falkor-integration #2
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WalkthroughThe changes introduce significant modifications to the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant MemoryGraph
participant GraphFactory
participant GraphProvider
User->>MemoryGraph: Execute query
MemoryGraph->>GraphFactory: Create graph instance
GraphFactory->>GraphProvider: Initialize connection
GraphProvider-->>GraphFactory: Connection established
GraphFactory-->>MemoryGraph: Graph instance created
MemoryGraph->>GraphProvider: Execute graph_query
GraphProvider-->>MemoryGraph: Query results returned
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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.
Co-authored-by: Anu <[email protected]>
Co-authored-by: Deshraj Yadav <[email protected]>
Co-authored-by: Dev Khant <[email protected]>
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Actionable comments posted: 1
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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
Ruff
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
Outdated
| 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) |
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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)
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- tests/llms/test_azure_openai.py (5 hunks)
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- tests/llms/test_ollama.py (1 hunks)
- tests/llms/test_openai.py (4 hunks)
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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
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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
Line range hint
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.
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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:
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Search for code that interacts with the `EmbedderConfig` class.
# Test: Search for references to the `EmbedderConfig` class. Expect: Relevant code locations.
rg --type python -A 5 $'EmbedderConfig'
Length of output: 69
Script:
#!/bin/bash
# Description: Search for EmbedderConfig in Python files and analyze its usage
# Search for EmbedderConfig in Python files
echo "Searching for EmbedderConfig in Python files:"
rg -g '*.py' 'EmbedderConfig' -A 5
# 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($$$)'
# Search for provider-related code
echo -e "\nSearching for provider-related code:"
rg -g '*.py' 'provider.*=.*("openai"|"ollama"|"huggingface"|"azure_openai"|"vertexai")' -A 5
Length of output: 42709
Script:
#!/bin/bash
# Description: Search for validate_config method and vertexai-related code
# Search for validate_config method
echo "Searching for validate_config method:"
rg -g '*.py' 'def validate_config' -A 10
# Search for vertexai-related code
echo -e "\nSearching for vertexai-related code:"
rg -g '*.py' 'vertexai' -A 5
# Search for EmbedderFactory or similar factory classes
echo -e "\nSearching for EmbedderFactory or similar factory classes:"
rg -g '*.py' 'class.*Factory' -A 10
Length of output: 19895
| @@ -22,7 +22,7 @@ def __init__(self, config: Optional[BaseLlmConfig] = None): | |||
| ) | |||
| else: | |||
| 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:
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify the impact of the change in the order of evaluating the `base_url` variable.
# 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
Script:
#!/bin/bash
# Description: Verify the impact of the change in the order of evaluating the `base_url` variable.
# Test 1: Search for usage of `OPENAI_API_BASE` environment variable.
echo "Searching for OPENAI_API_BASE usage:"
grep -R "OPENAI_API_BASE" .
# Test 2: Search for usage of `openai_base_url` configuration object.
echo "Searching for openai_base_url usage:"
grep -R "openai_base_url" .
Length of output: 3497
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- mem0/memory/graph_memory.py (9 hunks)
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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.
Description
Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
Fixes # (issue)
Type of change
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How Has This Been Tested?
Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration
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Checklist:
Maintainer Checklist
Summary by CodeRabbit
New Features
graph_querymethod for improved interaction with the graph database.Documentation
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