feat: add sqlalchemy as dlt destination #137
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After this change we can easily switch database providers. Default database is sqlite now.
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| GitGuardian id | GitGuardian status | Secret | Commit | Filename | |
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| 9573981 | Triggered | Generic Password | 766b6be | .github/workflows/test_python_3_11.yml | View secret |
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| 9573981 | Triggered | Generic Password | 766b6be | cognee/infrastructure/databases/relational/config.py | View secret |
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WalkthroughThe changes involve significant updates to the documentation and the introduction of a new demonstration notebook for the Changes
Sequence Diagram(s)sequenceDiagram
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participant Cognify
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participant KnowledgeGraph
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Cognify->>Dataset: Retrieve datasets
Dataset-->>Cognify: Return datasets
Cognify->>KnowledgeGraph: Process datasets into knowledge graph
KnowledgeGraph-->>Cognify: Return generated graph
Cognify-->>User: Provide results and insights
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Files ignored due to path filters (1)
poetry.lockis excluded by!**/*.lock
Files selected for processing (39)
- .github/workflows/test_neo4j.yml (0 hunks)
- .github/workflows/test_python_3_10.yml (0 hunks)
- .github/workflows/test_python_3_11.yml (0 hunks)
- .github/workflows/test_python_3_9.yml (0 hunks)
- .github/workflows/test_qdrant.yml (0 hunks)
- .github/workflows/test_weaviate.yml (0 hunks)
- cognee/api/client.py (1 hunks)
- cognee/api/v1/add/add.py (1 hunks)
- cognee/infrastructure/databases/relational/DatabaseEngine.py (0 hunks)
- cognee/infrastructure/databases/relational/FakeAsyncSession.py (0 hunks)
- cognee/infrastructure/databases/relational/init.py (1 hunks)
- cognee/infrastructure/databases/relational/config.py (2 hunks)
- cognee/infrastructure/databases/relational/create_db_and_tables.py (1 hunks)
- cognee/infrastructure/databases/relational/create_relational_engine.py (1 hunks)
- cognee/infrastructure/databases/relational/data_types/UUID.py (1 hunks)
- cognee/infrastructure/databases/relational/duckdb/DuckDBAdapter.py (0 hunks)
- cognee/infrastructure/databases/relational/relational_db_interface.py (0 hunks)
- cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py (4 hunks)
- cognee/infrastructure/databases/relational/sqlite/SqliteEngine.py (0 hunks)
- cognee/infrastructure/databases/relational/utils/init.py (0 hunks)
- cognee/infrastructure/databases/relational/utils/with_rollback.py (0 hunks)
- cognee/infrastructure/pipeline/models/Operation.py (2 hunks)
- cognee/modules/data/models/Data.py (1 hunks)
- cognee/modules/data/models/Dataset.py (1 hunks)
- cognee/modules/data/models/DatasetData.py (1 hunks)
- cognee/modules/pipelines/models/Pipeline.py (1 hunks)
- cognee/modules/pipelines/models/PipelineRun.py (1 hunks)
- cognee/modules/pipelines/models/PipelineTask.py (1 hunks)
- cognee/modules/pipelines/models/Task.py (1 hunks)
- cognee/modules/pipelines/models/TaskRun.py (1 hunks)
- cognee/modules/users/models/ACL.py (1 hunks)
- cognee/modules/users/models/ACLResources.py (1 hunks)
- cognee/modules/users/models/Group.py (1 hunks)
- cognee/modules/users/models/Permission.py (1 hunks)
- cognee/modules/users/models/Principal.py (1 hunks)
- cognee/modules/users/models/Resource.py (1 hunks)
- cognee/modules/users/models/User.py (1 hunks)
- cognee/modules/users/models/UserGroup.py (1 hunks)
- pyproject.toml (2 hunks)
Files not reviewed due to no reviewable changes (13)
- .github/workflows/test_neo4j.yml
- .github/workflows/test_python_3_10.yml
- .github/workflows/test_python_3_11.yml
- .github/workflows/test_python_3_9.yml
- .github/workflows/test_qdrant.yml
- .github/workflows/test_weaviate.yml
- cognee/infrastructure/databases/relational/DatabaseEngine.py
- cognee/infrastructure/databases/relational/FakeAsyncSession.py
- cognee/infrastructure/databases/relational/duckdb/DuckDBAdapter.py
- cognee/infrastructure/databases/relational/relational_db_interface.py
- cognee/infrastructure/databases/relational/sqlite/SqliteEngine.py
- cognee/infrastructure/databases/relational/utils/init.py
- cognee/infrastructure/databases/relational/utils/with_rollback.py
Files skipped from review due to trivial changes (2)
- cognee/modules/pipelines/models/PipelineTask.py
- cognee/modules/users/models/Permission.py
Additional comments not posted (58)
cognee/infrastructure/databases/relational/__init__.py (1)
7-7: Verify the usage of theUUIDdata type in the codebase.The import statement for the
UUIDdata type has been added correctly. This change may enhance the functionality of the module by allowing the use of UUIDs in relational database operations.Please ensure that:
- The
UUIDdata type is being utilized appropriately in other parts of the codebase.- The
UUIDdata type is compatible with the supported database engines and their respective data type mappings.Run the following script to verify the usage of the
UUIDdata type:Verification successful
The
UUIDdata type is widely and appropriately used throughout the codebase.The verification process has confirmed that the
UUIDdata type is extensively integrated into the project:
- It's used in database models as primary and foreign keys, ensuring unique identification of records.
- A custom
UUIDclass in the relational database module provides compatibility across different database systems.- The
UUIDtype is utilized in various modules including user management, pipeline operations, and data processing, indicating a consistent approach to unique identification throughout the system.- Both standard library and custom
UUIDimports are present, suggesting tailored UUID handling where necessary.The addition of the
UUIDimport incognee/infrastructure/databases/relational/__init__.pyis consistent with its usage across the project and appears to be a beneficial change that enhances the module's functionality.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of the `UUID` data type in the codebase. # Test 1: Search for the usage of the `UUID` data type. Expect: Occurrences of `UUID` data type. rg --type python -A 5 $'UUID' # Test 2: Search for the data type mappings in the SQLAlchemy models. Expect: Occurrences of `UUID` data type mapping. rg --type python -A 5 $'UUID\(\)'Length of output: 125
Script:
#!/bin/bash # Description: Verify the usage of the `UUID` data type in the codebase. # Test 1: Search for the import of the `UUID` data type echo "Searching for UUID import statements:" fd -e py | xargs grep -n "from .* import UUID" # Test 2: Search for the usage of the `UUID` data type echo "Searching for UUID usage:" fd -e py | xargs grep -n "UUID" # Test 3: Search for the data type mappings in the SQLAlchemy models echo "Searching for UUID data type mappings:" fd -e py | xargs grep -n "UUID()"Length of output: 13423
cognee/modules/users/models/UserGroup.py (2)
2-3: LGTM!The change in the import statement for
UUIDaligns with the new structure for managing database models. It does not introduce any apparent issues or inconsistencies.
10-10: Verify the impact of removingas_uuid=Trueon UUID handling.The removal of
as_uuid=Truefrom theuser_idcolumn definition could affect how UUIDs are processed or validated in the database context. It may lead to differences in behavior regarding UUID handling, which could have implications for data integrity or compatibility with other parts of the application.Run the following script to verify the UUID handling across the codebase:
Verification successful
Removal of
as_uuid=Trueis consistent across the codebaseThe change in
UserGroup.pyremovingas_uuid=Truefrom the UUID column definition is consistent with the rest of the codebase. Our verification shows:
- 25 occurrences of UUID columns without
as_uuid=True- 0 occurrences of UUID columns with
as_uuid=TrueThis indicates a standardized approach to UUID handling across the project, ensuring consistent behavior and maintaining data integrity.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `as_uuid=True` in UUID column definitions. # Test 1: Search for UUID column definitions without `as_uuid=True`. # Expect: No occurrences if the change is consistent across the codebase. rg --type python -g '*.py' $'Column\(UUID\)(?!.*as_uuid=True)' # Test 2: Search for UUID column definitions with `as_uuid=True`. # Expect: No occurrences if the change is consistent across the codebase. rg --type python -g '*.py' $'Column\(UUID.*as_uuid=True'Length of output: 402
Script:
#!/bin/bash # Description: Verify the usage of 'as_uuid=True' in UUID column definitions. # Search for UUID column definitions without 'as_uuid=True' echo "UUID columns without as_uuid=True:" rg -t py 'Column\(UUID[^)]*\)' | grep -v 'as_uuid=True' # Search for UUID column definitions with 'as_uuid=True' echo -e "\nUUID columns with as_uuid=True:" rg -t py 'Column\(UUID.*as_uuid=True' # Count occurrences of each type echo -e "\nCounts:" echo "UUID columns without as_uuid=True: $(rg -t py 'Column\(UUID[^)]*\)' | grep -v 'as_uuid=True' | wc -l)" echo "UUID columns with as_uuid=True: $(rg -t py 'Column\(UUID.*as_uuid=True' | wc -l)"Length of output: 3138
cognee/modules/data/models/DatasetData.py (2)
3-3: LGTM!The change aligns with the project's approach to handling UUIDs and does not introduce any issues.
10-11: Verify the impact of the UUID data type change.The change simplifies the column definitions and aligns with the project's approach to handling UUIDs. However, please ensure that:
- The change is consistently applied across the codebase.
- The change does not introduce any compatibility issues with existing data or other parts of the system.
Run the following script to verify the UUID usage across the codebase:
Verification successful
UUID usage change is consistent and approved.
The removal of
as_uuid=Truein theDatasetData.pyfile aligns with the UUID usage pattern across the entire codebase. Our verification confirms that:
- There are no remaining instances of
UUID(as_uuid=True)in the codebase.- The change is part of a consistent approach to handling UUIDs throughout the project.
- The modification doesn't introduce any compatibility issues with existing code or data structures.
The change is approved and maintains consistency with the rest of the codebase.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of UUID data type across the codebase. # Test 1: Search for the usage of UUID(as_uuid=True). Expect: No occurrences. rg --type python -g '!test_*.py' $'UUID\(as_uuid=True\)' # Test 2: Search for the usage of UUID without as_uuid parameter. Expect: Only occurrences of UUID without as_uuid. rg --type python -g '!test_*.py' $'UUID(?!\(as_uuid=True\))'Length of output: 382
Script:
#!/bin/bash # Description: Verify the usage of UUID data type across the codebase. # Test 1: Search for the usage of UUID(as_uuid=True). Expect: No occurrences. rg -g '*.py' -g '!test_*.py' 'UUID\(as_uuid=True\)' # Test 2: Search for the usage of UUID without as_uuid parameter. Expect: Only occurrences of UUID without as_uuid. rg -g '*.py' -g '!test_*.py' --pcre2 'UUID(?!\(as_uuid=True\))' # Test 3: Search for all occurrences of UUID to get a complete picture. rg -g '*.py' -g '!test_*.py' 'UUID'Length of output: 21419
cognee/modules/users/models/ACLResources.py (2)
11-11: Verify the impact of the UUID definition change.The simplification of the
resource_idcolumn definition to useUUIDdirectly is consistent with the updated import statement. However, please ensure that this change is applied consistently across the codebase and does not introduce any compatibility issues with existing data or queries.The same verification script suggested for the
acl_idcolumn can be used to verify the UUID usage across the codebase.
10-10: Verify the impact of the UUID definition change.The simplification of the
acl_idcolumn definition to useUUIDdirectly is consistent with the updated import statement. However, please ensure that this change is applied consistently across the codebase and does not introduce any compatibility issues with existing data or queries.Run the following script to verify the UUID usage across the codebase:
Verification successful
UUID usage is consistent across the codebase
The verification process confirms that the change to use
UUIDdirectly instead ofUUID(as_uuid=True)has been consistently applied across the codebase. No occurrences of the oldUUID(as_uuid=True)syntax were found, and the import ofUUIDfromcognee.infrastructure.databases.relationalis used consistently in multiple files, including theACLResources.pyfile under review.This consistency suggests that the change has been properly implemented and is unlikely to introduce compatibility issues with existing data or queries.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of UUID across the codebase. # Test 1: Search for the usage of `UUID(as_uuid=True)`. Expect: No occurrences. rg --type python $'UUID\(as_uuid=True\)' # Test 2: Search for the usage of `UUID` from `cognee.infrastructure.databases.relational`. Expect: Consistent usage across the codebase. rg --type python $'from cognee\.infrastructure\.databases\.relational import .*UUID'Length of output: 189
Script:
#!/bin/bash # Description: Verify the existence of Python files and search for UUID usage # Test 1: List Python files in the repository echo "Python files in the repository:" find . -name "*.py" # Test 2: Search for the usage of `UUID(as_uuid=True)`. Expect: No occurrences. echo -e "\nSearching for UUID(as_uuid=True):" grep -R "UUID(as_uuid=True)" . # Test 3: Search for the usage of `UUID` from `cognee.infrastructure.databases.relational`. Expect: Consistent usage across the codebase. echo -e "\nSearching for UUID import from cognee.infrastructure.databases.relational:" grep -R "from cognee.infrastructure.databases.relational import.*UUID" .Length of output: 16261
cognee/modules/pipelines/models/TaskRun.py (2)
3-4: LGTM!The change in import statement for
UUIDimproves code organization by centralizing the definition in a single location. It does not affect the functionality of the code.
9-9: Verify the impact of theUUIDtype change on the codebase.The
as_uuidparameter has been removed from theUUIDtype in theidcolumn declaration. This change may affect how UUIDs are generated or stored in the database.Please ensure that:
- This change is intentional and aligns with the desired behavior.
- Necessary adjustments have been made in other parts of the codebase that interact with this model or rely on the previous
UUIDbehavior.- Existing data in the database is compatible with this change and will not cause any issues.
Run the following script to verify the
UUIDusage across the codebase:Verification successful
The UUID type change in TaskRun.py is consistent with the codebase.
The removal of
as_uuid=Truefrom the UUID column declaration incognee/modules/pipelines/models/TaskRun.pyaligns with the UUID usage throughout the rest of the codebase. Other files consistently useColumn(UUID, ...)without additional parameters.Key observations:
- No other instances of
as_uuid=Truewere found in the codebase.- A custom UUID type is defined in
cognee/infrastructure/databases/relational/data_types/UUID.py, which likely handles UUID conversions internally.This change appears to be intentional and improves consistency across the project. The impact on existing data and functionality is expected to be minimal, as the custom UUID type should handle any necessary conversions.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `UUID` type across the codebase. # Test 1: Search for `UUID` type usage in SQLAlchemy column declarations. # Expect: Consistent usage of `UUID` without `as_uuid` parameter. rg --type python -A 5 $'Column\(UUID' # Test 2: Search for `UUID` type usage in function/method parameters and return types. # Expect: Consistent usage of `UUID` type. ast-grep --lang python --pattern $'def $_($$$): $$$ return UUID($_) $$$' # Test 3: Search for `UUID` type usage in variable assignments. # Expect: Consistent usage of `UUID` type. ast-grep --lang python --pattern $'$_ = UUID($_)'Length of output: 200
Script:
#!/bin/bash # Description: Verify the presence of Python files and UUID usage across the codebase. # Test 1: Check for the presence of Python files echo "Python files in the repository:" fd -e py # Test 2: Search for UUID import statements echo -e "\nUUID import statements:" rg "from uuid import" # Test 3: Search for UUID type usage in column declarations echo -e "\nUUID usage in column declarations:" rg "Column\(.*UUID" # Test 4: Search for UUID function calls echo -e "\nUUID function calls:" rg "UUID\(" # Test 5: Search for UUID type hints echo -e "\nUUID type hints:" rg ": UUID"Length of output: 22097
cognee/modules/pipelines/models/PipelineRun.py (2)
15-15: LGTM!The removal of the
as_uuidparameter from therun_idcolumn is consistent with the modification made to theidcolumn. This change looks good to me.
9-9: Verify the impact of removing theas_uuidparameter from theUUIDtype.The removal of the
as_uuidparameter may affect how UUIDs are represented in the database schema and processed within the application. Please ensure that this change aligns with the project's database support and configuration.Run the following script to verify the UUID representation in the database:
Verification successful
Removal of
as_uuidparameter is safe and consistent with the project's UUID handling.The change to remove the
as_uuidparameter from theUUIDtype in thePipelineRunclass is verified to be safe and consistent with the project's database configuration. Here's why:
- The project uses a custom
UUIDtype that adapts to both PostgreSQL and other databases.- The
idcolumn is correctly defined asColumn(UUID, primary_key = True, default = uuid4).- No direct usages of
PipelineRun.idwere found that might be affected by this change.- The custom
UUIDtype implementation handles the conversion between Python's UUID objects and database representations, making theas_uuidparameter unnecessary.This change aligns with the project's overall approach to UUID handling and should not cause any issues with database compatibility or application functionality.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the UUID representation in the database. # Test 1: Search for the `id` column definition in the `PipelineRun` table. # Expect: The `id` column should be defined as `UUID` without the `as_uuid` parameter. rg --type sql -A 5 $'CREATE TABLE pipeline_runs' # Test 2: Search for the usage of the `id` column in the codebase. # Expect: The `id` column should be used consistently without any type casting or conversion. rg --type python -A 5 $'PipelineRun.id'Length of output: 268
Script:
#!/bin/bash # Description: Locate PipelineRun class definition and usage # Find Python files containing PipelineRun echo "Python files containing PipelineRun:" fd -e py | xargs grep -l "class PipelineRun" # Search for PipelineRun class definition echo "\nPipelineRun class definition:" rg "class PipelineRun" -A 10 # Search for PipelineRun.id usage echo "\nPipelineRun.id usage:" rg "PipelineRun.id" # Search for database migration files echo "\nDatabase migration files:" fd -e py | xargs grep -l "db.Column" # Search for ORM configuration related to UUID echo "\nORM configuration for UUID:" rg "UUID|uuid"Length of output: 19375
cognee/infrastructure/databases/relational/create_relational_engine.py (1)
8-10: Update function calls to match the new signature.The changes to the function signature, including the renaming of
db_usertodb_usernameand the reordering of thedb_providerparameter, enhance clarity and flexibility. However, ensure that all calls tocreate_relational_engineare updated to match the new signature.Run the following script to verify the function usage:
Verification successful
No updates required for existing function calls
The changes to the
create_relational_enginefunction signature do not require updates to existing function calls. The search results show that the function is called using dictionary unpacking (**relational_config.to_dict()), which automatically adapts to changes in the function signature. This approach ensures that the code remains compatible as long as therelational_configobject contains the correct attributes.File: cognee/infrastructure/databases/relational/get_relational_engine.py
return create_relational_engine(**relational_config.to_dict())This implementation is flexible and future-proof. No further action is needed.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify all function calls to `create_relational_engine` match the new signature. # Test: Search for the function usage. Expect: Only occurrences of the new signature. rg --type python -A 5 $'create_relational_engine'Length of output: 79
Script:
#!/bin/bash # Description: Search for 'create_relational_engine' function calls in Python files # Using ripgrep with file extension matching echo "Searching with ripgrep:" rg -g '*.py' -A 5 'create_relational_engine\(' # Using ast-grep to search for function calls echo "Searching with ast-grep:" ast-grep --lang python --pattern 'create_relational_engine($_)'Length of output: 1055
cognee/infrastructure/databases/relational/create_db_and_tables.py (3)
1-3: LGTM!The new import statements are valid and relevant to the changes made in the function.
6-10: Excellent addition for SQLite support!The changes introduce a robust check to ensure the necessary directory exists before creating the SQLite database. This enhancement prevents potential runtime errors related to missing directories and improves the overall reliability of the database creation process.
The logic is sound, and the code is well-structured.
11-11: LGTM!The added empty line improves code readability and separates logical blocks.
cognee/modules/users/models/Group.py (2)
2-3: LGTM!The change in the import statement for
UUIDis a good refactoring effort to centralize database-related imports. As long as theUUIDtype is correctly defined in the new import location, this change should not affect the functionality of the code.
10-10: Verify the UUID type definition.The change in the instantiation of the
idcolumn fromUUID(as_uuid=True)toUUIDshould not affect the functionality of the code, as long as theUUIDtype is correctly defined in the new import location.To ensure the correctness of the change, please verify that the
UUIDtype incognee.infrastructure.databases.relationalis defined with the same behavior asUUID(as_uuid=True)fromsqlalchemy.cognee/modules/users/models/Principal.py (1)
9-9: Verify the impact of the UUID type change on the codebase.The change in the
idattribute definition to use theUUIDtype directly fromcognee.infrastructure.databases.relationallooks good.However, it's crucial to ensure that this change is consistently applied across the codebase and does not introduce any compatibility issues with existing data or other parts of the system.
Please run the following script to verify the usage of the
UUIDtype across the codebase:Verification successful
UUID type change consistently applied across the codebase
The verification process has confirmed that the UUID type change from using
as_uuid=Trueparameter to importingUUIDdirectly fromcognee.infrastructure.databases.relationalhas been consistently applied across the codebase. Key findings:
- No occurrences of
as_uuidparameter were found in Column definitions.- Multiple files are correctly importing
UUIDfromcognee.infrastructure.databases.relational.- Standard
uuidlibrary usage for UUID generation remains intact and doesn't conflict with the database column type change.These results indicate that the change has been implemented correctly and consistently throughout the project.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of the UUID type from cognee.infrastructure.databases.relational. # Test 1: Search for the UUID type usage. Expect: Only occurrences of the UUID type from cognee.infrastructure.databases.relational. rg --type python -A 5 $'UUID' # Test 2: Search for the as_uuid parameter usage. Expect: No occurrences of the as_uuid parameter. rg --type python -A 5 $'as_uuid'Length of output: 122
Script:
#!/bin/bash # Description: Verify the usage of the UUID type and as_uuid parameter in Python files # Find all Python files echo "Python files in the repository:" fd -e py # Test 1: Search for the UUID type usage in Python files echo -e "\nUUID type usage:" fd -e py -x grep -n -A 5 'UUID' {} || echo "No UUID usage found" # Test 2: Search for the as_uuid parameter usage in Python files echo -e "\nas_uuid parameter usage:" fd -e py -x grep -n -A 5 'as_uuid' {} || echo "No as_uuid parameter usage found"Length of output: 32233
cognee/modules/users/models/Resource.py (3)
4-5: LGTM!The imports are correct and necessary for defining the SQLAlchemy model.
11-11: Verify the impact of removingas_uuid=Truefrom theidcolumn definition.Ensure that this change is consistently applied across all relevant parts of the codebase and that it doesn't break any existing functionality related to UUID handling.
Run the following script to verify the usage of
UUID(as_uuid=True)in the codebase:Verification successful
Removal of
as_uuid=Trueis consistent with codebase practicesThe change in the
idcolumn definition inResource.pyhas been verified against the entire codebase. The removal ofas_uuid=Trueis consistent with other UUID column definitions throughout the project. No other occurrences ofas_uuid=Truewere found, and the updated definition aligns with the common pattern of usingColumn(UUID, primary_key = True, default = uuid4).This change does not introduce inconsistencies in UUID handling across the application and is unlikely to break existing functionality related to UUID handling.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `UUID(as_uuid=True)` in the codebase. # Test: Search for the usage of `UUID(as_uuid=True)`. Expect: No occurrences. rg --type python $'UUID\(as_uuid=True\)'Length of output: 72
Script:
#!/bin/bash # Description: Verify the usage of UUID in column definitions across the codebase. # Search for various UUID column definitions echo "Searching for UUID column definitions:" rg "Column\(UUID.*\)" echo -e "\nSearching for specific 'as_uuid' parameter:" rg "UUID.*as_uuid" # Use ast-grep to find more specific patterns echo -e "\nSearching for UUID column definitions using ast-grep:" ast-grep --lang python --pattern 'Column($uuid, $$$)' | grep -i 'uuid' echo -e "\nSearching for UUID imports:" rg "from uuid import"Length of output: 5817
16-16: Verify the impact of removingas_uuid=Truefrom theresource_idcolumn definition.Ensure that this change is consistently applied across all relevant parts of the codebase and that it doesn't break any existing functionality related to UUID handling.
Run the following script to verify the usage of
UUID(as_uuid=True)in the codebase:Verification successful
Removal of
as_uuid=Trueis consistent with codebase practicesThe change in the
resource_idcolumn definition aligns with the project's standard practice for defining UUID columns. All other UUID column definitions in the codebase useColumn(UUID, ...)without theas_uuid=Trueparameter. This consistency suggests that the change is intentional and follows the project's conventions.The codebase appears to use a custom UUID implementation (
cognee/infrastructure/databases/relational/data_types/UUID.py), which likely handles UUID conversions and storage. This custom implementation may eliminate the need foras_uuid=Truein column definitions.Based on the comprehensive analysis of UUID usage across the codebase, there's no evidence of inconsistency or potential functionality breakage due to this change.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `UUID(as_uuid=True)` in the codebase. # Test: Search for the usage of `UUID(as_uuid=True)`. Expect: No occurrences. rg --type python $'UUID\(as_uuid=True\)'Length of output: 72
Script:
#!/bin/bash # Description: Verify the usage of UUID in column definitions across the codebase. # Search for 'UUID(' without file type specification echo "Searching for 'UUID(' usage:" rg 'UUID\(' # Search for 'Column(UUID' to find UUID column definitions echo -e "\nSearching for 'Column(UUID' usage:" rg 'Column\(UUID' # Use ast-grep to find UUID column definitions in Python code echo -e "\nSearching for UUID column definitions using ast-grep:" ast-grep --lang python --pattern 'Column($uuid)' # Use ast-grep to find UUID imports in Python code echo -e "\nSearching for UUID imports using ast-grep:" ast-grep --lang python --pattern 'from $_ import UUID'Length of output: 5937
cognee/modules/pipelines/models/Pipeline.py (2)
5-5: Verify the impact of the custom UUID implementation on the codebase and database interactions.The import statement for
UUIDhas been changed to use a custom implementation fromcognee.infrastructure.databases.relational. This change suggests a specific configuration or custom handling of UUIDs within the application.Please ensure that this change is consistently applied throughout the codebase and that it does not introduce any unintended side effects or compatibility issues with the database interactions.
Run the following script to verify the usage of the custom
UUIDimplementation:
11-11: Verify the impact of the UUID column declaration change on the database.The declaration of the
idcolumn in thePipelineclass has been modified, replacingUUID(as_uuid=True)withUUID. This change may impact how the UUID values are handled and stored in the database.Please ensure that this modification does not introduce any unintended side effects or compatibility issues with the existing data in the database. Verify that the UUIDs are still being generated and stored correctly, and that the integrity of the
idfield as a primary key is maintained.Run the following script to verify the UUID generation and storage:
cognee/modules/pipelines/models/Task.py (2)
11-11: Clarify the reasoning behind the change in theidattribute.The
idattribute of theTaskclass has been modified fromUUID(as_uuid=True)toUUID. This change may affect how UUIDs are handled in the database context.
UUID(as_uuid=True)indicates that the UUID should be treated as a native UUID type in PostgreSQL.UUIDmay alter the compatibility or behavior with certain database operations.Please provide more context on the reasoning behind this change and how it aligns with the shift in database support from PostgreSQL to SQLite mentioned in the PR objectives.
If this change is intentional and well-justified based on the new database support, then it looks good to me.
4-5: LGTM!The updated import statements, particularly the import of
UUIDfromcognee.infrastructure.databases.relational, align with the changes made to theidattribute and the shift in database support.cognee/infrastructure/pipeline/models/Operation.py (2)
17-17: LGTM: The changes to the base class and data type for theidattribute are good improvements.The change in the base class for
OperationfromModelBasetoBasealigns with the transition to SQLAlchemy as mentioned in the PR objectives. This change is likely to improve the integration with the new ORM framework.The change in the data type for the
idattribute fromStringtoUUIDenhances the uniqueness and integrity of the identifier. This is a good practice for ensuring that each operation has a distinct and non-colliding identifier.Also applies to: 20-20
17-17: Verify the impact of these changes on the rest of the codebase and existing data.While the changes to the base class and data type for the
idattribute are good improvements, it's important to ensure that they are consistent with the rest of the project and do not introduce any compatibility issues.Please run the following script to verify the impact of these changes:
Additionally, please ensure that any existing data is migrated appropriately to accommodate the change in the data type for the
idattribute. This may require running database migrations or scripts to update the existing records.Also applies to: 20-20
Verification successful
Changes to base class and UUID type for 'id' attribute are consistently applied
The verification process has confirmed that the changes to the base class and the use of UUID type for the 'id' attribute have been consistently implemented across the codebase:
- All 'id' columns are now defined using UUID type.
- There are no remaining references to String-type 'id' columns.
- The 'Base' class is consistently imported and used.
However, please note:
- No specific migration files were found for this change. Ensure that any necessary data migrations have been handled appropriately to maintain data consistency.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the impact of the changes to the base class and data type for the `id` attribute. # Test 1: Search for references to the old base class `ModelBase`. Expect: No occurrences. rg --type python 'ModelBase' # Test 2: Search for references to the `Operation` class to ensure the new base class is used consistently. Expect: Only occurrences of `Operation(Base)`. rg --type python 'class Operation' # Test 3: Search for occurrences of `id` attribute with `String` type. Expect: No occurrences. rg --type python 'id = Column\(String' # Test 4: Search for occurrences of `id` attribute with `UUID` type to ensure it's used consistently. Expect: Only occurrences of `id = Column(UUID`. rg --type python 'id = Column\(UUID'Length of output: 269
Script:
#!/bin/bash # Description: Verify the impact of the changes to the base class and data type for the `id` attribute. # Test 1: Search for references to the old base class `ModelBase`. Expect: No occurrences. echo "Searching for 'ModelBase' references:" fd -e py -x grep -n "ModelBase" # Test 2: Search for references to the `Operation` class to ensure the new base class is used consistently. Expect: Only occurrences of `Operation(Base)`. echo -e "\nSearching for 'Operation' class definitions:" fd -e py -x grep -n "class Operation" # Test 3: Search for occurrences of `id` attribute with `String` type. Expect: No occurrences. echo -e "\nSearching for 'id' columns with String type:" fd -e py -x grep -n "id *= *Column(.*)String" # Test 4: Search for occurrences of `id` attribute with `UUID` type to ensure it's used consistently. Expect: Only occurrences of `id = Column(UUID`. echo -e "\nSearching for 'id' columns with UUID type:" fd -e py -x grep -n "id *= *Column(.*)UUID" # Test 5: Search for potential database migration files related to 'id' column changes echo -e "\nSearching for potential database migration files:" fd -e py "migration" -x grep -n "id"Length of output: 913
Script:
#!/bin/bash # Description: Further investigation of ModelBase imports and id column definitions # Check the content of files importing ModelBase echo "Checking files importing ModelBase:" fd -e py -x grep -n -A 5 "from .ModelBase import Base" {} # Search for any id column definitions echo -e "\nSearching for id column definitions:" fd -e py -x grep -n "id.*=.*Column" # Search for migration files or scripts echo -e "\nSearching for migration files or scripts:" fd -e py -x grep -n -i "migration|alembic" # Check for UUID imports echo -e "\nChecking for UUID imports:" fd -e py -x grep -n "import.*UUID"Length of output: 4780
cognee/modules/users/models/User.py (2)
5-5: Verify the compatibility of the new UUID implementation.The change in the import path for
UUIDsuggests a refactoring of the UUID implementation. This is likely part of a broader effort to consolidate database-related functionality.Please ensure that the new UUID implementation from
cognee.infrastructure.databases.relationalis compatible with the existing code and does not introduce any unintended side effects.
12-12: Ensure consistency and compatibility of the new UUID behavior.The change in the
UUIDtype used for theidcolumn suggests a shift in how UUIDs are handled in the database schema. This change is likely related to the update in the UUID import statement.Please ensure that this change is consistent across the codebase and does not break any existing functionality that relies on the previous UUID behavior. Verify that the new UUID implementation is compatible with all the code that interacts with user IDs, including:
- User creation and retrieval
- User authentication and authorization
- Any other modules or functions that use user IDs
Run the following script to verify the usage of the
User.idcolumn across the codebase:Verification successful
UUID usage is consistent across the codebase, including User.id
Based on the comprehensive analysis of UUID usage in the codebase, the change to
User.idappears to be consistent with the general pattern of UUID usage in other models. Here are the key findings:
- UUID is widely used for ID fields across multiple models, consistently using the
Column(UUID, ...)syntax.- The import statements for UUID are uniform, typically importing from the
uuidmodule.- Other references to
User.idin the codebase (e.g., in queries) are compatible with the UUID type.- Several
__init__methods and functions use UUID as a parameter type, indicating a standardized approach to handling UUIDs.Given these observations, the change to
User.idaligns with the project's overall UUID usage pattern and is unlikely to cause inconsistencies or compatibility issues.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of the `User.id` column across the codebase. # Test 1: Search for references to the `User.id` column. Expect: Only occurrences with the new UUID type. rg --type python -A 5 $'User.id' # Test 2: Search for usage of user IDs in function parameters and return values. Expect: Consistent usage of the new UUID type. ast-grep --lang python --pattern $'def $_($$$, user_id: $_, $$$): $$$ return $$$' ast-grep --lang python --pattern $'def $_($$$) -> $$$: $$$ return $$$user_id$$$'Length of output: 1399
Script:
#!/bin/bash # Description: Verify the usage of UUID and User.id across the codebase # Search for User.id references rg 'User\.id' # Search for UUID usage in column definitions rg 'Column\(UUID' # Search for UUID imports rg 'from uuid import UUID' rg 'import uuid' # Search for other models using UUID rg 'class.*\(.*\):\n.*Column\(UUID' # Search for functions using UUID as parameter or return type ast-grep --lang python --pattern 'def $_($$$, $_: UUID, $$$): $$$' ast-grep --lang python --pattern 'def $_($$$) -> UUID: $$$'Length of output: 9469
cognee/modules/users/models/ACL.py (4)
4-5: LGTM!The imports are correctly defined and necessary for the model definition.
16-16: LGTM!The change simplifies the UUID column definition for the foreign key and is consistent with the change made to the
idattribute.
17-17: LGTM!The change simplifies the UUID column definition for the foreign key and is consistent with the changes made to the
idandprincipal_idattributes.
11-11: Verify the UUID handling change across the codebase.The change simplifies the UUID column definition by using
UUIDdirectly without theas_uuidparameter. However, it's important to ensure that this change is consistently applied across the codebase and does not introduce any compatibility issues or unintended side effects.Run the following script to verify the UUID handling change:
Verification successful
UUID handling change consistently applied across the codebase
The verification process confirms that the UUID handling change has been consistently applied throughout the project. Key findings include:
- No instances of
UUID(as_uuid=True)were found, indicating the removal of this parameter is consistent.- Multiple occurrences of
UUIDusage were found across various parts of the codebase, including models, schemas, and utility functions.- Model definitions consistently use
Column(UUID, ...)without theas_uuidparameter, aligning with the change made in the ACL model.This change appears to be part of a broader standardization of UUID handling across the project, and no inconsistencies were detected.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify UUID handling change is consistently applied across the codebase. # Test 1: Search for occurrences of `UUID(as_uuid=True)`. Expect: No occurrences. rg --type python $'UUID\(as_uuid=True\)' # Test 2: Search for occurrences of `UUID` without `as_uuid` parameter. Expect: Multiple occurrences. rg --type python $'UUID(?!\(as_uuid=True\))'Length of output: 350
Script:
#!/bin/bash # Description: Verify UUID handling change across the codebase # Search for any remaining instances of UUID(as_uuid=True) echo "Searching for UUID(as_uuid=True):" rg "UUID\(as_uuid=True\)" # Look for general UUID usage echo "Searching for general UUID usage:" rg "UUID" # Use ast-grep to find UUID column definitions in models echo "Searching for UUID column definitions in models:" ast-grep --lang python --pattern 'Column(UUID, $$$)'Length of output: 14086
cognee/infrastructure/databases/relational/config.py (6)
2-2: LGTM!The
Uniontype from thetypingmodule is imported correctly. It will be useful for allowing configuration parameters to have multiple possible types.
10-10: LGTM!The change to make
db_hostan optional parameter of typeUnion[str, None]with a default value ofNoneprovides more flexibility in the database configuration.
11-11: LGTM!The change to make
db_portan optional parameter of typeUnion[str, None]with a default value ofNoneprovides more flexibility in the database configuration.
12-12: LGTM!The change to rename
db_usertodb_usernameimproves the clarity of the parameter name. Making it an optional parameter of typeUnion[str, None]with a default value ofNoneprovides more flexibility in the database configuration.
13-13: LGTM!The change to make
db_passwordan optional parameter of typeUnion[str, None]with a default value ofNoneprovides more flexibility in the database configuration.
14-14: Verify the impact of changing the default database provider.Changing the default database provider from PostgreSQL to SQLite simplifies the default database configuration for development and testing purposes. However, it's important to verify that this change does not break any existing code that relies on PostgreSQL-specific features.
Run the following script to search for PostgreSQL-specific code in the codebase:
Verification successful
Approval: Default database provider change is well-integrated and low-impact.
The change of the default
db_providerto "sqlite" in theRelationalConfigclass is consistent with the codebase structure and doesn't introduce breaking changes. Key findings:
- The
create_relational_engine.pyfile still supports both SQLite and PostgreSQL, maintaining flexibility.- No PostgreSQL-specific code was found, indicating the codebase was already database-agnostic.
- The
db_provideris used consistently across relevant files.This change simplifies development and testing setups while preserving the option to use PostgreSQL when needed. No further action is required.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Search for PostgreSQL-specific code in the codebase. # Test: Search for PostgreSQL-specific imports, functions, and SQL queries. # Expect: No occurrences of PostgreSQL-specific code. rg --type python -i $'import psycopg2|from psycopg2|import asyncpg|from asyncpg' rg --type python -i $'create_engine\("postgresql' rg --type sql -i $'CREATE EXTENSION|ALTER EXTENSION'Length of output: 397
Script:
#!/bin/bash # Description: Search for PostgreSQL and SQLite related code in the codebase # Find Python files and search for PostgreSQL-related imports and functions fd -e py -x grep -Hi 'import psycopg2\|from psycopg2\|import asyncpg\|from asyncpg\|create_engine("postgresql' # Find Python files and search for SQLite-related imports and functions fd -e py -x grep -Hi 'import sqlite3\|from sqlite3\|create_engine("sqlite' # Search for database configuration files fd -e yaml -e json -e toml -e ini -x grep -Hi 'database\|db_provider\|sqlite\|postgresql'Length of output: 320
Script:
#!/bin/bash # Description: Search for RelationalConfig usage and database connection setup # Search for RelationalConfig usage fd -e py -x grep -Hi 'RelationalConfig' # Search for db_provider usage fd -e py -x grep -Hi 'db_provider' # Search for database connection setup (SQLAlchemy or other ORM) fd -e py -x grep -Hi 'create_engine\|Database(' # Search for environment variable usage related to database configuration fd -e py -x grep -Hi 'os.environ.get.*DB\|os.getenv.*DB'Length of output: 1928
cognee/modules/data/models/Dataset.py (2)
5-6: LGTM!The imports are correct and necessary for defining the
Datasetclass and its attributes.
12-12: Verify the impact of the change in theUUIDtype import.The change in the
UUIDtype import is correct and consistent with the rest of the codebase. However, it's important to ensure that this change does not break any existing functionality.Run the following script to verify the impact of the change:
Verification successful
UUID import change verified and consistent across codebase
The modification of the UUID import has been successfully implemented throughout the project:
- The old import style
UUID(as_uuid=True)is no longer present in the codebase.- The new import
from cognee.infrastructure.databases.relational import UUIDis consistently used across multiple modules and model files.- The usage of
uuid4for generating default values for id columns remains unchanged, ensuring continued consistency in unique identifier generation.This change has been applied uniformly and does not appear to introduce any inconsistencies or potential issues.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the impact of the change in the `UUID` type import. # Test 1: Search for the old `UUID` type import. Expect: No occurrences. rg --type python -g '!test_*.py' $'UUID\(as_uuid=True\)' # Test 2: Search for the new `UUID` type import. Expect: Occurrences in relevant files. rg --type python -g '!test_*.py' $'from cognee\.infrastructure\.databases\.relational import .*UUID' # Test 3: Search for `uuid4` usage. Expect: No change in usage. rg --type python -g '!test_*.py' $'uuid4'Length of output: 293
Script:
#!/bin/bash # Description: Verify the impact of the change in the `UUID` type import using grep. # Test 1: Search for the old `UUID` type import. Expect: No occurrences. grep -R "UUID(as_uuid=True)" . --include="*.py" --exclude="test_*.py" --exclude-dir="__pycache__" # Test 2: Search for the new `UUID` type import. Expect: Occurrences in relevant files. grep -R "from cognee.infrastructure.databases.relational import.*UUID" . --include="*.py" --exclude="test_*.py" --exclude-dir="__pycache__" # Test 3: Search for `uuid4` usage. Expect: No change in usage. grep -R "uuid4" . --include="*.py" --exclude="test_*.py" --exclude-dir="__pycache__"Length of output: 4323
cognee/modules/data/models/Data.py (2)
6-6: Verify the impact of theUUIDimport change on the codebase.The import statement for
UUIDhas been updated to import it directly fromcognee.infrastructure.databases.relational. Ensure that this change is consistently applied throughout the codebase and that it does not introduce any compatibility issues or unintended consequences.Run the following script to verify the usage of
UUIDacross the codebase:Verification successful
The UUID import change has been consistently applied across the codebase.
The modification to import
UUIDfromcognee.infrastructure.databases.relationalhas been implemented consistently, particularly in model definitions and database-related code. This change doesn't appear to introduce any compatibility issues or unintended consequences. The codebase still usesUUIDfrom the standarduuidmodule andfastapi_userslibrary where appropriate, maintaining the expected functionality.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `UUID` across the codebase. # Test: Search for the usage of `UUID`. Expect: Consistent import and usage. rg --type python -A 5 $'UUID'Length of output: 59
Script:
#!/bin/bash # Description: Verify the usage of `UUID` across the codebase. # Search for UUID usage in Python files echo "Searching for UUID usage in Python files:" rg -g '*.py' -n -C 5 'UUID' # Search for import statements related to UUID echo "Searching for UUID import statements:" rg -g '*.py' -n 'import.*UUID' # Search for specific import from cognee.infrastructure.databases.relational echo "Searching for specific UUID import from cognee.infrastructure.databases.relational:" rg -g '*.py' -n 'from cognee\.infrastructure\.databases\.relational import.*UUID'Length of output: 58293
12-12: Verify the intentionality and impact of theidfield declaration change.The
idfield declaration has been modified to remove theas_uuid=Trueparameter from theUUIDtype. This change may affect how UUIDs are handled within the application and could have implications for data integrity and type handling.Please ensure that:
- The removal of
as_uuid=Trueis intentional and aligns with the desired behavior for UUID handling.- The codebase is updated accordingly to handle UUIDs correctly, considering the potential impact on data integrity and consistency.
- Thorough testing is performed to validate the behavior of UUID handling across the application.
Run the following script to verify the usage of the
idfield and UUID handling across the codebase:Verification successful
The
idfield declaration change is consistent and doesn't introduce apparent issues.After a thorough analysis of the codebase, I can confirm that the removal of
as_uuid=Truefrom theidfield declaration incognee/modules/data/models/Data.pyis consistent with other UUID column declarations across various models. The change doesn't appear to have introduced any inconsistencies or issues in UUID handling throughout the codebase.Key observations:
- Other models also use
UUIDtype foridfields without theas_uuid=Trueparameter.- UUID-related operations are widely used across the codebase, including in database queries, API endpoints, and data processing, without any apparent issues.
The change seems intentional and aligns with the overall UUID handling strategy in the project. No further action is required regarding this specific change.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of the `id` field and UUID handling across the codebase. # Test: Search for the usage of the `id` field. Expect: Consistent usage and handling of UUIDs. rg --type python -A 5 $'id = Column\(UUID' # Test: Search for UUID-related operations. Expect: Correct handling of UUIDs based on the updated field declaration. rg --type python -A 5 $'\.id'Length of output: 136
Script:
#!/bin/bash # Description: Search for the usage of the `id` field and UUID handling across the codebase. # Find Python files and search for the `id` field declaration echo "Searching for id field declarations:" fd -e py | xargs grep -n "id = Column(UUID" # Find Python files and search for UUID-related operations echo "Searching for UUID-related operations:" fd -e py | xargs grep -n "\.id"Length of output: 15723
cognee/infrastructure/databases/relational/data_types/UUID.py (4)
6-43: Excellent work on the UUID class implementation!The
UUIDclass provides a robust and flexible solution for handling UUIDs across different database systems. By extendingTypeDecoratorand adapting the storage based on the database dialect, it ensures consistent handling and storage of UUIDs.The
load_dialect_impl,process_bind_param, andprocess_result_valuemethods work together seamlessly to abstract away the differences between database dialects, making it easier for developers to work with UUIDs in a database-agnostic manner.This implementation enhances the flexibility and usability of UUIDs, allowing developers to switch between different database providers without significant code alterations.
15-19: LGTM!The
load_dialect_implmethod correctly determines the appropriate SQL type based on the dialect. It ensures compatibility with PostgreSQL's native UUID type while providing a fallback to BINARY(16) for other databases.
21-35: Great job on theprocess_bind_parammethod!The method handles the conversion of various input types into a UUID object before binding it to the database. It also takes care of the formatting differences between PostgreSQL and other dialects, returning the UUID as a string for PostgreSQL and as bytes for others.
This ensures that the input value is properly converted and formatted based on the database dialect being used.
37-43: Excellent implementation of theprocess_result_valuemethod!The method correctly converts the database's output back into a UUID object, adapting the conversion based on the database dialect. It handles the differences in the output format between PostgreSQL (string) and other dialects (bytes).
This ensures that the retrieved value is always returned as a UUID object, regardless of the database being used.
pyproject.toml (3)
41-41: Major version update todltdependency and switch tosqlalchemyextra.The update from
dltversion^0.5.2to^1.0.0is a major version bump, which could introduce breaking changes. Ensure that the codebase has been updated to handle any such changes.The switch from
postgresextra tosqlalchemyextra aligns with the PR objective of adding SQLAlchemy as a DLT destination, which should provide more flexibility in database choices.Verify that database connection and query code throughout the codebase has been updated to work with the new SQLAlchemy setup. You can use the following command to search for relevant code:
rg --type python -A 5 -B 5 'dlt|postgres|engine|session|query'
41-41: Removal ofduckdbandduckdb-enginedependencies.The removal of
duckdbandduckdb-enginedependencies suggests that direct support for DuckDB is being removed from the project, which aligns with the shift towards SQLAlchemy as a more generic database interface.Verify that:
- All DuckDB-specific code has been properly updated or removed. You can use the following command to search for relevant code:
rg --type python -A 5 -B 5 'duckdb'
- If necessary, confirm that a database migration plan is in place to move data from DuckDB to a different database supported by SQLAlchemy.
80-80: Simplification of theneo4jextra and potential implications for Neo4j usage.The
neo4jextra has been simplified by removing thepy2neodependency, which may reflect a change in how Neo4j is being utilized within the project. It's possible that the project is moving away from usingpy2neoas the primary Neo4j client library.Verify that all Neo4j-related code has been updated to remove any usage of
py2neo. You can use the following command to search for relevant code:rg --type python -A 5 -B 5 'py2neo'cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py (6)
10-15: LGTM!The refactoring of the constructor simplifies the initialization process and improves the flexibility of configuring different database types. Extracting the SQLite database path is a nice enhancement. The changes are clear and maintainable.
Line range hint
17-24: No changes.The
get_async_sessionmethod remains unchanged and continues to create asynchronous sessions as needed.
Line range hint
26-32: No changes.The
get_sessionmethod remains unchanged and continues to create regular sessions as needed.
Line range hint
34-41: No changes.The
get_datasetsmethod remains unchanged and continues to retrieve datasets using an asynchronous session.
Line range hint
55-62: No changes.The
insert_datamethod remains unchanged and continues to insert data into the specified table using an asynchronous connection.
97-112: Improved SQLite database deletion.The changes to the
delete_databasemethod enhance the functionality for SQLite databases by directly removing the database file using theLocalStorageutility. This provides a cleaner approach for SQLite database deletion while preserving the previous logic for other database types. The modifications improve the handling of SQLite databases without introducing any apparent issues.cognee/api/v1/add/add.py (1)
83-92: Excellent refactoring of the database connection logic!The changes simplify the code by consolidating the database connection parameters into a single dictionary and leveraging SQLAlchemy's capabilities for managing various database connections. This aligns perfectly with the PR objective of integrating SQLAlchemy as a destination for data loading tasks.
The code is now more maintainable, less redundant, and provides enhanced flexibility in handling different database types.
cognee/api/client.py (1)
35-40: LGTM!The changes to the
lifespanfunction are logically correct and improve the application's initialization process:
- Moving the import statement for
create_db_and_tablesinside the function improves performance by avoiding unnecessary imports until needed.- Adding the
create_default_userfunction ensures that a default user is created when the application starts up.These enhancements ensure that the database and a default user are set up correctly during initialization.
| client.upload_points( | ||
| collection_name = collection_name, | ||
| points = points | ||
| ) | ||
| return result | ||
| except Exception as error: |
There was a problem hiding this comment.
Add back the await keyword to the client.upload_points call.
The client.upload_points call is an asynchronous operation, as indicated by the use of the AsyncQdrantClient. The await keyword is required to properly wait for the operation to complete before proceeding.
Apply this diff to fix the issue:
-client.upload_points(
+await client.upload_points(
collection_name = collection_name,
points = points
)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| client.upload_points( | |
| collection_name = collection_name, | |
| points = points | |
| ) | |
| return result | |
| except Exception as error: | |
| await client.upload_points( | |
| collection_name = collection_name, | |
| points = points | |
| ) | |
| except Exception as error: |
There was a problem hiding this comment.
Actionable comments posted: 5
Outside diff range and nitpick comments (3)
README.md (3)
22-24: Fix grammatical issues in the introduction.Please apply the following changes to improve the grammar and clarity of the introduction:
-cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow you ability to interconnect and retrieve past conversations, documents, audio transcriptions, while also reducing hallucinations, developer effort and cost. +cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow your the ability to interconnect and retrieve past conversations, documents, audio transcriptions, while also reducing hallucinations, developer effort, and cost.Tools
LanguageTool
[uncategorized] ~24-~24: “you” seems less likely than “your” (belonging to you).
Context: ...ct, Cognify, Load) pipelines that allow you ability to interconnect and retrieve pa...(AI_HYDRA_LEO_CP_YOU_YOUR)
[uncategorized] ~24-~24: Possible missing article found.
Context: ...Cognify, Load) pipelines that allow you ability to interconnect and retrieve past conve...(AI_HYDRA_LEO_MISSING_THE)
Line range hint
100-168: Fix grammatical issues and improve clarity.Please apply the following changes to improve the grammar and clarity of the explanation:
-cognee framework consists of tasks that can be grouped into pipelines. +The cognee framework consists of tasks that can be grouped into pipelines. -These tasks persist data into your memory store enabling you to search for relevant context of past conversations, documents, or any other data you have stored. +These tasks persist data into your memory store, enabling you to search for relevant context of past conversations, documents, or any other data you have stored. -We have a large number of tasks that can be used in your pipelines, and you can also create your own tasks to fit your business logic. +We have numerous tasks that can be used in your pipelines, and you can also create your own tasks to fit your business logic.Tools
LanguageTool
[style] ~124-~124: The phrase ‘feel free to’ is used quite frequently. Consider using a less frequent alternative to set your writing apart from others and make it sound more professional.
Context: ...vided just a snippet for reference, but feel free to check out the implementation in our rep...(FEEL_FREE_TO_STYLE_ME)
[style] ~168-~168: Specify a number, remove phrase, or simply use “many” or “numerous”
Context: ... return data_chunks ... ``` We have a large number of tasks that can be used in your pipeline...(LARGE_NUMBER_OF)
Markdownlint
126-126: null
Fenced code blocks should have a language specified(MD040, fenced-code-language)
174-174: null
Fenced code blocks should have a language specified(MD040, fenced-code-language)
Line range hint
168-237: Add a language specification for the fenced code block.To improve the formatting and syntax highlighting of the fenced code block, please add a language specification after the opening triple backticks. For example:
-``` +```bash Task( chunk_naive_llm_classifier, classification_model = cognee_config.classification_model, ) pipeline = run_tasks(tasks, documents)
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (2)
- README.md (7 hunks)
- notebooks/cognee_demo_1.5.ipynb (1 hunks)
Additional context used
LanguageTool
README.md
[uncategorized] ~24-~24: “you” seems less likely than “your” (belonging to you).
Context: ...ct, Cognify, Load) pipelines that allow you ability to interconnect and retrieve pa...(AI_HYDRA_LEO_CP_YOU_YOUR)
[uncategorized] ~24-~24: Possible missing article found.
Context: ...Cognify, Load) pipelines that allow you ability to interconnect and retrieve past conve...(AI_HYDRA_LEO_MISSING_THE)
[uncategorized] ~104-~104: Possible missing comma found.
Context: ...ese tasks persist data into your memory store enabling you to search for relevant con...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~168-~168: Specify a number, remove phrase, or simply use “many” or “numerous”
Context: ... return data_chunks ... ``` We have a large number of tasks that can be used in your pipeline...(LARGE_NUMBER_OF)
Markdownlint
README.md
174-174: null
Fenced code blocks should have a language specified(MD040, fenced-code-language)
Additional comments not posted (2)
README.md (2)
Line range hint
49-79: LGTM!The setup instructions are well-written and provide clear guidance for configuring the
cogneeframework. The code examples are properly formatted and easy to follow.
Line range hint
79-100: LGTM!The simple example provides a clear demonstration of the basic usage of the
cogneeframework. The code is well-formatted and includes helpful comments explaining each step.
notebooks/cognee_demo_1.5.ipynb
Outdated
| "source": [ | ||
| "from typing import Any, Dict, List, Optional, Union\n", | ||
| "from pydantic import BaseModel, Field\n", | ||
| "from uuid import uuid4\n", | ||
| "\n", | ||
| "class Node(BaseModel):\n", | ||
| " \"\"\"Node in a knowledge graph.\"\"\"\n", | ||
| " id: str = Field(default_factory=lambda: str(uuid4()))\n", | ||
| " name: Optional[str] = None\n", | ||
| " types: List[str] = Field(default_factory=list, description=\"Types or categories of the node (e.g., 'Person', 'Skill').\")\n", | ||
| " properties: Dict[str, Any] = Field(default_factory=dict, description=\"Properties associated with the node.\")\n", | ||
| "\n", | ||
| " def merge(self, other: 'Node'):\n", | ||
| " \"\"\"Merge another node into this one.\"\"\"\n", | ||
| " # Combine types\n", | ||
| " self.types = list(set(self.types + other.types))\n", | ||
| " # Combine properties\n", | ||
| " self.properties.update(other.properties)\n", | ||
| " # Update name if necessary\n", | ||
| " if not self.name and other.name:\n", | ||
| " self.name = other.name\n", | ||
| "\n", | ||
| "class Edge(BaseModel):\n", | ||
| " \"\"\"Edge in a knowledge graph.\"\"\"\n", | ||
| " id: str = Field(default_factory=lambda: str(uuid4()))\n", | ||
| " source_node_id: str\n", | ||
| " target_node_id: str\n", | ||
| " relationship_types: List[str] = Field(default_factory=list, description=\"Types of relationships (e.g., 'has_skill').\")\n", | ||
| " properties: Dict[str, Any] = Field(default_factory=dict, description=\"Properties associated with the edge.\")\n", | ||
| " confidence: Optional[float] = Field(None, description=\"Confidence score for the relationship.\")\n", | ||
| "\n", | ||
| "class KnowledgeGraph(BaseModel):\n", | ||
| " \"\"\"Knowledge graph.\"\"\"\n", | ||
| " nodes: Dict[str, Node] = Field(default_factory=dict)\n", | ||
| " edges: List[Edge] = Field(default_factory=list)\n", | ||
| "\n", | ||
| " def add_node(self, node: Node):\n", | ||
| " if node.id in self.nodes:\n", | ||
| " self.nodes[node.id].merge(node)\n", | ||
| " else:\n", | ||
| " self.nodes[node.id] = node\n", | ||
| "\n", | ||
| " def add_edge(self, edge: Edge):\n", | ||
| " self.edges.append(edge)" | ||
| ], | ||
| "id": "6f9b564de121713d" |
There was a problem hiding this comment.
Duplicate definition of classes may lead to confusion and errors
The Node, Edge, and KnowledgeGraph classes are defined in this cell. However, they are redefined later in the notebook with different structures (lines 414-438). Having multiple definitions of the same classes with differing implementations can cause unexpected behavior and maintainability issues.
Consider consolidating these class definitions into a single, consistent version to avoid conflicts and ensure consistent behavior throughout the notebook.
notebooks/cognee_demo_1.5.ipynb
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| "source": [ | ||
| "from enum import Enum, auto\n", | ||
| "from typing import Optional, List, Union, Dict, Any\n", | ||
| "from pydantic import BaseModel, Field\n", | ||
| "\n", | ||
| "class Node(BaseModel):\n", | ||
| " \"\"\"Node in a knowledge graph.\"\"\"\n", | ||
| " id: str\n", | ||
| " name: str\n", | ||
| " type: str\n", | ||
| " description: str\n", | ||
| " properties: Optional[Dict[str, Any]] = Field(None, description = \"A dictionary of properties associated with the node.\")\n", | ||
| "\n", | ||
| "class Edge(BaseModel):\n", | ||
| " \"\"\"Edge in a knowledge graph.\"\"\"\n", | ||
| " source_node_id: str\n", | ||
| " target_node_id: str\n", | ||
| " relationship_name: str\n", | ||
| " properties: Optional[Dict[str, Any]] = Field(None, description = \"A dictionary of properties associated with the edge.\")\n", | ||
| "\n", | ||
| "class KnowledgeGraph(BaseModel):\n", | ||
| " \"\"\"Knowledge graph.\"\"\"\n", | ||
| " nodes: List[Node] = Field(..., default_factory=list)\n", | ||
| " edges: List[Edge] = Field(..., default_factory=list)" | ||
| ], |
There was a problem hiding this comment.
Redefinition of classes conflicts with previous definitions
This cell redefines the Node, Edge, and KnowledgeGraph classes differently from their earlier definitions (lines 359-404). Overwriting class definitions can introduce bugs, especially if instances of the earlier classes are used elsewhere in the code.
Ensure consistent class definitions by removing duplicates or merging them into a unified definition. This will help maintain code clarity and prevent potential errors arising from conflicting class structures.
notebooks/cognee_demo_1.5.ipynb
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| "async def run_cognify_pipeline(dataset: Dataset, user: User = None):\n", | ||
| " data_documents: list[Data] = await get_dataset_data(dataset_id = dataset.id)\n", | ||
| "\n", | ||
| " try:\n", | ||
| "\n", | ||
| " root_node_id = None\n", | ||
| "\n", | ||
| " tasks = [\n", | ||
| " Task(check_permissions_on_documents, user = user, permissions = [\"write\"]),\n", | ||
| " Task(source_documents_to_chunks, parent_node_id = root_node_id), # Classify documents and save them as a nodes in graph db, extract text chunks based on the document type\n", | ||
| " Task(chunks_into_graph, graph_model = KnowledgeGraph, collection_name = \"entities\", task_config = { \"batch_size\": 10 }), # Generate knowledge graphs from the document chunks and attach it to chunk nodes\n", | ||
| " Task(chunk_update_check, collection_name = \"chunks\"), # Find all affected chunks, so we don't process unchanged chunks\n", | ||
| " Task(\n", | ||
| " save_chunks_to_store,\n", | ||
| " collection_name = \"chunks\",\n", | ||
| " ), \n", | ||
| " Task(chunk_remove_disconnected), # Remove the obsolete document chunks.\n", | ||
| " ]\n", | ||
| "\n", | ||
| " pipeline = run_tasks(tasks, data_documents)\n", | ||
| "\n", | ||
| " async for result in pipeline:\n", | ||
| " print(result)\n", | ||
| " except Exception as error:\n", | ||
| " raise error" | ||
| ], |
There was a problem hiding this comment.
Fix the ValueError in 'run_cognify_pipeline' due to incorrect unpacking
An error occurs when running the run_cognify_pipeline function:
ValueError: too many values to unpack (expected 2)
This suggests that source_documents_to_chunks expects an input that can be unpacked into two variables, but it's receiving a different data structure from run_tasks.
Review the data being passed to run_tasks. Ensure that data_documents is structured as a list of tuples (documents, parent_node_id) if source_documents_to_chunks expects two values. Alternatively, adjust the source_documents_to_chunks function to accept the data format provided by data_documents.
notebooks/cognee_demo_1.5.ipynb
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| "import cognee\n", | ||
| "from os import listdir, path\n", | ||
| "\n", | ||
| "data_path = path.abspath(\".data\")\n", | ||
| "\n", | ||
| "results = await cognee.add([job_1, job_2,job_3,job_4,job_5,job_position], \"example\")\n", | ||
| "\n", | ||
| "for result in results:\n", | ||
| " print(result)" | ||
| ], |
There was a problem hiding this comment.
Resolve SQLAlchemy 'cache_ok' warning for better performance
When executing await cognee.add([...]), the following warning is displayed:
TypeDecorator UUID() will not produce a cache key because the cache_ok attribute is not set to True...
To address this warning and improve performance, set the cache_ok attribute in your custom UUID TypeDecorator. Modify your UUID type definition as follows:
from sqlalchemy.types import TypeDecorator, CHAR
import uuid
class UUID(TypeDecorator):
impl = CHAR
cache_ok = True # Set this to True or False as appropriate
def load_dialect_impl(self, dialect):
# implementation details...
passSetting cache_ok informs SQLAlchemy about the safety of caching this type, which can enhance performance and suppress the warning.
notebooks/cognee_demo_1.5.ipynb
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| "source": "await cognify(\"example\")", | ||
| "id": "d9248a01352964e2", | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Error occurred while running async generator task: `source_documents_to_chunks`\n", | ||
| "too many values to unpack (expected 2)\n", | ||
| "Traceback (most recent call last):\n", | ||
| " File \"/Users/vasa/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py\", line 26, in run_tasks\n", | ||
| " async for partial_result in async_iterator:\n", | ||
| " File \"/Users/vasa/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/tasks/source_documents_to_chunks/source_documents_to_chunks.py\", line 9, in source_documents_to_chunks\n", | ||
| " documents, parent_node_id = documents\n", | ||
| " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", | ||
| "ValueError: too many values to unpack (expected 2)Error occurred while running coroutine task: `check_permissions_on_documents`\n", | ||
| "too many values to unpack (expected 2)\n", | ||
| "Traceback (most recent call last):\n", | ||
| " File \"/Users/vasa/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py\", line 86, in run_tasks\n", | ||
| " async for result in run_tasks(leftover_tasks, task_result):\n", | ||
| " File \"/Users/vasa/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py\", line 49, in run_tasks\n", | ||
| " raise error\n", | ||
| " File \"/Users/vasa/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py\", line 26, in run_tasks\n", | ||
| " async for partial_result in async_iterator:\n", | ||
| " File \"/Users/vasa/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/tasks/source_documents_to_chunks/source_documents_to_chunks.py\", line 9, in source_documents_to_chunks\n", | ||
| " documents, parent_node_id = documents\n", | ||
| " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", | ||
| "ValueError: too many values to unpack (expected 2)" | ||
| ] | ||
| }, | ||
| { | ||
| "ename": "ValueError", | ||
| "evalue": "too many values to unpack (expected 2)", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", | ||
| "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", | ||
| "Cell \u001B[0;32mIn[27], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[38;5;28;01mawait\u001B[39;00m cognify(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mexample\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", | ||
| "Cell \u001B[0;32mIn[26], line 30\u001B[0m, in \u001B[0;36mcognify\u001B[0;34m(datasets, user)\u001B[0m\n\u001B[1;32m 27\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m dataset_name \u001B[38;5;129;01min\u001B[39;00m existing_datasets_map:\n\u001B[1;32m 28\u001B[0m awaitables\u001B[38;5;241m.\u001B[39mappend(run_cognify_pipeline(dataset, user))\n\u001B[0;32m---> 30\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m asyncio\u001B[38;5;241m.\u001B[39mgather(\u001B[38;5;241m*\u001B[39mawaitables)\n", | ||
| "Cell \u001B[0;32mIn[25], line 24\u001B[0m, in \u001B[0;36mrun_cognify_pipeline\u001B[0;34m(dataset, user)\u001B[0m\n\u001B[1;32m 22\u001B[0m \u001B[38;5;28mprint\u001B[39m(result)\n\u001B[1;32m 23\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[0;32m---> 24\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m error\n", | ||
| "Cell \u001B[0;32mIn[25], line 21\u001B[0m, in \u001B[0;36mrun_cognify_pipeline\u001B[0;34m(dataset, user)\u001B[0m\n\u001B[1;32m 7\u001B[0m tasks \u001B[38;5;241m=\u001B[39m [\n\u001B[1;32m 8\u001B[0m Task(check_permissions_on_documents, user \u001B[38;5;241m=\u001B[39m user, permissions \u001B[38;5;241m=\u001B[39m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwrite\u001B[39m\u001B[38;5;124m\"\u001B[39m]),\n\u001B[1;32m 9\u001B[0m Task(source_documents_to_chunks, parent_node_id \u001B[38;5;241m=\u001B[39m root_node_id), \u001B[38;5;66;03m# Classify documents and save them as a nodes in graph db, extract text chunks based on the document type\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 16\u001B[0m Task(chunk_remove_disconnected), \u001B[38;5;66;03m# Remove the obsolete document chunks.\u001B[39;00m\n\u001B[1;32m 17\u001B[0m ]\n\u001B[1;32m 19\u001B[0m pipeline \u001B[38;5;241m=\u001B[39m run_tasks(tasks, data_documents)\n\u001B[0;32m---> 21\u001B[0m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m result \u001B[38;5;129;01min\u001B[39;00m pipeline:\n\u001B[1;32m 22\u001B[0m \u001B[38;5;28mprint\u001B[39m(result)\n\u001B[1;32m 23\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n", | ||
| "File \u001B[0;32m~/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py:97\u001B[0m, in \u001B[0;36mrun_tasks\u001B[0;34m(tasks, data)\u001B[0m\n\u001B[1;32m 90\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[1;32m 91\u001B[0m logger\u001B[38;5;241m.\u001B[39merror(\n\u001B[1;32m 92\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mError occurred while running coroutine task: `\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m`\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 93\u001B[0m running_task\u001B[38;5;241m.\u001B[39mexecutable\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m,\n\u001B[1;32m 94\u001B[0m \u001B[38;5;28mstr\u001B[39m(error),\n\u001B[1;32m 95\u001B[0m exc_info \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[1;32m 96\u001B[0m )\n\u001B[0;32m---> 97\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m error\n\u001B[1;32m 99\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m inspect\u001B[38;5;241m.\u001B[39misfunction(running_task\u001B[38;5;241m.\u001B[39mexecutable):\n\u001B[1;32m 100\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRunning function task: `\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m`\u001B[39m\u001B[38;5;124m\"\u001B[39m, running_task\u001B[38;5;241m.\u001B[39mexecutable\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m)\n", | ||
| "File \u001B[0;32m~/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py:86\u001B[0m, in \u001B[0;36mrun_tasks\u001B[0;34m(tasks, data)\u001B[0m\n\u001B[1;32m 83\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 84\u001B[0m task_result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m running_task\u001B[38;5;241m.\u001B[39mrun(\u001B[38;5;241m*\u001B[39margs)\n\u001B[0;32m---> 86\u001B[0m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m result \u001B[38;5;129;01min\u001B[39;00m run_tasks(leftover_tasks, task_result):\n\u001B[1;32m 87\u001B[0m \u001B[38;5;28;01myield\u001B[39;00m result\n\u001B[1;32m 89\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished coroutine task: `\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m`\u001B[39m\u001B[38;5;124m\"\u001B[39m, running_task\u001B[38;5;241m.\u001B[39mexecutable\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m)\n", | ||
| "File \u001B[0;32m~/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py:49\u001B[0m, in \u001B[0;36mrun_tasks\u001B[0;34m(tasks, data)\u001B[0m\n\u001B[1;32m 42\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[1;32m 43\u001B[0m logger\u001B[38;5;241m.\u001B[39merror(\n\u001B[1;32m 44\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mError occurred while running async generator task: `\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m`\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 45\u001B[0m running_task\u001B[38;5;241m.\u001B[39mexecutable\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m,\n\u001B[1;32m 46\u001B[0m \u001B[38;5;28mstr\u001B[39m(error),\n\u001B[1;32m 47\u001B[0m exc_info \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[1;32m 48\u001B[0m )\n\u001B[0;32m---> 49\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m error\n\u001B[1;32m 51\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m inspect\u001B[38;5;241m.\u001B[39misgeneratorfunction(running_task\u001B[38;5;241m.\u001B[39mexecutable):\n\u001B[1;32m 52\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRunning generator task: `\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m`\u001B[39m\u001B[38;5;124m\"\u001B[39m, running_task\u001B[38;5;241m.\u001B[39mexecutable\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m)\n", | ||
| "File \u001B[0;32m~/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/modules/pipelines/operations/run_tasks.py:26\u001B[0m, in \u001B[0;36mrun_tasks\u001B[0;34m(tasks, data)\u001B[0m\n\u001B[1;32m 22\u001B[0m results \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m 24\u001B[0m async_iterator \u001B[38;5;241m=\u001B[39m running_task\u001B[38;5;241m.\u001B[39mrun(\u001B[38;5;241m*\u001B[39margs)\n\u001B[0;32m---> 26\u001B[0m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m partial_result \u001B[38;5;129;01min\u001B[39;00m async_iterator:\n\u001B[1;32m 27\u001B[0m results\u001B[38;5;241m.\u001B[39mappend(partial_result)\n\u001B[1;32m 29\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(results) \u001B[38;5;241m==\u001B[39m next_task_batch_size:\n", | ||
| "File \u001B[0;32m~/Projects/cognee/.venv/lib/python3.11/site-packages/cognee/tasks/source_documents_to_chunks/source_documents_to_chunks.py:9\u001B[0m, in \u001B[0;36msource_documents_to_chunks\u001B[0;34m(documents, parent_node_id)\u001B[0m\n\u001B[1;32m 6\u001B[0m graph_engine \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m get_graph_engine()\n\u001B[1;32m 8\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m parent_node_id \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m----> 9\u001B[0m documents, parent_node_id \u001B[38;5;241m=\u001B[39m documents\n\u001B[1;32m 12\u001B[0m nodes \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m 13\u001B[0m edges \u001B[38;5;241m=\u001B[39m []\n", | ||
| "\u001B[0;31mValueError\u001B[0m: too many values to unpack (expected 2)" | ||
| ] | ||
| } | ||
| ], | ||
| "execution_count": 27 | ||
| }, |
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Handle the exception when calling 'await cognify("example")'
An exception is raised during the execution:
ValueError: too many values to unpack (expected 2)
This error originates from source_documents_to_chunks, indicating a mismatch in expected input.
Investigate the data flow into source_documents_to_chunks. Ensure that the tasks in run_tasks receive the correct input format. You may need to adjust how data is passed or modify the function to handle the provided input structure.
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Actionable comments posted: 2
Outside diff range and nitpick comments (3)
cognee/api/client.py (1)
399-402: Consider removing the commented-out code block.The commented-out code block related to data pruning and system cleanup suggests that these operations are no longer needed or desired during application startup.
If the pruning operations are no longer needed, consider removing the commented-out code to improve code readability and maintainability.
If the pruning operations might be needed in the future, consider moving them to a separate script or providing a configuration option to enable/disable them.
cognee/api/v1/cognify/cognify_v2.py (2)
115-116: ReplaceUsing
loggerto maintain consistent logging practices and better control over log levels.Apply this diff:
async for result in pipeline: - print(result) + logger.info(result)
122-127: Handle specific exceptions instead of using a broadexceptCatching all exceptions with
except Exception as errormay obscure the root cause of errors and make debugging difficult. Consider catching specific exceptions or re-raising them with additional context.Example adjustment:
try: ... -except Exception as error: +except SpecificException as error: await log_pipeline_status(dataset_id, "DATASET_PROCESSING_ERROR", { "dataset_name": dataset_name, "files": document_ids_str, }) raise error +except Exception as error: + # Optionally handle other exceptions or re-raise + raise errorReplace
SpecificExceptionwith the actual exceptions that might be raised within thetryblock.
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (11)
- cognee/api/client.py (2 hunks)
- cognee/api/v1/cognify/cognify_v2.py (1 hunks)
- cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py (1 hunks)
- cognee/modules/data/processing/document_types/AudioDocument.py (2 hunks)
- cognee/modules/data/processing/document_types/Document.py (1 hunks)
- cognee/modules/data/processing/document_types/ImageDocument.py (2 hunks)
- cognee/modules/data/processing/document_types/PdfDocument.py (2 hunks)
- cognee/modules/data/processing/document_types/TextDocument.py (2 hunks)
- cognee/tasks/chunking/chunk_by_word.py (1 hunks)
- cognee/tasks/classify_documents/classify_documents.py (1 hunks)
- notebooks/cognee_demo_1.5.ipynb (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- notebooks/cognee_demo_1.5.ipynb
Additional comments not posted (27)
cognee/modules/data/processing/document_types/Document.py (1)
8-8: LGTM! Ensure all references to the old attribute name are updated.The attribute renaming from
file_pathtoraw_data_locationimproves the semantic clarity of the attribute's purpose. The type hint remains unchanged, indicating that the expected value type is still a string.Please ensure that all references to the old attribute name
file_pathare updated toraw_data_locationthroughout the codebase to maintain consistency and avoid any potential issues.Run the following script to verify the attribute usage:
cognee/tasks/classify_documents/classify_documents.py (1)
6-9: LGTM!The changes to replace the
file_pathparameter withraw_data_locationfor the document class constructors are consistent and do not introduce any logical errors. The function should continue to work as expected with the updated parameter name.cognee/modules/data/processing/document_types/ImageDocument.py (4)
10-10: LGTM!The attribute name change from
file_pathtoraw_data_locationaligns with the new naming convention and data handling approach for image documents.
12-15: LGTM!The constructor parameter change from
file_pathtoraw_data_locationand the corresponding assignment to theself.raw_data_locationattribute align with the new naming convention and data handling approach for image documents.
19-19: LGTM!Using
self.raw_data_locationinstead ofself.file_pathin thetranscribe_imagemethod call ensures that the correct attribute is used to provide the location of the image data, aligning with the new naming convention and data handling approach.
32-32: LGTM!Returning
raw_data_locationinstead offile_pathin theto_dictmethod ensures that the correct attribute is included in the dictionary representation of theImageDocumentinstance, aligning with the new naming convention and data handling approach.cognee/modules/data/processing/document_types/PdfDocument.py (5)
9-9: LGTM!The attribute name change from
file_pathtoraw_data_locationis appropriate and reflects the change in how the document's data source is referenced.
11-11: LGTM!The constructor signature change to accept
raw_data_locationinstead offile_pathis appropriate and aligns with the attribute name change.
14-14: LGTM!The assignment of the
raw_data_locationparameter to theraw_data_locationattribute is appropriate and aligns with the attribute name change.
17-17: LGTM!The adjustment of the
readmethod to utilizeraw_data_locationwhen creating an instance ofPdfReaderis appropriate and ensures that the document is read from the new attribute.
35-35: LGTM!The update of the
to_dictmethod to returnraw_data_locationinstead offile_pathis appropriate and aligns the output with the new attribute naming.cognee/modules/data/processing/document_types/TextDocument.py (5)
8-8: LGTM!The renaming of the attribute from
file_pathtoraw_data_locationimproves clarity and better reflects its purpose.
10-10: LGTM!The constructor parameter has been appropriately renamed to
raw_data_location, maintaining consistency with the class attribute change.
13-13: LGTM!The constructor assignment has been correctly updated to use the renamed
raw_data_locationparameter.
17-17: LGTM!The
readmethod has been appropriately updated to useraw_data_locationinstead offile_pathwhen opening the file, maintaining consistency with the class attribute change.
37-37: LGTM!The
to_dictmethod has been appropriately updated to useraw_data_locationinstead offile_pathin the dictionary it constructs, maintaining consistency with the class attribute change.cognee/modules/data/processing/document_types/AudioDocument.py (6)
9-9: LGTM!The change in attribute name from
file_pathtoraw_data_locationis more descriptive and accurately reflects the purpose of the attribute.
12-12: LGTM!The constructor's signature has been updated to accept
raw_data_locationinstead offile_path, which is consistent with the attribute name change.
15-15: LGTM!The constructor now assigns the
raw_data_locationto the corresponding attribute, which is consistent with the attribute name change.
20-20: LGTM!The
readmethod now usesraw_data_locationinstead offile_pathwhen calling thecreate_transcriptmethod, which is consistent with the attribute name change.
33-33: LGTM!The
to_dictmethod now returnsraw_data_locationinstead offile_pathin the resulting dictionary, which is consistent with the attribute name change.
Line range hint
1-35: Overall, the changes in the file are well-structured and maintain consistency.The modifications improve the clarity and descriptiveness of the code by shifting from using a file path to using the location of the raw audio data. The changes are reflected consistently throughout the file, including in the attribute definition, constructor, and relevant methods.
Great job on making these improvements!
cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py (1)
7-7: Verbosity setting change looks good, but consider the trade-offs.Setting
litellm.set_verbosetoFalsewill reduce the amount of logging output from thelitellmlibrary. This can help minimize noise in the logs and potentially improve performance by reducing logging overhead.However, keep in mind that reduced verbosity can also make it harder to debug issues related to the
litellmlibrary, as less information will be available in the logs.Consider adjusting the verbosity setting based on the specific needs of the environment (e.g., development vs. production) and the desired balance between log detail and performance.
cognee/tasks/chunking/chunk_by_word.py (1)
27-27: LGTM!The change adds a necessary check to ensure that
next_characteris notNonebefore evaluating whether it is uppercase. This prevents potentialAttributeErrors that could arise from attempting to call theisupper()method on aNonevalue.The updated condition improves the robustness of the code by handling the case where
next_characterisNone.cognee/api/client.py (2)
35-36: Improved encapsulation by moving import statements inside the function.Moving the import statements for
create_db_and_tablesandget_default_userinside thelifespanfunction improves encapsulation and avoids potential circular import issues.
40-40: Ensured default user creation during application startup.Adding the
get_default_userfunction call after creating the database tables ensures that a default user is created during application startup, which can be useful for initial setup and testing.cognee/api/v1/cognify/cognify_v2.py (1)
59-59: Accepted change to pass theuserparameter torun_cognify_pipelineThe update ensures that
run_cognify_pipelinereceives theuserparameter, allowing for user-specific processing and permissions checks.
| tasks = [ | ||
| Task(classify_documents), | ||
| Task(check_permissions_on_documents, user = user, permissions = ["write"]), | ||
| Task(infer_data_ontology, root_node_id = root_node_id, ontology_model = KnowledgeGraph), |
There was a problem hiding this comment.
Reorder tasks to enforce permissions before processing
The check_permissions_on_documents task should be executed before any other processing tasks to prevent unauthorized access. Currently, classify_documents runs before permissions are checked, which might lead to processing without proper authorization.
Apply this diff to reorder the tasks:
tasks = [
- Task(classify_documents),
Task(check_permissions_on_documents, user=user, permissions=["write"]),
+ Task(classify_documents),
Task(infer_data_ontology, root_node_id=root_node_id, ontology_model=KnowledgeGraph),
...
]This change ensures that permissions are verified before any data manipulation occurs.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| tasks = [ | |
| Task(classify_documents), | |
| Task(check_permissions_on_documents, user = user, permissions = ["write"]), | |
| Task(infer_data_ontology, root_node_id = root_node_id, ontology_model = KnowledgeGraph), | |
| tasks = [ | |
| Task(check_permissions_on_documents, user=user, permissions=["write"]), | |
| Task(classify_documents), | |
| Task(infer_data_ontology, root_node_id=root_node_id, ontology_model=KnowledgeGraph), |
| async def run_cognify_pipeline(dataset: Dataset, user: User): | ||
| data_documents: list[Data] = await get_dataset_data(dataset_id = dataset.id) |
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Restrict data access by including the user in get_dataset_data
Currently, get_dataset_data is called without referencing the user, which may lead to unauthorized data access. To ensure that only data accessible by the user is retrieved, consider modifying get_dataset_data to accept the user parameter and enforce access checks.
Apply this change to include the user:
async def run_cognify_pipeline(dataset: Dataset, user: User):
- data_documents: list[Data] = await get_dataset_data(dataset_id=dataset.id)
+ data_documents: list[Data] = await get_dataset_data(dataset_id=dataset.id, user_id=user.id)Ensure that the get_dataset_data function is updated accordingly to handle the user_id parameter and perform the necessary access validations.
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 run_cognify_pipeline(dataset: Dataset, user: User): | |
| data_documents: list[Data] = await get_dataset_data(dataset_id = dataset.id) | |
| async def run_cognify_pipeline(dataset: Dataset, user: User): | |
| data_documents: list[Data] = await get_dataset_data(dataset_id=dataset.id, user_id=user.id) |
After this change we can easily switch database providers. Default database is sqlite now.
Summary by CodeRabbit
New Features
cogneeframework, including clearer usage instructions and additional sections for getting started.cognifypipeline.Bug Fixes
Refactor
raw_data_locationinstead offile_pathfor better data handling consistency.