Example task extraction #127
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Warning Rate limit exceeded@Vasilije1990 has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 0 minutes and 42 seconds before requesting another review. How to resolve this issue?After the wait time has elapsed, a review can be triggered using the We recommend that you space out your commits to avoid hitting the rate limit. How do rate limits work?CodeRabbit enforces hourly rate limits for each developer per organization. Our paid plans have higher rate limits than the trial, open-source and free plans. In all cases, we re-allow further reviews after a brief timeout. Please see our FAQ for further information. WalkthroughThe recent updates significantly enhance the management and processing of knowledge graphs through new asynchronous functions and refined task implementations. Key improvements focus on chunk processing, graph topology management, and standardized node identification, leading to greater modularity and efficiency throughout the pipeline. Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant CognifyPipeline
participant GraphOntology
participant DataChunks
Client->>CognifyPipeline: run_cognify_pipeline()
CognifyPipeline->>DataChunks: source_documents_to_chunks()
DataChunks-->>CognifyPipeline: Return chunks
CognifyPipeline->>GraphOntology: chunk_to_graph_decomposition()
GraphOntology-->>CognifyPipeline: Return topology
CognifyPipeline-->>Client: Complete processing
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Actionable comments posted: 0
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (1)
- cognee/api/v1/tasks/graph_ontology/graph_ontology.py (1 hunks)
Additional comments not posted (5)
cognee/api/v1/tasks/graph_ontology/graph_ontology.py (5)
10-10: LGTM!The asynchronous function
ontology_taskis defined correctly.
11-11: LGTM!The return statement correctly returns a
Taskobject with the appropriate function, model, and task configuration.
6-6: Verify the import path.Ensure that the import path
cognee.shared.data_modelsis correct and that theKnowledgeGraphclass is necessary for this file.Verification successful
Verified: The import path is correct and the
KnowledgeGraphclass is used in the file.
- The
KnowledgeGraphclass is defined incognee/shared/data_models.py.- The
KnowledgeGraphclass is used incognee/api/v1/tasks/graph_ontology/graph_ontology.py.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence of the `KnowledgeGraph` class in the specified path. # Test: Search for the `KnowledgeGraph` class definition in the specified path. # Expect: The `KnowledgeGraph` class should be defined in the specified path. rg --type python -A 5 $'class KnowledgeGraph' cognee/shared/data_models.pyLength of output: 106
Script:
#!/bin/bash # Description: Verify the existence of the `KnowledgeGraph` class in the specified path. # Test: Search for the `KnowledgeGraph` class definition in the specified path. # Expect: The `KnowledgeGraph` class should be defined in the specified path. rg -A 5 $'class KnowledgeGraph' cognee/shared/data_models.pyLength of output: 267
Script:
#!/bin/bash # Description: Verify the usage of the `KnowledgeGraph` class in the specified file. # Test: Search for the usage of the `KnowledgeGraph` class in the specified file. # Expect: The `KnowledgeGraph` class should be used in the `cognee/api/v1/tasks/graph_ontology/graph_ontology.py` file. rg 'KnowledgeGraph' cognee/api/v1/tasks/graph_ontology/graph_ontology.pyLength of output: 236
5-5: Verify the import path.Ensure that the import path
cognee.modules.data.extraction.knowledge_graph.establish_graph_topologyis correct and that theestablish_graph_topologyfunction is necessary for this file.Verification successful
Verified the import path.
The import path
cognee.modules.data.extraction.knowledge_graph.establish_graph_topologyis correct, and theestablish_graph_topologyfunction is used within thecognee/api/v1/tasks/graph_ontology/graph_ontology.pyfile.
- The function
establish_graph_topologyis defined in the specified path.- The function is used in the file.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence of the `establish_graph_topology` function in the specified path. # Test: Search for the `establish_graph_topology` function definition in the specified path. # Expect: The `establish_graph_topology` function should be defined in the specified path. rg --type python -A 5 $'def establish_graph_topology' cognee/modules/data/extraction/knowledge_graph/establish_graph_topology.pyLength of output: 160
Script:
#!/bin/bash # Description: Verify the existence of the `establish_graph_topology` function in the specified path. # Test: Search for the `establish_graph_topology` function definition in the specified path. # Expect: The `establish_graph_topology` function should be defined in the specified path. rg -A 5 $'def establish_graph_topology' cognee/modules/data/extraction/knowledge_graph/establish_graph_topology.pyLength of output: 332
Script:
#!/bin/bash # Description: Verify the usage of the `establish_graph_topology` function in the specified file. # Test: Search for the usage of the `establish_graph_topology` function in the specified file. # Expect: The `establish_graph_topology` function should be used in the specified file. rg 'establish_graph_topology' cognee/api/v1/tasks/graph_ontology/graph_ontology.pyLength of output: 302
3-3: Verify the import path.Ensure that the import path
cognee.modules.pipelines.tasks.Taskis correct and that theTaskclass is necessary for this file.Verification successful
The import path is correct and necessary.
The import path
cognee.modules.pipelines.tasks.Taskis correct, and theTaskclass is necessary for the functionality ingraph_ontology.py.
- The
Taskclass is defined incognee/modules/pipelines/tasks/Task.py.- The
Taskclass is used within theontology_taskfunction ingraph_ontology.py.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence of the `Task` class in the specified path. # Test: Search for the `Task` class definition in the specified path. # Expect: The `Task` class should be defined in the specified path. rg --type python -A 5 $'class Task' cognee/modules/pipelines/tasks/Task.pyLength of output: 106
Script:
#!/bin/bash # Description: Verify the existence of the `Task` class in the specified path. # Test: Search for the `Task` class definition in the specified path. # Expect: The `Task` class should be defined in the specified path. rg --type py -A 5 $'class Task' cognee/modules/pipelines/tasks/Task.pyLength of output: 253
Script:
#!/bin/bash # Description: Review the remaining lines in `graph_ontology.py` to confirm the necessity of the `Task` class. # Test: Display the content of `graph_ontology.py`. # Expect: Review the usage of the `Task` class within the file. cat cognee/api/v1/tasks/graph_ontology/graph_ontology.pyLength of output: 417
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Actionable comments posted: 2
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (1)
- cognee/tasks/graph_ontology/graph_ontology.py (1 hunks)
Additional comments not posted (3)
cognee/tasks/graph_ontology/graph_ontology.py (3)
10-12: Verify the conditional logic forKnowledgeGraph.Ensure that the conditional check for
KnowledgeGraphis correct and that this early return is the desired behavior.
18-18: Consider potential performance impact.Returning the same
data_chunkslist after processing might have performance implications if the list is large. Consider if any optimizations are necessary.
21-22: Verify handling of edge cases.Ensure that the function correctly handles edge cases, such as empty strings or strings with special characters.
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| graph_engine = await get_graph_engine() | ||
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| await add_model_class_to_graph(topology_model, graph_engine) |
There was a problem hiding this comment.
Add error handling for add_model_class_to_graph.
The asynchronous call to add_model_class_to_graph should include error handling to manage potential failures.
- await add_model_class_to_graph(topology_model, graph_engine)
+ try:
+ await add_model_class_to_graph(topology_model, graph_engine)
+ except Exception as e:
+ # Handle the error appropriately, e.g., log it and return an empty list or raise a custom error
+ return []Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| await add_model_class_to_graph(topology_model, graph_engine) | |
| try: | |
| await add_model_class_to_graph(topology_model, graph_engine) | |
| except Exception as e: | |
| # Handle the error appropriately, e.g., log it and return an empty list or raise a custom error | |
| return [] |
| if topology_model == KnowledgeGraph: | ||
| return data_chunks | ||
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| graph_engine = await get_graph_engine() |
There was a problem hiding this comment.
Add error handling for get_graph_engine.
The asynchronous call to get_graph_engine should include error handling to manage potential failures.
- graph_engine = await get_graph_engine()
+ try:
+ graph_engine = await get_graph_engine()
+ except Exception as e:
+ # Handle the error appropriately, e.g., log it and return an empty list or raise a custom error
+ return []Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| graph_engine = await get_graph_engine() | |
| try: | |
| graph_engine = await get_graph_engine() | |
| except Exception as e: | |
| # Handle the error appropriately, e.g., log it and return an empty list or raise a custom error | |
| return [] |
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Actionable comments posted: 16
Outside diff range, codebase verification and nitpick comments (5)
cognee/tasks/chunk_remove_disconnected/chunk_remove_disconnected.py (1)
12-12: Optimize set comprehension.The set comprehension can be simplified for readability.
- document_ids = set((data_chunk.document_id for data_chunk in data_chunks)) + document_ids = {data_chunk.document_id for data_chunk in data_chunks}cognee/tasks/chunk_extract_summary/chunk_extract_summary.py (1)
15-17: Optimize asynchronous summary extraction.The list comprehension can be simplified for readability.
- chunk_summaries = await asyncio.gather( - *[extract_summary(chunk.text, summarization_model) for chunk in data_chunks] - ) + chunk_summaries = await asyncio.gather(*( + extract_summary(chunk.text, summarization_model) for chunk in data_chunks + ))cognee/tasks/source_documents_to_chunks/source_documents_to_chunks.py (1)
11-11: Optimize node existence check.The node existence check can be optimized for readability.
- if parent_node_id and await graph_engine.extract_node(parent_node_id) is None: + if parent_node_id and not await graph_engine.extract_node(parent_node_id):cognee/modules/classification/classify_text_chunks.py (2)
Line range hint
39-43:
Add error handling for vector engine operations.Consider adding try-except blocks to handle potential errors during retrieval and creation operations.
- if await vector_engine.has_collection(collection_name): - existing_data_points = await vector_engine.retrieve( - collection_name, - list(set(classification_data_points)), - ) if len(classification_data_points) > 0 else [] - existing_points_map = {point.id: True for point in existing_data_points} - else: - existing_points_map = {} - await vector_engine.create_collection(collection_name, payload_schema = Keyword) + try: + if await vector_engine.has_collection(collection_name): + existing_data_points = await vector_engine.retrieve( + collection_name, + list(set(classification_data_points)), + ) if len(classification_data_points) > 0 else [] + existing_points_map = {point.id: True for point in existing_data_points} + else: + existing_points_map = {} + await vector_engine.create_collection(collection_name, payload_schema = Keyword) + except Exception as e: + # Log the error or handle it appropriately + print(f"Error during vector engine operations: {e}")
Line range hint
54-142:
Optimize node and edge creation with batch processing.Consider batching node and edge creation to improve performance, especially for large datasets.
- for (chunk_index, data_chunk) in enumerate(data_chunks): - chunk_classification = chunk_classifications[chunk_index] - classification_type_label = chunk_classification.label.type - classification_type_id = uuid5(NAMESPACE_OID, classification_type_label) - if classification_type_id not in existing_points_map: - data_points.append( - DataPoint[Keyword]( - id = str(classification_type_id), - payload = Keyword.parse_obj({ - "uuid": str(classification_type_id), - "text": classification_type_label, - "chunk_id": str(data_chunk.chunk_id), - "document_id": str(data_chunk.document_id), - }), - embed_field = "text", - ) - ) - nodes.append(( - str(classification_type_id), - dict( - id = str(classification_type_id), - name = classification_type_label, - type = classification_type_label, - ) - )) - existing_points_map[classification_type_id] = True - edges.append(( - str(data_chunk.chunk_id), - str(classification_type_id), - "is_media_type", - dict( - relationship_name = "is_media_type", - source_node_id = str(data_chunk.chunk_id), - target_node_id = str(classification_type_id), - ), - )) - for classification_subclass in chunk_classification.label.subclass: - classification_subtype_label = classification_subclass.value - classification_subtype_id = uuid5(NAMESPACE_OID, classification_subtype_label) - if classification_subtype_id not in existing_points_map: - data_points.append( - DataPoint[Keyword]( - id = str(classification_subtype_id), - payload = Keyword.parse_obj({ - "uuid": str(classification_subtype_id), - "text": classification_subtype_label, - "chunk_id": str(data_chunk.chunk_id), - "document_id": str(data_chunk.document_id), - }), - embed_field = "text", - ) - ) - nodes.append(( - str(classification_subtype_id), - dict( - id = str(classification_subtype_id), - name = classification_subtype_label, - type = classification_subtype_label, - ) - )) - edges.append(( - str(classification_subtype_id), - str(classification_type_id), - "is_subtype_of", - dict( - relationship_name = "contains", - source_node_id = str(classification_type_id), - target_node_id = str(classification_subtype_id), - ), - )) - existing_points_map[classification_subtype_id] = True - edges.append(( - str(data_chunk.chunk_id), - str(classification_subtype_id), - "is_classified_as", - dict( - relationship_name = "is_classified_as", - source_node_id = str(data_chunk.chunk_id), - target_node_id = str(classification_subtype_id), - ), - )) + data_points.extend([ + DataPoint[Keyword]( + id = str(classification_type_id), + payload = Keyword.parse_obj({ + "uuid": str(classification_type_id), + "text": classification_type_label, + "chunk_id": str(data_chunk.chunk_id), + "document_id": str(data_chunk.document_id), + }), + embed_field = "text", + ) for chunk_index, data_chunk in enumerate(data_chunks) + if (classification_type_id := uuid5(NAMESPACE_OID, chunk_classifications[chunk_index].label.type)) not in existing_points_map + ]) + nodes.extend([ + ( + str(classification_type_id), + dict( + id = str(classification_type_id), + name = classification_type_label, + type = classification_type_label, + ) + ) for chunk_index, data_chunk in enumerate(data_chunks) + if (classification_type_id := uuid5(NAMESPACE_OID, chunk_classifications[chunk_index].label.type)) not in existing_points_map + ]) + edges.extend([ + ( + str(data_chunk.chunk_id), + str(classification_type_id), + "is_media_type", + dict( + relationship_name = "is_media_type", + source_node_id = str(data_chunk.chunk_id), + target_node_id = str(classification_type_id), + ), + ) for chunk_index, data_chunk in enumerate(data_chunks) + ]) + edges.extend([ + ( + str(classification_subtype_id), + str(classification_type_id), + "is_subtype_of", + dict( + relationship_name = "contains", + source_node_id = str(classification_type_id), + target_node_id = str(classification_subtype_id), + ), + ) for chunk_index, data_chunk in enumerate(data_chunks) + for classification_subclass in chunk_classifications[chunk_index].label.subclass + if (classification_subtype_id := uuid5(NAMESPACE_OID, classification_subclass.value)) not in existing_points_map + ]) + edges.extend([ + ( + str(data_chunk.chunk_id), + str(classification_subtype_id), + "is_classified_as", + dict( + relationship_name = "is_classified_as", + source_node_id = str(data_chunk.chunk_id), + target_node_id = str(classification_subtype_id), + ), + ) for chunk_index, data_chunk in enumerate(data_chunks) + for classification_subclass in chunk_classifications[chunk_index].label.subclass + ])
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (11)
- cognee/api/v1/cognify/cognify_v2.py (2 hunks)
- cognee/modules/classification/classify_text_chunks.py (1 hunks)
- cognee/modules/data/extraction/knowledge_graph/establish_graph_topology.py (1 hunks)
- cognee/tasks/chunk_extract_summary/chunk_extract_summary.py (1 hunks)
- cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.py (1 hunks)
- cognee/tasks/chunk_remove_disconnected/chunk_remove_disconnected.py (1 hunks)
- cognee/tasks/chunk_to_graph_decomposition/chunk_to_graph_decomposition.py (1 hunks)
- cognee/tasks/chunk_to_vector_graphstore/chunk_to_vector_graphstore.py (1 hunks)
- cognee/tasks/chunk_update_check/chunk_update_check.py (1 hunks)
- cognee/tasks/graph_decomposition_to_graph_nodes/graph_decomposition_to_graph_nodes.py (1 hunks)
- cognee/tasks/source_documents_to_chunks/source_documents_to_chunks.py (1 hunks)
Additional comments not posted (12)
cognee/modules/data/extraction/knowledge_graph/establish_graph_topology.py (2)
Line range hint
21-22:
LGTM!The function
generate_node_idis simple and performs the expected normalization of node IDs.
8-8: Verify the function usage in the codebase.Ensure that all function calls to
chunk_to_graph_decompositionmatch the new name.cognee/tasks/chunk_to_graph_decomposition/chunk_to_graph_decomposition.py (2)
21-22: LGTM!The function
generate_node_idis simple and performs the expected normalization of node IDs.
10-10: Verify the function usage in the codebase.Ensure that all function calls to
chunk_to_graph_decomposition_taskmatch the new name.Verification successful
Function usage verified successfully.
The function
chunk_to_graph_decomposition_taskis correctly referenced in the codebase.
cognee/api/v1/cognify/cognify_v2.pyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify all function calls to `chunk_to_graph_decomposition_task` match the new name. # Test: Search for the function usage. Expect: Only occurrences of the new name. rg --type python -A 5 $'chunk_to_graph_decomposition_task'Length of output: 88
Script:
#!/bin/bash # Description: Verify all function calls to `chunk_to_graph_decomposition_task` match the new name. # Test: Search for the function usage. Expect: Only occurrences of the new name. rg -A 5 $'chunk_to_graph_decomposition_task'Length of output: 2360
cognee/tasks/chunk_to_vector_graphstore/chunk_to_vector_graphstore.py (3)
28-37: LGTM! Ensure proper testing for data point creation.The code for creating data points in vector storage looks good. Ensure that this part is well-tested.
82-83: LGTM! Ensure proper testing for node and edge addition.The code for adding nodes and edges to graph storage looks good. Ensure that this part is well-tested.
89-91: LGTM! Ensure proper testing for edge cases.The logic for handling the first chunk is correct. Ensure that edge cases are covered in tests.
cognee/modules/classification/classify_text_chunks.py (1)
Line range hint
144-150:
LGTM! Ensure proper testing for node and edge addition.The code for adding nodes and edges to graph storage looks good. Ensure that this part is well-tested.
cognee/api/v1/cognify/cognify_v2.py (2)
30-38: LGTM! New task function imports are correct.The new task functions are imported correctly and seem necessary for the updated pipeline functionality.
112-131: LGTM! But verify the function usage in the codebase.The new task functions are integrated correctly into the
run_cognify_pipelinefunction.However, ensure that all function calls to
run_cognify_pipelineare updated if needed.Verification successful
All function calls to
run_cognify_pipelineare updated and consistent within the codebase.
- The function
run_cognify_pipelineis only referenced withincognee/api/v1/cognify/cognify_v2.py.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify all function calls to `run_cognify_pipeline` are updated if needed. # Test: Search for the function usage. Expect: Only occurrences of the new signature. rg --type python -A 5 $'run_cognify_pipeline'Length of output: 75
Script:
#!/bin/bash # Description: Verify all function calls to `run_cognify_pipeline` are updated if needed. # Find all Python files and search for the function usage within those files fd -e py | xargs rg 'run_cognify_pipeline'Length of output: 229
Script:
#!/bin/bash # Description: Verify all function calls to `run_cognify_pipeline` are updated if needed. # Search for the function usage in the entire codebase rg 'run_cognify_pipeline'Length of output: 212
cognee/tasks/graph_decomposition_to_graph_nodes/graph_decomposition_to_graph_nodes.py (2)
13-19: LGTM! TheEntityNodeclass is defined correctly.The class follows best practices for defining data models using Pydantic.
215-219: LGTM! The helper functions are defined correctly.The
generate_nameandgenerate_node_idfunctions follow best practices for string manipulations.
| for document_id in document_ids: | ||
| chunk_ids = await graph_engine.get_successor_ids(document_id, edge_label = "has_chunk") | ||
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| for chunk_id in chunk_ids: | ||
| previous_chunks = await graph_engine.get_predecessor_ids(chunk_id, edge_label = "next_chunk") | ||
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| if len(previous_chunks) == 0: | ||
| obsolete_chunk_ids.append(chunk_id) | ||
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There was a problem hiding this comment.
Consider breaking down nested loops for readability.
Nested loops can be hard to read and maintain. Consider breaking them down into smaller functions.
async def get_obsolete_chunk_ids(graph_engine, document_ids):
obsolete_chunk_ids = []
for document_id in document_ids:
chunk_ids = await graph_engine.get_successor_ids(document_id, edge_label="has_chunk")
for chunk_id in chunk_ids:
previous_chunks = await graph_engine.get_predecessor_ids(chunk_id, edge_label="next_chunk")
if len(previous_chunks) == 0:
obsolete_chunk_ids.append(chunk_id)
return obsolete_chunk_ids
async def chunk_remove_disconnected_task(data_chunks: list[DocumentChunk]) -> list[DocumentChunk]:
graph_engine = await get_graph_engine()
document_ids = {data_chunk.document_id for data_chunk in data_chunks}
obsolete_chunk_ids = await get_obsolete_chunk_ids(graph_engine, document_ids)
if obsolete_chunk_ids:
await graph_engine.delete_nodes(obsolete_chunk_ids)
disconnected_nodes = await graph_engine.get_disconnected_nodes()
if disconnected_nodes:
await graph_engine.delete_nodes(disconnected_nodes)
return data_chunks| disconnected_nodes = await graph_engine.get_disconnected_nodes() | ||
| if len(disconnected_nodes) > 0: | ||
| await graph_engine.delete_nodes(disconnected_nodes) |
There was a problem hiding this comment.
Optimize the deletion of disconnected nodes.
The deletion of disconnected nodes can be optimized by combining the checks and deletions.
- disconnected_nodes = await graph_engine.get_disconnected_nodes()
- if len(disconnected_nodes) > 0:
- await graph_engine.delete_nodes(disconnected_nodes)
+ disconnected_nodes = await graph_engine.get_disconnected_nodes()
+ if disconnected_nodes:
+ await graph_engine.delete_nodes(disconnected_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.
| disconnected_nodes = await graph_engine.get_disconnected_nodes() | |
| if len(disconnected_nodes) > 0: | |
| await graph_engine.delete_nodes(disconnected_nodes) | |
| disconnected_nodes = await graph_engine.get_disconnected_nodes() | |
| if disconnected_nodes: | |
| await graph_engine.delete_nodes(disconnected_nodes) |
| async def chunk_remove_disconnected_task(data_chunks: list[DocumentChunk]) -> list[DocumentChunk]: | ||
| graph_engine = await get_graph_engine() | ||
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| document_ids = set((data_chunk.document_id for data_chunk in data_chunks)) | ||
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| obsolete_chunk_ids = [] | ||
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| for document_id in document_ids: | ||
| chunk_ids = await graph_engine.get_successor_ids(document_id, edge_label = "has_chunk") | ||
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| for chunk_id in chunk_ids: | ||
| previous_chunks = await graph_engine.get_predecessor_ids(chunk_id, edge_label = "next_chunk") | ||
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| if len(previous_chunks) == 0: | ||
| obsolete_chunk_ids.append(chunk_id) | ||
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| if len(obsolete_chunk_ids) > 0: | ||
| await graph_engine.delete_nodes(obsolete_chunk_ids) | ||
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| disconnected_nodes = await graph_engine.get_disconnected_nodes() | ||
| if len(disconnected_nodes) > 0: | ||
| await graph_engine.delete_nodes(disconnected_nodes) | ||
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| return data_chunks |
There was a problem hiding this comment.
Ensure proper error handling for external calls.
The function makes several asynchronous calls to graph_engine. It's important to handle potential errors that might arise from these calls.
+ try:
+ graph_engine = await get_graph_engine()
+ except Exception as e:
+ # Handle the exception or log it
+ raise eAdd similar error handling for other asynchronous calls within the function.
Committable suggestion was skipped due to low confidence.
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| vector_engine = get_vector_engine() | ||
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| await vector_engine.create_collection(collection_name, payload_schema = TextSummary) |
There was a problem hiding this comment.
Ensure collection creation only if it does not exist.
Check if the collection already exists before attempting to create it.
+ if not await vector_engine.collection_exists(collection_name):
+ await vector_engine.create_collection(collection_name, payload_schema=TextSummary)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.
| await vector_engine.create_collection(collection_name, payload_schema = TextSummary) | |
| if not await vector_engine.collection_exists(collection_name): | |
| await vector_engine.create_collection(collection_name, payload_schema = TextSummary) |
| async def chunk_extract_summary_task(data_chunks: list[DocumentChunk], summarization_model: Type[BaseModel], collection_name: str = "summaries"): | ||
| if len(data_chunks) == 0: | ||
| return data_chunks |
There was a problem hiding this comment.
Ensure proper error handling for external calls.
The function makes several asynchronous calls. It's important to handle potential errors that might arise from these calls.
+ try:
+ if len(data_chunks) == 0:
+ return data_chunks
+ except Exception as e:
+ # Handle the exception or log it
+ raise eAdd similar error handling for other asynchronous calls within the function.
Committable suggestion was skipped due to low confidence.
| def get_previous_chunk_id(document_chunks: list[DocumentChunk], current_chunk: DocumentChunk) -> DocumentChunk: | ||
| if current_chunk.chunk_index == 0: | ||
| return current_chunk.document_id | ||
|
|
||
| for chunk in document_chunks: | ||
| if str(chunk.document_id) == str(current_chunk.document_id) \ | ||
| and chunk.chunk_index == current_chunk.chunk_index - 1: | ||
| return chunk.chunk_id | ||
|
|
||
| return None |
There was a problem hiding this comment.
Optimize loop for retrieving previous chunk ID.
Consider using a dictionary to map chunk indices to improve performance.
- for chunk in document_chunks:
- if str(chunk.document_id) == str(current_chunk.document_id) \
- and chunk.chunk_index == current_chunk.chunk_index - 1:
- return chunk.chunk_id
+ chunk_map = {chunk.chunk_index: chunk.chunk_id for chunk in document_chunks if str(chunk.document_id) == str(current_chunk.document_id)}
+ return chunk_map.get(current_chunk.chunk_index - 1, 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_previous_chunk_id(document_chunks: list[DocumentChunk], current_chunk: DocumentChunk) -> DocumentChunk: | |
| if current_chunk.chunk_index == 0: | |
| return current_chunk.document_id | |
| for chunk in document_chunks: | |
| if str(chunk.document_id) == str(current_chunk.document_id) \ | |
| and chunk.chunk_index == current_chunk.chunk_index - 1: | |
| return chunk.chunk_id | |
| return None | |
| def get_previous_chunk_id(document_chunks: list[DocumentChunk], current_chunk: DocumentChunk) -> DocumentChunk: | |
| if current_chunk.chunk_index == 0: | |
| return current_chunk.document_id | |
| chunk_map = {chunk.chunk_index: chunk.chunk_id for chunk in document_chunks if str(chunk.document_id) == str(current_chunk.document_id)} | |
| return chunk_map.get(current_chunk.chunk_index - 1, None) |
| await vector_engine.delete_data_points(collection_name, [str(chunk.chunk_id) for chunk in existing_chunks]) | ||
|
|
||
| await graph_engine.remove_connection_to_successors_of([chunk.chunk_id for chunk in existing_chunks], "next_chunk") | ||
| await graph_engine.remove_connection_to_predecessors_of([chunk.chunk_id for chunk in existing_chunks], "has_chunk") |
There was a problem hiding this comment.
Add error handling for vector and graph engine operations.
Consider adding try-except blocks to handle potential errors during deletion and removal operations.
- await vector_engine.delete_data_points(collection_name, [str(chunk.chunk_id) for chunk in existing_chunks])
- await graph_engine.remove_connection_to_successors_of([chunk.chunk_id for chunk in existing_chunks], "next_chunk")
- await graph_engine.remove_connection_to_predecessors_of([chunk.chunk_id for chunk in existing_chunks], "has_chunk")
+ try:
+ await vector_engine.delete_data_points(collection_name, [str(chunk.chunk_id) for chunk in existing_chunks])
+ await graph_engine.remove_connection_to_successors_of([chunk.chunk_id for chunk in existing_chunks], "next_chunk")
+ await graph_engine.remove_connection_to_predecessors_of([chunk.chunk_id for chunk in existing_chunks], "has_chunk")
+ except Exception as e:
+ # Log the error or handle it appropriately
+ print(f"Error during deletion or removal: {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.
| await vector_engine.delete_data_points(collection_name, [str(chunk.chunk_id) for chunk in existing_chunks]) | |
| await graph_engine.remove_connection_to_successors_of([chunk.chunk_id for chunk in existing_chunks], "next_chunk") | |
| await graph_engine.remove_connection_to_predecessors_of([chunk.chunk_id for chunk in existing_chunks], "has_chunk") | |
| try: | |
| await vector_engine.delete_data_points(collection_name, [str(chunk.chunk_id) for chunk in existing_chunks]) | |
| await graph_engine.remove_connection_to_successors_of([chunk.chunk_id for chunk in existing_chunks], "next_chunk") | |
| await graph_engine.remove_connection_to_predecessors_of([chunk.chunk_id for chunk in existing_chunks], "has_chunk") | |
| except Exception as e: | |
| # Log the error or handle it appropriately | |
| print(f"Error during deletion or removal: {e}") |
| for chunk in data_chunks: | ||
| chunk_nodes.append(( | ||
| str(chunk.chunk_id), | ||
| dict( | ||
| id = str(chunk.chunk_id), | ||
| chunk_id = str(chunk.chunk_id), | ||
| document_id = str(chunk.document_id), | ||
| word_count = chunk.word_count, | ||
| chunk_index = chunk.chunk_index, | ||
| cut_type = chunk.cut_type, | ||
| pages = chunk.pages, | ||
| ) | ||
| )) | ||
|
|
||
| chunk_edges.append(( | ||
| str(chunk.document_id), | ||
| str(chunk.chunk_id), | ||
| "has_chunk", | ||
| dict( | ||
| relationship_name = "has_chunk", | ||
| source_node_id = str(chunk.document_id), | ||
| target_node_id = str(chunk.chunk_id), | ||
| ), | ||
| )) | ||
|
|
||
| previous_chunk_id = get_previous_chunk_id(data_chunks, chunk) | ||
|
|
||
| if previous_chunk_id is not None: | ||
| chunk_edges.append(( | ||
| str(previous_chunk_id), | ||
| str(chunk.chunk_id), | ||
| "next_chunk", | ||
| dict( | ||
| relationship_name = "next_chunk", | ||
| source_node_id = str(previous_chunk_id), | ||
| target_node_id = str(chunk.chunk_id), | ||
| ), | ||
| )) |
There was a problem hiding this comment.
Optimize node and edge creation with batch processing.
Consider batching node and edge creation to improve performance, especially for large datasets.
- for chunk in data_chunks:
- chunk_nodes.append((
- str(chunk.chunk_id),
- dict(
- id = str(chunk.chunk_id),
- chunk_id = str(chunk.chunk_id),
- document_id = str(chunk.document_id),
- word_count = chunk.word_count,
- chunk_index = chunk.chunk_index,
- cut_type = chunk.cut_type,
- pages = chunk.pages,
- )
- ))
- chunk_edges.append((
- str(chunk.document_id),
- str(chunk.chunk_id),
- "has_chunk",
- dict(
- relationship_name = "has_chunk",
- source_node_id = str(chunk.document_id),
- target_node_id = str(chunk.chunk_id),
- ),
- ))
- previous_chunk_id = get_previous_chunk_id(data_chunks, chunk)
- if previous_chunk_id is not None:
- chunk_edges.append((
- str(previous_chunk_id),
- str(chunk.chunk_id),
- "next_chunk",
- dict(
- relationship_name = "next_chunk",
- source_node_id = str(previous_chunk_id),
- target_node_id = str(chunk.chunk_id),
- ),
- ))
+ chunk_nodes.extend([
+ (
+ str(chunk.chunk_id),
+ dict(
+ id = str(chunk.chunk_id),
+ chunk_id = str(chunk.chunk_id),
+ document_id = str(chunk.document_id),
+ word_count = chunk.word_count,
+ chunk_index = chunk.chunk_index,
+ cut_type = chunk.cut_type,
+ pages = chunk.pages,
+ )
+ ) for chunk in data_chunks
+ ])
+ chunk_edges.extend([
+ (
+ str(chunk.document_id),
+ str(chunk.chunk_id),
+ "has_chunk",
+ dict(
+ relationship_name = "has_chunk",
+ source_node_id = str(chunk.document_id),
+ target_node_id = str(chunk.chunk_id),
+ ),
+ ) for chunk in data_chunks
+ ])
+ chunk_edges.extend([
+ (
+ str(previous_chunk_id),
+ str(chunk.chunk_id),
+ "next_chunk",
+ dict(
+ relationship_name = "next_chunk",
+ source_node_id = str(previous_chunk_id),
+ target_node_id = str(chunk.chunk_id),
+ ),
+ ) for chunk in data_chunks if (previous_chunk_id := get_previous_chunk_id(data_chunks, chunk)) is not 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 chunk in data_chunks: | |
| chunk_nodes.append(( | |
| str(chunk.chunk_id), | |
| dict( | |
| id = str(chunk.chunk_id), | |
| chunk_id = str(chunk.chunk_id), | |
| document_id = str(chunk.document_id), | |
| word_count = chunk.word_count, | |
| chunk_index = chunk.chunk_index, | |
| cut_type = chunk.cut_type, | |
| pages = chunk.pages, | |
| ) | |
| )) | |
| chunk_edges.append(( | |
| str(chunk.document_id), | |
| str(chunk.chunk_id), | |
| "has_chunk", | |
| dict( | |
| relationship_name = "has_chunk", | |
| source_node_id = str(chunk.document_id), | |
| target_node_id = str(chunk.chunk_id), | |
| ), | |
| )) | |
| previous_chunk_id = get_previous_chunk_id(data_chunks, chunk) | |
| if previous_chunk_id is not None: | |
| chunk_edges.append(( | |
| str(previous_chunk_id), | |
| str(chunk.chunk_id), | |
| "next_chunk", | |
| dict( | |
| relationship_name = "next_chunk", | |
| source_node_id = str(previous_chunk_id), | |
| target_node_id = str(chunk.chunk_id), | |
| ), | |
| )) | |
| chunk_nodes.extend([ | |
| ( | |
| str(chunk.chunk_id), | |
| dict( | |
| id = str(chunk.chunk_id), | |
| chunk_id = str(chunk.chunk_id), | |
| document_id = str(chunk.document_id), | |
| word_count = chunk.word_count, | |
| chunk_index = chunk.chunk_index, | |
| cut_type = chunk.cut_type, | |
| pages = chunk.pages, | |
| ) | |
| ) for chunk in data_chunks | |
| ]) | |
| chunk_edges.extend([ | |
| ( | |
| str(chunk.document_id), | |
| str(chunk.chunk_id), | |
| "has_chunk", | |
| dict( | |
| relationship_name = "has_chunk", | |
| source_node_id = str(chunk.document_id), | |
| target_node_id = str(chunk.chunk_id), | |
| ), | |
| ) for chunk in data_chunks | |
| ]) | |
| chunk_edges.extend([ | |
| ( | |
| str(previous_chunk_id), | |
| str(chunk.chunk_id), | |
| "next_chunk", | |
| dict( | |
| relationship_name = "next_chunk", | |
| source_node_id = str(previous_chunk_id), | |
| target_node_id = str(chunk.chunk_id), | |
| ), | |
| ) for chunk in data_chunks if (previous_chunk_id := get_previous_chunk_id(data_chunks, chunk)) is not None | |
| ]) |
| async def chunk_naive_llm_classifier(data_chunks: list[DocumentChunk], classification_model: Type[BaseModel]): | ||
| if len(data_chunks) == 0: | ||
| return data_chunks |
There was a problem hiding this comment.
Add error handling for empty data chunks.
Consider logging a warning or raising an exception if data_chunks is empty to ensure proper handling.
- if len(data_chunks) == 0:
- return data_chunks
+ if len(data_chunks) == 0:
+ raise ValueError("data_chunks list is empty")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 chunk_naive_llm_classifier(data_chunks: list[DocumentChunk], classification_model: Type[BaseModel]): | |
| if len(data_chunks) == 0: | |
| return data_chunks | |
| async def chunk_naive_llm_classifier(data_chunks: list[DocumentChunk], classification_model: Type[BaseModel]): | |
| if len(data_chunks) == 0: | |
| raise ValueError("data_chunks list is empty") |
|
|
||
| edge_key = str(chunk.chunk_id) + node_id + "contains_entity" | ||
|
|
||
| if edge_key not in existing_edges_map: | ||
| graph_edges.append(( | ||
| str(chunk.chunk_id), | ||
| node_id, | ||
| "contains_entity", | ||
| dict( | ||
| relationship_name = "contains_entity", | ||
| source_node_id = str(chunk.chunk_id), | ||
| target_node_id = node_id, | ||
| ), | ||
| )) | ||
|
|
||
| # Add relationship between entity type and entity itself: "Jake is Person" | ||
| graph_edges.append(( | ||
| node_id, | ||
| type_node_id, | ||
| "is_entity_type", | ||
| dict( | ||
| relationship_name = "is_entity_type", | ||
| source_node_id = type_node_id, | ||
| target_node_id = node_id, | ||
| ), | ||
| )) | ||
|
|
||
| existing_edges_map[edge_key] = True | ||
|
|
||
| if type_node_id not in existing_nodes_map: | ||
| type_node_data = dict( | ||
| uuid = type_node_id, | ||
| name = type_node_name, | ||
| type = type_node_id, | ||
| description = type_node_name, | ||
| created_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | ||
| updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | ||
| ) | ||
|
|
||
| graph_nodes.append((type_node_id, dict( | ||
| **type_node_data, | ||
| properties = json.dumps(node.properties) | ||
| ))) | ||
|
|
||
| data_points.append(DataPoint[EntityNode]( | ||
| id = str(uuid5(NAMESPACE_OID, type_node_id)), | ||
| payload = type_node_data, | ||
| embed_field = "name", | ||
| )) | ||
|
|
||
| existing_nodes_map[type_node_id] = True | ||
|
|
||
| edge_key = str(chunk.chunk_id) + type_node_id + "contains_entity_type" | ||
|
|
||
| if edge_key not in existing_edges_map: | ||
| graph_edges.append(( | ||
| str(chunk.chunk_id), | ||
| type_node_id, | ||
| "contains_entity_type", | ||
| dict( | ||
| relationship_name = "contains_entity_type", | ||
| source_node_id = str(chunk.chunk_id), | ||
| target_node_id = type_node_id, | ||
| ), | ||
| )) | ||
|
|
||
| existing_edges_map[edge_key] = True | ||
|
|
||
| # Add relationship that came from graphs. | ||
| for edge in graph.edges: | ||
| source_node_id = generate_node_id(edge.source_node_id) | ||
| target_node_id = generate_node_id(edge.target_node_id) | ||
| relationship_name = generate_name(edge.relationship_name) | ||
| edge_key = source_node_id + target_node_id + relationship_name | ||
|
|
||
| if edge_key not in existing_edges_map: | ||
| graph_edges.append(( | ||
| generate_node_id(edge.source_node_id), | ||
| generate_node_id(edge.target_node_id), | ||
| edge.relationship_name, | ||
| dict( | ||
| relationship_name = generate_name(edge.relationship_name), | ||
| source_node_id = generate_node_id(edge.source_node_id), | ||
| target_node_id = generate_node_id(edge.target_node_id), | ||
| properties = json.dumps(edge.properties), | ||
| ), | ||
| )) | ||
| existing_edges_map[edge_key] = True | ||
|
|
||
| if len(data_points) > 0: | ||
| await vector_engine.create_data_points(collection_name, data_points) | ||
|
|
||
| if len(graph_nodes) > 0: | ||
| await graph_engine.add_nodes(graph_nodes) | ||
|
|
||
| if len(graph_edges) > 0: | ||
| await graph_engine.add_edges(graph_edges) | ||
|
|
||
| return data_chunks | ||
|
|
There was a problem hiding this comment.
Review the function logic and suggest improvements.
The graph_decomposition_to_graph_nodes_task function is well-structured and follows best practices. However, there are some areas for potential optimization and improvement.
- Optimize asyncio.gather usage: The
asyncio.gatherfunction call can be optimized by checking for empty input. - Improve error handling: Add error handling to manage potential exceptions during graph and vector engine operations.
- Refactor nested loops: The nested loops can be refactored for better readability and performance.
async def graph_decomposition_to_graph_nodes_task(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel], collection_name: str):
if not data_chunks:
return []
chunk_graphs = await asyncio.gather(
*[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
)
vector_engine = get_vector_engine()
graph_engine = await get_graph_engine()
has_collection = await vector_engine.has_collection(collection_name)
if not has_collection:
await vector_engine.create_collection(collection_name, payload_schema = EntityNode)
processed_nodes = {}
type_node_edges = []
entity_node_edges = []
type_entity_edges = []
for chunk_index, chunk in enumerate(data_chunks):
chunk_graph = chunk_graphs[chunk_index]
for node in chunk_graph.nodes:
type_node_id = generate_node_id(node.type)
entity_node_id = generate_node_id(node.id)
if type_node_id not in processed_nodes:
type_node_edges.append((str(chunk.chunk_id), type_node_id, "contains_entity_type"))
processed_nodes[type_node_id] = True
if entity_node_id not in processed_nodes:
entity_node_edges.append((str(chunk.chunk_id), entity_node_id, "contains_entity"))
type_entity_edges.append((entity_node_id, type_node_id, "is_entity_type"))
processed_nodes[entity_node_id] = True
graph_node_edges = [
(edge.source_node_id, edge.target_node_id, edge.relationship_name) \
for edge in chunk_graph.edges
]
existing_edges = await graph_engine.has_edges([
*type_node_edges,
*entity_node_edges,
*type_entity_edges,
*graph_node_edges,
])
existing_edges_map = {}
existing_nodes_map = {}
for edge in existing_edges:
existing_edges_map[edge[0] + edge[1] + edge[2]] = True
existing_nodes_map[edge[0]] = True
graph_nodes = []
graph_edges = []
data_points = []
for chunk_index, chunk in enumerate(data_chunks):
graph = chunk_graphs[chunk_index]
if graph is None:
continue
for node in graph.nodes:
node_id = generate_node_id(node.id)
node_name = generate_name(node.name)
type_node_id = generate_node_id(node.type)
type_node_name = generate_name(node.type)
if node_id not in existing_nodes_map:
node_data = dict(
uuid = node_id,
name = node_name,
type = node_name,
description = node.description,
created_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"),
updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"),
)
graph_nodes.append((
node_id,
dict(
**node_data,
properties = json.dumps(node.properties),
)
))
data_points.append(DataPoint[EntityNode](
id = str(uuid5(NAMESPACE_OID, node_id)),
payload = node_data,
embed_field = "name",
))
existing_nodes_map[node_id] = True
edge_key = str(chunk.chunk_id) + node_id + "contains_entity"
if edge_key not in existing_edges_map:
graph_edges.append((
str(chunk.chunk_id),
node_id,
"contains_entity",
dict(
relationship_name = "contains_entity",
source_node_id = str(chunk.chunk_id),
target_node_id = node_id,
),
))
# Add relationship between entity type and entity itself: "Jake is Person"
graph_edges.append((
node_id,
type_node_id,
"is_entity_type",
dict(
relationship_name = "is_entity_type",
source_node_id = type_node_id,
target_node_id = node_id,
),
))
existing_edges_map[edge_key] = True
if type_node_id not in existing_nodes_map:
type_node_data = dict(
uuid = type_node_id,
name = type_node_name,
type = type_node_id,
description = type_node_name,
created_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"),
updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"),
)
graph_nodes.append((type_node_id, dict(
**type_node_data,
properties = json.dumps(node.properties)
)))
data_points.append(DataPoint[EntityNode](
id = str(uuid5(NAMESPACE_OID, type_node_id)),
payload = type_node_data,
embed_field = "name",
))
existing_nodes_map[type_node_id] = True
edge_key = str(chunk.chunk_id) + type_node_id + "contains_entity_type"
if edge_key not in existing_edges_map:
graph_edges.append((
str(chunk.chunk_id),
type_node_id,
"contains_entity_type",
dict(
relationship_name = "contains_entity_type",
source_node_id = str(chunk.chunk_id),
target_node_id = type_node_id,
),
))
existing_edges_map[edge_key] = True
# Add relationship that came from graphs.
for edge in graph.edges:
source_node_id = generate_node_id(edge.source_node_id)
target_node_id = generate_node_id(edge.target_node_id)
relationship_name = generate_name(edge.relationship_name)
edge_key = source_node_id + target_node_id + relationship_name
if edge_key not in existing_edges_map:
graph_edges.append((
generate_node_id(edge.source_node_id),
generate_node_id(edge.target_node_id),
edge.relationship_name,
dict(
relationship_name = generate_name(edge.relationship_name),
source_node_id = generate_node_id(edge.source_node_id),
target_node_id = generate_node_id(edge.target_node_id),
properties = json.dumps(edge.properties),
),
))
existing_edges_map[edge_key] = True
if len(data_points) > 0:
await vector_engine.create_data_points(collection_name, data_points)
if len(graph_nodes) > 0:
await graph_engine.add_nodes(graph_nodes)
if len(graph_edges) > 0:
await graph_engine.add_edges(graph_edges)
return data_chunksCommittable 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 graph_decomposition_to_graph_nodes_task(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel], collection_name: str): | |
| chunk_graphs = await asyncio.gather( | |
| *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] | |
| ) | |
| vector_engine = get_vector_engine() | |
| graph_engine = await get_graph_engine() | |
| has_collection = await vector_engine.has_collection(collection_name) | |
| if not has_collection: | |
| await vector_engine.create_collection(collection_name, payload_schema = EntityNode) | |
| processed_nodes = {} | |
| type_node_edges = [] | |
| entity_node_edges = [] | |
| type_entity_edges = [] | |
| for (chunk_index, chunk) in enumerate(data_chunks): | |
| chunk_graph = chunk_graphs[chunk_index] | |
| for node in chunk_graph.nodes: | |
| type_node_id = generate_node_id(node.type) | |
| entity_node_id = generate_node_id(node.id) | |
| if type_node_id not in processed_nodes: | |
| type_node_edges.append((str(chunk.chunk_id), type_node_id, "contains_entity_type")) | |
| processed_nodes[type_node_id] = True | |
| if entity_node_id not in processed_nodes: | |
| entity_node_edges.append((str(chunk.chunk_id), entity_node_id, "contains_entity")) | |
| type_entity_edges.append((entity_node_id, type_node_id, "is_entity_type")) | |
| processed_nodes[entity_node_id] = True | |
| graph_node_edges = [ | |
| (edge.source_node_id, edge.target_node_id, edge.relationship_name) \ | |
| for edge in chunk_graph.edges | |
| ] | |
| existing_edges = await graph_engine.has_edges([ | |
| *type_node_edges, | |
| *entity_node_edges, | |
| *type_entity_edges, | |
| *graph_node_edges, | |
| ]) | |
| existing_edges_map = {} | |
| existing_nodes_map = {} | |
| for edge in existing_edges: | |
| existing_edges_map[edge[0] + edge[1] + edge[2]] = True | |
| existing_nodes_map[edge[0]] = True | |
| graph_nodes = [] | |
| graph_edges = [] | |
| data_points = [] | |
| for (chunk_index, chunk) in enumerate(data_chunks): | |
| graph = chunk_graphs[chunk_index] | |
| if graph is None: | |
| continue | |
| for node in graph.nodes: | |
| node_id = generate_node_id(node.id) | |
| node_name = generate_name(node.name) | |
| type_node_id = generate_node_id(node.type) | |
| type_node_name = generate_name(node.type) | |
| if node_id not in existing_nodes_map: | |
| node_data = dict( | |
| uuid = node_id, | |
| name = node_name, | |
| type = node_name, | |
| description = node.description, | |
| created_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| ) | |
| graph_nodes.append(( | |
| node_id, | |
| dict( | |
| **node_data, | |
| properties = json.dumps(node.properties), | |
| ) | |
| )) | |
| data_points.append(DataPoint[EntityNode]( | |
| id = str(uuid5(NAMESPACE_OID, node_id)), | |
| payload = node_data, | |
| embed_field = "name", | |
| )) | |
| existing_nodes_map[node_id] = True | |
| edge_key = str(chunk.chunk_id) + node_id + "contains_entity" | |
| if edge_key not in existing_edges_map: | |
| graph_edges.append(( | |
| str(chunk.chunk_id), | |
| node_id, | |
| "contains_entity", | |
| dict( | |
| relationship_name = "contains_entity", | |
| source_node_id = str(chunk.chunk_id), | |
| target_node_id = node_id, | |
| ), | |
| )) | |
| # Add relationship between entity type and entity itself: "Jake is Person" | |
| graph_edges.append(( | |
| node_id, | |
| type_node_id, | |
| "is_entity_type", | |
| dict( | |
| relationship_name = "is_entity_type", | |
| source_node_id = type_node_id, | |
| target_node_id = node_id, | |
| ), | |
| )) | |
| existing_edges_map[edge_key] = True | |
| if type_node_id not in existing_nodes_map: | |
| type_node_data = dict( | |
| uuid = type_node_id, | |
| name = type_node_name, | |
| type = type_node_id, | |
| description = type_node_name, | |
| created_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| ) | |
| graph_nodes.append((type_node_id, dict( | |
| **type_node_data, | |
| properties = json.dumps(node.properties) | |
| ))) | |
| data_points.append(DataPoint[EntityNode]( | |
| id = str(uuid5(NAMESPACE_OID, type_node_id)), | |
| payload = type_node_data, | |
| embed_field = "name", | |
| )) | |
| existing_nodes_map[type_node_id] = True | |
| edge_key = str(chunk.chunk_id) + type_node_id + "contains_entity_type" | |
| if edge_key not in existing_edges_map: | |
| graph_edges.append(( | |
| str(chunk.chunk_id), | |
| type_node_id, | |
| "contains_entity_type", | |
| dict( | |
| relationship_name = "contains_entity_type", | |
| source_node_id = str(chunk.chunk_id), | |
| target_node_id = type_node_id, | |
| ), | |
| )) | |
| existing_edges_map[edge_key] = True | |
| # Add relationship that came from graphs. | |
| for edge in graph.edges: | |
| source_node_id = generate_node_id(edge.source_node_id) | |
| target_node_id = generate_node_id(edge.target_node_id) | |
| relationship_name = generate_name(edge.relationship_name) | |
| edge_key = source_node_id + target_node_id + relationship_name | |
| if edge_key not in existing_edges_map: | |
| graph_edges.append(( | |
| generate_node_id(edge.source_node_id), | |
| generate_node_id(edge.target_node_id), | |
| edge.relationship_name, | |
| dict( | |
| relationship_name = generate_name(edge.relationship_name), | |
| source_node_id = generate_node_id(edge.source_node_id), | |
| target_node_id = generate_node_id(edge.target_node_id), | |
| properties = json.dumps(edge.properties), | |
| ), | |
| )) | |
| existing_edges_map[edge_key] = True | |
| if len(data_points) > 0: | |
| await vector_engine.create_data_points(collection_name, data_points) | |
| if len(graph_nodes) > 0: | |
| await graph_engine.add_nodes(graph_nodes) | |
| if len(graph_edges) > 0: | |
| await graph_engine.add_edges(graph_edges) | |
| return data_chunks | |
| async def graph_decomposition_to_graph_nodes_task(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel], collection_name: str): | |
| if not data_chunks: | |
| return [] | |
| chunk_graphs = await asyncio.gather( | |
| *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] | |
| ) | |
| vector_engine = get_vector_engine() | |
| graph_engine = await get_graph_engine() | |
| has_collection = await vector_engine.has_collection(collection_name) | |
| if not has_collection: | |
| await vector_engine.create_collection(collection_name, payload_schema = EntityNode) | |
| processed_nodes = {} | |
| type_node_edges = [] | |
| entity_node_edges = [] | |
| type_entity_edges = [] | |
| for chunk_index, chunk in enumerate(data_chunks): | |
| chunk_graph = chunk_graphs[chunk_index] | |
| for node in chunk_graph.nodes: | |
| type_node_id = generate_node_id(node.type) | |
| entity_node_id = generate_node_id(node.id) | |
| if type_node_id not in processed_nodes: | |
| type_node_edges.append((str(chunk.chunk_id), type_node_id, "contains_entity_type")) | |
| processed_nodes[type_node_id] = True | |
| if entity_node_id not in processed_nodes: | |
| entity_node_edges.append((str(chunk.chunk_id), entity_node_id, "contains_entity")) | |
| type_entity_edges.append((entity_node_id, type_node_id, "is_entity_type")) | |
| processed_nodes[entity_node_id] = True | |
| graph_node_edges = [ | |
| (edge.source_node_id, edge.target_node_id, edge.relationship_name) \ | |
| for edge in chunk_graph.edges | |
| ] | |
| existing_edges = await graph_engine.has_edges([ | |
| *type_node_edges, | |
| *entity_node_edges, | |
| *type_entity_edges, | |
| *graph_node_edges, | |
| ]) | |
| existing_edges_map = {} | |
| existing_nodes_map = {} | |
| for edge in existing_edges: | |
| existing_edges_map[edge[0] + edge[1] + edge[2]] = True | |
| existing_nodes_map[edge[0]] = True | |
| graph_nodes = [] | |
| graph_edges = [] | |
| data_points = [] | |
| for chunk_index, chunk in enumerate(data_chunks): | |
| graph = chunk_graphs[chunk_index] | |
| if graph is None: | |
| continue | |
| for node in graph.nodes: | |
| node_id = generate_node_id(node.id) | |
| node_name = generate_name(node.name) | |
| type_node_id = generate_node_id(node.type) | |
| type_node_name = generate_name(node.type) | |
| if node_id not in existing_nodes_map: | |
| node_data = dict( | |
| uuid = node_id, | |
| name = node_name, | |
| type = node_name, | |
| description = node.description, | |
| created_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| ) | |
| graph_nodes.append(( | |
| node_id, | |
| dict( | |
| **node_data, | |
| properties = json.dumps(node.properties), | |
| ) | |
| )) | |
| data_points.append(DataPoint[EntityNode]( | |
| id = str(uuid5(NAMESPACE_OID, node_id)), | |
| payload = node_data, | |
| embed_field = "name", | |
| )) | |
| existing_nodes_map[node_id] = True | |
| edge_key = str(chunk.chunk_id) + node_id + "contains_entity" | |
| if edge_key not in existing_edges_map: | |
| graph_edges.append(( | |
| str(chunk.chunk_id), | |
| node_id, | |
| "contains_entity", | |
| dict( | |
| relationship_name = "contains_entity", | |
| source_node_id = str(chunk.chunk_id), | |
| target_node_id = node_id, | |
| ), | |
| )) | |
| # Add relationship between entity type and entity itself: "Jake is Person" | |
| graph_edges.append(( | |
| node_id, | |
| type_node_id, | |
| "is_entity_type", | |
| dict( | |
| relationship_name = "is_entity_type", | |
| source_node_id = type_node_id, | |
| target_node_id = node_id, | |
| ), | |
| )) | |
| existing_edges_map[edge_key] = True | |
| if type_node_id not in existing_nodes_map: | |
| type_node_data = dict( | |
| uuid = type_node_id, | |
| name = type_node_name, | |
| type = type_node_id, | |
| description = type_node_name, | |
| created_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"), | |
| ) | |
| graph_nodes.append((type_node_id, dict( | |
| **type_node_data, | |
| properties = json.dumps(node.properties) | |
| ))) | |
| data_points.append(DataPoint[EntityNode]( | |
| id = str(uuid5(NAMESPACE_OID, type_node_id)), | |
| payload = type_node_data, | |
| embed_field = "name", | |
| )) | |
| existing_nodes_map[type_node_id] = True | |
| edge_key = str(chunk.chunk_id) + type_node_id + "contains_entity_type" | |
| if edge_key not in existing_edges_map: | |
| graph_edges.append(( | |
| str(chunk.chunk_id), | |
| type_node_id, | |
| "contains_entity_type", | |
| dict( | |
| relationship_name = "contains_entity_type", | |
| source_node_id = str(chunk.chunk_id), | |
| target_node_id = type_node_id, | |
| ), | |
| )) | |
| existing_edges_map[edge_key] = True | |
| # Add relationship that came from graphs. | |
| for edge in graph.edges: | |
| source_node_id = generate_node_id(edge.source_node_id) | |
| target_node_id = generate_node_id(edge.target_node_id) | |
| relationship_name = generate_name(edge.relationship_name) | |
| edge_key = source_node_id + target_node_id + relationship_name | |
| if edge_key not in existing_edges_map: | |
| graph_edges.append(( | |
| generate_node_id(edge.source_node_id), | |
| generate_node_id(edge.target_node_id), | |
| edge.relationship_name, | |
| dict( | |
| relationship_name = generate_name(edge.relationship_name), | |
| source_node_id = generate_node_id(edge.source_node_id), | |
| target_node_id = generate_node_id(edge.target_node_id), | |
| properties = json.dumps(edge.properties), | |
| ), | |
| )) | |
| existing_edges_map[edge_key] = True | |
| if len(data_points) > 0: | |
| await vector_engine.create_data_points(collection_name, data_points) | |
| if len(graph_nodes) > 0: | |
| await graph_engine.add_nodes(graph_nodes) | |
| if len(graph_edges) > 0: | |
| await graph_engine.add_edges(graph_edges) | |
| return data_chunks |
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Files selected for processing (1)
- cognee/tasks/chunk_extract_summary/chunk_extract_summary.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- cognee/tasks/chunk_extract_summary/chunk_extract_summary.py
cognee/tasks/chunk_to_vector_graphstore/chunk_to_vector_graphstore.py
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| created_at: datetime | ||
| updated_at: datetime | ||
|
|
||
| async def graph_decomposition_to_graph_nodes_task(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel], collection_name: str): |
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graph_decomposition_to_graph_nodes_task -> decompose_chunks_into_graph or something like that.
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Actionable comments posted: 1
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Configuration used: CodeRabbit UI
Review profile: CHILL
Files ignored due to path filters (1)
poetry.lockis excluded by!**/*.lock
Files selected for processing (2)
- cognee/api/v1/cognify/cognify_v2.py (3 hunks)
- cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py (2 hunks)
Additional context used
Ruff
cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py
104-104: Local variable
transis assigned to but never usedRemove assignment to unused variable
trans(F841)
Additional comments not posted (3)
cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py (1)
61-61: LGTM! TheCASCADEoption enhances table deletion robustness.The addition of the
CASCADEoption ensures that any dependent objects are also removed, preventing potential errors related to dependencies.cognee/api/v1/cognify/cognify_v2.py (2)
30-38: LGTM! The new imports enhance the pipeline's functionality.The new task functions replace existing ones, indicating a shift in the methodology used for various tasks in the pipeline.
112-131: LGTM! The updated tasks enhance pipeline capabilities.The changes focus on improved task specificity and potentially better performance in document processing and chunk management.
| async with connection.begin() as trans: | ||
| for table in Base.metadata.sorted_tables: | ||
| drop_table_query = text(f'DROP TABLE IF EXISTS {table.name} CASCADE') | ||
| await connection.execute(drop_table_query) | ||
| print("Database deleted successfully.") | ||
| except Exception as e: | ||
| print(f"Error deleting database: {e}") | ||
| print(f"Error deleting database: {e}") |
There was a problem hiding this comment.
LGTM! But remove the unused variable trans.
The changes improve the accuracy and reliability of the database deletion process. However, the variable trans is assigned but never used.
- async with connection.begin() as trans:
+ async with connection.begin():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 with connection.begin() as trans: | |
| for table in Base.metadata.sorted_tables: | |
| drop_table_query = text(f'DROP TABLE IF EXISTS {table.name} CASCADE') | |
| await connection.execute(drop_table_query) | |
| print("Database deleted successfully.") | |
| except Exception as e: | |
| print(f"Error deleting database: {e}") | |
| print(f"Error deleting database: {e}") | |
| async with connection.begin(): | |
| for table in Base.metadata.sorted_tables: | |
| drop_table_query = text(f'DROP TABLE IF EXISTS {table.name} CASCADE') | |
| await connection.execute(drop_table_query) | |
| print("Database deleted successfully.") | |
| except Exception as e: | |
| print(f"Error deleting database: {e}") |
Tools
Ruff
104-104: Local variable
transis assigned to but never usedRemove assignment to unused variable
trans(F841)
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Actionable comments posted: 3
Outside diff range, codebase verification and nitpick comments (4)
cognee/tasks/save_chunks_to_store/save_chunks_to_store.py (2)
12-24: Ensure atomic operations for chunk removal and unlinking.The current implementations of the functions
delete_data_points,remove_connection_to_successors_of, andremove_connection_to_predecessors_ofdo not ensure atomic operations. This could lead to partial deletions if an error occurs during execution. Please consider implementing transactions or other atomicity mechanisms to ensure that these operations are atomic.
cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.pycognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.pycognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.pycognee/infrastructure/databases/vector/falkordb/FalkorDBAdapter.pycognee/infrastructure/databases/graph/networkx/adapter.pycognee/infrastructure/databases/graph/neo4j_driver/adapter.pyAnalysis chain
Ensure efficient chunk removal and unlinking.
The function efficiently removes and unlinks existing chunks if the collection exists. Ensure that the operations are atomic to avoid partial deletions.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify atomicity of chunk removal and unlinking operations. # Test: Search for atomic operations in the functions. Expect: Proper atomicity mechanisms. rg --type python -A 5 $'def delete_data_points' rg --type python -A 5 $'def remove_connection_to_successors_of' rg --type python -A 5 $'def remove_connection_to_predecessors_of'Length of output: 273
Script:
#!/bin/bash # Description: Verify atomicity of chunk removal and unlinking operations. # Corrected Test: Search for atomic operations in the functions. Expect: Proper atomicity mechanisms. rg -A 5 $'def delete_data_points' rg -A 5 $'def remove_connection_to_successors_of' rg -A 5 $'def remove_connection_to_predecessors_of'Length of output: 5735
9-10: Ensure proper initialization of engines.The
get_vector_engineandget_graph_enginefunctions have minimal error handling. Thecreate_vector_enginefunction raises anEnvironmentErrorif the configuration is incorrect, but it could benefit from more specific exception handling and logging. Theget_graph_enginefunction uses bareexceptstatements, which should be replaced with specific exception handling and error logging.
- File:
cognee/infrastructure/databases/vector/create_vector_engine.py
- Improve error handling by catching specific exceptions and logging errors.
- File:
cognee/infrastructure/databases/graph/get_graph_engine.py
- Replace bare
exceptstatements with specific exception handling and error logging.Analysis chain
Ensure proper initialization of engines.
The vector and graph engines are correctly initialized. Ensure that
get_vector_engineandget_graph_enginehandle errors properly.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify error handling in `get_vector_engine` and `get_graph_engine`. # Test: Search for error handling in the functions. Expect: Proper error handling mechanisms. rg --type python -A 5 $'def get_vector_engine' rg --type python -A 5 $'def get_graph_engine'Length of output: 156
Script:
#!/bin/bash # Description: Verify error handling in `get_vector_engine` and `get_graph_engine`. # Test: Search for error handling in the functions. Expect: Proper error handling mechanisms. rg -A 10 'def get_vector_engine' rg -A 10 'def get_graph_engine'Length of output: 1406
Script:
#!/bin/bash # Description: Verify error handling in `create_vector_engine` and `get_vectordb_config`, and get more context for `get_graph_engine`. # Test: Search for error handling in `create_vector_engine` and `get_vectordb_config`. rg -A 10 'def create_vector_engine' rg -A 10 'def get_vectordb_config' # Get more context for `get_graph_engine`. rg -A 20 'def get_graph_engine'Length of output: 3387
cognee/tasks/chunks_into_graph/chunks_into_graph.py (2)
29-32: Consider logging collection creation.Adding logging when a new collection is created can help with debugging and monitoring.
+ await vector_engine.create_collection(collection_name, payload_schema = EntityNode) + print(f"Created new collection: {collection_name}")
203-210: Consider adding logging for node and edge additions.Adding logging when nodes and edges are added can help with debugging and monitoring.
+ await vector_engine.create_data_points(collection_name, data_points) + print(f"Added {len(data_points)} data points to collection: {collection_name}") + await graph_engine.add_nodes(graph_nodes) + print(f"Added {len(graph_nodes)} nodes to graph engine") + await graph_engine.add_edges(graph_edges) + print(f"Added {len(graph_edges)} edges to graph engine")
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (5)
- cognee/api/v1/cognify/cognify_v2.py (3 hunks)
- cognee/tasks/init.py (1 hunks)
- cognee/tasks/chunks_into_graph/chunks_into_graph.py (1 hunks)
- cognee/tasks/save_chunks_to_store/save_chunks_to_store.py (1 hunks)
- pyproject.toml (1 hunks)
Files skipped from review due to trivial changes (1)
- cognee/tasks/init.py
Additional comments not posted (20)
pyproject.toml (2)
86-86: Dependency addition looks good.The
langchaindependency has been correctly added to thenotebooksection.
89-89: Installation command addition looks good.The command for installing the
cogneepackage with thelangchainextra has been correctly added.cognee/tasks/save_chunks_to_store/save_chunks_to_store.py (4)
5-7: Handle empty data chunks gracefully.The function correctly handles the case where
data_chunksis empty by returning early.
88-97: Function looks good.The function correctly retrieves the previous chunk ID based on the chunk index.
39-83: Efficiently add nodes and edges to graph storage.The function efficiently adds nodes and edges to graph storage. Ensure that the
add_nodesandadd_edgesfunctions handle large data sets efficiently.
27-37: Efficiently add data points to vector storage.The function efficiently adds data points to vector storage. Ensure that the
create_data_pointsfunction handles large data sets efficiently.cognee/api/v1/cognify/cognify_v2.py (8)
38-38: Import looks good.The
source_documents_to_chunkstask function has been correctly imported.
30-30: Import looks good.The
chunk_extract_summary_tasktask function has been correctly imported.
31-31: Import looks good.The
chunk_naive_llm_classifier_tasktask function has been correctly imported.
32-32: Import looks good.The
chunk_remove_disconnected_tasktask function has been correctly imported.
33-33: Import looks good.The
chunk_to_graph_decomposition_tasktask function has been correctly imported.
34-34: Import looks good.The
save_chunks_to_store_tasktask function has been correctly imported.
35-35: Import looks good.The
chunk_update_check_tasktask function has been correctly imported.
36-37: Import looks good.The
chunks_into_graph_tasktask function has been correctly imported.cognee/tasks/chunks_into_graph/chunks_into_graph.py (6)
13-19: ClassEntityNodelooks good.The class definition is correct and follows Pydantic's syntax.
212-213: Return statement looks good.The return statement is correct and returns the processed data chunks.
215-216: Functiongenerate_namelooks good.The function correctly converts a name to a lowercase, underscored string.
218-219: Functiongenerate_node_idlooks good.The function correctly converts a node ID to a lowercase, underscored string.
1-11: Ensure all imports are necessary.Review the imports to ensure they are all required. For instance,
datetimeandtimezoneare used, but if they are not, they should be removed.Verification successful
The imports are necessary.
The
datetimeandtimezoneimports are used within the file.
created_at: datetimeupdated_at: datetimecreated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")updated_at = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of imported modules. # Test: Search for the usage of datetime and timezone. Expect: Occurrences of their usage. rg --type python 'datetime|timezone'Length of output: 69
Script:
#!/bin/bash # Description: Verify the usage of imported modules within the specific file. # Test: Search for the usage of datetime and timezone within the specific file. rg 'datetime|timezone' cognee/tasks/chunks_into_graph/chunks_into_graph.pyLength of output: 530
73-202: Ensure consistency in node and edge creation.Ensure that the node and edge creation logic is consistent and handles all edge cases.
| for (chunk_index, chunk) in enumerate(data_chunks): | ||
| chunk_graph = chunk_graphs[chunk_index] | ||
| for node in chunk_graph.nodes: | ||
| type_node_id = generate_node_id(node.type) | ||
| entity_node_id = generate_node_id(node.id) | ||
|
|
||
| if type_node_id not in processed_nodes: | ||
| type_node_edges.append((str(chunk.chunk_id), type_node_id, "contains_entity_type")) | ||
| processed_nodes[type_node_id] = True | ||
|
|
||
| if entity_node_id not in processed_nodes: | ||
| entity_node_edges.append((str(chunk.chunk_id), entity_node_id, "contains_entity")) | ||
| type_entity_edges.append((entity_node_id, type_node_id, "is_entity_type")) | ||
| processed_nodes[entity_node_id] = True | ||
|
|
||
| graph_node_edges = [ | ||
| (edge.source_node_id, edge.target_node_id, edge.relationship_name) \ | ||
| for edge in chunk_graph.edges | ||
| ] |
There was a problem hiding this comment.
Consider breaking down the loop into smaller functions.
The nested loops and conditionals can be refactored into smaller functions for better readability and maintainability.
async def process_chunk_graphs(data_chunks, chunk_graphs):
processed_nodes = {}
type_node_edges = []
entity_node_edges = []
type_entity_edges = []
for (chunk_index, chunk) in enumerate(data_chunks):
chunk_graph = chunk_graphs[chunk_index]
for node in chunk_graph.nodes:
type_node_id = generate_node_id(node.type)
entity_node_id = generate_node_id(node.id)
if type_node_id not in processed_nodes:
type_node_edges.append((str(chunk.chunk_id), type_node_id, "contains_entity_type"))
processed_nodes[type_node_id] = True
if entity_node_id not in processed_nodes:
entity_node_edges.append((str(chunk.chunk_id), entity_node_id, "contains_entity"))
type_entity_edges.append((entity_node_id, type_node_id, "is_entity_type"))
processed_nodes[entity_node_id] = True
return processed_nodes, type_node_edges, entity_node_edges, type_entity_edges| existing_edges = await graph_engine.has_edges([ | ||
| *type_node_edges, | ||
| *entity_node_edges, | ||
| *type_entity_edges, | ||
| *graph_node_edges, | ||
| ]) | ||
|
|
||
| existing_edges_map = {} | ||
| existing_nodes_map = {} | ||
|
|
||
| for edge in existing_edges: | ||
| existing_edges_map[edge[0] + edge[1] + edge[2]] = True | ||
| existing_nodes_map[edge[0]] = True |
There was a problem hiding this comment.
Consider optimizing edge existence check.
The current approach of checking for existing edges can be optimized for performance by reducing the number of calls to the graph engine.
existing_edges = await graph_engine.has_edges(type_node_edges + entity_node_edges + type_entity_edges + graph_node_edges)| async def chunks_into_graph_task(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel], collection_name: str): | ||
| chunk_graphs = await asyncio.gather( | ||
| *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] | ||
| ) | ||
|
|
||
| vector_engine = get_vector_engine() | ||
| graph_engine = await get_graph_engine() |
There was a problem hiding this comment.
Ensure error handling for asynchronous calls.
Consider adding error handling for the asynchronous calls to extract_content_graph, get_vector_engine, and get_graph_engine.
+ try:
+ chunk_graphs = await asyncio.gather(
+ *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
+ )
+ except Exception as e:
+ # Handle or log the exception
+ raise e
+ try:
+ vector_engine = get_vector_engine()
+ except Exception as e:
+ # Handle or log the exception
+ raise e
+ try:
+ graph_engine = await get_graph_engine()
+ except Exception as e:
+ # Handle or log the exception
+ raise eCommittable 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 chunks_into_graph_task(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel], collection_name: str): | |
| chunk_graphs = await asyncio.gather( | |
| *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] | |
| ) | |
| vector_engine = get_vector_engine() | |
| graph_engine = await get_graph_engine() | |
| async def chunks_into_graph_task(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel], collection_name: str): | |
| try: | |
| chunk_graphs = await asyncio.gather( | |
| *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks] | |
| ) | |
| except Exception as e: | |
| # Handle or log the exception | |
| raise e | |
| try: | |
| vector_engine = get_vector_engine() | |
| except Exception as e: | |
| # Handle or log the exception | |
| raise e | |
| try: | |
| graph_engine = await get_graph_engine() | |
| except Exception as e: | |
| # Handle or log the exception | |
| raise e |
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Actionable comments posted: 0
Outside diff range, codebase verification and nitpick comments (1)
cognee/api/v1/add/add.py (1)
131-131: Ensure proper transaction management and error handling.The addition of
await session.commit()ensures that changes are persisted to the database. However, there are no explicit error handling or transaction management mechanisms (e.g., try-except blocks, rollback) found in thecognee/api/v1/add/add.pyfile. Please verify and implement proper transaction management and error handling to ensure data consistency and reliability.
- File:
cognee/api/v1/add/add.py- Lines: Around the
data_resourcesfunction and its usage.Analysis chain
LGTM! Ensure proper transaction management and error handling.
The addition of
await session.commit()ensures that changes are persisted to the database. This is a crucial step for data consistency.However, verify that transaction management and error handling are properly handled throughout the function and any calling code.
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#!/bin/bash # Description: Verify proper transaction management and error handling in the calling code. # Test: Search for the function usage. Expect: Proper error handling and transaction management. rg --type python -A 5 $'data_resources'Length of output: 69
Script:
#!/bin/bash # Description: Verify proper transaction management and error handling in the calling code. # Test: Search for the function usage. Expect: Proper error handling and transaction management. rg --type py -A 5 $'data_resources'Length of output: 836
Script:
#!/bin/bash # Description: Fetch more context around the usage of `data_resources` to verify transaction management and error handling. # Test: Search for the function usage and fetch additional lines of context. rg --type py -A 10 -B 10 $'data_resources'Length of output: 2303
Script:
#!/bin/bash # Description: Search for transaction management and error handling mechanisms within the `cognee/api/v1/add/add.py` file. # Test: Search for try-except blocks and rollback calls to verify error handling and transaction management. rg --type py 'try:|except|rollback' cognee/api/v1/add/add.pyLength of output: 62
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- cognee/modules/search/llm/get_relevant_summary.py (1 hunks)
- cognee/tasks/chunk_extract_summary/chunk_extract_summary.py (2 hunks)
- cognee/tests/test_library.py (2 hunks)
Files skipped from review due to trivial changes (1)
- cognee/modules/search/llm/get_relevant_summary.py
Files skipped from review as they are similar to previous changes (2)
- cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py
- cognee/tasks/chunk_extract_summary/chunk_extract_summary.py
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cognee/tests/test_library.py (2)
9-9: Improved readability.The added empty line at the beginning of the
mainfunction improves readability.
73-73: Enabled debug mode for asyncio.The inclusion of
debug=Truein theasyncio.run(main(), debug=True)call enables debug mode for the asyncio event loop, aiding in identifying issues during asynchronous code execution.
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Actionable comments posted: 1
Outside diff range, codebase verification and nitpick comments (4)
cognee/tasks/document_to_ontology/document_to_ontology.py (1)
Line range hint
54-157:
Consider refactoringadd_graph_topology.The method
add_graph_topologycould be refactored to improve readability and efficiency. Consider breaking down the logic into smaller helper functions, especially for file processing and chunk handling.async def process_files(files, chunk_engine, chunk_strategy, chunk_config): initial_chunks_and_ids = [] for base_file in files: with open(base_file["file_path"], "rb") as file: try: file_type = guess_file_type(file) text = extract_text_from_file(file, file_type) subchunks, chunks_with_ids = chunk_engine.chunk_data(chunk_strategy, text, chunk_config.chunk_size, chunk_config.chunk_overlap) if chunks_with_ids[0][0] == 1: initial_chunks_and_ids.append({base_file["id"]: chunks_with_ids}) except FileTypeException: logger.warning("File (%s) has an unknown file type. We are skipping it.", file["id"]) return initial_chunks_and_ids # Usage in add_graph_topology initial_chunks_and_ids = await process_files(files, chunk_engine, chunk_strategy, chunk_config)docs/index.md (3)
4-4: Fix heading level and spacing.The heading level should increment by one, and there should be no multiple spaces after the hash.
-#### Deterministic LLMs Outputs for AI Engineers +## Deterministic LLMs Outputs for AI Engineers
8-8: Use a heading instead of emphasis.The introduction should use a heading for better readability and structure.
-_Open-source framework for loading and structuring LLM context to create accurate and explainable AI solutions using knowledge graphs and vector stores_ +### Open-source Framework for LLM Context Structuring
47-47: Remove or complete the empty link.The empty link placeholder should be removed or completed to avoid confusion.
-[//]: # (- **Case Studies** — Read about [case studies](case_studies.md) that show how cognee can be used in real-world applications.) +// Remove or complete this link placeholderTools
Markdownlint
47-47: null
No empty links(MD042, no-empty-links)
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Files selected for processing (3)
- cognee/api/v1/cognify/cognify_v2.py (3 hunks)
- cognee/tasks/document_to_ontology/document_to_ontology.py (4 hunks)
- docs/index.md (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- cognee/api/v1/cognify/cognify_v2.py
Additional context used
Markdownlint
docs/index.md
5-5: Expected: h2; Actual: h4
Heading levels should only increment by one level at a time(MD001, heading-increment)
5-5: null
Multiple spaces after hash on atx style heading(MD019, no-multiple-space-atx)
9-9: null
Emphasis used instead of a heading(MD036, no-emphasis-as-heading)
47-47: null
No empty links(MD042, no-empty-links)
Additional comments not posted (3)
cognee/tasks/document_to_ontology/document_to_ontology.py (2)
42-53: LGTM!The function
infer_data_topologyis well-implemented with proper error handling and logging.
32-39: Verify the system prompt file.Ensure that the
extract_topology.txtfile exists and is correctly formatted to avoid runtime errors.Verification successful
System prompt file exists and is non-empty.
The
extract_topology.txtfile is present and contains content. Please ensure that the file's content is correctly formatted according to your application's requirements to avoid runtime errors.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence and format of the system prompt file. # Test: Check if the file exists. Expect: File should exist. if [[ ! -f "cognee/infrastructure/llm/prompts/extract_topology.txt" ]]; then echo "File extract_topology.txt does not exist." exit 1 fi # Test: Check if the file is non-empty. Expect: File should have content. if [[ ! -s "cognee/infrastructure/llm/prompts/extract_topology.txt" ]]; then echo "File extract_topology.txt is empty." exit 1 fi echo "File extract_topology.txt exists and is non-empty."Length of output: 246
docs/index.md (1)
52-61: Vision and Architecture sections look great!These sections are well-structured and visually engaging.
| async def document_to_ontology(data, root_node_id): | ||
| cognee_config = get_cognify_config() | ||
| graph_config = get_graph_config() | ||
| root_node_id = None | ||
| if graph_config.infer_graph_topology and graph_config.graph_topology_task: | ||
| topology_engine = TopologyEngine(infer=graph_config.infer_graph_topology) | ||
| root_node_id = await topology_engine.add_graph_topology(files=data) | ||
| elif graph_config.infer_graph_topology and not graph_config.infer_graph_topology: | ||
|
|
||
| topology_engine = TopologyEngine(infer=graph_config.infer_graph_topology) | ||
| await topology_engine.add_graph_topology(graph_config.topology_file_path) | ||
| elif not graph_config.graph_topology_task: | ||
| root_node_id = "ROOT" | ||
|
|
||
| yield (data, root_node_id) |
There was a problem hiding this comment.
Simplify root node ID logic.
The logic for setting the root_node_id can be simplified by consolidating conditions.
root_node_id = "ROOT" if not graph_config.graph_topology_task else None
if graph_config.infer_graph_topology:
topology_engine = TopologyEngine(infer=True)
if graph_config.graph_topology_task:
root_node_id = await topology_engine.add_graph_topology(files=data)
else:
await topology_engine.add_graph_topology(graph_config.topology_file_path)
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Actionable comments posted: 0
Outside diff range, codebase verification and nitpick comments (7)
cognee/tasks/source_documents_to_chunks/source_documents_to_chunks.py (7)
2-2: Consider renaming the import for clarity.The import
Documentfromdocument_types.Documentmight be clearer if aliased to avoid confusion with other potentialDocumentclasses.from cognee.modules.data.processing.document_types.Document import Document as SourceDocument
11-11: Initialize lists after checkingparent_node_id.Initializing
nodesandedgesafter theparent_node_idcheck improves readability by logically grouping related operations.- nodes = [] - edges = [] if parent_node_id and await graph_engine.extract_node(parent_node_id) is None: nodes.append((parent_node_id, {})) + nodes = [] + edges = []
Line range hint
20-20:
Ensure proper error handling for node extraction.The call to
graph_engine.extract_nodesshould include error handling to manage potential exceptions.try: document_nodes = await graph_engine.extract_nodes([str(document.id) for document in documents]) except Exception as e: # Handle the exception or log it raise e
Line range hint
22-23:
Fix the logic for accessingdocument_nodes.The line
document_nodes[document_index] if document_index in document_nodesis incorrect.document_nodesis likely a list, not a dictionary, and should be accessed using the index directly.- document_node = document_nodes[document_index] if document_index in document_nodes else None + document_node = document_nodes[document_index] if document_index < len(document_nodes) else None
Line range hint
25-25:
Avoid redundant calls todocument.to_dict().The
document.to_dict()method is called twice for the same document. Store the result in a variable to avoid redundancy.- document_dict = document.to_dict() + document_dict = document.to_dict() document_dict["user"] = user document_dict["user_permissions"] = user_permissions - nodes.append((str(document.id), document.to_dict())) + nodes.append((str(document.id), document_dict))
Line range hint
40-40:
Consider handling exceptions foradd_nodesandadd_edges.Ensure that any exceptions during the addition of nodes and edges are handled to prevent the function from failing silently.
try: await graph_engine.add_nodes(nodes) await graph_engine.add_edges(edges) except Exception as e: # Handle the exception or log it raise e
Line range hint
46-48:
Consider yielding document chunks in a try-except block.Wrap the chunk reading and yielding logic in a try-except block to handle any potential errors during document reading.
try: for document_chunk in document_reader.read(max_chunk_size=1024): yield document_chunk except Exception as e: # Handle the exception or log it raise e
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cognee/tasks/source_documents_to_chunks/source_documents_to_chunks.py (1)
8-9: Verify the unpacking logic whenparent_node_idisNone.The line
documents, parent_node_id = documentsassumes thatdocumentsis a tuple containing both documents and a parent node ID. This may lead to runtime errors ifdocumentsis not structured as expected.
cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py
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- cognee/api/v1/cognify/cognify_v2.py (3 hunks)
- cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py (3 hunks)
Files skipped from review as they are similar to previous changes (1)
- cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py
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cognee/api/v1/cognify/cognify_v2.py (2)
22-31: Imports are appropriate for the task updates.The new imports correspond to the updated task functions and are necessary for the changes in the pipeline.
93-113: Task updates enhance pipeline functionality.The updates to task functions improve the modularity and specificity of the pipeline. Each task is now more focused on a specific operation, which can lead to better performance and maintainability. Ensure that these new task functions are thoroughly tested and integrated with the existing system.
Verification successful
Task functions confirmed in the codebase.
The new task functions exist and are defined in their respective modules. This confirms the updates to the pipeline are correctly implemented as per the review comment. Ensure these functions are integrated and tested within the system.
source_documents_to_chunksincognee/tasks/source_documents_to_chunks/source_documents_to_chunks.pychunk_to_graph_decomposition_taskincognee/tasks/chunk_to_graph_decomposition/chunk_to_graph_decomposition.pychunks_into_graph_taskincognee/tasks/chunks_into_graph/chunks_into_graph.pychunk_update_check_taskincognee/tasks/chunk_update_check/chunk_update_check.pysave_chunks_to_store_taskincognee/tasks/save_chunks_to_store/save_chunks_to_store.pychunk_extract_summary_taskincognee/tasks/chunk_extract_summary/chunk_extract_summary.pychunk_naive_llm_classifier_taskincognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.pychunk_remove_disconnected_taskincognee/tasks/chunk_remove_disconnected/chunk_remove_disconnected.pyScripts executed
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#!/bin/bash # Description: Verify the existence and usage of the new task functions in the repository. # Test: Search for the new task functions to confirm their existence and usage. ast-grep --lang python --pattern $'def source_documents_to_chunks($_):\n $$$' ast-grep --lang python --pattern $'def chunk_to_graph_decomposition_task($_):\n $$$' ast-grep --lang python --pattern $'def chunks_into_graph_task($_):\n $$$' ast-grep --lang python --pattern $'def chunk_update_check_task($_):\n $$$' ast-grep --lang python --pattern $'def save_chunks_to_store_task($_):\n $$$' ast-grep --lang python --pattern $'def chunk_extract_summary_task($_):\n $$$' ast-grep --lang python --pattern $'def chunk_naive_llm_classifier_task($_):\n $$$' ast-grep --lang python --pattern $'def chunk_remove_disconnected_task($_):\n $$$'Length of output: 639
Script:
#!/bin/bash # Description: Verify the existence and usage of the new task functions using ripgrep. # Test: Search for the new task function names to confirm their existence and usage. rg 'def source_documents_to_chunks' --glob '*.py' -A 5 rg 'def chunk_to_graph_decomposition_task' --glob '*.py' -A 5 rg 'def chunks_into_graph_task' --glob '*.py' -A 5 rg 'def chunk_update_check_task' --glob '*.py' -A 5 rg 'def save_chunks_to_store_task' --glob '*.py' -A 5 rg 'def chunk_extract_summary_task' --glob '*.py' -A 5 rg 'def chunk_naive_llm_classifier_task' --glob '*.py' -A 5 rg 'def chunk_remove_disconnected_task' --glob '*.py' -A 5Length of output: 5717
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Outside diff range, codebase verification and nitpick comments (5)
cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.py (5)
11-13: Ensure proper handling of empty input.The check for an empty
data_chunkslist is appropriate, but consider logging this event for better traceability.if len(data_chunks) == 0: logger.info("No data chunks provided for classification.") return data_chunks
Line range hint
21-30: Refactor UUID generation logic.The UUIDs for classification types and subclasses are generated twice. Consider refactoring to avoid redundancy.
classification_type_id = uuid5(NAMESPACE_OID, chunk_classification.label.type) classification_data_points.append(classification_type_id) for classification_subclass in chunk_classification.label.subclass: classification_subtype_id = uuid5(NAMESPACE_OID, classification_subclass.value) classification_data_points.append(classification_subtype_id)
88-90: Clarify edge relationship naming.The relationship name "is_media_type" might not be intuitive. Consider renaming for clarity or documenting its purpose.
edges.append(( str(data_chunk.chunk_id), str(classification_type_id), "has_classification_type", # Consider renaming for clarity dict( relationship_name="has_classification_type", source_node_id=str(data_chunk.chunk_id), target_node_id=str(classification_type_id), ), ))
138-140: Clarify edge relationship naming.The relationship name "is_classified_as" might not be intuitive. Consider renaming for clarity or documenting its purpose.
edges.append(( str(data_chunk.chunk_id), str(classification_subtype_id), "classified_as", # Consider renaming for clarity dict( relationship_name="classified_as", source_node_id=str(data_chunk.chunk_id), target_node_id=str(classification_subtype_id), ), ))
48-48: Add Conditional Logic for Collection CreationThe file
cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.pylacks the conditional check for creating a collection only if it does not already exist. Consider implementing this logic to prevent unnecessary collection creation.
- File:
cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.pyAnalysis chain
Create collection only when necessary.
Creating a collection should be conditional on its non-existence. Ensure this logic is correctly implemented.
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#!/bin/bash # Description: Verify that collection creation is conditional. # Test: Check if the collection creation logic is conditional on its non-existence. rg --type python $'if not await vector_engine.has_collection(collection_name)'Length of output: 110
Script:
#!/bin/bash # Description: Verify that collection creation is conditional. # Test: Check if the collection creation logic is conditional on its non-existence. rg 'if not await vector_engine.has_collection(collection_name)' --glob '*.py' -A 3Length of output: 84
Script:
#!/bin/bash # Description: Search for any usage of 'has_collection' to verify collection existence checks. # Test: Find occurrences of 'has_collection' in Python files. rg 'has_collection' --glob '*.py' -A 3Length of output: 4999
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Files selected for processing (8)
- cognee/api/v1/cognify/cognify_v2.py (3 hunks)
- cognee/tasks/chunk_extract_summary/chunk_extract_summary.py (2 hunks)
- cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.py (5 hunks)
- cognee/tasks/chunk_remove_disconnected/chunk_remove_disconnected.py (1 hunks)
- cognee/tasks/chunk_to_graph_decomposition/chunk_to_graph_decomposition.py (1 hunks)
- cognee/tasks/chunk_update_check/chunk_update_check.py (1 hunks)
- cognee/tasks/chunks_into_graph/chunks_into_graph.py (2 hunks)
- cognee/tasks/save_chunks_to_store/save_chunks_to_store.py (1 hunks)
Files skipped from review as they are similar to previous changes (6)
- cognee/tasks/chunk_extract_summary/chunk_extract_summary.py
- cognee/tasks/chunk_remove_disconnected/chunk_remove_disconnected.py
- cognee/tasks/chunk_to_graph_decomposition/chunk_to_graph_decomposition.py
- cognee/tasks/chunk_update_check/chunk_update_check.py
- cognee/tasks/chunks_into_graph/chunks_into_graph.py
- cognee/tasks/save_chunks_to_store/save_chunks_to_store.py
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cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.py (3)
Line range hint
15-18: Optimize asynchronous classification gathering.The use of
asyncio.gatheris correct for concurrent execution. Ensure thatextract_categoriesis implemented as an async function for optimal performance.Verification successful
Verification Successful:
extract_categoriesis async.The
extract_categoriesfunction is correctly implemented as an async function, confirming the appropriate use ofasyncio.gatherfor concurrent execution in the code snippet.
- Location:
cognee/modules/data/extraction/extract_categories.pyScripts executed
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Script:
#!/bin/bash # Description: Verify that `extract_categories` is an async function. # Test: Search for the function definition. Expect: The function should be async. rg --type python $'async def extract_categories'Length of output: 80
Script:
#!/bin/bash # Description: Verify that `extract_categories` is an async function. # Test: Search for the function definition. Expect: The function should be async. rg $'async def extract_categories'Length of output: 165
62-69: Use Pydantic'sparse_objcautiously.The use of
Keyword.parse_objis correct, but ensure that the input dictionary keys match the Pydantic model attributes to avoid runtime errors.Verification successful
Pydantic model keys verified.
The keys used in the
parse_objdictionary match the attributes of theKeywordPydantic model, ensuring compatibility and preventing runtime errors.
Keywordmodel attributes:uuid,text,chunk_id,document_id- Dictionary keys:
"uuid","text","chunk_id","document_id"Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the input dictionary keys match the Pydantic model attributes. # Test: Check the `Keyword` model definition for attribute consistency. ast-grep --lang python --pattern $'class Keyword(BaseModel) { $$$ }'Length of output: 524
Script:
#!/bin/bash # Description: Locate the `Keyword` class definition and inspect its attributes. # Search for the `Keyword` class definition in Python files. rg 'class Keyword' -A 10 --glob '*.py'Length of output: 1114
125-127: Ensure consistent relationship naming.The relationship name "contains" should consistently reflect its purpose. Verify if it aligns with the intended relationship semantics.
cognee/api/v1/cognify/cognify_v2.py (2)
22-31: Verify new task imports.Ensure that the new task imports are correct and exist in the specified paths.
Verification successful
Imports verified successfully.
All the task imports in the specified code snippet exist and are correctly referenced in the codebase. No further action is needed.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence and correct import paths for new tasks. # Test: Check for the existence of each new task file. fd 'chunk_extract_summary.py' --exec echo "chunk_extract_summary exists" fd 'chunk_naive_llm_classifier.py' --exec echo "chunk_naive_llm_classifier exists" fd 'chunk_remove_disconnected.py' --exec echo "chunk_remove_disconnected exists" fd 'chunk_to_graph_decomposition.py' --exec echo "chunk_to_graph_decomposition exists" fd 'save_chunks_to_store.py' --exec echo "save_chunks_to_store exists" fd 'chunk_update_check.py' --exec echo "chunk_update_check exists" fd 'chunks_into_graph.py' --exec echo "chunks_into_graph exists" fd 'source_documents_to_chunks.py' --exec echo "source_documents_to_chunks exists"Length of output: 1376
93-113: Verify task integration and configuration.Ensure that each task is correctly integrated and configured with the appropriate parameters and models.
Verification successful
Let's verify the presence of the
Taskclass definition in the codebase to ensure the pattern used was correct. I'll search for theTaskclass definition to confirm its existence.
Let's refine the search to focus on
Taskinstantiations within thecognify_v2.pyfile to verify the integration and configuration of tasks.
Tasks are correctly integrated and configured.
The tasks within the
run_cognify_pipelinefunction incognify_v2.pyare correctly instantiated with appropriate parameters and models, ensuring their intended functionality in the pipeline.
- Task instantiations include necessary parameters like
root_node_id,parent_node_id,topology_model,task_config,graph_model,collection_name,summarization_model, andclassification_model.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the integration and configuration of tasks in `run_cognify_pipeline`. # Test: Check for the existence of task definitions and their parameters. ast-grep --lang python --pattern $'class Task($_, $$$)' --jsonLength of output: 411
Script:
#!/bin/bash # Search for the definition of the `Task` class in the codebase. rg 'class Task' -A 5Length of output: 1252
Script:
#!/bin/bash # Search for Task instantiations in the cognify_v2.py file to verify parameter usage. rg 'Task\(' -A 3 cognee/api/v1/cognify/cognify_v2.pyLength of output: 1620
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.github/workflows/test_common.yml (4)
38-41: Consider cross-platform compatibility.The matrix strategy is now limited to Ubuntu. Ensure that cross-platform compatibility is not a requirement for your project, as this change will exclude macOS and Windows testing.
62-66: Approved: PostgreSQL health checks.The addition of health check options for the PostgreSQL service enhances the robustness of the workflow by ensuring the database is ready before running tests.
109-111: Approved: PostgreSQL readiness check.Using
docker execfor the PostgreSQL readiness check ensures accurate assessment of the database's readiness.
Line range hint
155-157:
Important: Matrix job result check.This step ensures that any failures or cancellations in the matrix jobs are reported, maintaining the integrity of the CI/CD pipeline.
Summary by CodeRabbit
New Features
Enhancements