as a JSON string. + Raises: + KeyError: If required keys are missing from the ontology structure. """ # Convert the ontology object to a JSON. ontology = ontology.to_json() + if not isinstance(ontology, dict) or "entities" not in ontology or "relations" not in ontology: + raise ValueError("Invalid ontology structure") + # Remove unique and required attributes from the ontology. for entity in ontology["entities"]: - for attribute in entity["attributes"]: - del attribute['unique'] - del attribute['required'] + if "attributes" in entity: + for attribute in entity["attributes"]: + attribute.pop('unique', None) + attribute.pop('required', None) for relation in ontology["relations"]: - for attribute in relation["attributes"]: - del attribute['unique'] - del attribute['required'] + if "attributes" in relation: + for attribute in relation["attributes"]: + attribute.pop('unique', None) + attribute.pop('required', None)
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graphrag_sdk/chat_session.py(3 hunks)graphrag_sdk/kg.py(1 hunks)graphrag_sdk/ontology.py(13 hunks)
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- graphrag_sdk/kg.py
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graphrag_sdk/ontology.py
131-131: Ambiguous variable name: l
(E741)
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🔇 Additional comments (5)
graphrag_sdk/chat_session.py (2)
49-53: LGTM! Clear purpose and implementation.The addition of ontology cleaning before prompt generation improves the quality of the system instruction by removing unnecessary attributes.
115-116: Formatting changes look good.graphrag_sdk/ontology.py (3)
Line range hint
84-105: LGTM! Clear method renaming.The rename from
from_graphtofrom_schema_graphbetter reflects the method's purpose and improves code clarity.
107-122: Well-documented new method.The method signature and documentation clearly explain the purpose and parameters.
137-151:⚠️ Potential issueSimilar security and robustness improvements needed for relationship extraction.
The relationship extraction code has the same issues with SQL injection vulnerability and error handling.
Apply these changes:
for e_type in e_types: for s_lbls in n_labels: for t_lbls in n_labels: - e_t = e_type[0] - s_l = s_lbls[0] - t_l = t_lbls[0] + edge_type = e_type[0] + source_label = s_lbls[0] + target_label = t_lbls[0] # Check if a relationship exists between the source and target entity labels - if graph.query(f"MATCH (s:{s_l})-[a:{e_t}]->(t:{t_l}) return a limit 1").result_set: + try: + relationship_exists = graph.query( + "MATCH (s:$source)-[a:$edge_type]->(t:$target) RETURN a LIMIT 1", + { + "source": source_label, + "edge_type": edge_type, + "target": target_label + } + ).result_set + if relationship_exists: + attributes = graph.query( + """MATCH ()-[a:$edge_type]->() + CALL { with a return [k in keys(a) | [k, typeof(a[k])]] as types } + WITH types limit $limit + UNWIND types as kt + RETURN kt, count(1)""", + {"edge_type": edge_type, "limit": sample_size} + ).result_set + ontology.add_relation( + Relation(edge_type, source_label, target_label, + [Attribute(attr[0][0], attr[0][1]) for attr in attributes]) + ) + except Exception as e: + logger.error(f"Failed to process relationship {edge_type}: {str(e)}") + raiseLikely invalid or redundant comment.
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🧹 Nitpick comments (1)
graphrag_sdk/ontology.py (1)
109-122: Enhance method documentation.While the docstring follows the standard format, it could be more detailed about:
- The sampling strategy for attribute extraction
- Handling of conflicting attribute types
- Performance implications of the sample size
Consider adding these details to the docstring:
""" Constructs an Ontology object from a given Knowledge Graph. This function queries the provided knowledge graph to extract: 1. Entities and their attributes. 2. Relationships between entities and their attributes. + Note: + - Attributes are sampled from a limited number of nodes/edges to improve performance + - If an attribute appears with different types, the first encountered type is used + - Large sample sizes may impact performance on big graphs + Args: graph (Graph): The graph object representing the knowledge graph. sample_size (int): The sample size for the attribute extraction. + Higher values provide better attribute coverage but slower performance. Returns: Ontology: The Ontology object constructed from the Knowledge Graph. """
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graphrag_sdk/ontology.py
131-131: Ambiguous variable name: l
(E741)
140-140: Undefined name e_t
(F821)
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🔇 Additional comments (5)
graphrag_sdk/kg.py (2)
71-72: LGTM! Good error handling for empty ontology.The check for empty ontology with a clear error message helps users understand the issue and how to resolve it.
61-69:⚠️ Potential issueAdd existence check for ontology_graph.
The code assumes
ontology_graphexists but doesn't validate this assumption. This could lead to runtime errors if the schema graph doesn't exist in the database.Add this validation before accessing the graph:
ontology_graph = self.db.select_graph("{" + name + "}" + "_schema") +if not self.db.graph_exists(ontology_graph.name): + raise Exception(f"Schema graph '{ontology_graph.name}' does not exist. Please create it first.")Likely invalid or redundant comment.
graphrag_sdk/ontology.py (3)
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84-105: LGTM! Clear method naming and documentation.The rename from
from_graphtofrom_schema_graphbetter reflects the method's purpose and improves code clarity.
172-172: LGTM! Good type annotation addition.The explicit return type annotation improves code clarity and helps with type checking.
130-136:⚠️ Potential issueFix security vulnerability and improve code quality.
- Use parameterized queries to prevent SQL injection
- Improve variable naming
- Add error handling
Apply these changes:
- l = lbls[0] + label = lbls[0] attributes = graph.query( - f"""MATCH (a:{l}) call {{ with a return [k in keys(a) | [k, typeof(a[k])]] as types }} - WITH types limit {sample_size} unwind types as kt RETURN kt, count(1)""").result_set + """MATCH (a:$label) + CALL { with a return [k in keys(a) | [k, typeof(a[k])]] as types } + WITH types limit $limit + UNWIND types as kt + RETURN kt, count(1)""", + {"label": label, "limit": sample_size} + ).result_setLikely invalid or redundant comment.
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131-131: Ambiguous variable name:
l(E741)
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Actionable comments posted: 2
🧹 Nitpick comments (1)
graphrag_sdk/ontology.py (1)
108-152: Add error handling for graph operations.The method should handle potential graph operation failures gracefully.
Add error handling:
@staticmethod def from_kg_graph(graph: Graph, sample_size: int = 100,): + try: ontology = Ontology() # Retrieve all node labels and edge types from the graph. n_labels = graph.call_procedure("db.labels").result_set e_types = graph.call_procedure("db.relationshipTypes").result_set # ... rest of the implementation ... return ontology + except Exception as e: + logger.error(f"Failed to create ontology from knowledge graph: {str(e)}") + raise🧰 Tools
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131-131: Ambiguous variable name:
l(E741)
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graphrag_sdk/ontology.py(13 hunks)
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graphrag_sdk/ontology.py
131-131: Ambiguous variable name: l
(E741)
🔇 Additional comments (3)
graphrag_sdk/ontology.py (3)
Line range hint
84-105: LGTM! Good improvements to method signatures.The changes enhance code clarity and type safety:
- Renaming
from_graphtofrom_schema_graphbetter reflects the method's purpose- Adding return type annotation to
to_jsonimproves type safetyAlso applies to: 172-182
108-122: LGTM! Well-documented method signature.The method signature and documentation clearly explain the purpose and parameters.
28-31: LGTM! Documentation improvements.The documentation changes enhance consistency and clarity:
- Standardized parameter descriptions using "Args"
- Added clear return type descriptions
- Improved parameter explanations
Also applies to: 45-52, 68-75, 88-93, 116-122, 158-160, 167-169, 188-193, 309-315, 321-326, 333-338, 345-350, 371-373
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Actionable comments posted: 3
🧹 Nitpick comments (1)
tests/test_ontology_from_kg.py (1)
121-124: Use path constants for test data files.Consider defining the test data file paths as class constants or using a test data helper to make the paths more maintainable.
+ CITIES_DATA_PATH = "tests/data/cities.json" + RESTAURANTS_DATA_PATH = "tests/data/restaurants.json" @classmethod def import_data( self, kg: KnowledgeGraph, ): - with open("tests/data/cities.json") as f: + with open(cls.CITIES_DATA_PATH) as f: cities = loads(f.read()) - with open("tests/data/restaurants.json") as f: + with open(cls.RESTAURANTS_DATA_PATH) as f: restaurants = loads(f.read())
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graphrag_sdk/ontology.py
131-131: Ambiguous variable name: l
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tests/test_ontology_from_kg.py (1)
168-173: Enhance test coverage with additional test cases.The current test only verifies serialization. Consider adding tests for:
- Edge cases (empty graph, missing attributes)
- Validation of the actual ontology structure
- Error cases (invalid graph, malformed data)
Would you like me to help generate additional test cases to improve coverage?
graphrag_sdk/ontology.py (1)
Line range hint
84-105: Method renaming improves clarity.The renaming of
from_graphtofrom_schema_graphbetter reflects the method's purpose and improves code readability.
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♻️ Duplicate comments (2)
graphrag_sdk/ontology.py (2)
130-136:⚠️ Potential issueFix SQL injection vulnerability and improve variable naming.
The implementation has two issues that need to be addressed:
- Direct string interpolation in queries is vulnerable to SQL injection
- Single-letter variable name 'l' is ambiguous
Apply this diff to fix both issues:
- for lbls in n_labels: - l = lbls[0] - attributes = graph.query( - f"""MATCH (a:{l}) call {{ with a return [k in keys(a) | [k, typeof(a[k])]] as types }} - WITH types limit {sample_size} unwind types as kt RETURN kt, count(1) ORDER BY kt[0]""").result_set + for label_entry in n_labels: + label = label_entry[0] + attributes = graph.query( + """MATCH (a:$label) + CALL { with a return [k in keys(a) | [k, typeof(a[k])]] as types } + WITH types limit $limit + UNWIND types as kt + RETURN kt, count(1) + ORDER BY kt[0]""", + {"label": label, "limit": sample_size} + ).result_set🧰 Tools
🪛 Ruff (0.8.2)
131-131: Ambiguous variable name:
l(E741)
139-152: 🛠️ Refactor suggestionRestructure edge attribute extraction for better efficiency.
The current implementation has several issues:
- Edge attribute extraction is inefficiently coupled with source-destination validation
- Uses string interpolation in queries
- Complex nested loops
Apply this restructured implementation:
for e_type in e_types: - e_t = e_type[0] - attributes = graph.query( - f"""MATCH ()-[a:{e_t}]->() call {{ with a return [k in keys(a) | [k, typeof(a[k])]] as types }} - WITH types limit {sample_size} unwind types as kt RETURN kt, count(1) ORDER BY kt[0]""").result_set - attributes = process_attributes_from_graph(attributes) - for s_lbls in n_labels: - for t_lbls in n_labels: - s_l = s_lbls[0] - t_l = t_lbls[0] - result_set = graph.query(f"MATCH (s:{s_l})-[a:{e_t}]->(t:{t_l}) return a limit 1").result_set - if len(result_set) > 0: - ontology.add_relation(Relation(e_t, s_l, t_l, attributes)) + edge_type = e_type[0] + + # Extract edge attributes + attributes = graph.query( + """MATCH ()-[a:$type]->() + CALL { with a return [k in keys(a) | [k, typeof(a[k])]] as types } + WITH types limit $limit + UNWIND types as kt + RETURN kt, count(1) + ORDER BY kt[0]""", + {"type": edge_type, "limit": sample_size} + ).result_set + edge_attributes = process_attributes_from_graph(attributes) + + # Find valid source-target combinations more efficiently + valid_pairs = graph.query( + """MATCH (s)-[r:$type]->(t) + WITH DISTINCT labels(s)[0] as source, labels(t)[0] as target + RETURN source, target""", + {"type": edge_type} + ).result_set + + # Add relations for valid combinations + for source, target in valid_pairs: + ontology.add_relation( + Relation(edge_type, source, target, edge_attributes) + )
🧹 Nitpick comments (1)
graphrag_sdk/attribute.py (1)
4-4: Replace wildcard import with explicit imports.Wildcard imports can lead to namespace pollution and make it unclear which symbols are being used from the module.
Replace the wildcard import with explicit imports of only the needed symbols:
-from graphrag_sdk.fixtures.regex import * +from graphrag_sdk.fixtures.regex import ( + # Add the specific regex patterns you need +)🧰 Tools
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4-4:
from graphrag_sdk.fixtures.regex import *used; unable to detect undefined names(F403)
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graphrag_sdk/attribute.py
4-4: from graphrag_sdk.fixtures.regex import * used; unable to detect undefined names
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193-193: Do not use bare except
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graphrag_sdk/ontology.py
131-131: Ambiguous variable name: l
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🔇 Additional comments (5)
graphrag_sdk/attribute.py (3)
18-24: LGTM! Well-structured type system expansion.The addition of new attribute types (POINT, MAP, VECTOR, DATE, etc.) and their corresponding synonyms enhances the system's type recognition capabilities while maintaining clean implementation.
Also applies to: 26-39
53-63: LGTM! Improved type validation and error handling.The use of _SYNONYMS for type validation and the switch to ValueError makes the implementation more robust and specific.
81-91: LGTM! Improved constructor signature.Making
uniqueparameter optional with a default value ofFalseimproves the API's usability. Documentation is properly updated to reflect this change.graphrag_sdk/ontology.py (2)
3-10: LGTM! Improved method naming and organization.The rename from
from_graphtofrom_schema_graphbetter reflects the method's purpose. Import changes are appropriate for the new functionality.Also applies to: 84-106
Line range hint
174-183: LGTM! Improved type clarity.The addition of the return type annotation
-> dictenhances code clarity and type safety.
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Actionable comments posted: 2
🧹 Nitpick comments (3)
tests/test_ontology_from_kg.py (3)
15-16: Consider using INFO level logging for tests.DEBUG level logging might be too verbose for CI/CD pipelines. Consider using INFO level unless actively debugging test issues.
-logging.basicConfig(level=logging.DEBUG) +logging.basicConfig(level=logging.INFO)
13-13: Move environment loading to conftest.py.Loading environment variables at the module level could affect other tests. Consider moving
load_dotenv()to a pytestconftest.pyfile to ensure consistent environment setup across all tests.
110-120: Add error handling to cleanup function.The cleanup function should handle potential errors to ensure resources are properly released even if deletion fails.
def cleanup(kg): logger.info("Cleaning up test graph...") - kg.delete() + try: + kg.delete() + except Exception as e: + logger.error(f"Failed to cleanup test graph: {e}") + raise
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♻️ Duplicate comments (1)
graphrag_sdk/attribute.py (1)
178-196:⚠️ Potential issueImprove error handling and type hints in process_attributes_from_graph.
The function needs better error handling and more specific type hints.
Apply this diff to improve the implementation:
-def process_attributes_from_graph(attributes: list[list[str]]) -> list[Attribute]: +def process_attributes_from_graph(attributes: list[tuple[tuple[str, str], int]]) -> list[Attribute]: """ Processes the attributes extracted from the graph and converts them into the SDK convention. Args: attributes (list[list[str]]): The attributes extracted from the graph. Returns: processed_attributes (list[Attribute]): The processed attributes. + + Raises: + ValueError: If attribute type is invalid """ processed_attributes = [] for attr in attributes: try: type = AttributeType.from_string(attr[0][1]) processed_attributes.append(Attribute(attr[0][0],type)) - except: + except ValueError as e: + logger.warning(f"Skipping attribute {attr[0][0]}: {str(e)}") continue + except Exception as e: + logger.error(f"Unexpected error processing attribute {attr[0][0]}: {str(e)}") + raise return processed_attributes🧰 Tools
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193-193: Do not use bare
except(E722)
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graphrag_sdk/attribute.py (1)
4-4: Replace wildcard import with explicit imports.Wildcard imports can lead to namespace pollution and make it harder to track dependencies. Consider explicitly importing only the needed regex patterns.
-from graphrag_sdk.fixtures.regex import * +from graphrag_sdk.fixtures.regex import ( + # Add specific patterns you need +)🧰 Tools
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4-4:
from graphrag_sdk.fixtures.regex import *used; unable to detect undefined names(F403)
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graphrag_sdk/attribute.py
4-4: from graphrag_sdk.fixtures.regex import * used; unable to detect undefined names
(F403)
193-193: Do not use bare except
(E722)
🔇 Additional comments (4)
graphrag_sdk/attribute.py (4)
18-39: LGTM! Well-structured type system enhancement.The addition of new attribute types and their synonyms improves type recognition flexibility while maintaining clear organization.
53-63: LGTM! Improved type conversion with better error handling.The updated implementation provides more robust type recognition using synonyms and clearer error messaging.
81-91: LGTM! More flexible constructor with improved documentation.Making
uniqueparameter optional with a default value ofFalseimproves API usability.
158-166: LGTM! Improved code organization in to_json method.Creating a dictionary before returning improves code readability while maintaining the same functionality.
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graphrag_sdk/ontology.py
132-132: Ambiguous variable name: l
(E741)
173-173: Do not use bare except
(E722)
graphrag_sdk/attribute.py
4-4: from graphrag_sdk.fixtures.regex import * used; unable to detect undefined names
(F403)
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🔇 Additional comments (7)
graphrag_sdk/attribute.py (4)
18-20: LGTM! New attribute types added.The addition of POINT, MAP, and VECTOR types expands the SDK's capabilities to handle more complex data structures.
24-34: LGTM! Well-structured type mapping.The _SYNONYMS dictionary provides a clean and maintainable way to normalize attribute types, with comprehensive coverage of all supported types including the newly added ones.
50-57: LGTM! Improved type validation.The from_string method now uses the _SYNONYMS dictionary for type validation, providing clear error messages for invalid types.
152-159: LGTM! Enhanced maintainability.Creating a json_data dictionary before returning improves code readability and maintainability.
graphrag_sdk/ontology.py (3)
Line range hint
85-107: LGTM! Method renamed for clarity.The renaming from
from_graphtofrom_schema_graphbetter reflects the method's purpose and improves code clarity.
140-153: 🛠️ Refactor suggestionRestructure edge attribute extraction for better efficiency.
The current implementation has several issues:
- Edge attribute extraction is inefficiently coupled with source-destination validation
- Uses string interpolation in queries (security risk)
- Complex nested loops
Apply this restructured implementation:
for e_type in e_types: - e_t = e_type[0] - attributes = graph.query( - f"""MATCH ()-[a:{e_t}]->() call {{ with a return [k in keys(a) | [k, typeof(a[k])]] as types }} - WITH types limit {sample_size} unwind types as kt RETURN kt, count(1) ORDER BY kt[0]""").result_set - attributes = ontology.process_attributes_from_graph(attributes) - for s_lbls in n_labels: - for t_lbls in n_labels: - s_l = s_lbls[0] - t_l = t_lbls[0] - result_set = graph.query(f"MATCH (s:{s_l})-[a:{e_t}]->(t:{t_l}) return a limit 1").result_set - if len(result_set) > 0: - ontology.add_relation(Relation(e_t, s_l, t_l, attributes)) + edge_type = e_type[0] + + # Extract edge attributes + attributes = graph.query( + """MATCH ()-[a:$type]->() + CALL { with a return [k in keys(a) | [k, typeof(a[k])]] as types } + WITH types limit $limit + UNWIND types as kt + RETURN kt, count(1) + ORDER BY kt[0]""", + {"type": edge_type, "limit": sample_size} + ).result_set + edge_attributes = ontology.process_attributes_from_graph(attributes) + + # Find valid source-target combinations more efficiently + valid_pairs = graph.query( + """MATCH (s)-[r:$type]->(t) + WITH DISTINCT labels(s)[0] as source, labels(t)[0] as target + RETURN source, target""", + {"type": edge_type} + ).result_set + + # Add relations for valid combinations + for source, target in valid_pairs: + ontology.add_relation( + Relation(edge_type, source, target, edge_attributes) + )Likely invalid or redundant comment.
109-109: Add tests for the new functionality.The new
from_kg_graphmethod needs test coverage to ensure it works correctly.Run this script to check for existing tests:
Would you like me to help generate comprehensive test cases for this new functionality?
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🔇 Additional comments (2)
graphrag_sdk/ontology.py (2)
104-104: LGTM! Method rename and type annotation improvements.The changes improve clarity by:
- Renaming
from_graphtofrom_schema_graphto better reflect its purpose- Adding return type annotation to
to_jsonAlso applies to: 194-194
128-128: Add tests for the new functionality.Please ensure that comprehensive tests are added for the new
from_kg_graphmethod.Would you like me to help generate test cases for this method?
PR Type
Enhancement, Bug fix
Description
Introduced
include_allparameter for JSON serialization in multiple classes.Enhanced ontology creation with
from_kg_graphmethod for Knowledge Graphs.Fixed incorrect method usage for ontology schema graph loading.
Improved docstrings for consistency and clarity across files.
Changes walkthrough 📝
attribute.py
Enhanced JSON serialization for attributes.graphrag_sdk/attribute.py
include_allparameter toto_jsonmethod.include_all.entity.py
Enhanced JSON serialization for entities.graphrag_sdk/entity.py
include_allparameter toto_jsonmethod.include_all.ontology.py
Added Knowledge Graph ontology creation and JSON enhancements.graphrag_sdk/ontology.py
from_kg_graphmethod for ontology creation from KnowledgeGraphs.
to_jsonmethod withinclude_allparameter.from_graphtofrom_schema_graph.relation.py
Enhanced JSON serialization for relations.graphrag_sdk/relation.py
include_allparameter toto_jsonmethod.include_all.chat_session.py
Adjusted ontology JSON usage in chat session.graphrag_sdk/chat_session.py
cypher_system_instructionto useto_jsonwithinclude_all=False.kg.py
Fixed ontology schema graph loading method.graphrag_sdk/kg.py
Ontology.from_graphwithOntology.from_schema_graph.Summary by CodeRabbit
Release Notes
New Features
clean_ontology_for_promptmethod in the ChatSession class for improved ontology processing.from_kg_graphmethod in the Ontology class for enhanced Knowledge Graph processing.process_attributes_from_graphto streamline attribute processing.Improvements
uniqueparameter in the Attribute class.Technical Updates
from_graphmethod tofrom_schema_graphin Ontology class.from_stringmethods in both AttributeType and Attribute classes for improved error handling and type normalization.