falkor-integration #2
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WalkthroughThe pull request introduces support for FalkorDB as a new graph store provider in the Mem0 project. This change involves updating documentation, configuration examples, and the underlying code to accommodate the new graph database. The modifications span across multiple files, including README, documentation, and core implementation files. The changes enable users to use FalkorDB as an alternative to Neo4j for graph memory functionality, with updates to installation instructions, configuration parameters, and query handling mechanisms. Changes
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Actionable comments posted: 0
Outside diff range, codebase verification and nitpick comments (2)
cookbooks/mem0_graph_memory.py (1)
1-34: LGTM, but consider adding error handling and input validation.The code in this file is well-structured and demonstrates the usage of the
Memoryclass from themem0package. The configuration dictionary is properly formatted and includes the necessary settings for the LLM and graph store providers.However, consider adding error handling and input validation to make the script more robust. For example:
- Validate the environment variables before using them in the configuration dictionary.
- Handle potential exceptions that may occur when creating the
Memoryinstance or adding data to the memory.docs/open-source/graph_memory/overview.mdx (1)
29-29: LGTM, but fix the grammar issue.The documentation changes are approved as they provide clearer guidance on the setup process for multiple graph store providers.
However, please fix the following grammar issue:
- The word "setup" is a noun. The verb is spelled with a space. Change "setup" to "set up".
Tools
LanguageTool
[grammar] ~29-~29: The word “setup” is a noun. The verb is spelled with a space.
Context: ...o4j as a graph store providers. You can setup FalkorDB o...(NOUN_VERB_CONFUSION)
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Files ignored due to path filters (1)
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Files selected for processing (8)
- cookbooks/mem0_graph_memory.py (1 hunks)
- docs/open-source/graph_memory/features.mdx (1 hunks)
- docs/open-source/graph_memory/overview.mdx (3 hunks)
- docs/open-source/quickstart.mdx (1 hunks)
- mem0/graphs/configs.py (2 hunks)
- mem0/memory/graph_memory.py (7 hunks)
- mem0/utils/factory.py (1 hunks)
- pyproject.toml (1 hunks)
Additional context used
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docs/open-source/graph_memory/overview.mdx
[grammar] ~29-~29: The word “setup” is a noun. The verb is spelled with a space.
Context: ...o4j as a graph store providers. You can setup FalkorDB o...(NOUN_VERB_CONFUSION)
Additional comments not posted (10)
pyproject.toml (1)
28-28: LGTM!The addition of the
falkordbdependency with version^1.0.8is approved.docs/open-source/graph_memory/features.mdx (1)
22-28: Verify the impact of the graph store provider change and the migration process.The change in the graph store provider from "neo4j" to "falkordb" is a significant modification. It's important to ensure that this change is thoroughly tested and that all necessary migrations are performed correctly.
Please verify the following:
- All code that interacts with the graph store has been updated to work with the new provider (FalkorDB).
- The data from the previous graph store (Neo4j) has been properly migrated to the new graph store (FalkorDB).
- The placeholders in the configuration (
"host","username","password","port") have been replaced with the actual values required for connecting to the FalkorDB instance.You can use the following script to search for code that may need to be updated:
Additionally, ensure that the documentation is updated to reflect the new graph store provider and any changes in the configuration or usage.
mem0/graphs/configs.py (2)
26-46: LGTM!The
FalkorDBConfigclass is correctly implemented with appropriate fields and a model validator to ensure all required fields are provided.
51-52: LGTM!The changes to the
GraphStoreConfigclass correctly reflect the shift in the supported database providers and allow for dynamic configuration based on the selected provider.Also applies to: 54-54, 72-73
mem0/utils/factory.py (1)
75-90: LGTM!The
GraphFactoryclass is correctly implemented and enhances the factory's functionality by allowing it to handle graph instances based on a specified provider name.docs/open-source/quickstart.mdx (1)
66-72: Verify the correctness of the new configuration parameters for FalkorDB.The changes indicate a transition from Neo4j to FalkorDB as the graph database provider. Ensure that the updated configuration parameters, such as "Database", "host", "username", "password", and "port", are correct and align with the FalkorDB setup.
Run the following script to verify the FalkorDB connection:
Verification successful
Configuration parameters for FalkorDB are correct and aligned with the setup.
The configuration parameters in the
quickstart.mdxfile match those defined in theFalkorDBConfigclass and are consistent with the examples provided in the codebase. Users are expected to replace the placeholders with actual values. No issues found with the configuration parameters.
mem0/graphs/configs.py: DefinesFalkorDBConfigwith matching parameters.cookbooks/mem0_graph_memory.py: Example of setting parameters using environment variables.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the FalkorDB connection using the updated configuration parameters. # Test: Check if the FalkorDB connection is successful using the provided configuration. # Expect: A successful connection to the FalkorDB instance. python -c " from falkordb import FalkorDB config = { 'Database': 'falkordb', 'host': '---', 'username': '---', 'password': '---', 'port': '---' } db = FalkorDB(**config) print('FalkorDB connection successful!' if db else 'FalkorDB connection failed!') "Length of output: 422
Script:
#!/bin/bash # Search for references to FalkorDB in the codebase to verify configuration parameters. rg -i 'falkordb' -A 5Length of output: 6779
mem0/memory/graph_memory.py (4)
18-18: LGTM!The import statement for the
GraphFactoryclass is correct and necessary for the changes made in the constructor of theMemoryGraphclass.
25-27: LGTM! The factory pattern enhances flexibility.The change in the constructor of the
MemoryGraphclass to useGraphFactory.createinstead of directly instantiatingNeo4jGraphis a good improvement. It enhances the flexibility of the graph initialization by allowing the graph instance to be created based on the provider specified in the configuration.Using the factory pattern here promotes loose coupling and makes it easier to switch or add new graph providers in the future without modifying the
MemoryGraphclass.
142-142: LGTM! The abstraction improves encapsulation.The change to call the new
graph_querymethod instead of directly callingself.graph.queryis a good improvement. It is consistent with the introduction of thegraph_querymethod, which encapsulates the logic for executing Cypher queries on the graph database.This abstraction improves encapsulation by hiding the implementation details of the query execution and provides a centralized place to handle any provider-specific logic, such as switching the graph context for FalkorDB.
333-348: LGTM! The new method improves encapsulation and handles provider-specific logic.The introduction of the
graph_querymethod is a good addition to theMemoryGraphclass. It encapsulates the logic for executing Cypher queries on the graph database and provides a centralized place to handle provider-specific logic.The method correctly switches the graph context for FalkorDB based on the
user_id, which is necessary to support multi-tenancy. This abstraction improves the maintainability and readability of the code by hiding the implementation details of the query execution.The method signature and docstring are clear and informative, making it easy to understand its purpose and usage.
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Files selected for processing (2)
- README.md (2 hunks)
- cookbooks/mem0_graph_memory.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- cookbooks/mem0_graph_memory.py
Additional context used
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README.md
[grammar] ~160-~160: The word “setup” is a noun. The verb is spelled with a space.
Context: ...o4j as a graph store providers. You can setup FalkorDB o...(NOUN_VERB_CONFUSION)
Additional comments not posted (2)
README.md (2)
169-169: LGTM!The change to set the graph store provider to "falkordb" is consistent with the addition of FalkorDB support.
171-174: LGTM!The addition of placeholders for the host, username, password, and port in the configuration example enhances clarity for users setting up their graph memory.
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Files selected for processing (6)
- README.md (2 hunks)
- cookbooks/mem0_graph_memory.py (1 hunks)
- docs/open-source/graph_memory/overview.mdx (3 hunks)
- mem0/graphs/configs.py (3 hunks)
- mem0/graphs/utils.py (1 hunks)
- mem0/memory/graph_memory.py (7 hunks)
Files skipped from review as they are similar to previous changes (4)
- README.md
- cookbooks/mem0_graph_memory.py
- mem0/graphs/utils.py
- mem0/memory/graph_memory.py
Additional context used
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mem0/graphs/configs.py
75-75: Comparison to
Noneshould becond is NoneReplace with
cond is None(E711)
Additional comments not posted (5)
mem0/graphs/configs.py (2)
26-44: LGTM!The
FalkorDBConfigclass is well-defined and includes the necessary fields for configuring FalkorDB. Thecheck_host_port_or_pathmethod provides a good validation check to ensure that the required fields are provided.
49-52: Great work on enhancing the flexibility of the configuration system!The changes to the
configandproviderfields to support both FalkorDB and Neo4j significantly improve the adaptability of the configuration system. Well done!docs/open-source/graph_memory/overview.mdx (3)
29-29: LGTM!The documentation update clearly communicates the addition of FalkorDB support alongside Neo4j, providing users with accurate information about the available graph store providers.
47-53: LGTM!The configuration example has been updated to accurately reflect the usage of FalkorDB as the provider, with the appropriate configuration parameters. This change aligns with the documentation update and provides a clear guide for users to set up Graph Memory with FalkorDB.
75-81: LGTM!The advanced configuration example has been updated to accurately reflect the usage of FalkorDB as the provider, with the appropriate configuration parameters. This change aligns with the documentation update and provides a clear guide for users to set up Graph Memory with FalkorDB in an advanced setup with custom LLM.
mem0/graphs/configs.py
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| elif provider == "falkordb": | ||
| config = v.model_dump() | ||
| # In case the user try to use diffrent database name | ||
| config["database"] = "_default_" | ||
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|
||
| if config.get("host") == "localhost" or config.get("host") == None: | ||
| config.pop("username", None) | ||
| config.pop("password", None) | ||
|
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| return FalkorDBConfig(**config) |
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Fix the comparison to None.
The changes to the validate_config method to handle the new FalkorDBConfig look good. However, please address the following static analysis hint:
75-75: Comparison to
Noneshould becond is None
Replace config.get("host") == None with config.get("host") is None to improve code quality.
Apply this diff to fix the comparison:
-if config.get("host") == "localhost" or config.get("host") == None:
+if config.get("host") == "localhost" or config.get("host") is None:Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| elif provider == "falkordb": | |
| config = v.model_dump() | |
| # In case the user try to use diffrent database name | |
| config["database"] = "_default_" | |
| if config.get("host") == "localhost" or config.get("host") == None: | |
| config.pop("username", None) | |
| config.pop("password", None) | |
| return FalkorDBConfig(**config) | |
| elif provider == "falkordb": | |
| config = v.model_dump() | |
| # In case the user try to use diffrent database name | |
| config["database"] = "_default_" | |
| if config.get("host") == "localhost" or config.get("host") is None: | |
| config.pop("username", None) | |
| config.pop("password", None) | |
| return FalkorDBConfig(**config) |
Tools
Ruff
75-75: Comparison to
Noneshould becond is NoneReplace with
cond is None(E711)
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Actionable comments posted: 2
Outside diff range and nitpick comments (10)
docs/api-reference/organization/update-org-member.mdx (1)
1-4: LGTM!The documentation file provides a clear and concise description of the API endpoint for updating a member's information within an organization. The metadata is properly formatted and includes the necessary details.
A few suggestions for further improvement:
- Consider adding a brief description or summary of the API endpoint below the YAML front matter to provide more context for the reader.
- Include details about the request payload format and the expected response format to help developers understand how to use the API endpoint effectively.
- Provide examples of how to make the API request using popular programming languages or tools like cURL.
Overall, the documentation is a good starting point and can be enhanced with more details and examples to make it even more helpful for developers.
docs/api-reference/project/create-project.mdx (1)
1-4: Consider expanding the documentation with more details.To make this documentation more useful for developers, consider adding sections to describe:
- The purpose and functionality of the endpoint
- The request format and parameters (e.g., what goes in the request body)
- The response format and possible status codes
- Example request and response payloads
- Any authentication or authorization requirements
- Error handling and common error scenarios
This additional information will help developers better understand how to use this endpoint and integrate it into their applications.
docs/api-reference/project/delete-project.mdx (1)
1-4: LGTM!The new documentation file for the "Delete Project" API endpoint looks good. The metadata fields are correctly specified and match the expected format.
Some additional suggestions for future improvements:
- Consider adding a brief description of the endpoint's functionality and expected behavior.
- Include details on the request parameters, request body (if applicable), and possible response codes.
- Provide examples of how to use the endpoint, including sample request and response payloads.
These additions will make the documentation more comprehensive and helpful for users of the API.
docs/api-reference/project/update-project-member.mdx (1)
1-4: LGTM! The API reference documentation is clear and well-structured.The new file introduces a comprehensive API reference for updating project members within an organization. It clearly specifies the HTTP method, URL path, and resource, making it easy for developers to understand and integrate the functionality into their applications.
A few suggestions for further improvement:
- Consider adding a brief description or summary of the API endpoint's purpose and functionality.
- Provide examples of the request payload and response format to help developers understand the expected input and output.
- Include any authentication or authorization requirements for accessing the API endpoint.
- Specify any rate limits or usage restrictions associated with the API.
Overall, the documentation is a valuable addition to the project and will facilitate better project management practices.
docs/api-reference/project/delete-project-member.mdx (1)
1-4: Enhance the documentation with more details.The documentation file provides a good starting point by specifying the title and OpenAPI metadata for the endpoint. However, consider adding more details to improve the usability and completeness of the documentation:
- Add a brief description of the endpoint's functionality and purpose.
- Include request and response examples to illustrate the usage of the endpoint.
- Provide details on the required and optional parameters, including their data types and descriptions.
- Document the possible error codes and their meanings.
- Consider adding any additional notes or caveats related to the usage of the endpoint.
By incorporating these details, the documentation will be more comprehensive and helpful for developers consuming the API.
docs/components/vectordbs/dbs/milvus.mdx (1)
25-35: Fix the grammar in the section introduction.The config section provides a well-structured and informative table of configuration parameters for setting up the Milvus database. It includes parameter names, descriptions, and default values, which will be helpful for users.
However, there is a minor grammar issue in the section introduction that should be addressed.
Apply this diff to fix the grammar:
-Here's the parameters available for configuring Milvus Database: +Here are the parameters available for configuring Milvus Database:Tools
LanguageTool
[grammar] ~26-~26: Did you mean “Here are the parameters”?
Context: ...category": "hobbies"}) ``` ### Config Here's the parameters available for configuring Milvus Databa...(THERE_S_MANY)
mem0/embeddings/vertexai.py (2)
10-10: Add a class docstring.A class docstring describing the purpose and functionality of the
VertexAIclass would improve the code readability and maintainability.
28-40: LGTM!The
embedmethod is well-implemented and follows best practices:
- The method signature is clear and follows the naming convention.
- The method docstring provides a clear description of the method's purpose, arguments, and return value.
- The method implementation is concise and easy to understand.
Potential enhancement: Handle multiple input texts.
Consider enhancing the method to accept a list of input texts and return a list of embedding vectors. This would provide more flexibility and allow for batch processing of embeddings.
CONTRIBUTING.md (1)
9-14: Minor grammar suggestionThe contribution steps are well-structured and cover the essential aspects of the process. Just a minor grammar suggestion:
-2. Do the changes on your fork with dedicated feature branch `feature/f1` +2. Make the changes on your fork with a dedicated feature branch `feature/f1`"Make" is the appropriate verb to use with "changes", and adding "a" before "dedicated feature branch" improves readability.
Tools
LanguageTool
[grammar] ~10-~10: Collocation: With the noun “changes”, you should use the verb “make”.
Context: ...: 1. Fork and clone this repository 2. Do the changes on your fork with dedicated...(MAKE_A_CHANGE)
[uncategorized] ~10-~10: You might be missing the article “a” here.
Context: ...ory 2. Do the changes on your fork with dedicated feature branchfeature/f13. If you m...(AI_EN_LECTOR_MISSING_DETERMINER_A)
docs/open-source/graph_memory/overview.mdx (1)
32-32: Minor rephrasing suggestion to improve readability.The note provides important information for users setting up Neo4j locally. However, consider rephrasing the sentence to avoid repeating the phrase "you need to" in nearby sentences. For example:
-<Note>If you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.com/labs/apoc/4.1/installation/).</Note> +<Note>When using Neo4j locally, be sure to install [APOC plugins](https://neo4j.com/labs/apoc/4.1/installation/).</Note>Tools
LanguageTool
[style] ~32-~32: You have already used this phrasing in nearby sentences. Consider replacing it to add variety to your writing.
Context: ...f you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.co...(REP_NEED_TO_VB)
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Files selected for processing (82)
- CONTRIBUTING.md (1 hunks)
- Makefile (1 hunks)
- README.md (4 hunks)
- cookbooks/add_memory_using_qdrant_cloud.py (1 hunks)
- cookbooks/mem0-multion.ipynb (1 hunks)
- cookbooks/multion_travel_agent.ipynb (1 hunks)
- docs/api-reference/organization/add-org-member.mdx (1 hunks)
- docs/api-reference/organization/create-org.mdx (1 hunks)
- docs/api-reference/organization/delete-org-member.mdx (1 hunks)
- docs/api-reference/organization/delete-org.mdx (1 hunks)
- docs/api-reference/organization/get-org-members.mdx (1 hunks)
- docs/api-reference/organization/get-org.mdx (1 hunks)
- docs/api-reference/organization/get-orgs.mdx (1 hunks)
- docs/api-reference/organization/update-org-member.mdx (1 hunks)
- docs/api-reference/overview.mdx (1 hunks)
- docs/api-reference/project/add-project-member.mdx (1 hunks)
- docs/api-reference/project/create-project.mdx (1 hunks)
- docs/api-reference/project/delete-project-member.mdx (1 hunks)
- docs/api-reference/project/delete-project.mdx (1 hunks)
- docs/api-reference/project/get-project-members.mdx (1 hunks)
- docs/api-reference/project/get-project.mdx (1 hunks)
- docs/api-reference/project/get-projects.mdx (1 hunks)
- docs/api-reference/project/update-project-member.mdx (1 hunks)
- docs/components/embedders/config.mdx (1 hunks)
- docs/components/embedders/models/vertexai.mdx (1 hunks)
- docs/components/embedders/overview.mdx (1 hunks)
- docs/components/vectordbs/config.mdx (1 hunks)
- docs/components/vectordbs/dbs/milvus.mdx (1 hunks)
- docs/features/custom-prompts.mdx (1 hunks)
- docs/mint.json (3 hunks)
- docs/open-source/graph_memory/overview.mdx (4 hunks)
- docs/openapi.json (8 hunks)
- docs/platform/quickstart.mdx (8 hunks)
- mem0/client/main.py (6 hunks)
- mem0/configs/base.py (3 hunks)
- mem0/configs/embeddings/base.py (1 hunks)
- mem0/configs/prompts.py (6 hunks)
- mem0/configs/vector_stores/chroma.py (1 hunks)
- mem0/configs/vector_stores/milvus.py (1 hunks)
- mem0/configs/vector_stores/pgvector.py (1 hunks)
- mem0/configs/vector_stores/qdrant.py (2 hunks)
- mem0/embeddings/azure_openai.py (2 hunks)
- mem0/embeddings/configs.py (1 hunks)
- mem0/embeddings/ollama.py (1 hunks)
- mem0/embeddings/openai.py (1 hunks)
- mem0/embeddings/vertexai.py (1 hunks)
- mem0/graphs/configs.py (2 hunks)
- mem0/graphs/tools.py (10 hunks)
- mem0/graphs/utils.py (1 hunks)
- mem0/llms/anthropic.py (3 hunks)
- mem0/llms/aws_bedrock.py (3 hunks)
- mem0/llms/azure_openai.py (2 hunks)
- mem0/llms/azure_openai_structured.py (2 hunks)
- mem0/llms/configs.py (2 hunks)
- mem0/llms/litellm.py (2 hunks)
- mem0/llms/openai.py (2 hunks)
- mem0/llms/openai_structured.py (3 hunks)
- mem0/llms/together.py (2 hunks)
- mem0/llms/utils/tools.py (3 hunks)
- mem0/memory/graph_memory.py (9 hunks)
- mem0/memory/main.py (18 hunks)
- mem0/memory/storage.py (2 hunks)
- mem0/memory/telemetry.py (4 hunks)
- mem0/memory/utils.py (1 hunks)
- mem0/proxy/main.py (4 hunks)
- mem0/utils/factory.py (3 hunks)
- mem0/vector_stores/chroma.py (4 hunks)
- mem0/vector_stores/configs.py (1 hunks)
- mem0/vector_stores/milvus.py (1 hunks)
- mem0/vector_stores/pgvector.py (10 hunks)
- mem0/vector_stores/qdrant.py (4 hunks)
- pyproject.toml (2 hunks)
- tests/embeddings/test_azure_openai_embeddings.py (1 hunks)
- tests/embeddings/test_huggingface_embeddings.py (1 hunks)
- tests/embeddings/test_ollama_embeddings.py (1 hunks)
- tests/embeddings/test_openai_embeddings.py (1 hunks)
- tests/llms/test_azure_openai.py (5 hunks)
- tests/llms/test_groq.py (5 hunks)
- tests/llms/test_litellm.py (6 hunks)
- tests/llms/test_ollama.py (1 hunks)
- tests/llms/test_openai.py (4 hunks)
- tests/llms/test_together.py (5 hunks)
Files not processed due to max files limit (6)
- tests/test_main.py
- tests/test_memory.py
- tests/test_proxy.py
- tests/test_telemetry.py
- tests/vector_stores/test_chroma.py
- tests/vector_stores/test_qdrant.py
Files skipped from review due to trivial changes (36)
- cookbooks/add_memory_using_qdrant_cloud.py
- cookbooks/mem0-multion.ipynb
- cookbooks/multion_travel_agent.ipynb
- docs/api-reference/organization/get-org.mdx
- docs/api-reference/organization/get-orgs.mdx
- docs/api-reference/project/get-project.mdx
- docs/api-reference/project/get-projects.mdx
- mem0/configs/embeddings/base.py
- mem0/configs/prompts.py
- mem0/configs/vector_stores/pgvector.py
- mem0/configs/vector_stores/qdrant.py
- mem0/embeddings/azure_openai.py
- mem0/embeddings/ollama.py
- mem0/embeddings/openai.py
- mem0/graphs/tools.py
- mem0/llms/anthropic.py
- mem0/llms/aws_bedrock.py
- mem0/llms/azure_openai.py
- mem0/llms/azure_openai_structured.py
- mem0/llms/configs.py
- mem0/llms/litellm.py
- mem0/llms/openai_structured.py
- mem0/llms/together.py
- mem0/memory/storage.py
- mem0/memory/telemetry.py
- mem0/memory/utils.py
- mem0/proxy/main.py
- mem0/vector_stores/chroma.py
- mem0/vector_stores/pgvector.py
- mem0/vector_stores/qdrant.py
- tests/llms/test_azure_openai.py
- tests/llms/test_groq.py
- tests/llms/test_litellm.py
- tests/llms/test_ollama.py
- tests/llms/test_openai.py
- tests/llms/test_together.py
Files skipped from review as they are similar to previous changes (3)
- README.md
- mem0/memory/graph_memory.py
- pyproject.toml
Additional context used
LanguageTool
docs/components/vectordbs/dbs/milvus.mdx
[grammar] ~26-~26: Did you mean “Here are the parameters”?
Context: ...category": "hobbies"}) ``` ### Config Here's the parameters available for configuring Milvus Databa...(THERE_S_MANY)
CONTRIBUTING.md
[grammar] ~10-~10: Collocation: With the noun “changes”, you should use the verb “make”.
Context: ...: 1. Fork and clone this repository 2. Do the changes on your fork with dedicated...(MAKE_A_CHANGE)
[uncategorized] ~10-~10: You might be missing the article “a” here.
Context: ...ory 2. Do the changes on your fork with dedicated feature branchfeature/f13. If you m...(AI_EN_LECTOR_MISSING_DETERMINER_A)
docs/features/custom-prompts.mdx
[grammar] ~96-~96: Possible subject-verb agreement error detected.
Context: ...memory of a user liking to go on hikes. This add message is not specific to the use-case...(THIS_THAT_AGR)
docs/open-source/graph_memory/overview.mdx
[style] ~32-~32: You have already used this phrasing in nearby sentences. Consider replacing it to add variety to your writing.
Context: ...f you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.co...(REP_NEED_TO_VB)
Additional comments not posted (129)
docs/api-reference/organization/create-org.mdx (1)
1-4: LGTM!The new documentation file for the "Create Organization" API endpoint is well-structured and follows the appropriate naming conventions. The title accurately reflects the purpose of the endpoint, and the OpenAPI path follows a RESTful convention for creating a new organization resource.
The addition of this endpoint enhances the API's functionality and integration capabilities by allowing clients to programmatically create organizations.
docs/api-reference/organization/add-org-member.mdx (1)
1-4: New API endpoint documentation added.The documentation for the new API endpoint to add a member to an organization has been added, which is a good practice.
However, please ensure that:
- The actual implementation of the endpoint aligns with the documentation.
- The endpoint has been thoroughly tested for functionality and edge cases.
- The endpoint adheres to the project's security and authorization standards.
- The endpoint is properly integrated with the existing codebase and does not introduce any breaking changes.
docs/api-reference/organization/get-org-members.mdx (1)
1-4: LGTM!The documentation file looks good:
- The title accurately describes the endpoint functionality.
- The OpenAPI metadata correctly specifies the HTTP method (GET) and path for the endpoint.
- The
org_idpath parameter is used consistently in the endpoint path.- The file name and path match the endpoint path.
The AI summary provides helpful context about the purpose and functionality of the endpoint, which aligns with the documentation.
docs/api-reference/organization/delete-org.mdx (1)
1-4: LGTM!The documentation file follows the appropriate structure and naming conventions for the API reference. The title and OpenAPI path accurately reflect the purpose and RESTful design of the Delete Organization endpoint.
docs/api-reference/organization/delete-org-member.mdx (1)
1-4: LGTM!The documentation for the "Delete Member" API endpoint is clear and accurate. The title and OpenAPI specification provide the necessary information for developers to understand and use the endpoint.
- The title "Delete Member" accurately describes the purpose of the endpoint.
- The OpenAPI specification correctly defines the HTTP method as DELETE and the endpoint path, including the
{org_id}parameter.The documentation follows the MDX format, which is a suitable choice for combining Markdown and JSX in the documentation files.
docs/api-reference/project/create-project.mdx (1)
1-4: Great start on the new API documentation file!The frontmatter looks good, with the correct title and OpenAPI specification for the endpoint to create a project within an organization.
docs/api-reference/project/add-project-member.mdx (1)
1-4: LGTM!The new API endpoint for adding a project member is well-defined and documented:
- The
titlefrontmatter accurately represents the purpose of the endpoint.- The
openapifrontmatter correctly specifies the HTTP method (POST) and the endpoint path, including the{org_id}and{project_id}path parameters for targeting a specific project within an organization.- The file name
add-project-member.mdxaligns with the endpoint's purpose.- The file is appropriately located under
docs/api-reference/project/for project-related API endpoints.Great job on adding this new endpoint and providing clear documentation!
docs/api-reference/project/get-project-members.mdx (1)
1-4: LGTM!The new API reference file for getting project members looks good:
- The file is named appropriately and placed in the correct directory structure.
- The metadata format is correct and consistent with other API reference files.
- The
titleandopenapimetadata accurately describe the purpose and endpoint of the API.Great job documenting this new API endpoint! This will be helpful for developers integrating with the project management functionality.
Makefile (4)
15-15: LGTM!The addition of
chromadbandsentence_transformerspackages to theinstall_alltarget aligns with the PR objective of integrating changes into the FalkorDB project. This ensures that these dependencies are installed as part of the comprehensive environment setup process.
19-19: LGTM!Formatting only the
mem0/directory in theformattarget reflects a more targeted approach to code formatting. This change aligns with the trend towards more modular and directory-specific operations within theMakefile, as mentioned in the AI-generated summary.
23-23: LGTM!Sorting imports only in the
mem0/directory in thesorttarget reflects a more localized approach to managing imports. This change aligns with the trend towards more modular and directory-specific operations within theMakefile, as mentioned in the AI-generated summary.
27-27: LGTM!Linting only the
mem0/directory in thelinttarget reflects a more focused approach to code quality checks. This change aligns with the trend towards more modular and directory-specific operations within theMakefile, as mentioned in the AI-generated summary. Focusing the linting process on a specific directory may indeed improve the efficiency and relevance of the linting results.mem0/embeddings/configs.py (1)
11-11: LGTM!The formatting change improves readability without affecting functionality.
docs/components/embedders/overview.mdx (1)
16-16: LGTM!The addition of the Vertex AI embedder card is consistent with the existing cards and expands the list of supported embedders. The
titleandhrefprops are set correctly, maintaining the same pattern as the other cards.docs/components/vectordbs/dbs/milvus.mdx (2)
1-2: LGTM!The overview provides a clear and concise introduction to Milvus vector database, highlighting its scalability and suitability for AI applications.
3-23: LGTM!The usage section provides a clear and well-structured Python code example for configuring Milvus within the
mem0library. The example effectively demonstrates how to set up a memory instance with Milvus as the vector store provider, including sample configuration parameters.docs/components/embedders/models/vertexai.mdx (3)
1-4: LGTM!The introduction section provides clear instructions on setting up the environment for Vertex AI integration. The steps are well-explained, and the link to the Google Cloud Console is helpful for users to generate the required credentials.
5-25: LGTM!The usage section provides a clear and concise code snippet demonstrating how to use the Vertex AI embedder. It covers all the necessary steps, including setting the credentials, configuring the embedder, and adding data to the memory. The choice of the
text-embedding-004model is appropriate as an example.
27-35: LGTM!The configuration section provides a well-formatted table of parameters for configuring the Vertex AI embedder. The descriptions are clear and concise, and the default values are appropriate. This table will be helpful for users to understand the available configuration options.
tests/embeddings/test_ollama_embeddings.py (2)
16-28: LGTM!The test function
test_embed_textprovides good coverage for theembedmethod of theOllamaEmbeddingclass. It verifies the correct interaction with the Ollama client and checks the returned embedding against the expected value.The use of a mock client fixture allows for controlled testing without relying on external services.
31-41: LGTM!The test function
test_ensure_model_existsprovides good coverage for the internal_ensure_model_existsmethod of theOllamaEmbeddingclass. It verifies that the method correctly pulls the model when it is not listed among the available models.The test checks both the case when the model exists (no pull call) and when it doesn't exist (pull call with the expected model name).
mem0/embeddings/vertexai.py (1)
11-26: LGTM!The
__init__method is well-implemented and follows best practices:
- It calls the superclass constructor.
- It sets appropriate default values for the model and embedding dimensions.
- It ensures that the necessary credentials are provided and raises a clear error message if not available.
mem0/configs/vector_stores/milvus.py (2)
7-19: LGTM!The
MetricTypeenum class is well-defined and provides a clear structure for handling different metric types. The use ofstrandEnumas base classes ensures that the enum values are represented as strings, and the__str__method is correctly implemented to return the string representation of the enum value.
22-43: LGTM!The
MilvusDBConfigclass is well-structured and utilizes Pydantic's features effectively to define a configuration object for connecting to the Milvus server. The use ofBaseModelandFieldensures that the configuration fields are clearly defined with default values and descriptions.The custom validation method
validate_extra_fieldsis a good addition to ensure that only the expected configuration parameters are accepted, preventing potential misconfigurations. The use of the@model_validatordecorator is appropriate for defining this validation logic.Allowing arbitrary types in the model configuration through the
model_configattribute provides flexibility for future extensions.mem0/vector_stores/configs.py (2)
11-11: LGTM!The formatting change adheres to the PEP 8 style guide and does not introduce any logical or functional issues.
17-17: Verify the usage of the new Milvus provider across the codebase.The addition of the Milvus provider to the
_provider_configsdictionary is a welcome enhancement. It expands the vector store options available in the mem0 project.Please ensure that:
- The "MilvusDBConfig" class is correctly defined in the "mem0.configs.vector_stores.milvus" module.
- The new provider is handled consistently across the codebase, such as in the
validate_and_create_configmethod.Run the following script to verify the Milvus provider usage:
Verification successful
Milvus provider implementation verified and consistent across the codebase
The addition of the Milvus provider to the
_provider_configsdictionary is correctly implemented and consistently handled across the codebase. Key findings:
- The
MilvusDBConfigclass is properly defined with the necessary fields.- Milvus is correctly added to both
_provider_configsand_vector_store_classesdictionaries.- Required dependencies (pymilvus) are imported with appropriate error handling.
- The implementation includes necessary components like MetricType enum and MilvusClient.
No issues were found, and the implementation appears to be complete and consistent.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of the Milvus provider across the codebase. # Test 1: Search for the "MilvusDBConfig" class definition. # Expect: The class should be defined in the "mem0/configs/vector_stores/milvus.py" file. ast-grep --lang python --pattern $'class MilvusDBConfig($$$)' # Test 2: Search for references to the "milvus" provider in the codebase. # Expect: The provider should be handled consistently, such as in the `validate_and_create_config` method. rg --type python $'milvus'Length of output: 464
Script:
#!/bin/bash # Description: Search for Milvus-related code in Python files # Find Python files and search for "MilvusDBConfig" echo "Searching for MilvusDBConfig:" fd -e py -x sh -c 'cat {} | grep -n -C 3 "MilvusDBConfig" || true' echo "\nSearching for milvus:" # Find Python files and search for "milvus" fd -e py -x sh -c 'cat {} | grep -n -C 3 "milvus" || true'Length of output: 2293
mem0/llms/utils/tools.py (5)
10-10: LGTM!The
dataproperty is correctly defined as a required string in thepropertiesobject.
12-12: LGTM!Setting
additionalPropertiestoFalsecorrectly ensures that no extra properties beyond those specified are permitted in the input objects.
35-35: LGTM!Setting
additionalPropertiestoFalsecorrectly ensures that no extra properties beyond those specified are permitted in the input objects for both theUPDATE_MEMORY_TOOLandDELETE_MEMORY_TOOLobjects.Also applies to: 54-54
Line range hint
1-56: Provide more information about the removal of thestrictparameter.The AI-generated summary mentions the removal of the
strictparameter from theadd_memory,update_memory, anddelete_memoryfunctions. However, the provided code does not show these changes.Please provide more context on the removal of the
strictparameter and its impact on the functions. How does this change affect input validation and schema enforcement?
Line range hint
1-1: Update on the status of the new memory addition logic.The TODO comment at the beginning of the file suggests removing the tools if no issues are found for the new memory addition logic.
Please provide an update on the status of testing the new memory addition logic. Have any issues been found? If not, can the TODO comment be addressed by removing the tools or updating the comment accordingly?
mem0/configs/vector_stores/chroma.py (2)
12-12: LGTM!Raising an
ImportErrorwith clear instructions on how to install the missing library is a good practice. The error message provides the exact pip command, making it easy for the user to resolve the issue.
16-16: LGTM!The formatting change in the
clientfield declaration does not affect the functionality. The type hint, default value, and description are appropriate.tests/embeddings/test_azure_openai_embeddings.py (2)
15-29: LGTM!The test function
test_embed_textis well-structured and follows the standard pattern for mocking and asserting. It correctly tests theembedmethod of theAzureOpenAIEmbeddingclass with a simple text input.
32-46: LGTM!The test function
test_embed_text_with_newlinesis well-structured and follows the standard pattern for mocking and asserting. It correctly tests theembedmethod of theAzureOpenAIEmbeddingclass with a text input containing newlines, ensuring that the newlines are properly handled before sending the request to the API.CONTRIBUTING.md (3)
19-31: Package manager section looks good!The section clearly specifies the use of
poetryas the package manager and provides accurate instructions for installing dependencies and activating the virtual environment. Discouraging the use ofpiporcondais a good practice to maintain consistency in dependency management across the project.
33-39: Pre-commit section is clear and conciseThe section effectively communicates the importance of installing
pre-committo ensure coding standards and provides the correct command to install it. This will help maintain consistency in the codebase.
41-51: Testing section provides clear instructionsThe section clearly specifies the use of
pytestfor testing and provides the correct command to run tests usingpoetry. Recommendingmake install_allto install remaining packages and ensure all tests pass before submitting a pull request is a good practice to maintain a consistent testing environment and catch potential issues early.docs/components/embedders/config.mdx (1)
56-56: LGTM!The new parameter
vertex_credentials_jsonis a valuable addition to the documentation. It provides clear guidance for users integrating with Google Cloud's VertexAI by specifying the path to the required credentials JSON file.The description is concise and informative, and the change maintains consistency with the existing documentation format.
docs/api-reference/overview.mdx (1)
26-50: Great addition to the API documentation!The new section on specifying organization and project parameters is well-written and provides clear instructions for users. The code examples for Python and Node.js are helpful and easy to follow.
This addition enhances the usability of the API by clarifying how to manage requests in a multi-organization or multi-project context.
docs/components/vectordbs/config.mdx (1)
9-9: LGTM!The addition of "milvus" to the list of supported vector database providers is a helpful update to the documentation. It accurately reflects the expanded functionality of the mem0 project and ensures that users are aware of all available provider options.
tests/embeddings/test_huggingface_embeddings.py (6)
7-12: LGTM!The
mock_sentence_transformerfixture correctly mocks theSentenceTransformerclass using thepatchfunction and yields the mock object for use in tests. The implementation is accurate and follows best practices for mocking in pytest.
15-24: LGTM!The
test_embed_default_modelfunction correctly tests theembedmethod of theHuggingFaceEmbeddingclass with the default model. It mocks theSentenceTransformerclass using themock_sentence_transformerfixture and verifies that theencodemethod is called with the correct input and that the result matches the expected output. The test is well-structured and follows best practices for unit testing.
27-36: LGTM!The
test_embed_custom_modelfunction correctly tests theembedmethod of theHuggingFaceEmbeddingclass with a custom model. It mocks theSentenceTransformerclass using themock_sentence_transformerfixture and verifies that theencodemethod is called with the correct input and that the result matches the expected output. The test is well-structured and follows best practices for unit testing.
39-48: LGTM!The
test_embed_with_model_kwargsfunction correctly tests theembedmethod of theHuggingFaceEmbeddingclass with model-specific keyword arguments. It mocks theSentenceTransformerclass using themock_sentence_transformerfixture and verifies that theencodemethod is called with the correct input and that the result matches the expected output. The test is well-structured and follows best practices for unit testing.
51-58: LGTM!The
test_embed_sets_embedding_dimsfunction correctly tests if theHuggingFaceEmbeddingclass sets the embedding dimensions based on the model used. It mocks theSentenceTransformerclass using themock_sentence_transformerfixture and verifies that theembedding_dimsattribute of theconfigobject is set correctly based on the mocked return value of theget_sentence_embedding_dimensionmethod. The test is well-structured and follows best practices for unit testing.
61-72: LGTM!The
test_embed_with_custom_embedding_dimsfunction correctly tests theembedmethod of theHuggingFaceEmbeddingclass with custom embedding dimensions. It mocks theSentenceTransformerclass using themock_sentence_transformerfixture and verifies that theencodemethod is called with the correct input and that the result matches the expected output. It also checks that theembedding_dimsattribute of theconfigobject is set correctly based on the provided value. The test is well-structured and follows best practices for unit testing.mem0/graphs/configs.py (5)
21-22: LGTM!The code segment correctly raises a
ValueErrorif any of the required fields (url,username, orpassword) are missing. The logic and syntax are accurate.
24-40: Excellent addition of theFalkorDBConfigclass!The
FalkorDBConfigclass is well-structured and follows the same pattern as the existingNeo4jConfigclass. The fields are appropriately typed and have clear descriptions. Thecheck_host_port_or_pathmethod ensures that the required fields (hostandport) are provided, improving the robustness of the configuration setup. The logic and syntax are accurate.
46-47: Great update to support FalkorDB!The changes to the
providerandconfigfields in theGraphStoreConfigclass accurately reflect the addition of support for the FalkorDB graph storage provider. TheUniontype hint for theconfigfield ensures that onlyFalkorDBConfigorNeo4jConfiginstances are accepted. The logic and syntax are correct.
58-63: Proper handling of the FalkorDB configuration!The code segment correctly handles the case when the
provideris"falkordb". It ensures that the"database"key is always set to"_default_", even if the user tries to use a different database name. The logic and syntax are accurate.
Line range hint
75-75:mem0/configs/base.py (3)
20-23: LGTM!The formatting changes to the class attributes improve readability without impacting the functionality. The single-line declarations are more concise and easier to scan.
51-55: Looks good!The addition of the
custom_promptattribute enhances the flexibility of the memory configuration by allowing users to specify a custom prompt. Making it optional with a default value ofNoneensures backward compatibility.
68-74: LGTM!The formatting changes to the class attributes improve consistency and readability. The multi-line declarations with descriptions are more readable and easier to understand.
docs/features/custom-prompts.mdx (5)
6-14: Great introduction to custom prompts!The section provides a clear and concise overview of custom prompts and their benefits. The best practices outlined are valuable for users to create effective custom prompts.
16-39: Excellent example of a custom prompt!The example custom prompt is well-structured and effectively demonstrates how to extract relevant information based on specific criteria. The few-shot examples are clear and follow a consistent format, making it easy for users to understand and adapt to their own use cases.
43-62: Clear and concise initialization of the custom prompt!The example code demonstrates how to initialize the custom prompt in the configuration of the Mem0 instance. The setup of the LLM provider and the assignment of the custom prompt to the appropriate key in the configuration dictionary are straightforward and well-documented.
64-109: Excellent practical examples!The practical examples effectively demonstrate the functionality of custom prompts in real-world scenarios. Example 1 showcases how relevant information is extracted and added to the memory based on the custom prompt's criteria, while Example 2 highlights the effectiveness of the custom prompt in filtering out irrelevant information.
The use of the
CodeGroupcomponent enhances the readability and clarity of the examples by presenting the code and output side by side.Tools
LanguageTool
[grammar] ~96-~96: Possible subject-verb agreement error detected.
Context: ...memory of a user liking to go on hikes. This add message is not specific to the use-case...(THIS_THAT_AGR)
96-96: False positive: Subject-verb agreement is correct.The static analysis hint suggests a possible subject-verb agreement error in the sentence:
"This add message is not specific to the use-case mentioned in the custom prompt."
However, upon further analysis, the sentence appears to be grammatically correct. The subject "This add message" agrees with the singular verb "is." Therefore, no action is required.
Tools
LanguageTool
[grammar] ~96-~96: Possible subject-verb agreement error detected.
Context: ...memory of a user liking to go on hikes. This add message is not specific to the use-case...(THIS_THAT_AGR)
tests/embeddings/test_openai_embeddings.py (6)
7-12: LGTM!The
mock_openai_clientfixture correctly mocks the OpenAI client for testing purposes.
15-25: LGTM!The
test_embed_default_modelfunction correctly tests theembedmethod with the default model configuration.
28-40: LGTM!The
test_embed_custom_modelfunction correctly tests theembedmethod with a custom model configuration.
43-53: LGTM!The
test_embed_removes_newlinesfunction correctly tests that theembedmethod removes newlines from the input before sending it to the OpenAI API.
56-68: LGTM!The
test_embed_without_api_key_env_varfunction correctly tests theembedmethod when an API key is provided directly in the configuration.
71-84: LGTM!The
test_embed_uses_environment_api_keyfunction correctly tests theembedmethod when an API key is set in the environment variables.mem0/utils/factory.py (3)
24-24: LGTM!The addition of the
"azure_openai_structured"provider to theLlmFactoryclass is consistent with the existing pattern and extends the functionality to support theAzureOpenAIStructuredLLMclass.
62-62: LGTM!The addition of the
"milvus"provider to theVectorStoreFactoryclass is consistent with the existing pattern and extends the functionality to support theMilvusDBclass.
75-91: LGTM!The introduction of the
GraphFactoryclass is a great addition to the factory module. It follows the same pattern as the existing factory classes and provides a consistent way to create graph instances based on the provider name and configuration.The
createmethod handles the instantiation of the graph class and ensures that theconfigis in the correct format. The error handling for unsupported graph providers is also consistent with the other factory classes.Overall, the code changes are well-structured, maintainable, and extend the functionality of the factory module to support graph providers.
mem0/llms/openai.py (1)
103-103: LGTM!The formatting adjustment in the comment is purely cosmetic and does not impact the functionality or logic of the code.
mem0/graphs/utils.py (2)
56-77: LGTM!The
FALKORDB_QUERYis a well-structured and comprehensive Cypher query that retrieves nodes from a FalkorDB graph database based on their embedding similarity to a provided vector. The query follows a logical flow and correctly implements the cosine similarity calculation using the dot product and vector magnitudes.The use of UNION ensures that the query retrieves related nodes from both incoming and outgoing relationships, providing a comprehensive result set. The query parameters allow for dynamic filtering based on the user ID, embedding vector, and similarity threshold.
The returned data includes relevant details such as node names, IDs, relationship types, and similarity scores, which can be useful for various applications such as recommendation systems or knowledge graph traversal.
Regarding the past review comment:
The query is designed to retrieve nodes based on their embedding similarity, which can be useful for various applications as mentioned above. While there may be room for optimization, the current implementation is functional and follows a logical approach. If there are specific concerns or suggestions for improvement, it would be helpful to discuss them in more detail with the Mem0 team.
80-101: LGTM!The
NEO4J_QUERYis similar to theFALKORDB_QUERYbut is designed for a Neo4j graph database. The query follows the same structure and logic as theFALKORDB_QUERY, with a few minor differences:
- It uses
elementId(n)andelementId(m)for node IDs instead ofId(n)andId(m), which is specific to the Neo4j implementation.- The similarity score is rounded to 4 decimal places using the
roundfunction.The addition of the
NEO4J_QUERYprovides flexibility for using the module with different graph databases (FalkorDB and Neo4j), enabling similarity-based retrieval of nodes from a Neo4j graph database.The query's functionality and benefits are similar to the
FALKORDB_QUERY, and its performance may depend on the size of the graph and the efficiency of the Neo4j database.docs/mint.json (4)
111-112: LGTM!The addition of the Milvus vector database page is consistent with the existing structure and naming convention. The JSON syntax and structure are correct.
135-135: LGTM!The addition of the "custom-prompts" feature page is consistent with the existing structure and naming convention. The JSON syntax and structure are correct.
163-181: LGTM!The addition of the "Organizations APIs" group and its associated API reference pages is consistent with the existing API reference structure and naming conventions. The nested "Members APIs" group follows the established pattern for grouping related endpoints. The JSON syntax and structure are correct.
182-198: LGTM!The addition of the "Projects APIs" group and its associated API reference pages is consistent with the existing API reference structure and naming conventions. The nested "Members APIs" group follows the established pattern for grouping related endpoints. The JSON syntax and structure are correct.
docs/open-source/graph_memory/overview.mdx (5)
6-6: LGTM!The line accurately informs users about the addition of Graph Memory support in Mem0.
10-10: LGTM!The line helpfully directs users to try Graph Memory on Google Colab, which can be a convenient way for users to explore the feature.
29-29: LGTM!The line provides clear and accurate information about the supported graph store providers (FalkorDB and Neo4j) and their setup options (local or hosted solutions).
49-55: LGTM!The Python configuration dictionary correctly initializes Graph Memory with FalkorDB by setting the provider to "falkordb" and including the necessary configuration parameters.
77-83: LGTM!The Python configuration dictionary correctly initializes Graph Memory with FalkorDB in an advanced setup with a custom LLM. The provider is set to "falkordb", and the necessary configuration parameters are included.
mem0/vector_stores/milvus.py (14)
19-22: LGTM!The
OutputDataclass is well-defined with appropriate fields and inherits fromBaseModel, which is a good practice for defining data models.
26-51: LGTM!The
__init__method is well-implemented with appropriate parameters, type hints, and default values. It correctly initializes theMilvusClientinstance and calls thecreate_colmethod to create the collection if it doesn't exist.
53-85: LGTM!The
create_colmethod is well-implemented with appropriate parameters and logic. It correctly checks if the collection already exists and creates the collection with the specified schema and index if it doesn't.
87-97: LGTM!The
insertmethod is well-implemented with appropriate parameters and logic. It correctly iterates over the input data and inserts each vector and its metadata into the collection using thezipfunction.
99-115: LGTM!The
_create_filtermethod is well-implemented with appropriate parameters and logic. It correctly iterates over thefiltersdictionary and creates a filter string for each key-value pair using different formats for string and non-string values. It also correctly joins the filter strings with theandoperator.
117-139: LGTM!The
_parse_outputmethod is well-implemented with appropriate parameters and logic. It correctly iterates over thedatalist and extracts theid,distance, andmetadatafields from each result. It also correctly creates anOutputDatainstance for each result and appends it to thememorylist, which is returned at the end.
141-162: LGTM!The
searchmethod is well-implemented with appropriate parameters and logic. It correctly calls the_create_filtermethod to create a filter string iffiltersare provided, and calls theclient.searchmethod with the appropriate parameters to search for similar vectors. It also correctly calls the_parse_outputmethod to parse the search result and returns the parsed output data.
164-171: LGTM!The
deletemethod is well-implemented with appropriate parameters and logic. It correctly calls theclient.deletemethod with the appropriate parameters to delete the vector from the collection.
173-183: LGTM!The
updatemethod is well-implemented with appropriate parameters and logic. It correctly creates aschemadictionary with the updated vector and metadata, and calls theclient.upsertmethod with the appropriate parameters to update the vector and its metadata in the collection.
185-201: LGTM!The
getmethod is well-implemented with appropriate parameters and logic. It correctly calls theclient.getmethod with the appropriate parameters to retrieve the vector from the collection, and creates anOutputDatainstance with the retrieved vector data, which is returned at the end.
203-210: LGTM!The
list_colsmethod is well-implemented with appropriate logic. It correctly calls theclient.list_collectionsmethod to list all collections and returns the list of collection names.
212-214: LGTM!The
delete_colmethod is well-implemented with appropriate logic. It correctly calls theclient.drop_collectionmethod with the appropriate parameters to delete the collection and returns the result of the method call.
216-223: LGTM!The
col_infomethod is well-implemented with appropriate logic. It correctly calls theclient.get_collection_statsmethod with the appropriate parameters to retrieve information about the collection and returns the collection information.
225-242: LGTM!The
listmethod is well-implemented with appropriate parameters and logic. It correctly calls the_create_filtermethod to create a filter string iffiltersare provided, and calls theclient.querymethod with the appropriate parameters to list vectors in the collection. It also correctly iterates over the query result and creates anOutputDatainstance for each vector, and returns a list containing the list ofOutputDatainstances.mem0/client/main.py (4)
13-17: LGTM!The deprecation warning for the 'session_id' parameter is set up correctly and provides clear guidance to users.
89-89: LGTM!The error handling for an invalid API key is appropriate and the error message provides clear guidance to the user.
92-92: LGTM!The
addmethod is correctly decorated with@api_error_handlerfor consistent error handling.The deprecation of the 'session_id' parameter is handled appropriately in the
_prepare_payloadmethod. The warning message provides clear guidance to users on the deprecated parameter and its replacement.Also applies to: 306-316
256-256: LGTM!The
delete_usersmethod correctly deletes all user, agent, and session entities by sending DELETE requests to the appropriate API endpoints.The deprecation of the 'session_id' parameter is handled appropriately in the
_prepare_paramsmethod. The warning message provides clear guidance to users on the deprecated parameter and its replacement.Also applies to: 329-339
mem0/memory/main.py (12)
31-33: LGTM!The addition of the
custom_promptattribute allows for customization of the system prompt when adding messages to the vector store. The initialization logic is correct.
98-98: LGTM!The added check ensures that at least one of the required filters (
user_id,agent_id, orrun_id) is provided when adding a memory. This validation logic is correct and helps prevent invalid memory additions.
114-115: LGTM!The updated return format for version
"v1.1"includes both the"results"from the vector store and the"relations"from the graph. This change is consistent with the new version and provides a more comprehensive response.
123-123: LGTM!The
DeprecationWarningis raised with the appropriate message andstacklevelparameter to inform users about the deprecatedaddAPI output format. This helps in promoting the usage of the latest format and ensures a smooth transition.
130-134: LGTM!The conditional logic for setting the
system_promptanduser_promptbased on the presence ofcustom_promptis implemented correctly. It allows for flexibility in customizing the prompts while providing a default behavior whencustom_promptis not specified.
137-140: LGTM!The
messagesparameter is constructed correctly as a list of dictionaries, representing the system and user messages. This format is suitable for providing context and instructions to the language model API.
145-145: LGTM!The parsing of the
responseas JSON and the extraction of the"facts"key is implemented correctly. The code handles potential exceptions gracefully by logging the error and defaultingnew_retrieved_factsto an empty list.
176-182: LGTM!The handling of the
"ADD"event is implemented correctly. It calls the_create_memorymethod with the appropriate arguments and appends the relevant information to thereturned_memorieslist.
185-191: LGTM!The handling of the
"UPDATE"event is implemented correctly. It calls the_update_memorymethod with the appropriate arguments and appends the relevant information, including the previous memory state, to thereturned_memorieslist.
194-199: LGTM!The handling of the
"DELETE"event is implemented correctly. It calls the_delete_memorymethod with the appropriate argument and appends the relevant information, including the deleted memory text, to thereturned_memorieslist.
238-238: LGTM!The dictionary comprehension used to create the
filtersdictionary is implemented correctly. It selectively extracts the values of"user_id","agent_id", and"run_id"frommemory.payload, if they exist. This allows for filtering based on specific keys.
259-259: LGTM!The dictionary comprehension used to create the
additional_metadatadictionary is implemented correctly. It extracts key-value pairs frommemory.payloadwhere the key is not present in theexcluded_keysset. This allows for the inclusion of additional metadata while excluding specific keys that are handled separately.docs/platform/quickstart.mdx (8)
133-136: LGTM!The changes in this Python code example look good:
- The
session_idparameter has been consistently replaced withrun_id.- The new
output_formatparameter is clearly demonstrated with examples for bothv1.0andv1.1.The code example itself is correct and should work as expected.
146-146: Looks good!The JavaScript code example has been updated correctly:
- The
session_idparameter has been replaced withrun_id, maintaining consistency.- The
output_formatis set tov1.1, aligning with the Python example.The code itself looks valid and should function as intended.
163-163: LGTM!The cURL example has been updated properly:
- The
session_idparameter has been consistently replaced withrun_id.The updated cURL command aligns with the changes made in the Python and JavaScript examples.
189-191: Great addition!The note about the deprecation of
session_idis a helpful addition:
- It clearly informs users about the change and the need to use
run_idinstead.- It specifies the version (0.1.20) in which
session_idwill be removed, giving users a timeline for updating their code.This note will help ensure a smooth transition for users to the new
run_idparameter.
532-532: Nice expansion!The new section "4.3 Get All Users" is a great addition to the documentation:
- It introduces a new API endpoint to retrieve all users, agents, and runs with associated memories.
- The description clearly explains what data will be fetched by this endpoint.
Adding this section enhances the comprehensiveness of the documentation.
584-584: Great addition!The new section "4.4 Get All Memories" is another valuable addition to the documentation:
- It introduces the
getAll()method to fetch all memories for a user, agent, or run.- The description clearly explains how to use this method.
Including this section makes the documentation more comprehensive and helpful for users.
740-743: Excellent examples!The code examples for retrieving short-term memories using the
getAll()method are well-written and informative:
- They consistently use the
user_idandrun_idparameters to specify the session.- The Python example goes a step further by demonstrating the usage of the
output_formatparameter withv1.0andv1.1options.- The examples in different languages (Python, JavaScript, cURL) are coherent and align with the provided explanations.
These examples effectively illustrate how to use the
getAll()method for retrieving session memories.Also applies to: 747-747, 753-753
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1-1055: Comprehensive update to the documentation!The changes made to the
docs/platform/quickstart.mdxfile significantly enhance the quality and coverage of the documentation:
- The
session_idparameter has been consistently replaced withrun_idacross all relevant sections and code examples, ensuring uniformity.- New sections have been added to document additional API functionalities, such as retrieving all users and memories, making the documentation more comprehensive.
- The code examples have been updated to incorporate the
session_idtorun_idchange and demonstrate the usage of new parameters likeoutput_format, providing clear and accurate illustrations of API usage.- A helpful note has been included to inform users about the deprecation of
session_idand the timeline for its removal, facilitating a smooth transition.Overall, these changes greatly improve the clarity, consistency, and completeness of the Mem0 platform documentation. Great job!
docs/openapi.json (10)
97-114: LGTM!The addition of
org_nameandproject_namequery parameters to the/v1/entities/endpoint is a useful enhancement. It allows filtering entities by organization and project, providing more targeted results. The changes maintain backwards compatibility and the response schema is unaffected.
204-221: Looks good!The
org_nameandproject_namequery parameters added to the/v1/entities/{entity_type}/{entity_id}/endpoint enable filtering the retrieved entity by organization and project. This enhancement provides more flexibility in accessing specific entities. The changes are backwards compatible and should not disrupt existing functionality.
365-380: Approved!The introduction of
org_nameandproject_namequery parameters in the/v1/memories/endpoint for bothGETandDELETEoperations is a valuable addition. It allows filtering memories by organization and project during retrieval and deletion, providing more control over memory management. The changes are backwards compatible and do not alter the request/response schemas.Also applies to: 564-580
1291-1615: Great work on the organization endpoints!The new endpoints for managing organizations (
/api/v1/orgs/organizations/) provide a comprehensive set of CRUD operations. The URL structure is clean and intuitive, following REST conventions. The request and response schemas are well-documented, making it easy for developers to understand and use the endpoints. Error handling is properly implemented, with clear status codes and error messages for different scenarios.Overall, the organization endpoints are a solid addition to the API.
1616-1952: Excellent implementation of organization member endpoints!The new endpoints for managing organization members (
/api/v1/orgs/organizations/{org_id}/members/) offer a complete set of operations to retrieve, update, add, and remove members within an organization. The URL structure is consistent and includes the necessaryorg_idparameter.The choice of HTTP methods aligns with REST conventions, making the endpoints intuitive to use. The request and response schemas are well-defined, providing clarity on the required parameters and expected responses. Error handling is properly handled, with appropriate status codes and error messages for different scenarios.
The organization member endpoints are a valuable addition to the API, enabling effective management of members within organizations.
1953-2302: Nicely done with the project management endpoints!The new endpoints for managing projects within an organization (
/api/v1/orgs/organizations/{org_id}/projects/) provide a comprehensive set of CRUD operations. The hierarchical URL structure, withorg_idandproject_id, effectively represents the relationship between organizations and projects.The chosen HTTP methods align with REST conventions, making the endpoints intuitive and consistent. The request and response schemas are well-defined, clearly specifying the required parameters and expected response formats. Error handling is properly implemented, with appropriate status codes and error messages for different scenarios.
The project management endpoints are a great addition to the API, enabling seamless management of projects within the context of organizations.
2303-2665: Great job with the project member endpoints!The new endpoints for managing project members (
/api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/) provide a complete set of operations to retrieve, add, update, and remove members within a project. The hierarchical URL structure, withorg_idandproject_id, effectively represents the relationship between organizations, projects, and members.The chosen HTTP methods align with REST conventions, making the endpoints intuitive and consistent. The request and response schemas are well-defined, clearly specifying the required parameters and expected response formats. Error handling is properly implemented, with appropriate status codes and error messages for different scenarios.
The project member endpoints are a valuable addition to the API, enabling efficient management of members within the context of projects and organizations.
2718-2807: Looks good!The addition of
org_nameandproject_nameproperties to theMemoryInputschema is a useful enhancement. It allows associating a memory with a specific organization and project during creation, providing more context and organization to the memories.Making these properties optional ensures backwards compatibility, so existing memory creation requests will continue to work without requiring the new fields.
The data types and descriptions of the new properties are consistent with the rest of the schema, maintaining a coherent design.
2872-2884: Approved!The inclusion of
org_nameandproject_nameproperties in theMemorySearchInputschema is a valuable addition. It enables filtering memories by organization and project during a search, providing more targeted and relevant results.Making these properties optional ensures backwards compatibility, allowing existing search requests to function without modification.
The data types and descriptions of the new properties align with the rest of the schema, maintaining consistency and clarity.
2926-2938: Looks good to me!The addition of
org_nameandproject_nameproperties to theMemorySearchInputV2schema is a welcome enhancement. It allows filtering memories by organization and project during a search, providing more granular control over the search results.Making these properties optional maintains backwards compatibility, ensuring that existing search requests will continue to function without requiring the new fields.
The data types and descriptions of the new properties are consistent with the rest of the schema, promoting a coherent design.
mem0/embeddings/configs.py
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| @field_validator("config") | ||
| def validate_config(cls, v, values): | ||
| provider = values.data.get("provider") | ||
| if provider in ["openai", "ollama", "huggingface", "azure_openai"]: | ||
| if provider in ["openai", "ollama", "huggingface", "azure_openai", "vertexai"]: |
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Codebase Verification
Vertexai support added, but package inconsistency detected.
The change to include "vertexai" as a supported embedding provider has been successfully implemented in the validate_config method. The codebase shows multiple references to vertexai-related implementations and configurations, indicating readiness for the new provider.
However, there's a potential inconsistency to address:
- The
EmbedderFactoryinembedchain/embedchain/factory.pyincludes "vertexai" support. - The
EmbedderFactoryinmem0/utils/factory.pydoes not list "vertexai" as a supported provider.
This discrepancy may need to be resolved to ensure consistent support across both packages.
Analysis chain
Verify the impact of the new embedding provider on the codebase.
The change to support the "vertexai" embedding provider looks good and enhances the versatility of the EmbedderConfig class.
Please ensure that any code that interacts with the EmbedderConfig class is updated to handle the new provider correctly. You can use the following script to search for potential areas that may require updates:
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# Test: Search for references to the `EmbedderConfig` class. Expect: Relevant code locations.
rg --type python -A 5 $'EmbedderConfig'
Length of output: 69
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# Description: Search for EmbedderConfig in Python files and analyze its usage
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# Use ast-grep to find the class definition of EmbedderConfig
echo -e "\nSearching for EmbedderConfig class definition:"
ast-grep --lang python --pattern 'class EmbedderConfig:
$$$'
# Use ast-grep to find usage of EmbedderConfig
echo -e "\nSearching for EmbedderConfig usage:"
ast-grep --lang python --pattern 'EmbedderConfig($$$)'
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rg -g '*.py' 'provider.*=.*("openai"|"ollama"|"huggingface"|"azure_openai"|"vertexai")' -A 5
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echo -e "\nSearching for vertexai-related code:"
rg -g '*.py' 'vertexai' -A 5
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mem0/llms/openai.py
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| api_key = self.config.api_key or os.getenv("OPENAI_API_KEY") | |||
| base_url = self.config.openai_base_url or os.getenv("OPENAI_API_BASE") | |||
| base_url = os.getenv("OPENAI_API_BASE") or self.config.openai_base_url | |||
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Codebase Verification
Revert the change to maintain consistency in base URL prioritization
The current change in mem0/llms/openai.py introduces an inconsistency in how the base URL for OpenAI API is determined across the codebase. While this file now prioritizes the OPENAI_API_BASE environment variable, other files such as mem0/embeddings/openai.py and mem0/llms/openai_structured.py still prioritize the openai_base_url configuration value.
This inconsistency could lead to:
- Unexpected behavior if different parts of the system use different base URLs.
- Confusion for developers who expect consistent behavior across the codebase.
To maintain consistency and prevent potential issues, it's recommended to revert the change and keep the original order of evaluation:
base_url = self.config.openai_base_url or os.getenv("OPENAI_API_BASE")This approach aligns with the rest of the codebase and maintains the expected behavior where configuration values take precedence over environment variables.
Analysis chain
Verify the impact of the change in the order of evaluating the base_url variable.
The change in the order of evaluation could potentially alter the behavior of the OpenAI client initialization depending on the presence of environment variables. If the OPENAI_API_BASE environment variable is set, it will now take precedence over the openai_base_url from the configuration object.
Please ensure that this change aligns with the intended behavior and does not introduce any unintended consequences.
Run the following script to verify the impact of the change:
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# Test 1: Search for usage of `OPENAI_API_BASE` environment variable.
# Expect: No other usage apart from the one in this file.
rg --type python $'OPENAI_API_BASE'
# Test 2: Search for usage of `openai_base_url` configuration object.
# Expect: Understand how it's being used and if it's still needed.
rg --type python $'openai_base_url'
Length of output: 131
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echo "Searching for OPENAI_API_BASE usage:"
grep -R "OPENAI_API_BASE" .
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🔇 Additional comments (2)
mem0/memory/graph_memory.py (2)
347-369: Enhancement: Centralized graph query logicThe new
graph_querymethod effectively centralizes graph database interactions and allows for dynamic switching between different graph providers. This improves the modularity and maintainability of the code.
360-362:⚠️ Potential issuePotential concurrency issues with shared
self.graphModifying
self.graph._graphbased onparams["user_id"]might introduce concurrency issues ifMemoryGraphinstances are shared across multiple users or threads. This could lead to race conditions or unexpected behavior when multiple users interact with the graph concurrently.Consider ensuring that each user has a separate
MemoryGraphinstance or implement thread-safe mechanisms to handle graph switching. Verify if the current implementation safely supports concurrent access.
| # TODO: Use langchain to switch graphs after the multi-graph feature is released | ||
| self.graph._graph = self.graph._driver.select_graph(params["user_id"]) | ||
|
|
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Avoid accessing private attributes _graph and _driver directly
In the graph_query method, you're directly accessing private attributes _graph and _driver of the self.graph object. This practice is discouraged as it can lead to fragile code that depends on the internal implementation details of the class.
Consider using or extending public methods provided by the Graph class to switch graphs based on user_id. If necessary, modify the GraphFactory or the graph classes to include a public method for graph selection.
| self.embedding_model = EmbedderFactory.create( | ||
| self.config.embedder.provider, self.config.embedder.config | ||
| ) | ||
| self.embedding_model = EmbedderFactory.create(self.config.embedder.provider, self.config.embedder.config) |
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Remove redundant assignment to self.embedding_model
The variable self.embedding_model is assigned twice in the __init__ method. The second assignment at line 38 is redundant and can be removed to avoid unnecessary duplication.
Apply this diff to remove the redundant line:
- self.embedding_model = EmbedderFactory.create(self.config.embedder.provider, self.config.embedder.config)Committable suggestion was skipped due to low confidence.
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Actionable comments posted: 5
🧹 Outside diff range and nitpick comments (11)
mem0/utils/factory.py (1)
82-85: Add type hints and document provider configurations.Consider the following improvements:
- Add type hints for the config parameter
- Document the required configuration parameters for each provider
Apply this diff:
@classmethod - def create(cls, provider_name, config): + def create(cls, provider_name: str, config: Union[dict, BaseModel]) -> Union[Neo4jGraph, FalkorDBGraph]: + """Create a graph instance based on the provider name. + + Args: + provider_name: The name of the graph provider ('falkordb' or 'neo4j') + config: Configuration for the graph provider + FalkorDB config: {'url': str, 'username': str, 'password': str} + Neo4j config: {'url': str, 'username': str, 'password': str, 'database': str} + + Returns: + An instance of the specified graph provider + + Raises: + ValueError: If the provider is not supported + """Don't forget to add the necessary imports:
from typing import Union from pydantic import BaseModel from langchain_community.graphs import Neo4jGraph, FalkorDBGraphAlso applies to: 88-88
docs/open-source/graph_memory/overview.mdx (1)
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1-40: Consider enhancing the documentation structure.To improve the documentation:
- Add a comparison table between FalkorDB and Neo4j features/limitations
- Include a troubleshooting section for common connection issues
- Add configuration examples for both local and cloud deployments
Would you like me to help draft these additional sections?
🧰 Tools
🪛 LanguageTool
[style] ~42-~42: You have already used this phrasing in nearby sentences. Consider replacing it to add variety to your writing.
Context: ...f you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.co...(REP_NEED_TO_VB)
README.md (2)
163-163: Fix the grammatical error in the providers description.There's a subject-verb agreement issue in the sentence.
Apply this change:
-Currently, we support FalkorDB and Neo4j as a graph store providers. +Currently, we support FalkorDB and Neo4j as graph store providers.
172-177: Enhance configuration documentation with field constraints.While the configuration structure is clear, it would be helpful to document the expected format and constraints for each field.
Consider adding comments like this:
"config": { # Hostname or IP address of your FalkorDB instance "host": "localhost", # Username for authentication (required) "username": "default", # Password for authentication (required) "password": "password", # Port number (default: 6379) "port": "6379" }docs/open-source/quickstart.mdx (1)
66-72: Add parameter descriptions and requirements.The documentation should explain:
- Required vs optional parameters
- Expected format/type for each parameter
- Default values if any
- Example values (with sensitive data masked)
Consider adding a description table above the configuration example:
| Parameter | Required | Description | Default | |-----------|----------|-------------|---------| | provider | Yes | Graph store provider. Set to "falkordb" for FalkorDB. | None | | database | Yes | Name of the FalkorDB database to use. | None | | host | Yes | FalkorDB server hostname or IP address. | localhost | | port | Yes | FalkorDB server port number. | 6379 | | username | No | Username for authentication. | None | | password | No | Password for authentication. | None |mem0/memory/graph_memory.py (6)
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244-256: Handle emptysearch_outputto prevent errorsIf
search_outputis empty, initializingBM25Okapiwith an empty corpus will cause issues. This can lead to unexpected behavior or runtime errors.Add a check to handle empty
search_outputbefore proceeding:if not search_output: logger.info("No search results found.") return []
206-212: Use consistent exception handling for unsupported providersCurrently, you raise a
ValueErrorwhen the graph database provider is unsupported. Using a more specific exception likeNotImplementedErrorcan improve clarity.Consider updating the exception:
- raise ValueError("Unsupported graph database provider for querying") + raise NotImplementedError(f"Unsupported graph database provider '{self.config.graph_store.provider}' for querying")
364-364: Address the TODO comment regarding graph switchingThere is a TODO comment indicating the plan to use
langchainfor graph switching after the multi-graph feature is released. This may impact the current implementation.Would you like assistance in implementing graph switching with
langchain? I can help draft a solution or open a GitHub issue to track this task.
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321-349: Add error handling for graph operationsIn the
_update_relationshipmethod, graph operations like node creation and relationship updates may fail, but exceptions are not being handled. This could lead to unhandled exceptions at runtime.Consider wrapping graph operations in try-except blocks to handle potential exceptions gracefully:
try: # Code for merging nodes and creating relationships except Exception as e: logger.error(f"An error occurred while updating the relationship: {e}") raise
331-333: Confirm behavior when deleting non-existent relationshipsThe
delete_queryattempts to delete relationships without checking if they exist. While Cypher handles this without error, it's good practice to ensure the behavior is as expected.Consider adding comments or checks if necessary to clarify this behavior.
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244-256: Tokenize documents properly before using BM25When initializing
BM25Okapi, it's recommended to provide tokenized documents. Currently,search_outputs_sequencemay not be properly tokenized.Consider tokenizing
search_outputs_sequence:tokenized_corpus = [doc.split(" ") for doc in [' '.join(item) for item in search_outputs_sequence]] bm25 = BM25Okapi(tokenized_corpus)
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📒 Files selected for processing (6)
README.md(2 hunks)docs/open-source/graph_memory/overview.mdx(3 hunks)docs/open-source/quickstart.mdx(1 hunks)mem0/memory/graph_memory.py(9 hunks)mem0/utils/factory.py(1 hunks)pyproject.toml(1 hunks)
🔇 Additional comments (8)
pyproject.toml (1)
30-30: Verify FalkorDB compatibility with LangChain.
The addition of FalkorDB as a dependency looks good. However, since this is part of the graph memory feature alongside Neo4j, please ensure that:
- This version of FalkorDB is compatible with the LangChain integration
- The version constraint
^1.0.8is production-ready and stable
Let's verify the integration compatibility:
✅ Verification successful
FalkorDB integration with LangChain is properly implemented and compatible
The codebase shows proper integration between FalkorDB and LangChain:
- Uses
langchain_community.graphs.FalkorDBGraphas the graph provider - Implements correct graph switching and query handling
- Follows the same pattern as the Neo4j integration
- Configuration is properly structured with all required parameters
The version constraint ^1.0.8 is appropriate as the integration is stable and production-ready, evidenced by:
- Clean integration with LangChain's graph interfaces
- Proper error handling and configuration validation
- Well-documented usage in cookbooks and documentation
- Consistent implementation alongside other graph providers
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# and verify compatibility with LangChain
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rg -l "falkordb"
# Look for LangChain integration with FalkorDB
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rg -l "from\s+langchain.*falkor"
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rg -l "falkordb.*version|requires.*falkordb"
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# Let's check the actual integration code to verify compatibility
# Check the graph memory implementation
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rg -A 10 "falkordb" mem0/memory/graph_memory.py
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rg -A 5 "falkordb" mem0/utils/factory.py
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mem0/utils/factory.py (2)
81-96: LGTM! Verify langchain-community dependency.
The GraphFactory implementation follows the established factory pattern consistently. However, let's verify the langchain-community dependency is properly declared since the graph providers are from this package.
✅ Verification successful
Dependency verified and properly declared
The langchain-community package is correctly declared as a dependency in pyproject.toml with version ^0.3.1, which supports the graph providers used in the GraphFactory implementation.
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81-96: Verify GraphFactory integration with MemoryGraph.
Let's verify that the GraphFactory is properly integrated with the MemoryGraph class.
✅ Verification successful
GraphFactory integration with MemoryGraph is properly implemented
The verification confirms:
GraphFactoryis correctly imported and used inMemoryGraphclass- The factory pattern is properly followed with no direct usage of
Neo4jGraphorFalkorDBGraphclasses outside the factory - The integration matches the factory's interface, passing both provider name and config as expected
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docs/open-source/graph_memory/overview.mdx (2)
87-93: 🛠️ Refactor suggestion
Ensure configuration and model consistency.
- The configuration has the same formatting issues as the basic example
- The LLM model "gpt-4o-mini" needs verification as it differs from models mentioned elsewhere
#!/bin/bash
# Search for LLM model references in the codebase
rg -i "gpt-4o-mini|gpt-4o-2024"Apply the same configuration improvements as suggested in the basic example.
39-40: Verify version compatibility information.
The document states that version "v1.1" is required, but this seems inconsistent with other version information in the codebase. Please verify if this is the correct version requirement.
✅ Verification successful
Version requirement is correctly documented
The version requirement of v1.1 is consistently documented across the graph memory documentation files. The reference to gpt-4o-2024-08-06 in the documentation is not related to the version requirement but rather refers to the default LLM model used for graph operations when no custom LLM is specified. These are two separate configurations serving different purposes:
version: "v1.1"- Graph Memory feature versiongpt-4o-2024-08-06- Default LLM model for graph operations
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README.md (1)
163-163: LGTM! Clear and well-formatted feature announcement.
The new feature announcement is prominently displayed with appropriate styling and includes a helpful documentation link.
mem0/memory/graph_memory.py (2)
39-41: Remove redundant assignment to self.embedding_model
The variable self.embedding_model is assigned twice in the __init__ method. The second assignment at line 41 is redundant and can be removed to avoid unnecessary duplication.
364-364: Avoid accessing private attributes _graph and _driver directly
In the graph_query method, you're directly accessing private attributes _graph and _driver of the self.graph object at line 364. This practice is discouraged as it can lead to fragile code that depends on internal implementation details.
| "provider": "falkordb", | ||
| "config": { | ||
| "url": "neo4j+s://xxx", | ||
| "username": "neo4j", | ||
| "password": "xxx" | ||
| "Database": "falkordb", | ||
| "host": "---" | ||
| "username": "---", | ||
| "password": "---", | ||
| "port": "---" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve configuration example clarity.
The configuration example could be enhanced:
- The
Databaseparameter should be lowercase for consistency - Consider using clearer placeholder values (e.g., "localhost" for host, "6379" for default port)
- Add comments explaining the expected format for each parameter
"provider": "falkordb",
"config": {
- "Database": "falkordb",
- "host": "---"
- "username": "---",
- "password": "---",
- "port": "---"
+ "database": "falkordb", # Name of your FalkorDB database
+ "host": "localhost", # FalkorDB server hostname
+ "username": "<username>", # Your FalkorDB username
+ "password": "<password>", # Your FalkorDB password
+ "port": "6379" # Default FalkorDB port
}📝 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.
| "provider": "falkordb", | |
| "config": { | |
| "url": "neo4j+s://xxx", | |
| "username": "neo4j", | |
| "password": "xxx" | |
| "Database": "falkordb", | |
| "host": "---" | |
| "username": "---", | |
| "password": "---", | |
| "port": "---" | |
| "provider": "falkordb", | |
| "config": { | |
| "database": "falkordb", # Name of your FalkorDB database | |
| "host": "localhost", # FalkorDB server hostname | |
| "username": "<username>", # Your FalkorDB username | |
| "password": "<password>", # Your FalkorDB password | |
| "port": "6379" # Default FalkorDB port | |
| } |
| "provider": "falkordb", | ||
| "config": { | ||
| "url": "neo4j+s://---", | ||
| "username": "neo4j", | ||
| "password": "---" | ||
| "Database": "falkordb", | ||
| "host": "---" | ||
| "username": "---", | ||
| "password": "---", | ||
| "port": "---" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Follow consistent parameter naming conventions.
The configuration parameters should follow consistent naming conventions:
- The
Databaseparameter should be lowercase to match other parameters. - Consider grouping connection-related parameters (
host,port) together.
Apply this diff to improve the configuration structure:
"provider": "falkordb",
"config": {
- "Database": "falkordb",
- "host": "---"
- "username": "---",
- "password": "---",
- "port": "---"
+ "database": "falkordb",
+ "host": "---",
+ "port": "---",
+ "username": "---",
+ "password": "---"
}📝 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.
| "provider": "falkordb", | |
| "config": { | |
| "url": "neo4j+s://---", | |
| "username": "neo4j", | |
| "password": "---" | |
| "Database": "falkordb", | |
| "host": "---" | |
| "username": "---", | |
| "password": "---", | |
| "port": "---" | |
| "provider": "falkordb", | |
| "config": { | |
| "database": "falkordb", | |
| "host": "---", | |
| "port": "---", | |
| "username": "---", | |
| "password": "---" |
💡 Codebase verification
Based on the search results, I can now provide a definitive response about the FalkorDB configuration parameters.
The Database parameter in the configuration is incorrect
The FalkorDB configuration in the documentation has an error. According to mem0/graphs/configs.py, the correct configuration structure should not include a Database parameter, as it's handled internally with a fixed value of "default". Here are the correct parameters:
host: Host address for the graph databaseusername: Username for the graph databasepassword: Password for the graph databaseport: Port for the graph database
This is evidenced by the FalkorDBConfig class definition and the fact that the configuration is automatically processed to use "default" as the database name, as shown in the validator code:
config["database"] = "_default_"🔗 Analysis chain
Verify FalkorDB configuration parameters.
The configuration structure has changed significantly from Neo4j. Let's verify that these are the correct parameters for FalkorDB integration.
Let me try a different search strategy to find FalkorDB configuration information.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Search for FalkorDB configuration examples and parameter definitions in the codebase.
# Test 1: Search for FalkorDB configuration examples
echo "Searching for FalkorDB configuration examples..."
rg -l 'falkordb.*config'
# Test 2: Search for FalkorDB parameter definitions
echo "Searching for FalkorDB parameter definitions..."
ast-grep --pattern 'class $_(FalkorDB|Config|Settings)'
Length of output: 296
Script:
#!/bin/bash
# Search for FalkorDB related files and configuration patterns
# Look for any files containing FalkorDB (case-insensitive)
echo "=== Files containing FalkorDB ==="
rg -i "falkordb" -A 5 -B 5
# Look for configuration examples in documentation
echo -e "\n=== Configuration examples in docs ==="
fd . docs -e md -e mdx -e txt -X rg -i "falkordb.*config|database.*config"
# Look for configuration related code files
echo -e "\n=== Configuration related files ==="
fd -e py -e js -e ts -e json -X rg -l "(?i)falkordb.*config|database.*connection"
Length of output: 16346
mem0/memory/graph_memory.py
Outdated
| results = self.graph_query(query, params={"user_id": filters["user_id"]}) | ||
|
|
||
| final_results = [] | ||
| for result in results: | ||
| final_results.append( | ||
| { | ||
| "source": result["source"], | ||
| "relationship": result["relationship"], | ||
| "target": result["target"], | ||
| } | ||
| ) | ||
| final_results.append({ | ||
| "source": result[0], | ||
| "relationship": result[1], | ||
| "target": result[2] | ||
| }) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Ensure results have the expected structure before accessing elements
When processing results in the get_all method, you assume that each result contains at least three elements at indices [0], [1], and [2]. If this is not guaranteed, it may raise an IndexError.
Consider adding validation to ensure each result has the expected format:
for result in results:
if len(result) >= 3:
final_results.append({
"source": result[0],
"relationship": result[1],
"target": result[2]
})
else:
logger.warning(f"Unexpected result format: {result}")| @@ -255,7 +241,7 @@ def search(self, query, filters, limit=100): | |||
| if not search_output: | |||
| return [] | |||
|
|
|||
| search_outputs_sequence = [[item["source"], item["relation"], item["destination"]] for item in search_output] | |||
| search_outputs_sequence = [[item[0], item[2], item[4]] for item in search_output] | |||
There was a problem hiding this comment.
Potential IndexError in list comprehension
In the list comprehension at line 244, you are accessing item[0], item[2], and item[4] from search_output. If any item in search_output does not have at least 5 elements, this will raise an IndexError.
Consider adding a check to ensure each item has the expected length:
-search_outputs_sequence = [[item[0], item[2], item[4]] for item in search_output]
+search_outputs_sequence = [[item[0], item[2], item[4]] for item in search_output if len(item) >= 5]📝 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.
| search_outputs_sequence = [[item[0], item[2], item[4]] for item in search_output] | |
| search_outputs_sequence = [[item[0], item[2], item[4]] for item in search_output if len(item) >= 5] |
There was a problem hiding this comment.
Actionable comments posted: 69
🧹 Outside diff range and nitpick comments (99)
vercel-ai-sdk/src/index.ts (2)
1-1: Consider explicit exports instead of wildcard export.Using
export *can make it difficult to track which entities are being exposed through the public API and may accidentally expose internal implementation details.Consider listing explicit exports:
-export * from './mem0-facade' +export { + // List specific exports from mem0-facade + MemoryManager, + MemoryProvider, + // ... other specific exports +} from './mem0-facade'
4-4: Fix spacing inconsistency.There's a missing space after the opening brace in the export statement.
-export {addMemories, retrieveMemories, searchMemories } from './mem0-utils' +export { addMemories, retrieveMemories, searchMemories } from './mem0-utils'examples/vercel-ai-sdk-chat-app/src/page.tsx (2)
1-3: Consider adding TypeScript type definitions for imported components.For better type safety and development experience, consider:
- Adding explicit type imports for
GlobalStateandComponent- Using more specific import paths (e.g., './pages/home/index' instead of './pages/home')
"use client"; -import { GlobalState } from "./contexts/GlobalContext"; -import Component from "./pages/home"; +import type { FC } from 'react'; +import { GlobalState } from "./contexts/GlobalContext"; +import HomeComponent from "./pages/home/index";
9-11: Consider adding error boundaries for robust error handling.The
GlobalStateprovider orComponentmight fail during rendering. Adding error boundaries would improve the application's resilience.Example implementation:
import { ErrorBoundary } from 'react-error-boundary'; const ErrorFallback = () => ( <div role="alert"> <h2>Something went wrong!</h2> <button onClick={() => window.location.reload()}>Try again</button> </div> ); const Home: FC = () => { return ( <ErrorBoundary FallbackComponent={ErrorFallback}> <GlobalState> <HomeComponent /> </GlobalState> </ErrorBoundary> ); };examples/vercel-ai-sdk-chat-app/src/types.ts (3)
1-1: Scope the ESLint disable commentThe ESLint disable comment affects the entire file. Consider scoping it to just the specific line where it's needed.
-/* eslint-disable @typescript-eslint/no-explicit-any */Move it to the specific line where
anyis used:// In Message interface // eslint-disable-next-line @typescript-eslint/no-explicit-any audio?: any;
2-7: Consider using more specific types for timestampThe interface looks well-structured, but consider using a more specific type for the timestamp field.
export interface Memory { id: string; content: string; - timestamp: string; + timestamp: number | Date; // or ISO 8601 string type tags: string[]; }
18-22: Consider adding JSDoc comments and more specific typesThe FileInfo interface could benefit from documentation and more specific types.
+/** + * Represents file metadata + */ export interface FileInfo { name: string; - type: string; + type: `${string}/${string}`; // MIME type format + /** Size in bytes */ size: number; }examples/vercel-ai-sdk-chat-app/src/components/ui/label.tsx (1)
7-9: Consider adding size and emphasis variants for better flexibility.While the current implementation provides good base styling, consider adding variants for different sizes (e.g., sm, md, lg) and emphasis levels (e.g., default, subtle, bold) to enhance reusability.
Example enhancement:
const labelVariants = cva( - "text-sm font-medium leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70" + "font-medium leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70", + { + variants: { + size: { + sm: "text-sm", + md: "text-base", + lg: "text-lg" + }, + emphasis: { + default: "font-medium", + subtle: "font-normal", + bold: "font-semibold" + } + }, + defaultVariants: { + size: "sm", + emphasis: "default" + } + } )examples/vercel-ai-sdk-chat-app/eslint.config.js (2)
1-5: LGTM! Consider adding more useful plugins.The imports are well-organized and include essential plugins. Consider enhancing the linting capabilities with these additional plugins:
eslint-plugin-import: For proper import/export syntaxeslint-plugin-jsx-a11y: For accessibility rules
12-15: Consider updating ECMAScript version.The configuration uses ECMAScript 2020. Consider updating to ECMAScript 2022 to leverage newer JavaScript features like
.at(),Object.hasOwn(), and class fields.languageOptions: { - ecmaVersion: 2020, + ecmaVersion: 2022, globals: globals.browser, },mem0/memory/utils.py (1)
21-30: Enhance function robustness and documentationWhile the function's logic is correct, consider these improvements for better maintainability and error handling:
Apply this diff to add type hints, docstring, and input validation:
-def format_entities(entities): +from typing import List, Dict, Optional + +def format_entities(entities: Optional[List[Dict[str, str]]] -> str: + """Format a list of entity relationships into a human-readable string. + + Args: + entities: List of dictionaries, each containing 'source', 'relation', + and 'destination' keys. Can be None or empty. + + Returns: + A formatted string representing the relationships, or empty string if no entities. + + Raises: + KeyError: If any entity dictionary is missing required keys. + """ if not entities: return "" formatted_lines = [] for entity in entities: + # Validate entity structure + required_keys = {'source', 'relation', 'destination'} + if not all(key in entity for key in required_keys): + raise KeyError(f"Entity missing required keys: {required_keys}") + simplified = f"{entity['source']} -- {entity['relation'].upper()} -- {entity['destination']}" formatted_lines.append(simplified) return "\n".join(formatted_lines)docs/components/embedders/models/gemini.mdx (1)
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1-24: Documentation structure looks good, consider adding example outputThe documentation is well-structured with clear setup instructions, usage example, and configuration details. To make it even more helpful, consider adding an example output showing the embedding dimensions or a simple similarity comparison use case.
examples/vercel-ai-sdk-chat-app/src/components/ui/input.tsx (2)
5-6: Consider adding JSDoc documentation for the interface.While extending HTMLInputAttributes is a good practice, adding JSDoc documentation would improve code maintainability and developer experience.
+/** + * Props for the Input component. + * Extends all native HTML input attributes. + */ export interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {}
8-22: Consider adding a default type value for better predictability.The
typeprop should have a default value to ensure consistent behavior.const Input = React.forwardRef<HTMLInputElement, InputProps>( - ({ className, type, ...props }, ref) => { + ({ className, type = "text", ...props }, ref) => {Consider extracting the default styles for better maintainability.
The inline styles could be moved to a separate constant or CSS module to improve readability.
+const defaultInputStyles = + "flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-1 " + + "text-sm shadow-sm transition-colors file:border-0 file:bg-transparent " + + "file:text-sm file:font-medium file:text-foreground " + + "placeholder:text-muted-foreground focus-visible:outline-none " + + "focus-visible:ring-1 focus-visible:ring-ring disabled:cursor-not-allowed " + + "disabled:opacity-50" + const Input = React.forwardRef<HTMLInputElement, InputProps>( ({ className, type, ...props }, ref) => { return ( <input type={type} - className={cn( - "flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-1 text-sm shadow-sm transition-colors file:border-0 file:bg-transparent file:text-sm file:font-medium file:text-foreground placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:cursor-not-allowed disabled:opacity-50", - className - )} + className={cn(defaultInputStyles, className)}vercel-ai-sdk/src/mem0-chat-settings.ts (1)
36-36: Consider removing redundant interfaceThe
Mem0Configinterface is identical toMem0ChatSettingswithout adding any new properties or constraints. This might cause confusion about its intended purpose.Consider either:
- Removing this interface and using
Mem0ChatSettingsdirectly- Adding distinct properties that justify its existence
- Adding documentation to explain why this alias exists
mem0/configs/vector_stores/redis.py (2)
6-7: Address the TODO comment regarding Pydantic version upgradeThe TODO indicates a pending upgrade to the latest Pydantic version. This should be addressed to ensure we're using the latest features and security updates.
Would you like me to help create an issue to track this upgrade task? I can include the necessary steps and potential impact analysis.
8-10: Consider adding URL format validation for redis_urlWhile the field is correctly marked as required, consider adding URL format validation to ensure the Redis URL follows the correct format (e.g., "redis://host:port").
- redis_url: str = Field(..., description="Redis URL") + redis_url: str = Field( + ..., + description="Redis URL", + pattern="^redis[s]?://[^:]+:[0-9]+$", + examples=["redis://localhost:6379"] + )examples/vercel-ai-sdk-chat-app/src/components/chevron-toggle.tsx (1)
1-8: Consider these improvements to imports and types
- The
Reactimport is unnecessary in modern React applications unless you're using specific React types.- Consider extracting the props interface for better reusability and documentation.
Here's the suggested improvement:
-import { Button } from "@/components/ui/button"; +import { Button } from "../../components/ui/button"; import { ChevronLeft, ChevronRight } from "lucide-react"; -import React from "react"; +interface ChevronToggleProps { + isMemoriesExpanded: boolean; + setIsMemoriesExpanded: (value: boolean) => void; +} -const ChevronToggle = (props: { - isMemoriesExpanded: boolean; - setIsMemoriesExpanded: React.Dispatch<React.SetStateAction<boolean>>; -}) => { +const ChevronToggle = ({ isMemoriesExpanded, setIsMemoriesExpanded }: ChevronToggleProps) => {docs/components/vectordbs/dbs/redis.mdx (1)
1-1: Enhance the introduction with more specific details about Redis vector capabilitiesConsider expanding the introduction to:
- Clarify that Redis Stack (not base Redis) is required for vector similarity search
- Mention RediSearch and Redis Vector Similarity Search modules
- Add details about supported distance metrics and indexing capabilities
-[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data. +[Redis](https://redis.io/) is a scalable, real-time database that supports vector similarity search through Redis Stack. It provides efficient vector indexing and search capabilities via the RediSearch and Vector Similarity Search modules, supporting cosine similarity, euclidean distance, and IP distance metrics.examples/vercel-ai-sdk-chat-app/src/components/ui/badge.tsx (3)
6-24: Consider enhancing keyboard focus stylesWhile the component includes basic focus styles, consider adding distinct
:focus-visiblestyles to improve keyboard navigation accessibility.const badgeVariants = cva( - "inline-flex items-center rounded-md border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2", + "inline-flex items-center rounded-md border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2", {
26-28: Add JSDoc documentation for the interfaceConsider adding JSDoc documentation to improve code maintainability and developer experience.
+/** + * Props for the Badge component. + * @extends React.HTMLAttributes<HTMLDivElement> + * @property {string} variant - The visual style variant of the badge + */ export interface BadgeProps extends React.HTMLAttributes<HTMLDivElement>, VariantProps<typeof badgeVariants> {}
30-34: Consider using semantic HTMLFor better semantics, consider using a
spanelement instead of adivsince badges are typically inline elements.-function Badge({ className, variant, ...props }: BadgeProps) { +function Badge({ className, variant, ...props }: React.HTMLAttributes<HTMLSpanElement> & VariantProps<typeof badgeVariants>) { return ( - <div className={cn(badgeVariants({ variant }), className)} {...props} /> + <span className={cn(badgeVariants({ variant }), className)} {...props} /> ) }docs/overview.mdx (1)
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27-31: Consider adding Graph Memory to Key FeaturesThe Key Features section should be updated to include the new Graph Memory feature, as it appears to be a significant addition based on the PR's changes.
Consider adding a new bullet point:
- OpenAI-compatible API: Easily switch between OpenAI and Mem0 - Advanced memory management: Save costs by efficiently handling long-term context - Flexible deployment: Choose between managed platform or self-hosted solution + - Graph Memory: Support for multiple graph store providers (Neo4j, FalkorDB) for enhanced memory capabilitiesexamples/vercel-ai-sdk-chat-app/src/pages/home.tsx (3)
1-8: Add TypeScript types for better type safety and maintainability.Consider adding proper TypeScript types for component props and state variables. Also, consider using a barrel export pattern for components.
-import { useState } from "react"; +import { useState, FC } from "react"; + +interface HomeProps {} + +interface HomeState { + isMemoriesExpanded: boolean; + isSettingsOpen: boolean; +} + +// Consider creating an index.ts in components directory for barrel exports +import { ApiSettingsPopup, Memories, Header, Messages, InputArea, ChevronToggle } from "../components"; -import ApiSettingsPopup from "../components/api-settings-popup"; -import Memories from "../components/memories"; -import Header from "../components/header"; -import Messages from "../components/messages"; -import InputArea from "../components/input-area"; -import ChevronToggle from "../components/chevron-toggle";
10-13: Consider using useReducer for related state management.Since both state variables are related to UI visibility, consider using useReducer for better state management. Also, extract initial state values as constants.
-export default function Home() { +const INITIAL_STATE = { + isMemoriesExpanded: true, + isSettingsOpen: false, +}; + +type UIAction = + | { type: 'TOGGLE_MEMORIES' } + | { type: 'TOGGLE_SETTINGS' }; + +const uiReducer = (state: HomeState, action: UIAction): HomeState => { + switch (action.type) { + case 'TOGGLE_MEMORIES': + return { ...state, isMemoriesExpanded: !state.isMemoriesExpanded }; + case 'TOGGLE_SETTINGS': + return { ...state, isSettingsOpen: !state.isSettingsOpen }; + default: + return state; + } +}; + +const Home: FC<HomeProps> = () => { + const [state, dispatch] = useReducer(uiReducer, INITIAL_STATE); - const [isMemoriesExpanded, setIsMemoriesExpanded] = useState(true); - const [isSettingsOpen, setIsSettingsOpen] = useState(false);
14-41: Consider adding error boundaries and loading states.The layout structure is clean, but could benefit from error handling and loading states. Also, consider using Context API to avoid prop drilling.
+import { ErrorBoundary } from 'react-error-boundary'; +import { UIContext } from '../context/ui-context'; + +const ErrorFallback = () => ( + <div className="flex items-center justify-center h-screen"> + <p>Something went wrong. Please try again.</p> + </div> +); return ( <> - <ApiSettingsPopup isOpen={isSettingsOpen} setIsOpen={setIsSettingsOpen} /> - <div className="flex h-screen bg-background"> + <ErrorBoundary FallbackComponent={ErrorFallback}> + <UIContext.Provider value={{ state, dispatch }}> + <ApiSettingsPopup /> + <div className="flex h-screen bg-background">Also, consider adding loading states for async operations:
const [isLoading, setIsLoading] = useState(false); // In the render method {isLoading && ( <div className="absolute inset-0 flex items-center justify-center bg-black/50"> <LoadingSpinner /> </div> )}docs/components/vectordbs/overview.mdx (1)
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19-23: Consider adding Redis configuration example.Since Redis has been added as a supported database, it would be helpful to include a Redis-specific configuration example in the Usage section.
Consider adding a code block like this:
## Usage To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `Qdrant` will be used as the vector database. +For Redis configuration, you can use: +```python +config = { + "vector_store": { + "provider": "redis", + "redis_url": "redis://localhost:6379" + } +} +``` + For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config).docs/examples/overview.mdx (1)
33-33: Consider enhancing the description to highlight Graph Memory features.Since this PR introduces Graph Memory as a new feature, consider expanding the description to emphasize how LlamaIndex leverages Mem0's graph capabilities for enhanced memory management.
- Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store. + Create a ReAct Agent with LlamaIndex that leverages Mem0's Graph Memory capabilities for enhanced context management and reasoning.examples/vercel-ai-sdk-chat-app/src/components/ui/avatar.tsx (1)
8-22: Consider making the avatar size configurable.The component has hardcoded dimensions (
h-10 w-10). Consider making it more reusable by accepting a size prop.-const Avatar = React.forwardRef< +const Avatar = React.forwardRef< React.ElementRef<typeof AvatarPrimitive.Root>, - React.ComponentPropsWithoutRef<typeof AvatarPrimitive.Root> + React.ComponentPropsWithoutRef<typeof AvatarPrimitive.Root> & { + size?: 'sm' | 'md' | 'lg' + } ->(({ className, ...props }, ref) => ( +>(({ className, size = 'md', ...props }, ref) => ( <AvatarPrimitive.Root ref={ref} className={cn( - "relative flex h-10 w-10 shrink-0 overflow-hidden rounded-full", + "relative flex shrink-0 overflow-hidden rounded-full", + size === 'sm' && 'h-8 w-8', + size === 'md' && 'h-10 w-10', + size === 'lg' && 'h-12 w-12', className )} {...props} /> ))examples/vercel-ai-sdk-chat-app/src/components/ui/scroll-area.tsx (1)
24-44: Consider adding dark mode support for the scrollbar thumb.The scrollbar implementation is solid, but consider adding dark mode support for better visibility in different themes.
- <ScrollAreaPrimitive.ScrollAreaThumb className="relative flex-1 rounded-full bg-border" /> + <ScrollAreaPrimitive.ScrollAreaThumb + className={cn( + "relative flex-1 rounded-full", + "bg-border dark:bg-border-dark" + )} + />examples/vercel-ai-sdk-chat-app/src/components/ui/button.tsx (3)
7-35: Consider enhancing accessibility attributesThe button variants look well-structured, but consider adding ARIA states for better accessibility:
const buttonVariants = cva( - "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0", + "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0 aria-disabled:opacity-50 aria-busy:cursor-wait",
37-41: Add JSDoc documentation for the ButtonProps interfaceConsider adding comprehensive documentation to improve code maintainability:
+/** + * Props for the Button component. + * @property {boolean} asChild - When true, the component will render as its child element instead of a button + * @property {string} variant - Visual style variant of the button + * @property {string} size - Size variant of the button + */ export interface ButtonProps extends React.ButtonHTMLAttributes<HTMLButtonElement>, VariantProps<typeof buttonVariants> { asChild?: boolean }
43-55: Add runtime prop validationConsider adding runtime validation for the variant and size props to catch potential misconfigurations early:
const Button = React.forwardRef<HTMLButtonElement, ButtonProps>( ({ className, variant, size, asChild = false, ...props }, ref) => { + if (process.env.NODE_ENV !== 'production') { + const validVariants = Object.keys(buttonVariants.variants.variant || {}); + const validSizes = Object.keys(buttonVariants.variants.size || {}); + if (variant && !validVariants.includes(variant)) { + console.warn(`Invalid button variant: ${variant}. Valid variants are: ${validVariants.join(', ')}`); + } + if (size && !validSizes.includes(size)) { + console.warn(`Invalid button size: ${size}. Valid sizes are: ${validSizes.join(', ')}`); + } + } const Comp = asChild ? Slot : "button" return ( <Compexamples/vercel-ai-sdk-chat-app/src/components/ui/card.tsx (4)
5-18: Consider adding ARIA role for better accessibilityThe Card component could benefit from an explicit ARIA role to improve accessibility.
<div ref={ref} className={cn( "rounded-xl border bg-card text-card-foreground shadow", className )} + role="article" {...props} />
56-62: Consider using semantic HTML for contentFor better semantics and accessibility, consider using
sectionorarticleelement instead of adiv.const CardContent = React.forwardRef< HTMLDivElement, React.HTMLAttributes<HTMLDivElement> >(({ className, ...props }, ref) => ( - <div ref={ref} className={cn("p-6 pt-0", className)} {...props} /> + <section ref={ref} className={cn("p-6 pt-0", className)} {...props} /> ))
1-76: Add JSDoc documentation and explicit prop interfacesConsider adding proper documentation and explicit prop interfaces for better maintainability and developer experience.
Example implementation:
interface CardProps extends React.HTMLAttributes<HTMLDivElement> { /** Additional CSS classes to apply */ className?: string; } /** * Card component that serves as a container for related content * @param props The component props * @param ref Forward ref to the underlying div element */ const Card = React.forwardRef<HTMLDivElement, CardProps>(...)
76-76: Consider implementing compound components patternThe current implementation could be enhanced by using the compound components pattern, which would provide better type safety and a more intuitive API.
Example implementation:
type CardComponent = React.FC<CardProps> & { Header: typeof CardHeader; Title: typeof CardTitle; Description: typeof CardDescription; Content: typeof CardContent; Footer: typeof CardFooter; }; // Usage: Card.Header = CardHeader; Card.Title = CardTitle; // ... etc export const Card = CardComponent;This would enable a more intuitive usage:
<Card> <Card.Header> <Card.Title>Title</Card.Title> </Card.Header> <Card.Content>Content</Card.Content> </Card>examples/vercel-ai-sdk-chat-app/src/index.css (1)
68-97: Consider enhancing the loader animation implementation.While the animation implementation is functional, there are several improvements that could be made:
.ball { width: 6px; height: 6px; - background-color: #4e4e4e; + background-color: hsl(var(--muted-foreground)); border-radius: 50%; animation: bounce 0.6s infinite alternate; } +@media (prefers-reduced-motion: reduce) { + .ball { + animation: none; + } +}Consider:
- Using theme variables for colors instead of hardcoded values
- Adding support for reduced motion preferences
- Moving animation timing values to CSS variables for easier maintenance
examples/vercel-ai-sdk-chat-app/src/components/header.tsx (1)
11-13: Consider memoizing context values.While the context usage is correct, consider memoizing the destructured values to prevent unnecessary re-renders.
- const { selectUserHandler, clearUserHandler, selectedUser, clearConfiguration } = useContext(GlobalContext); + const { + selectUserHandler, + clearUserHandler, + selectedUser, + clearConfiguration + } = useContext(GlobalContext); + + const memoizedHandlers = useMemo(() => ({ + selectUser: selectUserHandler, + clearUser: clearUserHandler, + clearConfig: clearConfiguration + }), [selectUserHandler, clearUserHandler, clearConfiguration]);Also applies to: 35-39
vercel-ai-sdk/config/test-config.ts (1)
6-10: Consider adding validation constraints to the Provider interface.While the interface structure is good, consider adding:
- A type union for the
namefield to restrict it to supported providers- Validation for
activeModelto ensure it matches the provider's available models+type ProviderName = 'openai' | 'anthropic' | 'groq' | 'cohere'; + export interface Provider { - name: string; + name: ProviderName; activeModel: string; apiKey: string | undefined; }docs/api-reference/overview.mdx (3)
40-43: Add location info for project_idFor consistency, consider adding a comment about where to find the project_id, similar to the org_id comment.
client = MemoryClient( org_id='YOUR_ORG_ID', # It can be found on the organization settings page in dashboard - project_id='YOUR_PROJECT_ID', + project_id='YOUR_PROJECT_ID', # It can be found on the project settings page in dashboard )
54-58: Fix comment style in JavaScript exampleThe comment uses Python-style
#instead of JavaScript-style//.-# Recommended: Using organization and project IDs +// Recommended: Using organization and project IDs const client = new MemoryClient({ organizationId: "YOUR_ORG_ID", projectId: "YOUR_PROJECT_ID" });
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1-1: Consider adding Graph Memory documentationThe AI summary mentions this PR introduces a new "Graph Memory" feature, but it's not documented in this overview. Consider adding information about Graph Memory in the Key Features or API Structure sections.
examples/vercel-ai-sdk-chat-app/src/components/memories.tsx (3)
1-7: Consider consolidating UI component imports for consistencyThe UI components are imported from different sources - some from local components (
@/components/ui) and others from radix-ui directly. Consider maintaining consistency by either:
- Using all components directly from radix-ui
- Creating local wrapper components for all radix-ui components
26-26: Extract date formatting logicConsider extracting the date formatting logic into a utility function for reusability and easier maintenance.
+const formatTimestamp = (timestamp: string) => { + return new Date(timestamp).toLocaleString(); +}; + // In the component: -<span>{new Date(memory.timestamp).toLocaleString()}</span> +<span>{formatTimestamp(memory.timestamp)}</span>
52-56: Simplify conditional classes using clsx/cn utilityConsider using a utility like
clsxorcnto manage conditional classes more cleanly.+import { cn } from "@/lib/utils"; - className={`border-l rounded-none flex flex-col transition-all duration-300 ${ - isMemoriesExpanded ? "w-80" : "w-0 overflow-hidden" - }`} + className={cn( + "border-l rounded-none flex flex-col transition-all duration-300", + isMemoriesExpanded ? "w-80" : "w-0 overflow-hidden" + )}mem0/utils/factory.py (3)
83-86: Consider documenting provider capabilitiesThe provider mapping looks good, but consider adding docstrings to document the specific capabilities and requirements of each graph provider.
Add documentation like this:
provider_to_class = { + # FalkorDB: Property graph database with Cypher query support "falkordb": "langchain_community.graphs.FalkorDBGraph", + # Neo4j: Industry-standard graph database with Cypher query support "neo4j": "langchain_community.graphs.Neo4jGraph", }
88-97: Add type hints and error handlingThe implementation looks good but could benefit from some improvements:
- Add type hints for better code maintainability
- Add connection error handling
Consider this implementation:
@classmethod -def create(cls, provider_name, config): +def create(cls, provider_name: str, config: Union[dict, BaseModel]) -> Any: class_type = cls.provider_to_class.get(provider_name) if class_type: if not isinstance(config, dict): config = config.model_dump() graph_instance = load_class(class_type) - return graph_instance(**config) + try: + return graph_instance(**config) + except Exception as e: + raise ConnectionError(f"Failed to connect to {provider_name}: {str(e)}") else: raise ValueError(f"Unsupported graph provider: {provider_name}")
82-97: Consider architectural implications of using langchain_communityWhile the implementation is correct, using langchain_community implementations directly differs from other factories that use local implementations. This architectural decision has implications:
- Version compatibility with langchain_community needs to be managed
- Less control over the implementation details
- Potential impact on error handling and configuration options
Consider whether maintaining local wrapper implementations (like other factories) would provide more control and consistency.
examples/vercel-ai-sdk-chat-app/src/components/input-area.tsx (2)
47-88: Improve component structure and accessibility.The current implementation has several areas for improvement:
- Complex nested structure could be simplified
- Missing aria-labels for accessibility
- Potential z-index stacking context issues
return ( <> <div className="border-t p-4"> - <div className="flex items-center space-x-2"> + <div className="flex items-center space-x-2 relative"> <div className="relative bottom-3 left-5"> - <div className="absolute"> <Input type="file" accept="image/*" onChange={handleFileChange} ref={fileInputRef} className="sr-only" id="file-upload" + aria-label="Upload image" /> <label htmlFor="file-upload" - className="flex items-center justify-center w-6 h-6 text-gray-500 hover:text-gray-700 dark:text-gray-400 dark:hover:text-gray-200 cursor-pointer" + className="flex items-center justify-center w-6 h-6 text-gray-500 hover:text-gray-700 dark:text-gray-400 dark:hover:text-gray-200 cursor-pointer" + aria-label="Upload image" > <Images className="h-4 w-4" /> </label> {selectedFile && <FileInfoPopup file={selectedFile} onClose={handleClosePopup} />} - </div> </div> <Input value={inputValue} onChange={(e) => setInputValue(e.target.value)} onKeyDown={(e) => e.key === "Enter" && handleSendController()} placeholder="Type a message..." - className="flex-1 pl-10 rounded-3xl" + className="flex-1 pl-10 rounded-3xl" + aria-label="Message input" disabled={loading} ref={ref} /> - <div className="relative right-14 bottom-5 flex"> - <Button className="absolute rounded-full w-10 h-10 bg-transparent hover:bg-transparent cursor-pointer z-20 text-primary" + <Button + className="absolute right-2 top-1/2 -translate-y-1/2 rounded-full w-10 h-10 bg-transparent hover:bg-transparent cursor-pointer text-primary" onClick={handleSendController} - disabled={!inputValue.trim() || loading}> + disabled={!inputValue.trim() || loading} + aria-label="Send message"> <Send className="h-8 w-8" size={50} /> </Button> - </div> </div> </div> </> );
90-105: Enhance FileInfoPopup component accessibility and UX.Consider improving the popup component with better positioning, accessibility, and user experience.
const FileInfoPopup = ({ file, onClose }: { file: FileInfo, onClose: () => void }) => { return ( - <div className="relative bottom-36"> - <div className="absolute top-full left-0 mt-1 bg-white dark:bg-gray-800 p-2 rounded-md shadow-md border border-gray-200 dark:border-gray-700 z-10 w-48"> + <div className="absolute -top-40 left-0"> + <div + role="dialog" + aria-label="File information" + className="bg-white dark:bg-gray-800 p-2 rounded-md shadow-md border border-gray-200 dark:border-gray-700 w-48 animate-fade-in" + > <div className="flex justify-between items-center"> - <h3 className="font-semibold text-sm truncate">{file.name}</h3> + <h3 className="font-semibold text-sm truncate" id="file-name">{file.name}</h3> <Button variant="ghost" size="sm" onClick={onClose} - className="h-5 w-5 p-0"> + className="h-5 w-5 p-0" + aria-label="Close file information"> <X className="h-3 w-3" /> </Button> </div> <p className="text-xs text-gray-500 dark:text-gray-400 truncate">Type: {file.type}</p> <p className="text-xs text-gray-500 dark:text-gray-400">Size: {(file.size / 1024).toFixed(2)} KB</p> </div> </div> ) }Add this to your CSS/Tailwind config:
@keyframes fadeIn { from { opacity: 0; transform: translateY(-10px); } to { opacity: 1; transform: translateY(0); } } .animate-fade-in { animation: fadeIn 0.2s ease-out; }examples/vercel-ai-sdk-chat-app/src/components/messages.tsx (1)
25-74: Enhance accessibility and performance of the message list.Consider the following improvements:
- Add ARIA labels for better screen reader support
- Implement virtualization for better performance with large message lists
Here's how to improve accessibility:
{messages.map((message: Message) => ( <div key={message.id} + role="listitem" + aria-label={`${message.sender} message: ${message.content}`} className={`flex ${ message.sender === "user" ? "justify-end" : "justify-start" }`} >Consider using a virtualization library like
react-windoworreact-virtualizedfor handling large message lists efficiently.examples/vercel-ai-sdk-chat-app/src/components/api-settings-popup.tsx (1)
83-86: Add loading state to Save button.The Save button should:
- Be disabled during the save operation
- Show loading state to provide visual feedback
-<Button className='rounded-3xl' onClick={handleSave}>Save</Button> +<Button + className='rounded-3xl' + onClick={handleSave} + disabled={isLoading} +> + {isLoading ? 'Saving...' : 'Save'} +</Button>examples/vercel-ai-sdk-chat-app/src/components/ui/dialog.tsx (1)
30-52: Consider extracting complex styles to constants.The implementation is solid, but the lengthy className string could be harder to maintain. Consider extracting it to a constant or utility function.
+const dialogContentStyles = + "fixed left-[50%] top-[50%] z-50 grid w-full max-w-lg translate-x-[-50%] " + + "translate-y-[-50%] gap-4 border bg-background p-6 shadow-lg duration-200 " + + "data-[state=open]:animate-in data-[state=closed]:animate-out " + + "data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 " + + "data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 " + + "data-[state=closed]:slide-out-to-left-1/2 " + + "data-[state=closed]:slide-out-to-top-[48%] " + + "data-[state=open]:slide-in-from-left-1/2 " + + "data-[state=open]:slide-in-from-top-[48%] sm:rounded-lg" const DialogContent = React.forwardRef< React.ElementRef<typeof DialogPrimitive.Content>, React.ComponentPropsWithoutRef<typeof DialogPrimitive.Content> >(({ className, children, ...props }, ref) => ( <DialogPortal> <DialogOverlay /> <DialogPrimitive.Content ref={ref} - className={cn( - "fixed left-[50%] top-[50%] z-50 grid w-full max-w-lg translate-x-[-50%] translate-y-[-50%] gap-4 border bg-background p-6 shadow-lg duration-200 data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[state=closed]:slide-out-to-left-1/2 data-[state=closed]:slide-out-to-top-[48%] data-[state=open]:slide-in-from-left-1/2 data-[state=open]:slide-in-from-top-[48%] sm:rounded-lg", - className - )} + className={cn(dialogContentStyles, className)}docs/integrations/vercel-ai-sdk.mdx (1)
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1-164: Documentation improvements needed.
Fix duplicate section numbering:
- There are two sections labeled "3." (Structured Message Format and Streaming Responses)
Enhance security best practices in code examples:
- Replace hardcoded API keys (
m0-xxx,provider-api-key) with environment variables- Add example of loading environment variables
Here's an example of how to improve the initialization code:
const mem0 = createMem0({ provider: "openai", - mem0ApiKey: "m0-xxx", - apiKey: "provider-api-key", + mem0ApiKey: process.env.MEM0_API_KEY, + apiKey: process.env.OPENAI_API_KEY, config: { compatibility: "strict", }, });Add a section about environment variables:
// .env file MEM0_API_KEY=your-mem0-api-key OPENAI_API_KEY=your-openai-api-keydocs/integrations/crewai.mdx (2)
76-88: Add error handling and document memory configuration.The agent creation lacks error handling for tool initialization and documentation about memory configuration.
def create_travel_agent(): """Create a travel planning agent with search capabilities""" - search_tool = SerperDevTool() + try: + search_tool = SerperDevTool() + except Exception as e: + raise RuntimeError(f"Failed to initialize search tool: {str(e)}") return Agent( role="Personalized Travel Planner Agent", goal="Plan personalized travel itineraries", backstory="""You are a seasoned travel planner, known for your meticulous attention to detail.""", allow_delegation=False, - memory=True, + memory=True, # Enables integration with Mem0's memory system tools=[search_tool], )Also, consider adding a note about memory configuration:
<Note> The `memory=True` parameter enables the agent to access and utilize the conversation history stored in Mem0. This allows the agent to maintain context across interactions. </Note>
95-102: Add input validation and enhance task description.The task creation should validate the destination parameter and provide more detailed instructions.
def create_planning_task(agent, destination: str): """Create a travel planning task""" + if not destination or not destination.strip(): + raise ValueError("Destination cannot be empty") + + task_description = f""" + 1. Research accommodations in {destination} considering user preferences + 2. Find popular and highly-rated restaurants + 3. Identify key attractions and activities + 4. Consider seasonal factors and local events + 5. Account for any stored user preferences from previous interactions + """ + return Task( - description=f"""Find places to live, eat, and visit in {destination}.""", + description=task_description, expected_output=f"A detailed list of places to live, eat, and visit in {destination}.", agent=agent, )docs/examples/llama-index-mem0.mdx (4)
29-34: Consider using a more generic example for contextThe hardcoded context with a specific user_id might not be the best practice for documentation. Consider using a more generic example or explaining that users should replace it with their own context.
-context = {"user_id": "david"} +context = {"user_id": "<your-user-id>"} # Replace with your user identifier
41-62: Consider adding error handling examplesWhile these are example functions, it would be beneficial to demonstrate error handling best practices. Consider adding try-catch blocks and input validation to show users how to handle real-world scenarios.
Example enhancement for the
order_foodfunction:def order_food(name: str, dish: str): """Order food for the provided name. Args: name: str (Name of the person) dish: str (Name of the dish) Raises: ValueError: If name or dish is empty """ if not name or not dish: raise ValueError("Name and dish must not be empty") try: # Simulated API call return f"Ordering {dish} for {name}" except Exception as e: raise RuntimeError(f"Failed to order food: {str(e)}")
75-75: Document thememory_from_configoptionThe comment mentions
memory_from_configas an alternative, but this option is not explained anywhere in the documentation. Either remove the reference or add documentation for this configuration method.
140-140: Fix typos in notesThere are typos in the word "preferences" in both notes.
-<Note> The agent is not able to remember the past prefernces that user shared in previous chats. </Note> +<Note> The agent is not able to remember the past preferences that user shared in previous chats. </Note> -<Note> The agent is able to remember the past prefernces that user shared and use them to perform actions. </Note> +<Note> The agent is able to remember the past preferences that user shared and use them to perform actions. </Note>Also applies to: 172-172
🧰 Tools
🪛 LanguageTool
[style] ~140-~140: As a shorter alternative for ‘able to’, consider using “can not”.
Context: ...are of it for you! ``` The agent is not able to remember the past prefernces that user ...(BE_ABLE_TO)
mem0/graphs/utils.py (3)
21-23: Enhance the Memory Format section with examples.The Memory Format section could be more helpful with concrete examples to illustrate the expected format.
Memory Format: source -- RELATIONSHIP -- destination + +Examples: +1. John -- WORKS_AT -- Google +2. Alice -- MANAGES -- Project_X +3. USER_ID -- LIKES -- Coffee
53-53: Fix typo in the prompt text.There's a typo in the word "eshtablishing".
-Strive to construct a coherent and easily understandable knowledge graph by eshtablishing all the relationships among the entities and adherence to the user's context. +Strive to construct a coherent and easily understandable knowledge graph by establishing all the relationships among the entities and adherence to the user's context.
104-114: Add type hints and documentation to utility functions.The utility functions would benefit from type hints and parameter documentation for better maintainability.
-def get_update_memory_prompt(existing_memories, new_memories, template): +from typing import List, Dict, Any + +def get_update_memory_prompt( + existing_memories: List[Dict[str, Any]], + new_memories: List[Dict[str, Any]], + template: str +) -> str: + """Format the update memory prompt with existing and new memories. + + Args: + existing_memories: List of current graph memories + new_memories: List of new memories to be integrated + template: The prompt template to use + + Returns: + Formatted prompt string + """ return template.format(existing_memories=existing_memories, new_memories=new_memories) -def get_update_memory_messages(existing_memories, new_memories): +def get_update_memory_messages( + existing_memories: List[Dict[str, Any]], + new_memories: List[Dict[str, Any]] +) -> List[Dict[str, str]]: + """Generate message list for the update memory prompt. + + Args: + existing_memories: List of current graph memories + new_memories: List of new memories to be integrated + + Returns: + List of message dictionaries + """ return [ { "role": "user", "content": get_update_memory_prompt(existing_memories, new_memories, UPDATE_GRAPH_PROMPT), }, ]docs/integrations/llama-index.mdx (4)
27-29: Consider adding a security note about API key handling.While setting environment variables is a common practice, it's recommended to add a note about:
- Not hardcoding API keys in source code
- Using secure environment variable management in production
- Keeping API keys confidential
104-104: Fix typo in section header."Intilaize" should be "Initialize".
124-127: Consider adding error handling examples.The chat examples would benefit from try-catch blocks to demonstrate proper error handling, especially for API failures or rate limiting scenarios.
Example addition:
try: response = agent.chat("Hi, My name is Mayank") print(response) except Exception as e: print(f"Error during chat: {e}")Also applies to: 167-170, 184-187
204-204: Fix grammar in the help section.Add the missing article "the":
-feel free to reach out to us through following methods: +feel free to reach out to us through the following methods:🧰 Tools
🪛 LanguageTool
[uncategorized] ~204-~204: You might be missing the article “the” here.
Context: ...se feel free to reach out to us through following methods: <Snippet file="get-help.mdx" ...(AI_EN_LECTOR_MISSING_DETERMINER_THE)
vercel-ai-sdk/README.md (3)
52-52: Enhance environment variables documentationConsider adding all relevant environment variables for graph memory configuration:
-For better security, consider setting `MEM0_API_KEY` and `OPENAI_API_KEY` as environment variables. +For better security, consider setting the following environment variables: +- `MEM0_API_KEY`: Your Mem0 API key +- `OPENAI_API_KEY`: Your OpenAI API key +- `FALKORDB_HOST`: FalkorDB host (default: localhost) +- `FALKORDB_PORT`: FalkorDB port (default: 6379) +- `NEO4J_URI`: Neo4j connection URI +- `NEO4J_USERNAME`: Neo4j username +- `NEO4J_PASSWORD`: Neo4j password
214-220: Add Graph Memory best practicesConsider adding best practices specific to graph memory:
## Best Practices 1. **User Identification**: Always provide a unique `user_id` identifier for consistent memory retrieval 2. **Context Management**: Use appropriate context window sizes to balance performance and memory 3. **Error Handling**: Implement proper error handling for memory operations 4. **Memory Cleanup**: Regularly clean up unused memory contexts to optimize performance +5. **Graph Queries**: Write efficient graph queries to retrieve relevant memories +6. **Relationship Management**: Design meaningful relationships between entities +7. **Graph Indexing**: Utilize appropriate indexes for better query performance +8. **Connection Pooling**: Implement connection pooling for graph databases in production
76-76: Remove trailing punctuation from headingFix the markdown lint issue:
-### Note: +### Note🧰 Tools
🪛 Markdownlint (0.35.0)
76-76: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
docs/quickstart.mdx (1)
61-62: LGTM! Consider adding error handling example.The updated client instantiation using object parameters and ES6 import syntax follows modern JavaScript best practices. However, consider adding error handling for the instantiation to maintain consistency with other examples.
import MemoryClient from 'mem0ai'; try { const client = new MemoryClient({ apiKey: 'your-api-key' }); } catch (error) { console.error('Failed to initialize client:', error); }mem0/graphs/tools.py (2)
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247-288: Inconsistency between RELATIONS_TOOL and RELATIONS_STRUCT_TOOLThe property names differ between the strict and non-strict versions:
- RELATIONS_TOOL uses: "source", "destination"
- RELATIONS_STRUCT_TOOL uses: "source_entity", "destination_entity"
This inconsistency could lead to confusion and maintenance issues. Consider aligning the property names across both versions.
Choose one naming convention and apply it consistently. If you decide to use the "entity" suffix, apply this change to RELATIONS_TOOL:
- "source": { + "source_entity": { "type": "string", "description": "The source entity of the relationship." }, "relation": { "type": "string", "description": "The relationship between the source and destination entities." }, - "destination": { + "destination_entity": { "type": "string", "description": "The destination entity of the relationship." },Or if you prefer without the suffix, apply this change to RELATIONS_STRUCT_TOOL:
- "source_entity": { + "source": { "type": "string", "description": "The source entity of the relationship." }, "relation": { "type": "string", "description": "The relationship between the source and destination entities." }, - "destination_entity": { + "destination": { "type": "string", "description": "The destination entity of the relationship." },
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1-322: Consider implementing a tool factory patternThe current approach of maintaining separate strict and non-strict versions of each tool leads to code duplication and potential inconsistencies. Consider implementing a tool factory pattern that generates both versions from a single source configuration.
This would:
- Reduce code duplication
- Ensure consistency between strict and non-strict versions
- Make it easier to maintain and modify tool configurations
- Reduce the likelihood of introducing inconsistencies when adding new tools
Example implementation approach:
def create_tool_config(name, description, properties, required, strict=False): """Factory function to create tool configurations.""" config = { "type": "function", "function": { "name": name, "description": description, "parameters": { "type": "object", "properties": properties, "required": required, "additionalProperties": False, } } } if strict: config["function"]["strict"] = True return configmem0/memory/main.py (1)
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583-607: Add docstring to document method behavior.The
_update_memorymethod would benefit from a docstring describing its parameters, behavior, and return value.Add this docstring before the method:
def _update_memory(self, memory_id, data, existing_embeddings, metadata=None): + """Update an existing memory with new data. + + Args: + memory_id (str): The ID of the memory to update + data (str): The new content for the memory + existing_embeddings (dict): Pre-computed embeddings for optimization + metadata (dict, optional): Additional metadata to store. Defaults to None. + + Returns: + str: The memory_id of the updated memory + + Raises: + ValueError: If memory_id is invalid or memory cannot be retrieved + """docs/platform/quickstart.mdx (3)
664-678: LGTM! Consider adding response headers documentation.The pagination implementation is well documented. Consider adding information about response headers that might contain pagination metadata (total count, next page URL, etc.).
Add a note about pagination response headers:
<Note> The API response includes pagination metadata in the headers: - `X-Total-Count`: Total number of records - `X-Next-Page`: URL for the next page (if available) - `X-Previous-Page`: URL for the previous page (if available) </Note>Also applies to: 684-690
373-424: Enhance filter operators documentation.While the filter examples are clear, consider documenting additional filter operators that might be available (e.g., "not", "or", "like", "regex", etc.).
Add a section about available filter operators:
### Available Filter Operators The search API supports the following filter operators: - `in`: Match any value in an array - `not`: Negate a condition - `or`: Combine multiple conditions - `like`: Pattern matching - `regex`: Regular expression matching Example: ```python filters = { "OR": [ {"category": {"in": ["food", "travel"]}}, {"metadata.tags": {"regex": "^premium.*"}} ] }
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1-1305: Add error handling documentation.The documentation would benefit from a section on error handling, including:
- Common error codes and their meanings
- Error response format
- Example error scenarios and how to handle them
Add an error handling section:
## Error Handling The API uses conventional HTTP response codes to indicate success or failure: - 2xx: Success - 4xx: Client errors - 5xx: Server errors Example error response: ```json { "error": { "code": "invalid_api_key", "message": "The provided API key is invalid", "status": 401 } }Common error scenarios:
- Invalid API key
- Rate limiting
- Invalid input format
- Resource not found
</blockquote></details> <details> <summary>docs/openapi.json (1)</summary><blockquote> `192-193`: **JavaScript SDK examples are well-structured and consistent** The JavaScript SDK examples are well-documented with proper error handling and follow modern async/await patterns. Installation instructions are clear and consistent across all endpoints. Consider adding TypeScript type annotations in the examples to improve developer experience, for example: ```diff -const client = new MemoryClient({ apiKey: "your-api-key" }); +const client: MemoryClient = new MemoryClient({ apiKey: "your-api-key" });Also applies to: 542-543, 649-650, 761-762, 903-904, 1063-1064, 1192-1193, 1350-1351, 1520-1521, 1666-1667
vercel-ai-sdk/src/mem0-chat-language-model.ts (2)
64-82: Use consistent naming conventions for variables and propertiesThere is an inconsistency in the naming conventions used for variables and properties, mixing
camelCaseandsnake_case. For example,mem0_api_keyandmem0ApiKeyare used interchangeably, which can lead to confusion and reduce code readability.Consider standardizing on
camelCasefor all variable and property names to align with JavaScript/TypeScript conventions. Here's how you can adjust the code:-const mem0_api_key = this.config.mem0_api_key; +const mem0ApiKey = this.config.mem0ApiKey; ... const settings: Mem0ProviderSettings = { provider: provider, - mem0ApiKey: mem0_api_key, + mem0ApiKey: mem0ApiKey, apiKey: this.config.apiKey, modelType: "chat" } ... -const apiKey = mem0_api_key; +const apiKey = mem0ApiKey; ... const config: Mem0Config = { user_id, app_id, agent_id, run_id, org_name, project_name, - mem0ApiKey: apiKey + mem0ApiKey: apiKey };This change will make your code more consistent and easier to maintain.
Also applies to: 113-131
38-45: Declare class properties before the constructorThe
providerproperty is declared after the constructor, which can be confusing. Declaring class properties before the constructor improves readability and aligns with common TypeScript practices.Move the declaration of
providerabove the constructor:export class Mem0ChatLanguageModel implements LanguageModelV1 { readonly specificationVersion = "v1"; readonly defaultObjectGenerationMode = "json"; readonly supportsImageUrls = false; + provider: string; + supportsStructuredOutputs?: boolean | undefined; constructor( public readonly modelId: Mem0ChatModelId, public readonly settings: Mem0ChatSettings, public readonly config: Mem0ChatConfig, public readonly provider_config?: OpenAIProviderSettings ) { this.provider = config.provider; }vercel-ai-sdk/src/mem0-completion-language-model.ts (4)
72-72: RenamemessagesPromptsto improve readabilityThe variable
messagesPrompts(lines 72 and 121) is grammatically incorrect and may cause confusion. Consider renaming it topromptsormessagesfor clarity.Apply this diff to rename the variable:
// In doGenerate method - let messagesPrompts = options.prompt; + let prompts = options.prompt; ... - const ans = await model.generateText(messagesPrompts, config); + const ans = await model.generateText(prompts, config);// In doStream method - let messagesPrompts = options.prompt; + let prompts = options.prompt; ... - const response = await model.streamText(messagesPrompts, config); + const response = await model.streamText(prompts, config);Also applies to: 121-121
63-82: Refactor duplicated code into a helper functionThe code blocks initializing variables and settings in
doGenerate(lines 63-82) anddoStream(lines 112-132) are nearly identical. Extracting this code into a shared private method will reduce duplication and improve maintainability.Create a private method to handle the shared logic:
private initializeModelAndConfig() { const provider = this.config.provider; const mem0ApiKey = this.config.mem0ApiKey; const settings: Mem0ProviderSettings = { provider: provider, mem0ApiKey: mem0ApiKey, apiKey: this.config.apiKey, modelType: "completion" }; const selector = new Mem0ClassSelector(this.modelId, settings, this.provider_config); const model = selector.createProvider(); const { user_id, app_id, agent_id, run_id, org_name, project_name } = this.settings; const config: Mem0Config = { user_id, app_id, agent_id, run_id, org_name, project_name, mem0ApiKey, modelType: "completion" }; return { model, config }; }Then update
doGenerateanddoStreammethods:// In doGenerate method const { model, config } = this.initializeModelAndConfig(); const ans = await model.generateText(options.prompt, config);// In doStream method const { model, config } = this.initializeModelAndConfig(); const response = await model.streamText(options.prompt, config);Also applies to: 112-132
100-102: Include the original error message when throwing new errorsIn the catch blocks (lines 100-102 and 146-148), the original error is logged but not included in the thrown error. Including the original error message can aid in debugging by providing more context.
Modify the error handling to include the original error message:
// In doGenerate method console.error("Error in doGenerate:", error); - throw new Error("Failed to generate response."); + throw new Error(`Failed to generate response: ${error.message}`);// In doStream method console.error("Error in doStream:", error); - throw new Error("Streaming failed or method not implemented."); + throw new Error(`Streaming failed: ${error.message}`);Also applies to: 146-148
48-48: Remove unused propertysupportsStructuredOutputsThe property
supportsStructuredOutputsat line 48 is declared but not used elsewhere in the class. If it's not required, consider removing it to keep the code clean and focused.examples/vercel-ai-sdk-chat-app/src/components/ui/select.tsx (3)
83-87: Review conditional classNames based onpositionSince the
positionprop is not utilized bySelectPrimitive.Content, the conditional classNames based onposition === "popper"may be unnecessary. Review these conditions to ensure they are still relevant or simplify them if they are no longer needed.
40-74: Refactor duplicate code in scroll button componentsThe
SelectScrollUpButtonandSelectScrollDownButtoncomponents have similar structures. To adhere to the DRY principle, consider creating a singleSelectScrollButtoncomponent that handles both directions by accepting direction-specific props.Example refactor:
const SelectScrollButton = React.forwardRef< React.ElementRef<typeof SelectPrimitive.ScrollAreaScrollButton>, React.ComponentPropsWithoutRef<typeof SelectPrimitive.ScrollAreaScrollButton> & { direction: 'up' | 'down' } >(({ direction, className, ...props }, ref) => ( <SelectPrimitive.ScrollAreaScrollButton ref={ref} className={cn( "flex cursor-default items-center justify-center py-1", className )} {...props} > {direction === 'up' ? <ChevronUpIcon /> : <ChevronDownIcon />} </SelectPrimitive.ScrollAreaScrollButton> ));Usage:
<SelectScrollButton direction="up" /> <SelectScrollButton direction="down" />
119-138: Enhance accessibility inSelectItemcomponentWhile customizing the
SelectItemcomponent, ensure that it retains all necessary accessibility attributes provided by Radix UI, such asrole,aria-selected, and proper keyboard interaction handling. This will maintain a consistent and accessible user experience.mem0/vector_stores/redis.py (6)
83-83: Avoid usingidas a variable nameThe variable
idin the loop shadows the built-in functionid(). This can lead to confusion or unintended behavior.Consider renaming
idtovector_idto avoid shadowing the built-in name.
105-107: Avoid usingfilteras a variable nameUsing
filtershadows the built-in functionfilter(), which can lead to confusion.Consider renaming
filtertofilter_expressionorquery_filterto avoid shadowing the built-in name.
202-204: Avoid re-initializingquerywhenlimitis specifiedRe-initializing
querywhenlimitis provided can lead to redundant code and potential errors.Modify the existing
queryobject instead of creating a new one. You can chain thepagingmethod conditionally.For example:
query = Query(str(filter_expression)).sort_by("created_at", asc=False) if limit is not None: query = query.paging(0, limit)
Line range hint
207-237: Correct list comprehension to prevent nested listsThe
listmethod's return statement creates a nested list due to extra brackets, resulting in[[...]]instead of[...].Apply this diff to fix the issue:
- return [ - [ - MemoryResult( - id=result["memory_id"], - ... - ) - for result in results.docs - ] - ] + return [ + MemoryResult( + id=result["memory_id"], + ... + ) + for result in results.docs + ]
77-79: Implement or remove the unimplementedcreate_colmethodThe
create_colmethod raisesNotImplementedError, indicating incomplete functionality.If this method is planned for future development, consider adding a
TODOcomment with details. Otherwise, remove the method to avoid confusion.
40-44: SimplifyMemoryResultusing@dataclassThe
MemoryResultclass primarily stores data. Using@dataclasscan reduce boilerplate code.Apply the following changes:
+from dataclasses import dataclass - class MemoryResult: + @dataclass + class MemoryResult: def __init__(self, id: str, payload: dict, score: float = None): self.id = id self.payload = payload self.score = scoreSimplify the class definition accordingly.
examples/vercel-ai-sdk-chat-app/src/contexts/GlobalContext.tsx (3)
1-1: Avoid Disabling ESLint Rule GloballyDisabling
@typescript-eslint/no-explicit-anyat the file level can mask type safety issues across the entire file. It's advisable to enable the rule and address specific instances whereanyis unavoidable to maintain strong type safety.
55-55: Type thepropsParameter inGlobalStateComponentThe
propsparameter is currently typed asany. Defining a specific type forpropswill enhance type safety and make the code more readable and maintainable.
66-70: Memoizemem0to Prevent Unnecessary Re-initializationsThe
mem0instance is recreated on every render, which could impact performance. UseuseMemoto memoizemem0based on its dependencies.Proposed change:
+ import { useMemo } from "react"; const GlobalState = (props: any) => { const [memories, setMemories] = useState<Memory[]>([]); // ...other state variables... - const mem0 = createMem0({ + const mem0 = useMemo(() => createMem0({ provider: selectedProvider, mem0ApiKey: selectedMem0Key, apiKey: selectedOpenAIKey, - }); + }), [selectedProvider, selectedMem0Key, selectedOpenAIKey]);mem0/memory/graph_memory.py (2)
23-23: Remove unused importEXTRACT_ENTITIES_PROMPTThe
EXTRACT_ENTITIES_PROMPTis imported but not used in the code. Removing unused imports improves code clarity and maintainability.Apply this diff to remove the unused import:
-from mem0.graphs.utils import ( - EXTRACT_ENTITIES_PROMPT, EXTRACT_RELATIONS_PROMPT, FALKORDB_QUERY, NEO4J_QUERY, get_update_memory_messages, )🧰 Tools
🪛 Ruff (0.7.0)
23-23:
mem0.graphs.utils.EXTRACT_ENTITIES_PROMPTimported but unusedRemove unused import:
mem0.graphs.utils.EXTRACT_ENTITIES_PROMPT(F401)
360-360: Correct grammatical error in the docstringThere's a grammatical error in the docstring of the
graph_querymethod:Original:
FalkorDB supported multi-graph usage, the graphs is switched based on the user_id.Correction:
FalkorDB supports multi-graph usage; the graph is switched based on the user_id.Apply this diff to correct the docstring:
- FalkorDB supported multi-graph usage, the graphs is switched based on the user_id. + FalkorDB supports multi-graph usage; the graph is switched based on the user_id.
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embedchain/poetry.lockis excluded by!**/*.lockexamples/vercel-ai-sdk-chat-app/public/mem0_logo.jpegis excluded by!**/*.jpegexamples/vercel-ai-sdk-chat-app/src/assets/mem0_logo.jpegis excluded by!**/*.jpegexamples/vercel-ai-sdk-chat-app/src/assets/react.svgis excluded by!**/*.svgexamples/vercel-ai-sdk-chat-app/src/assets/user.jpgis excluded by!**/*.jpg
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(parse)
examples/vercel-ai-sdk-chat-app/tsconfig.node.json
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(parse)
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(parse)
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(parse)
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(parse)
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(parse)
🪛 Ruff (0.7.0)
mem0/memory/graph_memory.py
23-23: mem0.graphs.utils.EXTRACT_ENTITIES_PROMPT imported but unused
Remove unused import: mem0.graphs.utils.EXTRACT_ENTITIES_PROMPT
(F401)
mem0/memory/utils.py
1-1: json imported but unused
Remove unused import: json
(F401)
🪛 Markdownlint (0.35.0)
vercel-ai-sdk/README.md
76-76: Punctuation: ':'
Trailing punctuation in heading
(MD026, no-trailing-punctuation)
🔇 Additional comments (96)
vercel-ai-sdk/src/index.ts (1)
1-4: Verify the existence of imported modules.
Let's ensure all referenced modules exist in the project structure.
✅ Verification successful
Let me generate additional scripts to verify the specific exports since we found that the module files exist but need to check their contents more thoroughly.
All required modules and exports are present and correctly defined
The verification confirms that:
- All three module files exist:
mem0-facade.ts,mem0-provider.ts, andmem0-utils.ts mem0-provider.tscorrectly exports:Mem0ProviderinterfaceMem0ProviderSettingsinterfacecreateMem0functionmem0constant
mem0-utils.tsexports all required functions:addMemories,retrieveMemories, andsearchMemoriesmem0-facade.tsexports theMem0class
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify the existence of imported modules and their exports
# Check if the module files exist
echo "Checking module files..."
for module in "mem0-facade" "mem0-provider" "mem0-utils"; do
fd -t f "^${module}\.(ts|js|tsx|jsx)$" ./vercel-ai-sdk/src
# Check TypeScript declarations
fd -t f "^${module}\.d\.ts$" ./vercel-ai-sdk/src
done
# Verify exported types and functions exist in mem0-provider
echo "Checking mem0-provider exports..."
ast-grep --pattern 'export type Mem0Provider' ./vercel-ai-sdk/src
ast-grep --pattern 'export type Mem0ProviderSettings' ./vercel-ai-sdk/src
ast-grep --pattern 'export (const|function) createMem0' ./vercel-ai-sdk/src
ast-grep --pattern 'export (const|function) mem0' ./vercel-ai-sdk/src
# Verify utility functions exist in mem0-utils
echo "Checking mem0-utils exports..."
ast-grep --pattern 'export (const|function) addMemories' ./vercel-ai-sdk/src
ast-grep --pattern 'export (const|function) retrieveMemories' ./vercel-ai-sdk/src
ast-grep --pattern 'export (const|function) searchMemories' ./vercel-ai-sdk/src
Length of output: 1378
Script:
#!/bin/bash
# Check the contents of mem0-provider.ts for the exported types and functions
echo "=== mem0-provider.ts contents ==="
rg "export.*Mem0Provider|export.*Mem0ProviderSettings|export.*createMem0|export.*mem0" ./vercel-ai-sdk/src/mem0-provider.ts -A 1
echo -e "\n=== mem0-utils.ts contents ==="
rg "export.*addMemories|export.*retrieveMemories|export.*searchMemories" ./vercel-ai-sdk/src/mem0-utils.ts -A 1
echo -e "\n=== mem0-facade.ts contents ==="
rg "export" ./vercel-ai-sdk/src/mem0-facade.ts -A 1
Length of output: 985
examples/vercel-ai-sdk-chat-app/src/page.tsx (1)
2-3: Verify component integration with the application structure.
Let's verify the imported components and their integration with the application routing.
✅ Verification successful
Let me try a different approach to verify the component integration.
Let me check the actual implementation of these components to verify their integration.
Component integration is properly implemented and functional
The verification shows that:
- The
GlobalStateprovider is correctly implemented with a comprehensive context management system - The home component is properly structured with all necessary sub-components
- The page component correctly wraps the home component with the GlobalState provider
- All imports resolve to existing files and components
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if the imported components exist and verify the application structure
# Check for GlobalContext implementation
echo "Checking GlobalContext implementation..."
rg -l "GlobalState.*provider" --type=typescript
# Check for the home component implementation
echo "Checking home component implementation..."
rg -l "export.*default.*Component" --type=typescript --glob "pages/home/**"
# Check for Next.js routing configuration
echo "Checking Next.js routing configuration..."
fd "layout.tsx|route.ts" --type f
Length of output: 510
Script:
#!/bin/bash
# Check for GlobalContext implementation
echo "Checking GlobalContext implementation..."
rg -l "GlobalState"
# Check for the home component implementation
echo "Checking home component implementation..."
rg -l "Component.*from.*['\"]./pages/home['\"]"
# Look for the actual component files
echo "Looking for component files..."
fd -e tsx -e ts
Length of output: 2767
Script:
#!/bin/bash
# Check the GlobalContext implementation
echo "Checking GlobalContext content..."
cat examples/vercel-ai-sdk-chat-app/src/contexts/GlobalContext.tsx
# Check the home component implementation
echo "Checking home component content..."
cat examples/vercel-ai-sdk-chat-app/src/pages/home.tsx
# Check the page component that imports these
echo "Checking page component content..."
cat examples/vercel-ai-sdk-chat-app/src/page.tsx
Length of output: 10744
examples/vercel-ai-sdk-chat-app/src/components/ui/label.tsx (4)
1-5: LGTM! Well-structured imports with good accessibility foundations.
The imports are well-organized and the use of Radix UI primitives ensures proper accessibility support.
11-22: LGTM! Well-implemented React component with proper TypeScript support.
The implementation follows React best practices with:
- Proper ref forwarding
- Well-defined TypeScript types
- Correct props handling and className merging
24-24: LGTM! Clean named export.
The named export is appropriate and follows best practices.
1-24: Verify consistent usage patterns across the application.
Let's ensure this Label component is being used consistently across the application and follows the same patterns as other form-related components.
✅ Verification successful
Let me gather more information about the actual usage patterns of the Label component to verify its consistency.
Label component follows consistent UI patterns and integration
The Label component is being used correctly and consistently in the application:
- It's properly integrated with other form components (Input, Select) in a standardized grid layout
- It follows the same React forwarded ref pattern as other UI components
- It maintains consistent styling through the shared
cnutility - It's correctly paired with form controls using htmlFor/id relationships
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check usage patterns of the Label component and related form components
# Find all files that import this Label component
rg -l "import.*Label.*from.*components/ui/label"
# Check for other form-related components following similar patterns
fd -e tsx -e ts -p "components/ui/(input|select|checkbox|radio|form)" --exec cat {} \;
Length of output: 6685
Script:
#!/bin/bash
# Let's examine the actual usage of Label in api-settings-popup.tsx and look for any form-related patterns
cat examples/vercel-ai-sdk-chat-app/src/components/api-settings-popup.tsx
# Also check if there are any other form-related components in the UI directory
fd -e tsx -e ts -p "components/ui/form" --exec cat {} \;
Length of output: 3923
examples/vercel-ai-sdk-chat-app/eslint.config.js (1)
7-28: Verify ESLint configuration compatibility with build setup.
The flat config format is correctly used, but please ensure it's compatible with your build setup and CI/CD pipeline.
✅ Verification successful
ESLint configuration is compatible with the build setup
The ESLint configuration is properly aligned with the project's Vite + TypeScript setup:
- Using modern flat config format which is compatible with ESLint v9
- TypeScript and build configurations are properly configured without conflicts
- Required ESLint plugins for React are correctly installed and configured
- No legacy ESLint configurations exist that could cause conflicts
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for any package.json scripts or build configurations that might conflict with ESLint
rg -g "package.json" -g "tsconfig.json" "eslint"
# Check for any existing ESLint configurations that might conflict
fd -g ".eslintrc*"
Length of output: 976
Script:
#!/bin/bash
# Check for build configuration files and their content
fd -e config.js -e config.mjs -e config.cjs
# Check the package.json for build-related scripts and dependencies
rg -g "package.json" "\"(build|dev|start)\""
# Check for any TypeScript configuration
fd -e tsconfig.json
Length of output: 940
Script:
#!/bin/bash
# Check the Vite configuration since the project uses Vite
fd -g "vite.config*"
# Get content of the Vite config to verify ESLint integration
rg -g "vite.config*" "eslint"
# Check for any TypeScript configuration content
fd -g "tsconfig.json" --exec cat {}
Length of output: 1153
mem0/memory/utils.py (1)
21-30: Verify function usage in graph memory implementation
Let's verify how this utility function is integrated with the graph memory feature.
✅ Verification successful
Function is properly integrated with graph memory implementation
The format_entities function is correctly integrated with the graph memory feature:
- It's imported and used in
graph_memory.pyfor formatting graph relationships - Used in two critical graph memory operations:
- Formatting search output:
search_output_string = format_entities(search_output) - Formatting extracted relations:
extracted_relations_string = format_entities(extracted_relations)
- Formatting search output:
- The formatted output is used in the update memory prompt system for graph memory operations
The function's implementation aligns with its usage in the graph memory system, properly formatting entity relationships in a human-readable format: source -- RELATION -- destination.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for usage of format_entities in the codebase
rg "format_entities" --type python -B 2 -A 2
# Look for potential graph-related files that might need this formatting
fd -e py -E "tests" | xargs rg "graph" --type python
Length of output: 157
Script:
#!/bin/bash
# Search for usage of format_entities in the codebase
rg "format_entities" -B 2 -A 2
# Look for graph-related files and their content
rg "graph" -i
Length of output: 36031
examples/vercel-ai-sdk-chat-app/src/components/ui/input.tsx (2)
1-3: LGTM! Clean and necessary imports.
The imports are well-organized and include only the required dependencies.
23-25: LGTM! Clean export with proper displayName.
The component is properly named and exported following React best practices.
vercel-ai-sdk/src/mem0-chat-settings.ts (2)
1-1: Verify OpenAI SDK compatibility
Let's ensure the imported OpenAIChatSettings type is compatible with the current implementation.
✅ Verification successful
OpenAI SDK integration appears correctly implemented
The Mem0ChatSettings interface properly extends OpenAIChatSettings from "@ai-sdk/openai/internal" and adds its own specific configuration properties. The implementation is consistent across the codebase with both chat and completion settings following the same pattern.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check OpenAI SDK version and usage
rg -l "OpenAIChatSettings"
# Look for other files that might be affected by these settings
fd -e ts -e tsx | xargs rg "Mem0ChatSettings|Mem0Config"
Length of output: 4318
Script:
#!/bin/bash
# Check the implementation of Mem0ChatSettings to verify OpenAI SDK compatibility
ast-grep --pattern 'interface Mem0ChatSettings extends OpenAIChatSettings {
$$$
}'
# Check OpenAIChatSettings import source
fd -e ts -e tsx | xargs rg "@ai-sdk/openai/internal"
Length of output: 1029
3-23: Review model versioning and type safety concerns
- Several model versions reference future dates (e.g., 2024-08-06, 2024-10-01). Please verify if these are intended release dates or if they should be adjusted.
- Consider grouping models by provider (o1 vs GPT) for better organization.
- The catch-all type might allow invalid model IDs. Consider using a more restrictive type if possible.
Let's verify the model versions across the codebase:
✅ Verification successful
Future model versions are valid and in active use
The model versions with future dates (2024-05-13, 2024-08-06, 2024-10-01) are legitimate and actively used in the codebase:
- "gpt-4o-2024-08-06" is set as the default model in both Azure and OpenAI structured LLM implementations
- These models are properly configured in the pricing configuration file (model_prices_and_context_window.json)
- The models are documented in the official documentation (docs/components/llms/models/openai.mdx)
The catch-all type is also appropriate as it allows for future model versions without requiring type updates.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for any references to these model versions to ensure consistency
rg -l "2024-(05|08|10)-\d{2}"
# Check if there are any configuration files that might define these versions
fd -e json -e yaml -e toml | xargs rg "2024-(05|08|10)-\d{2}"
Length of output: 515
Script:
#!/bin/bash
# Let's check the content of these files to understand the context of these model versions
rg -A 5 -B 5 "2024-(05|08|10)-\d{2}" docs/components/llms/models/openai.mdx docs/features/direct-import.mdx
# Check the model configuration in the JSON file
cat embedchain/embedchain/config/model_prices_and_context_window.json
# Look for any implementation details in the Python files
rg -A 5 -B 5 "2024-(05|08|10)-\d{2}" mem0/llms/azure_openai_structured.py mem0/llms/openai_structured.py
Length of output: 31187
mem0/configs/vector_stores/redis.py (2)
12-22: LGTM! Well-implemented validator with clear error messages
The validator effectively prevents configuration pollution by unknown fields and provides helpful error messages. The use of set operations makes the implementation efficient.
24-26: Verify if arbitrary_types_allowed is necessary
The arbitrary_types_allowed setting is quite permissive for a configuration class. Consider if this setting is actually needed, as it could potentially allow unexpected types to be passed through.
examples/vercel-ai-sdk-chat-app/src/components/chevron-toggle.tsx (2)
35-35: LGTM!
The export statement is appropriate for a single component file.
1-35: Verify integration with Home component
The AI summary indicates this component is used in the Home component. Let's verify the integration to ensure proper usage.
✅ Verification successful
ChevronToggle integration is properly implemented
The component is correctly imported and used in home.tsx with all required props:
- Imported from the correct path
- Used with both required props:
isMemoriesExpandedandsetIsMemoriesExpanded - Props are properly managed using useState hook in the Home component
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify ChevronToggle usage in Home component
# Expected: Find usage of ChevronToggle in Home component with proper props
# Search for ChevronToggle imports and usage
rg -A 5 "ChevronToggle" "src/pages"
Length of output: 83
Script:
#!/bin/bash
# Let's first find where the Home component might be located
fd -t f "home" -i
# Then search for ChevronToggle usage across the entire codebase
rg -A 5 "ChevronToggle"
Length of output: 1822
examples/vercel-ai-sdk-chat-app/src/components/ui/badge.tsx (2)
36-36: LGTM!
The exports are well-structured and follow React component library conventions.
1-4: Verify path alias configuration
The @/lib/utils import uses a path alias. Ensure that this alias is properly configured in your TypeScript/build configuration.
mem0/vector_stores/base.py (1)
41-41:
Breaking Change: Restore the name parameter in delete_col
The removal of the name parameter from delete_col is a breaking change that would affect all implementing classes. Without this parameter, it's unclear which collection should be deleted.
Apply this fix:
- def delete_col(self):
+ def delete_col(self, name):Let's verify the impact:
docs/overview.mdx (1)
6-6: LGTM! The CrewAI integration note is well-formatted.
The update correctly highlights the new CrewAI integration with proper markdown formatting and linking.
docs/examples/overview.mdx (1)
32-34: LGTM! Card structure and placement are correct.
The new example card follows the established pattern and maintains consistency with the existing documentation structure.
examples/vercel-ai-sdk-chat-app/src/components/ui/avatar.tsx (4)
1-7: LGTM! Proper setup with client-side directive and necessary imports.
The file correctly sets up client-side rendering and imports all required dependencies.
23-34: LGTM! Well-implemented image component with proper aspect ratio handling.
The component correctly handles image scaling and maintains aspect ratio.
50-50: LGTM! Clean named exports.
The components are properly exported using named exports.
35-48: Verify the availability of the bg-muted utility class.
The component uses the bg-muted class. Please ensure this utility class is defined in your Tailwind configuration.
✅ Verification successful
The bg-muted utility class is properly configured in the Tailwind theme
The muted color is defined in the Tailwind configuration with both DEFAULT and foreground variants, which means the bg-muted utility class will be available for use in the component.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if bg-muted is defined in the Tailwind config
fd -e js -e ts -e json -g "*tailwind*config*" | xargs cat | grep -A 10 -B 10 "muted"
Length of output: 838
mem0/vector_stores/configs.py (1)
19-19: Verify the RedisDBConfig implementation
The addition of Redis as a vector store provider looks good. However, we need to ensure the referenced RedisDBConfig class exists and is properly implemented.
✅ Verification successful
RedisDBConfig implementation is properly integrated
The RedisDBConfig class is correctly implemented in mem0/configs/vector_stores/redis.py with all necessary fields and validation logic. It's properly used by the Redis vector store implementation in mem0/vector_stores/redis.py, which accepts the same configuration parameters defined in the config class.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify the existence and implementation of RedisDBConfig
# Check if the Redis config file exists and contains the RedisDBConfig class
ast-grep --pattern 'class RedisDBConfig' mem0/configs/vector_stores/redis.py
# Check for any imports of RedisDBConfig
rg "from.*RedisDBConfig.*import" -A 2
Length of output: 118
Script:
#!/bin/bash
# Let's try a broader search approach
# Search for any file containing RedisDBConfig class definition
fd . -t f -e py -x ast-grep --pattern 'class RedisDBConfig'
# Search for Redis-related config files
fd redis -t f -e py
# Search for any mention of RedisDBConfig
rg "RedisDBConfig" --type py
Length of output: 308
Script:
#!/bin/bash
# Let's verify the implementation of RedisDBConfig
# Check the RedisDBConfig class implementation
cat mem0/configs/vector_stores/redis.py
# Check if there are any imports of this config in the Redis vector store implementation
cat mem0/vector_stores/redis.py
Length of output: 10030
examples/vercel-ai-sdk-chat-app/src/components/ui/scroll-area.tsx (3)
1-4: LGTM! Dependencies are properly imported.
The imports are well-organized and include all necessary dependencies for the scroll area implementation.
46-46: LGTM! Clean exports.
The components are properly exported for external use.
6-22: Verify height behavior in parent components.
While the implementation is solid, note that the ScrollArea relies on parent components to define its height. The viewport is set to h-full, but the root container needs an explicit height to function properly.
Let's verify how this component is being used:
✅ Verification successful
ScrollArea height is properly constrained in parent components
The verification shows that ScrollArea is used correctly with proper height constraints:
- In
messages.tsxandmemories.tsx, ScrollArea hasflex-1class which allows it to grow and fill available space in a flex container - Both usages include padding (
p-4) for proper content spacing
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for ScrollArea usage to verify height constraints
rg -l "ScrollArea" --type tsx | xargs rg -B 2 -A 2 "<ScrollArea"
Length of output: 3429
examples/vercel-ai-sdk-chat-app/tailwind.config.js (4)
1-62: Well-structured Tailwind configuration!
The configuration follows Tailwind best practices with a comprehensive theme setup, proper typing, and good organization. The semantic color system will help maintain consistency across the application.
10-60: Verify CSS variable definitions
The theme uses CSS variables (e.g., --background, --primary, etc.) that must be defined in your CSS. Ensure these variables are properly defined in your global styles.
✅ Verification successful
CSS variables are properly defined
All CSS variables used in the Tailwind configuration are correctly defined in examples/vercel-ai-sdk-chat-app/src/index.css. The file includes both light (:root) and dark (.dark) theme variables for all the required values including background, foreground, card, popover, primary, secondary, muted, accent, destructive, border, input, ring, chart colors, and border radius.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for CSS variable definitions
echo "Searching for CSS variable definitions..."
rg -t css -t scss "(:root|html|body)\s*{[^}]*--(\w|-)*:" .
# List all CSS/SCSS files that might contain these definitions
fd -e css -e scss
Length of output: 422
Script:
#!/bin/bash
# Let's check the content of CSS files to find variable definitions
echo "Content of examples/vercel-ai-sdk-chat-app/src/index.css:"
cat "examples/vercel-ai-sdk-chat-app/src/index.css"
echo -e "\nContent of embedchain/examples/full_stack/frontend/src/styles/globals.css:"
cat "embedchain/examples/full_stack/frontend/src/styles/globals.css"
Length of output: 2619
61-61: Verify tailwindcss-animate installation
Ensure that tailwindcss-animate is listed as a dependency in your package.json.
✅ Verification successful
tailwindcss-animate is properly installed
The tailwindcss-animate package is correctly listed as a dependency in package.json with version ^1.0.7. The plugin configuration in tailwind.config.js is valid and matches the installed dependency.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if tailwindcss-animate is in package.json
if [ -f "package.json" ]; then
echo "Checking for tailwindcss-animate in dependencies..."
jq '.dependencies["tailwindcss-animate"] // .devDependencies["tailwindcss-animate"]' package.json
fi
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fi
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echo "Directory structure:"
ls -la
echo -e "\nSearching for package.json files:"
fd package.json
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# Check the package.json in the vercel-ai-sdk-chat-app example
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Length of output: 1602
1-6: Verify ESM import compatibility with the build system
The use of ESM import syntax (import tailwindcssAnimate from 'tailwindcss-animate') might require specific Node.js configuration. Consider using CommonJS syntax if you encounter build issues.
✅ Verification successful
ESM imports are correctly configured and supported
The project is properly configured for ESM imports:
- Package.json explicitly sets
"type": "module" tailwindcss-animateis correctly installed as a dependency- The project uses Vite, which fully supports ESM imports
- No build errors related to Tailwind imports were found
🏁 Scripts executed
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#!/bin/bash
# Check if package.json exists and verify the type field
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echo "Checking package.json type field..."
jq '.type' package.json
fi
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fd -e config.js -e config.mjs
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#!/bin/bash
# Check the content of the package.json in the example directory
cat examples/vercel-ai-sdk-chat-app/package.json
# Check if tailwindcss-animate is listed in dependencies
if [ -f "examples/vercel-ai-sdk-chat-app/package.json" ]; then
echo -e "\nChecking dependencies..."
jq '.dependencies["tailwindcss-animate"], .devDependencies["tailwindcss-animate"]' examples/vercel-ai-sdk-chat-app/package.json
fi
# Check if there are any build errors or issues reported in the repository
rg -i "tailwindcss.*error|tailwind.*failed" -g '!node_modules'
Length of output: 1811
examples/vercel-ai-sdk-chat-app/src/components/ui/button.tsx (2)
57-57: LGTM! Exports look good
The exports provide good flexibility by exposing both the Button component and buttonVariants for custom usage.
1-5: Verify path alias configuration
The import using @/lib/utils relies on path alias configuration. Ensure this is properly set up in your TypeScript/build configuration.
✅ Verification successful
Path alias configuration is correctly set up
The @ path alias is properly configured in multiple TypeScript configuration files, consistently mapping @/* to ./src/*. This confirms that the import @/lib/utils will correctly resolve to src/lib/utils.
🏁 Scripts executed
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# Check for tsconfig.json or other configuration files that might define path aliases
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examples/vercel-ai-sdk-chat-app/src/components/ui/card.tsx (1)
1-4: Verify the cn utility implementation
The code relies on the cn utility from "@/lib/utils". Let's ensure it exists and is properly implemented.
examples/vercel-ai-sdk-chat-app/src/index.css (2)
59-66: LGTM! Clean and consistent base styling.
The base styles follow Tailwind best practices and ensure consistent theming across elements.
1-58: LGTM! Consider verifying color contrast ratios.
The theme setup is well-structured with semantic naming and comprehensive coverage of UI elements. The color system appears to follow accessibility best practices.
examples/vercel-ai-sdk-chat-app/src/components/header.tsx (2)
1-11: LGTM! Well-structured imports and prop types.
The component follows TypeScript best practices with proper type definitions and organized imports.
1-81: Verify integration with FalkorDB configuration.
Since this PR is about FalkorDB integration, ensure that the settings and configuration handling in this component properly support FalkorDB-specific settings.
vercel-ai-sdk/config/test-config.ts (2)
1-4: LGTM! Environment setup is properly configured.
The environment setup follows best practices with dotenv configuration at the module level.
12-50:
Critical: Fix inconsistencies and potential security issues in the configuration.
Several issues need attention:
- Model inconsistency: The
anthropicmodel differs between the providers array ("claude-3-5-sonnet-20240620") and models object ("claude-3-haiku-20240307") - Security concern: Hardcoded user ID could lead to test conflicts
- Redundant API key storage increases maintenance burden
export const testConfig = {
apiKey: process.env.MEM0_API_KEY,
- userId: "mem0-ai-sdk-test-user-1134774",
+ userId: process.env.TEST_USER_ID || "mem0-ai-sdk-test-user-1134774",
deleteId: "",
providers: [
{
name: "openai",
activeModel: "gpt-4-turbo",
- apiKey: process.env.OPENAI_API_KEY,
+ apiKey: undefined, // Will be populated from apiKeys
},
// ... other providers
],
models: {
openai: "gpt-4-turbo",
- anthropic: "claude-3-haiku-20240307",
+ anthropic: "claude-3-5-sonnet-20240620", // Match the provider config
groq: "gemma2-9b-it",
cohere: "command-r-plus"
},docs/api-reference/overview.mdx (2)
28-33: LGTM! Clear and comprehensive organization capabilities section.
The documentation effectively outlines the key features of organization and project management.
46-46: Verify the deprecation version number
The deprecation notice is well-structured. Let's verify if version 0.1.40 aligns with the project's versioning plan.
✅ Verification successful
Version 0.1.40 appears to be a future version
The current version in the main pyproject.toml is 0.1.32, and the deprecation notice mentions removing these parameters in version 0.1.40. This aligns well with semantic versioning as it:
- Gives users advance notice of the breaking change
- Plans the removal in a future minor version
- Provides enough version gap (8 minor versions) for users to migrate
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Check current version and recent version history
# Check version in package files
echo "Current versions in package files:"
rg -g "package.json" -g "pyproject.toml" -g "setup.py" "version" -A 1
# Check for version references in changelog
echo "\nVersion references in changelog:"
fd "CHANGELOG.md" --exec rg "0\.1\." {} || true
Length of output: 3813
examples/vercel-ai-sdk-chat-app/src/components/memories.tsx (1)
4-4: Verify Memory type compatibility with FalkorDB integration
Ensure that the Memory type definition includes all necessary properties from the FalkorDB integration.
✅ Verification successful
Memory type is compatible with the integration
The Memory type is correctly defined and used throughout the codebase:
- It's defined in
examples/vercel-ai-sdk-chat-app/src/types.tswith required properties - It's properly integrated with FalkorDB through
@mem0/vercel-ai-providerin the GlobalContext - The memories component correctly imports and uses the type for rendering memory items
🏁 Scripts executed
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Script:
#!/bin/bash
# Check Memory type definition and its usage
ast-grep --pattern 'type Memory = {
$$$
}'
# Search for Memory type usage across the codebase
rg "Memory.*{" --type ts --type tsx
Length of output: 166
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# Check Memory type definition
rg "type Memory" -A 5 --type ts --type tsx
# Find Memory interface or type usage
rg "Memory(\s+|{|;|=)" --type ts --type tsx
# Find files importing Memory type
rg "import.*Memory" --type ts --type tsx
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# Search for Memory type definition
fd -e ts -e tsx | xargs rg "type Memory"
# Find Memory type usage
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# Look for Memory-related code
fd -e ts -e tsx | xargs rg "Memory" -B 2 -A 2
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mem0/utils/factory.py (1)
68-68: Verify Redis implementation existence
The addition of Redis provider looks good, following the existing pattern.
Let's verify the Redis implementation exists:
✅ Verification successful
Redis implementation is properly implemented
The RedisDB class exists in mem0/vector_stores/redis.py and correctly implements the VectorStoreBase interface with all required vector store operations (insert, search, delete, update, get, etc.). The implementation is complete and follows the codebase's patterns.
🏁 Scripts executed
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# Verify the Redis implementation file exists and contains the RedisDB class
fd "redis.py" --type f --exec ast-grep --pattern 'class RedisDB' {}
Length of output: 69
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#!/bin/bash
# Let's first check if the redis.py file exists in the repository
fd "redis.py"
# Let's also check the vector_stores directory structure
fd . --type f mem0/vector_stores
# Let's search for any Redis-related imports or references
rg -l "redis" mem0/
Length of output: 520
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# Let's check the content of redis.py to verify the RedisDB class implementation
cat mem0/vector_stores/redis.py
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examples/vercel-ai-sdk-chat-app/src/components/input-area.tsx (3)
1-6: LGTM! Imports are well-organized and necessary.
The imports are clean and purposeful, properly separating UI components, context, types, and icons.
8-15: LGTM! State management is well-structured.
The component properly manages local state with useState and global state through context. The use of refs is appropriate for DOM manipulation needs.
3-3: Verify GlobalContext implementation.
The component relies heavily on GlobalContext for file and message handling. Let's verify the implementation of these methods.
Also applies to: 10-10
✅ Verification successful
GlobalContext implementation is properly integrated with file handling and message management
The GlobalContext implementation in examples/vercel-ai-sdk-chat-app/src/contexts/GlobalContext.tsx shows:
- Complete
handleSendimplementation that handles both text and file (image/audio) uploads - Proper state management for files using
[selectedFile, setSelectedFile]useState hook - Comprehensive message handling with proper typing and state updates
- Error handling and fallback mechanisms
🏁 Scripts executed
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Script:
#!/bin/bash
# Search for GlobalContext implementation and its usage
rg -l "GlobalContext" --type tsx --type ts
# Search for handleSend implementation
ast-grep --pattern 'const handleSend = $_'
# Search for file-related state management
ast-grep --pattern 'const [selectedFile, setSelectedFile] = $_'
Length of output: 13242
examples/vercel-ai-sdk-chat-app/src/components/messages.tsx (2)
1-9: LGTM! Imports are well-organized and necessary.
All required dependencies are properly imported for the component's functionality.
10-13: LGTM! Hooks are properly implemented.
The component correctly uses context and ref hooks with appropriate typing.
examples/vercel-ai-sdk-chat-app/src/components/api-settings-popup.tsx (2)
1-10: LGTM! Well-structured imports and component definition.
The component is properly typed with TypeScript and follows React best practices.
14-14: Verify GlobalContext implementation.
Let's verify the implementation of GlobalContext and selectorHandler to ensure proper integration.
Also applies to: 18-18
✅ Verification successful
GlobalContext implementation is properly integrated
The verification confirms that:
GlobalContextis correctly implemented insrc/contexts/GlobalContext.tsxselectorHandleris properly defined with the expected parameters (mem0, openai, provider)- The context provides all the required values:
selectorHandler,selectedOpenAIKey,selectedMem0Key, andselectedProvider - The implementation includes proper state management and persistence in localStorage
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Search for GlobalContext implementation
echo "Searching for GlobalContext implementation..."
rg -t typescript -t javascript "GlobalContext" --glob "!{node_modules,dist}/**"
# Search for selectorHandler implementation
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ast-grep --pattern 'const selectorHandler = $_'
Length of output: 1226
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# Search for GlobalContext implementation with proper file type
echo "Searching for GlobalContext implementation..."
rg -l "GlobalContext" --type-add 'ts:*.{ts,tsx}' -t ts
# Get the content of GlobalContext file
echo -e "\nContent of GlobalContext implementation:"
cat examples/vercel-ai-sdk-chat-app/src/contexts/GlobalContext.tsx
Length of output: 9587
examples/vercel-ai-sdk-chat-app/src/components/ui/dialog.tsx (5)
1-14: LGTM! Clean imports and primitive exports.
The imports are well-organized and the pattern of re-exporting Radix UI primitives is appropriate.
54-81: LGTM! Well-implemented responsive components.
The DialogHeader and DialogFooter components are well-structured with appropriate responsive design considerations.
82-107: LGTM! Accessible and well-typed components.
The DialogTitle and DialogDescription components are properly implemented with good accessibility considerations.
109-120: LGTM! Clean and organized exports.
All components are properly exported in a logical order.
15-29: Verify z-index and animation classes.
The implementation looks good, but please verify:
- The z-index (z-50) is appropriate in the context of other overlays in the application
- The animation classes are properly defined in your Tailwind configuration
✅ Verification successful
Let me try a different approach to find the z-index usage and animation configurations without relying on specific file extensions.
Z-index and animation classes are appropriately used
The verification shows that:
- The z-50 value is consistent with other overlay components in the codebase (Select and Dialog components use the same z-50 for their overlays)
- The animation classes are properly supported through the
tailwindcss-animateplugin which is configured in the project's tailwind config
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Search for other z-index values and animation classes in the codebase
echo "Checking for z-index usage:"
rg "z-\d+" --type=tsx --type=css
echo -e "\nChecking for animation class definitions:"
fd "tailwind.config" --exec cat {} | grep -A 5 "animation:"
Length of output: 286
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#!/bin/bash
# Search for z-index values in all files
echo "Checking for z-index usage:"
rg "z-\d+"
echo -e "\nChecking for tailwind config files:"
fd "tailwind" --extension js --extension cjs --extension ts
echo -e "\nChecking content of found config files:"
fd "tailwind" --extension js --extension cjs --extension ts --exec cat {}
Length of output: 5658
embedchain/pyproject.toml (2)
99-99: Verify compatibility with chromadb 0.5.x.
The update from chromadb ^0.4.24 to ^0.5.10 is a significant version jump that might introduce breaking changes.
Let's check for any breaking changes in chromadb:
✅ Verification successful
ChromaDB 0.5.x upgrade appears compatible with current implementation
The codebase's usage of ChromaDB is limited to core functionality that remains stable across 0.4.x and 0.5.x versions:
- Basic client initialization and collection management
- Standard operations: add, query, delete, reset
- Core features: embeddings, metadata, and where clauses
The implementation handles potential breaking changes:
- Uses try-except for imports
- Has proper error handling for InvalidDimensionException
- Includes fallback for SQLite issues
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Check chromadb changelog and issues for breaking changes between 0.4.x and 0.5.x
# Check for migration guides or breaking changes in chromadb repository
gh api repos/chroma-core/chroma/releases | jq -r '.[].body' | grep -i -A 5 -B 5 "breaking"
# Search for issues related to migration
gh api search/issues -f q='repo:chroma-core/chroma "0.4" to "0.5" migration' | jq '.items[].title'
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# Also check for specific chromadb class/method usage
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# Let's also check the test files to see if there are any chromadb-specific tests
fd "test.*\.py" --exec grep -l "chroma"
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# Also check the test file to see what features are being tested
cat embedchain/tests/vectordb/test_chroma_db.py
Length of output: 20178
106-106: Verify mem0ai update compatibility with new graph features.
The update to mem0ai ^0.1.29 might be related to the new graph memory features mentioned in the AI summary.
Let's verify the changes in mem0ai that might be related to graph features:
✅ Verification successful
Based on the search results, I can see that mem0ai version 0.1.29 introduces graph memory features, as evidenced by:
- The README.md shows graph memory support with both FalkorDB and Neo4j:
### Graph Memory
To initialize Graph Memory you'll need to set up your configuration with graph store providers.
Currently, we support FalkorDB and Neo4j as a graph store providers.
-
The codebase has graph memory implementation in
mem0/memory/graph_memory.pywith related utilities and tools. -
The version 1.1 API is required for graph memory features as mentioned in README:
Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*).
Since the update to mem0ai ^0.1.29 aligns with the graph memory features and the required version 1.1 API support, this appears to be a compatible and intentional update.
The dependency update is compatible with the new graph features.
🏁 Scripts executed
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#!/bin/bash
# Description: Check mem0ai changelog for graph-related features
# Search for graph-related changes in mem0ai repository
gh api search/commits -f q='repo:mem0ai/mem0 graph' | jq '.items[].commit.message'
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# Let's check the actual usage of mem0ai in the codebase
rg "mem0" -A 5 -B 5
# Also check if there are any graph-related features that use mem0ai
rg "graph" --type python -A 5 -B 5
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docs/integrations/vercel-ai-sdk.mdx (1)
164-164: LGTM!
The documentation link to the Vercel AI SDK is correct and appropriate.
docs/integrations/crewai.mdx (1)
19-19: 🛠️ Refactor suggestion
Add version constraints for dependencies.
To ensure reproducibility and prevent breaking changes, specify version constraints for the dependencies.
-pip install crewai crewai-tools mem0ai
+pip install crewai==0.x.x crewai-tools==0.x.x mem0ai==0.x.xLikely invalid or redundant comment.
mem0/graphs/utils.py (1)
57-78: 🛠️ Refactor suggestion
Optimize the similarity calculation query.
The current query implementation has several opportunities for optimization:
- The similarity calculation is duplicated in both parts of the UNION
- Complex nested reduce operations might impact performance
- Missing index hints for optimal execution plan
Consider refactoring the query to:
- Calculate similarity once and reuse it
- Use vector operations if supported by FalkorDB
- Add appropriate index hints
MATCH (n)
WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
+USING INDEX n:node(embedding)
WITH n,
reduce(dot = 0.0, i IN range(0, size(n.embedding)-1) | dot + n.embedding[i] * $n_embedding[i]) /
(sqrt(reduce(l2 = 0.0, i IN range(0, size(n.embedding)-1) | l2 + n.embedding[i] * n.embedding[i])) *
sqrt(reduce(l2 = 0.0, i IN range(0, size($n_embedding)-1) | l2 + $n_embedding[i] * $n_embedding[i]))) AS similarity
WHERE similarity >= $threshold
+WITH n, similarity
MATCH (n)-[r]->(m)
RETURN n.name AS source, Id(n) AS source_id, type(r) AS relation, Id(r) AS relation_id, m.name AS destination, Id(n) AS destination_id, similarity
UNION
-MATCH (n)
-WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
-WITH n,
- reduce(dot = 0.0, i IN range(0, size(n.embedding)-1) | dot + n.embedding[i] * $n_embedding[i]) /
- (sqrt(reduce(l2 = 0.0, i IN range(0, size(n.embedding)-1) | l2 + n.embedding[i] * n.embedding[i])) *
- sqrt(reduce(l2 = 0.0, i IN range(0, size($n_embedding)-1) | l2 + $n_embedding[i] * $n_embedding[i]))) AS similarity
-WHERE similarity >= $threshold
MATCH (m)-[r]->(n)
RETURN m.name AS source, Id(m) AS source_id, type(r) AS relation, Id(r) AS relation_id, n.name AS destination, Id(n) AS destination_id, similarityLet's verify if FalkorDB supports vector operations:
docs/integrations/llama-index.mdx (2)
1-10: LGTM! Clear and well-structured introduction.
The documentation starts with proper frontmatter and effectively communicates the key features.
81-81: Verify the model name "gpt-4o".
This appears to be a typo. The standard OpenAI model name is "gpt-4".
docs/mint.json (3)
212-212: LGTM! Verify CrewAI integration documentation.
The CrewAI integration is properly added to the navigation structure.
Let's verify the CrewAI integration documentation:
✅ Verification successful
Documentation for CrewAI integration is complete and well-structured
The CrewAI integration documentation is properly implemented with:
- Comprehensive setup instructions and configuration details
- Clear code examples for integration with Mem0
- Well-explained features and benefits
- Proper code snippets with Python implementation
- Links to relevant documentation resources
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Verify CrewAI integration documentation and example files
# Check if CrewAI documentation exists
fd -t f "crewai.mdx" docs/integrations/
# Check for CrewAI integration code
rg -l "crewai" --type python
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#!/bin/bash
# Let's verify the content of the CrewAI documentation file
cat docs/integrations/crewai.mdx
# Also check for any CrewAI related code or examples
rg -l "crewai"
Length of output: 5005
114-115: LGTM! Verify Redis documentation.
The addition of Redis as a supported vector database is properly structured.
Let's verify the Redis documentation exists:
✅ Verification successful
Redis integration is properly documented and implemented ✅
The Redis vector database integration is complete with:
- Comprehensive documentation in
redis.mdxwith installation and usage examples - Full implementation in
mem0/vector_stores/redis.pywith proper Redis client setup - Configuration schema in
mem0/configs/vector_stores/redis.py - Factory registration in
mem0/utils/factory.py
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify Redis documentation and configuration files exist
# Check if Redis documentation exists
fd -t f "redis" docs/components/vectordbs/dbs/
# Check if Redis is properly configured in the codebase
rg -l "redis" --type python
Length of output: 145
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#!/bin/bash
# Let's verify Redis implementation and configuration
# Check Redis documentation content
cat docs/components/vectordbs/dbs/redis.mdx
# Search for Redis-related code files
fd redis
# Search for Redis-related imports and configurations
rg -i "redis" --type-add 'py:*.py' --type py
Length of output: 2372
227-228: LGTM! Verify example documentation.
The examples are properly added to the navigation structure.
Let's verify the example documentation files:
✅ Verification successful
Documentation verified and properly structured
The example documentation files are present and well-documented:
docs/examples/personal-travel-assistant.mdxcontains a complete guide with setup instructions, code examples (both for before and after v1.1), and detailed explanationsdocs/examples/llama-index-mem0.mdxprovides comprehensive documentation for creating a ReAct Agent with LlamaIndex using Mem0, including setup, code samples, and usage examples
Both examples are properly structured, contain working code samples, and provide clear instructions for implementation.
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The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Verify example documentation and implementation files
# Check if example documentation exists
fd -t f "personal-travel-assistant.mdx|llama-index-mem0.mdx" docs/examples/
# Check for example implementation files
rg -l "personal[_-]travel[_-]assistant|llama[_-]index[_-]mem0" --type python
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# Let's verify the content of these example documentation files
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fd -e py examples/
Length of output: 12123
docs/quickstart.mdx (1)
Line range hint 1-24: LGTM! Documentation changes are comprehensive and well-structured.
The updates to method signatures and error handling follow JavaScript best practices:
- Consistent use of object parameters across all methods
- Proper Promise handling with
.then()and.catch() - Clear parallel structure between different language examples
mem0/graphs/tools.py (2)
127-158: LGTM: EXTRACT_ENTITIES_TOOL configuration is well-structured
The tool configuration is consistent and properly defined with matching properties and required fields.
290-322: LGTM: EXTRACT_ENTITIES_STRUCT_TOOL configuration is consistent
The strict version maintains consistency with its non-strict counterpart (EXTRACT_ENTITIES_TOOL) while adding the strict flag.
mem0/memory/main.py (1)
585-585: Verify hash usage in the codebase.
Let's confirm how the hash is being used across the codebase to ensure the change won't have unintended consequences.
✅ Verification successful
MD5 hash is used only for content tracking, not security purposes
Based on the code analysis:
- The hash is stored in metadata during memory creation and updates (lines 562, 585)
- It's only used as a content identifier in memory records (lines 277, 364, 460)
- There are no security validations or comparisons using this hash
- The hash is part of the memory metadata structure alongside creation timestamps and user IDs
🏁 Scripts executed
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#!/bin/bash
# Description: Check how the hash is used across the codebase
# Expected results: Hash is only used for content tracking, not security validation
# Search for hash usage in Python files
echo "Searching for hash usage in code..."
rg -p "hash.*=.*hashlib"
# Search for hash field references
echo "Searching for hash field references..."
rg -p "payload\[.[\"']hash.[\"']\]|metadata\[.[\"']hash.[\"']\]"
# Search for security-related comments mentioning hash
echo "Searching for security-related comments..."
rg -p "(?i)#.*security.*hash|#.*hash.*security"
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# Let's check how the hash is being used in the context
rg -p -B 5 -A 5 "metadata\[.?[\"']hash[\"']\]|new_metadata\[.?[\"']hash[\"']\]" mem0/memory/main.py
# Check where these hashes are being compared or validated
rg -p "hash" mem0/memory/main.py
# Look for any hash comparisons
ast-grep --pattern 'if $_ == $_: $$$' mem0/memory/main.py
Length of output: 7258
docs/platform/quickstart.mdx (2)
80-88: LGTM! Well-documented metadata feature.
The metadata parameter is consistently documented across all language examples with clear usage patterns.
Also applies to: 103-106
35-36: 🛠️ Refactor suggestion
Update Python example to match JavaScript's object-based initialization.
The JavaScript client initialization has been updated to use an object parameter, but the Python example still uses a string parameter. This creates inconsistency in the documentation.
Update the Python example to match:
-client = MemoryClient(api_key="your-api-key")
+client = MemoryClient({"api_key": "your-api-key"})Likely invalid or redundant comment.
docs/openapi.json (1)
161-166: LGTM! Well-defined entity type enumeration
The entity type enumeration is well-defined and consistently used throughout the API specification. The values are clear and follow a logical pattern.
examples/vercel-ai-sdk-chat-app/src/components/ui/select.tsx (2)
14-19: Components are correctly re-exported from Radix UI primitives
The components Select, SelectGroup, and SelectValue are properly assigned to their respective Radix UI primitives, ensuring seamless integration and usage within the application.
12-13: Verify the correct import path for the cn utility function
The cn utility function is imported from @/lib/utils. Ensure that this import path is correct and that the utils module exports the cn function. An incorrect path or missing export could lead to runtime errors.
Run the following script to confirm the cn function is exported properly:
Expected result: The script should output lines confirming that cn is exported from the utils module.
mem0/vector_stores/redis.py (2)
162-162: Verify the use of keys parameter in self.index.load
In the update method, you're specifying both data and keys when calling self.index.load. Ensure this aligns with the method's expected parameters.
Check the documentation for self.index.load to confirm that providing both data and keys simultaneously is correct and will produce the desired outcome.
144-145: Verify that drop_keys correctly deletes entries
Ensure that self.index.drop_keys effectively removes the vector from the index.
Run the following script to confirm that drop_keys functions as intended:
✅ Verification successful
drop_keys implementation is reliable for vector deletion
The implementation is correct and reliable because:
- It uses the Redis Vector Library (redisvl)
drop_keysmethod which is designed for this purpose - The key format
{self.schema['index']['prefix']}:{vector_id}matches the same format used in insert/update operations - The deletion is atomic as it's handled directly by Redis
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify that drop_keys removes the vector from the index
# Check if the key exists before deletion
redis-cli EXISTS "mem0:${collection_name}:${vector_id}"
# Delete the key using drop_keys
# Note: Adjust command if necessary based on actual implementation
redis-cli FT.DEL "${collection_name}" "mem0:${collection_name}:${vector_id}"
# Check if the key has been deleted
redis-cli EXISTS "mem0:${collection_name}:${vector_id}"
Length of output: 381
Script:
#!/bin/bash
# Let's examine the implementation of drop_keys in the Redis index class
ast-grep --pattern 'def drop_keys'
# Also check for any tests related to deletion
rg -l "test.*delete" --type python
Length of output: 104
Script:
#!/bin/bash
# Let's examine the implementation of drop_keys and related code
rg "def drop_keys" -A 5
rg "test.*delete"
Length of output: 600
Script:
#!/bin/bash
# Let's check the Redis implementation file and test file content
rg -g "redis.py" "class.*Redis" -A 10
cat tests/vector_stores/test_redis.py 2>/dev/null || echo "Redis test file not found"
Length of output: 1603
Script:
#!/bin/bash
# Let's examine the Redis implementation file more thoroughly
cat mem0/vector_stores/redis.py
Length of output: 9030
examples/vercel-ai-sdk-chat-app/src/contexts/GlobalContext.tsx (2)
6-6: Verify 'Buffer' Availability in Browser Environment
Importing Buffer from 'buffer' may not work in a browser environment without a polyfill or appropriate bundler configuration. Ensure that Buffer is available in the target environment or consider alternative methods for handling binary data.
176-201: Replace map with forEach When Not Using Return Value
The map function is used to iterate over messages, but its return value is not utilized. Replace map with forEach to better convey the intent and avoid confusion.
Proposed change:
- messages.map((message) => {
+ messages.forEach((message) => {
const messageContent: any = {
role: message.sender,
content: [
{
type: "text",
text: message.content,
},
],
};
// ...rest of the code...Review Logic for Excluding Messages with Audio
The condition if (!message.audio) excludes messages containing audio from messagesForPrompt. This might unintentionally skip user messages with audio content. Verify if this behavior is intended.
mem0/client/main.py (15)
3-3: Importing warnings module is appropriate
The addition of import warnings is necessary for issuing deprecation warnings later in the code.
59-60: Add org_id and project_id parameters to the constructor
Including org_id and project_id as optional parameters allows users to specify organization and project IDs directly, aligning with updated API requirements.
68-71: Update docstrings to reflect parameter changes
The docstrings correctly indicate that organization and project are deprecated and advise using org_id and project_id instead. Documenting the new parameters enhances clarity for users.
80-81: Assign new parameters to instance variables
Storing org_id and project_id in self.org_id and self.project_id ensures they are accessible throughout the class methods.
87-94: Issue deprecation warning for old parameters
The code correctly uses warnings.warn to alert users that organization and project are deprecated. This encourages migration to the new parameters while maintaining backward compatibility.
125-125: Use _prepare_params method in add method
By invoking kwargs = self._prepare_params(kwargs), the add method now ensures consistent parameter handling and supports both new and deprecated parameters.
197-198: Update search method to prepare parameters correctly
The search method now uses _prepare_params to handle parameters, which standardizes parameter preparation and accommodates deprecation.
279-279: Prepare parameters in users method
Using _prepare_params() without arguments ensures that org_id and project_id are included in the request parameters when needed.
288-288: Include prepared parameters in delete_users method
The addition of params = self._prepare_params() ensures that delete_users operates within the correct organizational and project context.
345-381: Implement _prepare_params method for consistent parameter handling
The _prepare_params method effectively manages both new and deprecated parameters, raises a ValueError when conflicting parameters are provided, and adds clarity to parameter usage across methods.
394-395: Add org_id and project_id to AsyncMemoryClient constructor
Including the new parameters in the asynchronous client ensures feature parity with the synchronous MemoryClient.
397-404: Initialize MemoryClient with new parameters in AsyncMemoryClient
Passing org_id and project_id to the MemoryClient within AsyncMemoryClient ensures consistent configuration between the two clients.
419-419: Prepare parameters in add method of AsyncMemoryClient
Using kwargs = self.sync_client._prepare_params(kwargs) aligns the asynchronous add method with the synchronous version, ensuring consistent parameter handling.
494-494: Update users method in AsyncMemoryClient to include parameters
Including params = self.sync_client._prepare_params() ensures that the users method in AsyncMemoryClient respects the organizational context.
502-502: Prepare parameters in delete_users method of AsyncMemoryClient
By adding params = self.sync_client._prepare_params(), the method ensures it deletes users within the correct organization and project scope.
| export default function Home() { | ||
| return ( | ||
| <div> | ||
| <GlobalState> | ||
| <Component /> | ||
| </GlobalState> | ||
| </div> | ||
| ); | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add TypeScript types and remove unnecessary wrapper.
The component could be improved with proper TypeScript typing and a cleaner structure.
-export default function Home() {
+const Home: FC = () => {
return (
- <div>
<GlobalState>
- <Component />
+ <HomeComponent />
</GlobalState>
- </div>
);
-}
+};
+
+export default Home;Committable suggestion skipped: line range outside the PR's diff.
| export interface Message { | ||
| id: string; | ||
| content: string; | ||
| sender: "user" | "assistant"; | ||
| timestamp: string; | ||
| image?: string; | ||
| audio?: any; | ||
| } |
There was a problem hiding this comment.
Replace 'any' type with a more specific type for audio
The Message interface uses 'any' type for the audio property, which bypasses TypeScript's type checking. Consider defining a more specific type based on your audio data structure.
export interface Message {
id: string;
content: string;
sender: "user" | "assistant";
- timestamp: string;
+ timestamp: number | Date;
image?: string;
- audio?: any;
+ audio?: {
+ url: string;
+ duration?: number;
+ mimeType: string;
+ };
}📝 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.
| export interface Message { | |
| id: string; | |
| content: string; | |
| sender: "user" | "assistant"; | |
| timestamp: string; | |
| image?: string; | |
| audio?: any; | |
| } | |
| export interface Message { | |
| id: string; | |
| content: string; | |
| sender: "user" | "assistant"; | |
| timestamp: number | Date; | |
| image?: string; | |
| audio?: { | |
| url: string; | |
| duration?: number; | |
| mimeType: string; | |
| }; | |
| } |
| rules: { | ||
| ...reactHooks.configs.recommended.rules, | ||
| 'react-refresh/only-export-components': [ | ||
| 'warn', | ||
| { allowConstantExport: true }, | ||
| ], | ||
| }, |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance TypeScript and React rules configuration.
While the basic rules are set up correctly, consider adding these TypeScript-specific rules for better type safety:
rules: {
...reactHooks.configs.recommended.rules,
+ '@typescript-eslint/explicit-function-return-type': ['error', {
+ allowExpressions: true,
+ allowTypedFunctionExpressions: true,
+ }],
+ '@typescript-eslint/strict-boolean-expressions': 'error',
'react-refresh/only-export-components': [
'warn',
{ allowConstantExport: true },
],
},Also, add a comment explaining the React Refresh rule:
rules: {
...reactHooks.configs.recommended.rules,
+ // Ensures components can be properly HMR'd during development
'react-refresh/only-export-components': [
'warn',
{ allowConstantExport: true },
],
},📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| rules: { | |
| ...reactHooks.configs.recommended.rules, | |
| 'react-refresh/only-export-components': [ | |
| 'warn', | |
| { allowConstantExport: true }, | |
| ], | |
| }, | |
| rules: { | |
| ...reactHooks.configs.recommended.rules, | |
| '@typescript-eslint/explicit-function-return-type': ['error', { | |
| allowExpressions: true, | |
| allowTypedFunctionExpressions: true, | |
| }], | |
| '@typescript-eslint/strict-boolean-expressions': 'error', | |
| // Ensures components can be properly HMR'd during development | |
| 'react-refresh/only-export-components': [ | |
| 'warn', | |
| { allowConstantExport: true }, | |
| ], | |
| }, |
mem0/memory/utils.py
Outdated
| @@ -1,3 +1,5 @@ | |||
| import json | |||
There was a problem hiding this comment.
Remove unused import
The json module is imported but not used anywhere in this file.
Apply this diff to remove the unused import:
-import json📝 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.
| import json |
🧰 Tools
🪛 Ruff (0.7.0)
1-1: json imported but unused
Remove unused import: json
(F401)
| @@ -11,6 +11,7 @@ import os | |||
| from mem0 import Memory | |||
|
|
|||
| os.environ["GOOGLE_API_KEY"] = "key" | |||
| os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM | |||
There was a problem hiding this comment.
💡 Codebase verification
Remove redundant OPENAI_API_KEY from non-OpenAI embedder documentation
The verification reveals that OPENAI_API_KEY with the comment "# For LLM" is consistently added across multiple non-OpenAI embedder documentation files (Gemini, VertexAI, Together, Ollama, HuggingFace, Azure OpenAI). This appears to be a documentation issue where:
- The OpenAI API key setup is incorrectly included in documentation for other embedding providers
- The comment "For LLM" doesn't explain the purpose or relationship to the embedder being documented
- Only the OpenAI and Azure OpenAI embedder docs should require their respective API keys
Please remove the OPENAI_API_KEY environment variable setup from:
- docs/components/embedders/models/gemini.mdx
- docs/components/embedders/models/vertexai.mdx
- docs/components/embedders/models/together.mdx
- docs/components/embedders/models/ollama.mdx
- docs/components/embedders/models/huggingface.mdx
If there's a general LLM requirement that needs OpenAI integration, this should be documented separately in a dedicated LLM configuration section.
🔗 Analysis chain
Clarify the requirement for OPENAI_API_KEY in Gemini embedder documentation
The addition of OPENAI_API_KEY in the Gemini embedder documentation seems inconsistent. This is a Google product-specific documentation, and it's unclear why an OpenAI API key would be required. The comment indicates it's "For LLM" but doesn't explain the integration between Gemini embeddings and OpenAI's LLM.
Let's verify if this is a pattern across other embedding model documentations:
Please clarify:
- Why is an OpenAI API key required for Gemini embeddings?
- What LLM functionality requires this key?
- Should this be documented in a separate LLM integration section instead?
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if other embedding model docs require OPENAI_API_KEY
# Expected: Find similar patterns in other embedding docs if this is intentional
echo "Checking other embedding model docs for OPENAI_API_KEY requirement:"
rg -A 2 -B 2 "OPENAI_API_KEY" "docs/components/embedders/models/"
Length of output: 4068
mem0/vector_stores/redis.py
Outdated
| def list(self, filters: dict = None, limit: int = None) -> list: | ||
| """ | ||
| List all recent created memories from the vector store. | ||
| """ | ||
| conditions = [Tag(key) == value for key, value in filters.items() if value is not None] | ||
| filter = reduce(lambda x, y: x & y, conditions) | ||
| query = Query(str(filter)).sort_by("created_at", asc=False) |
There was a problem hiding this comment.
Handle empty or None filters in the list method
Similar to the search method, if filters is None or empty, calling filters.items() will raise an exception, and reduce on an empty list will fail.
Update the list method to handle None or empty filters. Ensure that the query construction accounts for the absence of filters.
Example modification:
if filters:
conditions = [Tag(key) == value for key, value in filters.items() if value is not None]
filter_expression = reduce(lambda x, y: x & y, conditions)
else:
filter_expression = None
mem0/vector_stores/redis.py
Outdated
| def update(self, vector_id=None, vector=None, payload=None): | ||
| data = { | ||
| "memory_id": vector_id, | ||
| "hash": payload["hash"], | ||
| "memory": payload["data"], | ||
| "created_at": int(datetime.fromisoformat(payload["created_at"]).timestamp()), | ||
| "updated_at": int(datetime.fromisoformat(payload["updated_at"]).timestamp()), | ||
| "embedding": np.array(vector, dtype=np.float32).tobytes(), | ||
| } | ||
|
|
||
| for field in ["agent_id", "run_id", "user_id"]: | ||
| if field in payload: | ||
| data[field] = payload[field] | ||
|
|
There was a problem hiding this comment.
Validate inputs in the update method
Parameters vector_id, vector, and payload default to None, but the method assumes they are provided. This can lead to errors if they are not passed.
Add input validation to ensure that vector_id, vector, and payload are not None. Raise appropriate exceptions or handle missing inputs.
Example:
if not vector_id or vector is None or payload is None:
raise ValueError("vector_id, vector, and payload must be provided")| import { Message, Memory, FileInfo } from "@/types"; | ||
| import { Buffer } from 'buffer'; | ||
|
|
||
| const GlobalContext = createContext<any>({}); |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Define a Proper Type for GlobalContext
Using any in createContext<any>({}) diminishes the benefits of TypeScript's type checking. Define a proper interface or type for your context value to improve type safety and catch potential bugs at compile time.
| model: mem0(getModel(selectedProvider), { | ||
| user_id: selectedUser || "", | ||
| }), |
There was a problem hiding this comment.
Ensure mem0 is Callable as a Function
In the call to streamText, mem0 is invoked as a function: mem0(getModel(selectedProvider), { ... }). If mem0 is not a function but an object, this will cause a runtime error. Confirm that mem0 is intended to be callable.
| async function getFileBuffer(file: any) { | ||
| const response = await fetch(file); | ||
| const arrayBuffer = await response.arrayBuffer(); | ||
| const buffer = Buffer.from(arrayBuffer); | ||
| return buffer; | ||
| } |
There was a problem hiding this comment.
Correct getFileBuffer to Properly Read File Objects
The getFileBuffer function attempts to fetch a File object, which is not appropriate because fetch expects a URL or Request. Instead, use a FileReader to read the file's contents as an ArrayBuffer.
Proposed fix:
async function getFileBuffer(file: any) {
- const response = await fetch(file);
- const arrayBuffer = await response.arrayBuffer();
- const buffer = Buffer.from(arrayBuffer);
- return buffer;
+ return new Promise<ArrayBuffer>((resolve, reject) => {
+ const reader = new FileReader();
+ reader.onload = () => resolve(reader.result as ArrayBuffer);
+ reader.onerror = (error) => reject(error);
+ reader.readAsArrayBuffer(file);
+ });
}📝 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 function getFileBuffer(file: any) { | |
| const response = await fetch(file); | |
| const arrayBuffer = await response.arrayBuffer(); | |
| const buffer = Buffer.from(arrayBuffer); | |
| return buffer; | |
| } | |
| async function getFileBuffer(file: any) { | |
| return new Promise<ArrayBuffer>((resolve, reject) => { | |
| const reader = new FileReader(); | |
| reader.onload = () => resolve(reader.result as ArrayBuffer); | |
| reader.onerror = (error) => reject(error); | |
| reader.readAsArrayBuffer(file); | |
| }); | |
| } |
There was a problem hiding this comment.
Copilot reviewed 6 out of 11 changed files in this pull request and generated 1 comment.
Files not reviewed (5)
- docs/open-source/graph_memory/features.mdx: Language not supported
- docs/open-source/graph_memory/overview.mdx: Language not supported
- docs/open-source/quickstart.mdx: Language not supported
- pyproject.toml: Evaluated as low risk
- README.md: Evaluated as low risk
Comments suppressed due to low confidence (1)
mem0/graphs/utils.py:61
- [nitpick] The variable name 'n_embedding' could be more descriptive. Consider renaming it to 'node_embedding'.
reduce(dot = 0.0, i IN range(0, size(n.embedding)-1) | dot + n.embedding[i] * $n_embedding[i]) /
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (4)
mem0/memory/graph_memory.py (4)
Line range hint
4-7: Enhance the import error message for rank_bm25The error message could be more helpful by including installation context.
- raise ImportError("rank_bm25 is not installed. Please install it using pip install rank-bm25") + raise ImportError( + "rank_bm25 is not installed. This package is required for search functionality. " + "Please install it using: pip install rank-bm25" + )
Line range hint
89-137: Parameterize the Cypher query in add methodThe current implementation uses f-strings for query construction, which could be improved for better maintainability and security.
- cypher = f""" - MERGE (n:{source_type} {{name: $source_name, user_id: $user_id}}) - ON CREATE SET n.created = timestamp(), n.embedding = $source_embedding - ON MATCH SET n.embedding = $source_embedding - MERGE (m:{destination_type} {{name: $dest_name, user_id: $user_id}}) - ON CREATE SET m.created = timestamp(), m.embedding = $dest_embedding - ON MATCH SET m.embedding = $dest_embedding - MERGE (n)-[rel:{relation}]->(m) - ON CREATE SET rel.created = timestamp() - RETURN n, rel, m - """ + cypher = """ + MERGE (n:$source_label {name: $source_name, user_id: $user_id}) + ON CREATE SET n.created = timestamp(), n.embedding = $source_embedding + ON MATCH SET n.embedding = $source_embedding + MERGE (m:$dest_label {name: $dest_name, user_id: $user_id}) + ON CREATE SET m.created = timestamp(), m.embedding = $dest_embedding + ON MATCH SET m.embedding = $dest_embedding + MERGE (n)-[rel:$relation_type]->(m) + ON CREATE SET rel.created = timestamp() + RETURN n, rel, m + """ params = { "source_name": source, "dest_name": destination, "source_embedding": source_embedding, "dest_embedding": dest_embedding, "user_id": filters["user_id"], + "source_label": source_type, + "dest_label": destination_type, + "relation_type": relation }
173-178: Consider using an enum for graph store providersUsing string literals for provider types could lead to maintenance issues.
from enum import Enum, auto class GraphProvider(Enum): FALKORDB = auto() NEO4J = auto() # Usage: if self.config.graph_store.provider == GraphProvider.FALKORDB.name.lower(): cypher_query = FALKORDB_QUERY
Line range hint
1-378: Consider implementing a proper abstraction layer for graph providersThe current implementation mixes provider-specific logic throughout the code. Consider introducing a proper abstraction layer:
- Create separate classes for each graph provider implementing a common interface
- Move provider-specific query handling into their respective classes
- Use dependency injection for the graph provider
Example structure:
from abc import ABC, abstractmethod class GraphProvider(ABC): @abstractmethod def execute_query(self, query: str, params: dict) -> list: pass class FalkorDBProvider(GraphProvider): def execute_query(self, query: str, params: dict) -> list: self._graph = self._driver.select_graph(params["user_id"]) return self.graph.query(query, params=params) class Neo4jProvider(GraphProvider): def execute_query(self, query: str, params: dict) -> list: results = self.graph.query(query, params=params) return [list(d.values()) for d in results]
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mem0/memory/graph_memory.py(9 hunks)
🔇 Additional comments (3)
mem0/memory/graph_memory.py (3)
39-42: Remove redundant assignment toself.embedding_modelThe variable
self.embedding_modelis assigned twice in the__init__method.
369-371: Avoid accessing private attributes_graphand_driverdirectlyDirect access to private attributes makes the code fragile and dependent on implementation details.
211-211: PotentialIndexErrorin list comprehensionThe list comprehension assumes each item has at least 5 elements.
mem0/memory/graph_memory.py
Outdated
| def graph_query(self, query, params): | ||
| """ | ||
| Execute a Cypher query on the graph database. | ||
| FalkorDB supported multi-graph usage, the graphs is switched based on the user_id. | ||
|
|
||
| Args: | ||
| query (str): The Cypher query to execute. | ||
| params (dict): A dictionary containing params to be applied during the query. | ||
|
|
||
| Returns: | ||
| list: A list of dictionaries containing the results of the query. | ||
| """ | ||
| if self.config.graph_store.provider == "falkordb": | ||
| # TODO: Use langchain to switch graphs after the multi-graph feature is released | ||
| self.graph._graph = self.graph._driver.select_graph(params["user_id"]) | ||
|
|
||
| query_output = self.graph.query(query, params=params) | ||
|
|
||
| if self.config.graph_store.provider == "neo4j": | ||
| query_output = [list(d.values()) for d in query_output] | ||
|
|
||
|
|
||
| return query_output |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling to graph_query method
The method should handle potential database connection errors and query execution failures.
def graph_query(self, query, params):
+ try:
if self.config.graph_store.provider == "falkordb":
self.graph._graph = self.graph._driver.select_graph(params["user_id"])
query_output = self.graph.query(query, params=params)
if self.config.graph_store.provider == "neo4j":
query_output = [list(d.values()) for d in query_output]
return query_output
+ except Exception as e:
+ logger.error(f"Graph query failed: {str(e)}")
+ logger.debug(f"Query: {query}, Params: {params}")
+ raiseCommittable suggestion skipped: line range outside the PR's diff.
There was a problem hiding this comment.
Actionable comments posted: 2
🧹 Nitpick comments (2)
docs/open-source/graph_memory/overview.mdx (1)
39-40: Minor grammar fix
"Currently, we support FalkorDB and Neo4j as a graph store providers." → "Currently, we support FalkorDB and Neo4j as graph store providers."-Currently, we support FalkorDB and Neo4j as a graph store providers. +Currently, we support FalkorDB and Neo4j as graph store providers.mem0/memory/graph_memory.py (1)
108-108: Deletion query usage
You’re callinggraph_querybut not processing its return value. If needed, confirm that no further handling of the deletion count or response is required.
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📒 Files selected for processing (5)
README.md(1 hunks)docs/open-source/graph_memory/overview.mdx(3 hunks)mem0/memory/graph_memory.py(12 hunks)mem0/memory/utils.py(1 hunks)pyproject.toml(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- README.md
- pyproject.toml
🔇 Additional comments (15)
mem0/memory/utils.py (1)
27-27: Potential IndexError when accessing list elements
This code assumes that eachentityhas at least five elements. If any entry inentitiesis missing indices [0], [2], or [4], you'll encounter an IndexError. Consider verifying the list length before indexing or adding defensive checks.docs/open-source/graph_memory/overview.mdx (2)
59-65: Use consistent parameter naming ()
A previous review suggested renaming"Database"to"database"for consistency and clarity. Additionally, consider swapping placeholder values like"---"with something more descriptive (e.g.,"localhost","6379", etc.).
87-93: Use consistent parameter naming ()
Same note for the advanced configuration block. Rename"Database"to"database"and introduce more recognizable placeholder host/port values if possible.mem0/memory/graph_memory.py (12)
18-21: Imports look good
It’s clear you’re pulling in newly needed symbols frommem0.graphs.utils. No issues noted here.
30-32: Verify dynamic graph creation
Ensure thatGraphFactory.create(...)gracefully handles invalid or missing configuration parameters. A try/except might help detect misconfigurations at this step.
88-88: Potential IndexError ()
Accessingitem[0],item[2], anditem[4]can fail if the list is shorter than 5 elements. Ensure each item insearch_outputmeets the required length before indexing.
129-129: Query looks solid
Fetching relationships with the given query parameters is clear. No concerns noted here.
133-137: Check result length before indexing ()
This block indexesresult[0],result[1], andresult[2]. Ensureresultentries always have at least three elements, or add a guard check.
246-246: Good use of the helper method
Callingself.graph_query(cypher_query, params=params)maintains consistent query handling.
302-302: Deletion query is straightforward
No issues noted. The return value is stored inresult, so presumably used for downstream processing.
350-350: Query result accepted
Again, consistent usage ofgraph_query, no immediate issues seen here.
375-375: Solid approach
You handle the graph insertion consistently. No concerns noted.
420-420: Consistent query usage
Looks fine. The query result is appended toresultsfor later processing.
461-461: Good usage
Storing the returned value inresultensures you can handle it if needed.
496-519: Directly accessing private attributes_graphand_driver()
At line 511,self.graph._graphandself.graph._driverare used, which was flagged in a previous review as prone to break if their internal implementation changes. Consider public methods or more robust patterns to handle multi-graph switching.
| @@ -343,14 +340,14 @@ def _add_entities(self, to_be_added, user_id, entity_type_map): | |||
| """ | |||
|
|
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| params = { | |||
| "source_id": source_node_search_result[0]['elementId(source_candidate)'], | |||
| "source_id": source_node_search_result[0][0], | |||
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Risk of empty list
source_node_search_result[0][0] can raise an IndexError if source_node_search_result is empty. Verify that the list contains items before indexing.
| "source_id": source_node_search_result[0][0], | ||
| "destination_id": destination_node_search_result[0][0], |
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Both source_id and destination_id indexing
Similar to earlier lines, indexing [0][0] may fail if source_node_search_result or destination_node_search_result are empty. Consider verifying their length.
Also applies to: 396-396
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Actionable comments posted: 0
🧹 Nitpick comments (5)
cookbooks/mem0_graph_memory.py (5)
1-1: Use consistent naming for FalkorDB.
The introductory comment uses "falkordb," whereas other references and official documentation use "FalkorDB." Consider unifying the naming for clarity and consistency.
13-14: Fix typos in comments.
The term “carditionals” appears to be a misspelling or placeholder. If you intended “credentials” or another meaningful term, update these references accordingly to maintain clarity in the documentation.Also applies to: 20-20
23-26: Handle missing environment variables gracefully.
Directly indexing environment variables (e.g.,os.environ['HOST']) will raise aKeyErrorif the variable is not set. Consider usingos.environ.get(...)with fallback values or error-handling logic.
36-40: Remove or replace debug prints before production release.
Repeated print statements are useful for demonstrations or debugging but may clutter logs or expose data in production. Consider using a logger with controlled verbosity instead.
39-39: Address grammatical issues in memory text.
The text "My friend name is john ..." could be rephrased to "My friend's name is John..." to improve clarity and correctness.
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Actionable comments posted: 1
🧹 Nitpick comments (1)
mem0/utils/factory.py (1)
83-98: Enhance error handling in GraphFactory.The error handling in the
createmethod could be improved to provide more specific error messages and handle potential configuration issues.Apply this diff to enhance error handling:
@classmethod def create(cls, provider_name, config): + if not provider_name: + raise ValueError("Provider name is required") + if not config: + raise ValueError("Configuration is required") + class_type = cls.provider_to_class.get(provider_name) if class_type: if not isinstance(config, dict): - config = config.model_dump() + try: + config = config.model_dump() + except AttributeError as e: + raise ValueError(f"Invalid configuration format: {e}") graph_instance = load_class(class_type) - return graph_instance(**config) + try: + return graph_instance(**config) + except Exception as e: + raise ValueError(f"Failed to initialize {provider_name} graph: {e}") else: - raise ValueError(f"Unsupported graph provider: {provider_name}") + supported = ", ".join(cls.provider_to_class.keys()) + raise ValueError( + f"Unsupported graph provider: {provider_name}. " + f"Supported providers are: {supported}" + )
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⛔ Files ignored due to path filters (1)
poetry.lockis excluded by!**/*.lock
📒 Files selected for processing (6)
README.md(1 hunks)docs/open-source/graph_memory/overview.mdx(3 hunks)mem0/memory/graph_memory.py(12 hunks)mem0/memory/utils.py(1 hunks)mem0/utils/factory.py(1 hunks)pyproject.toml(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- mem0/memory/utils.py
- README.md
🔇 Additional comments (4)
docs/open-source/graph_memory/overview.mdx (1)
61-65: Improve FalkorDB configuration example.The configuration example needs improvement for better clarity and usability.
Apply this diff to enhance the configuration example:
- "Database": "falkordb", - "host": "---" - "username": "---", - "password": "---", - "port": "---" + "database": "falkordb", # Name of your FalkorDB database + "host": "localhost", # FalkorDB server hostname + "username": "<username>", # Your FalkorDB username + "password": "<password>", # Your FalkorDB password + "port": "6379" # Default FalkorDB portmem0/memory/graph_memory.py (3)
88-88:⚠️ Potential issueAdd validation for search output items.
The list comprehension assumes each item in
search_outputhas at least 5 elements, which could lead to anIndexError.Apply this diff to add validation:
- search_outputs_sequence = [[item[0], item[2], item[4]] for item in search_output] + search_outputs_sequence = [ + [item[0], item[2], item[4]] + for item in search_output + if len(item) >= 5 + ] + if not search_outputs_sequence: + logger.warning("No valid search outputs found") + return []Likely invalid or redundant comment.
133-137:⚠️ Potential issueAdd validation for result items.
The result processing assumes each result has at least 3 elements, which could lead to an
IndexError.Apply this diff to add validation:
final_results = [] for result in results: + if len(result) < 3: + logger.warning(f"Skipping invalid result: {result}") + continue final_results.append({ "source": result[0], "relationship": result[1], "target": result[2] })Likely invalid or redundant comment.
496-519:⚠️ Potential issueImprove graph_query method implementation.
The
graph_querymethod has several issues that need to be addressed:
- Direct access to private attributes
_graphand_driver- Missing error handling
- Lack of type hints
Apply this diff to improve the implementation:
def graph_query(self, query: str, params: dict) -> list: """ Execute a Cypher query on the graph database. FalkorDB supported multi-graph usage, the graphs is switched based on the user_id. Args: query (str): The Cypher query to execute. params (dict): A dictionary containing params to be applied during the query. Returns: list: A list of dictionaries containing the results of the query. + + Raises: + ValueError: If the query or params are invalid + RuntimeError: If the query execution fails """ + if not query or not isinstance(query, str): + raise ValueError("Query must be a non-empty string") + if not params or not isinstance(params, dict): + raise ValueError("Params must be a non-empty dictionary") + if self.config.graph_store.provider == "falkordb": query = query.replace("elementId", "Id").replace("round(", "").replace(", 4)", "") - # TODO: Use langchain to switch graphs after the multi-graph feature is released - self.graph._graph = self.graph._driver.select_graph(params["user_id"]) + try: + # TODO: Replace with public method after multi-graph feature is released + self.graph._graph = self.graph._driver.select_graph(params["user_id"]) + except Exception as e: + raise RuntimeError(f"Failed to switch graph: {e}") - query_output = self.graph.query(query, params=params) + try: + query_output = self.graph.query(query, params=params) + except Exception as e: + raise RuntimeError(f"Query execution failed: {e}") if self.config.graph_store.provider == "neo4j": - query_output = [list(d.values()) for d in query_output] + try: + query_output = [list(d.values()) for d in query_output] + except Exception as e: + raise RuntimeError(f"Failed to process Neo4j results: {e}") return query_outputLikely invalid or redundant comment.
| @@ -25,6 +25,7 @@ sqlalchemy = "^2.0.31" | |||
| langchain-community = "^0.3.1" | |||
| neo4j = "^5.23.1" | |||
| rank-bm25 = "^0.2.2" | |||
| falkordb = "^1.0.8" | |||
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🛠️ Refactor suggestion
Add falkordb to the graph extras.
The falkordb dependency should be added to the [tool.poetry.extras] section under graph since it's a graph provider.
Apply this diff to add falkordb to the graph extras:
[tool.poetry.extras]
-graph = ["langchain-community", "neo4j", "rank-bm25"]
+graph = ["langchain-community", "neo4j", "rank-bm25", "falkordb"]Committable suggestion skipped: line range outside the PR's diff.
Description
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