The LLM Graph Builder project, designed to create knowledge graphs from unstructured data using Large Language Models and store them in a Neo4j database, is experiencing a high volume of open issues (78) despite active development efforts. This suggests potential challenges in managing feature requests and bug fixes effectively.
Recent issues and pull requests (PRs) indicate a focus on enhancing file processing resilience (#695), addressing critical bugs (#691), and improving user experience through UI enhancements (#668). The development team is actively engaged in refining both backend and frontend functionalities, with significant contributions from team members such as Kartik Persistent, Prakriti Solankey, and Vasanthasaikalluri. Recent commits include improvements to error handling, UI loading states, and the integration of hybrid search capabilities.
Kartik Persistent
Prakriti Solankey
Vasanthasaikalluri
Aashipandya
Pravesh Kumar
Michael Hunger
Jerome Choo
Abhishekkumar-27
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
Jayanth T | 4 | 0/0/0 | 9 | 27 | 42839 | |
Prakriti Solankey (prakriti-solankey) | 11 | 12/5/3 | 28 | 38 | 8711 | |
kartikpersistent | 9 | 6/7/0 | 61 | 72 | 8453 | |
Pravesh Kumar (praveshkumar1988) | 9 | 3/2/0 | 19 | 103 | 6596 | |
None (aashipandya) | 5 | 2/2/0 | 13 | 50 | 5378 | |
None (vasanthasaikalluri) | 6 | 5/3/1 | 13 | 12 | 4284 | |
abhishekkumar-27 | 4 | 0/0/0 | 5 | 5 | 1053 | |
jayanth | 1 | 0/0/0 | 1 | 1 | 619 | |
None (destiny966113) | 1 | 1/1/0 | 2 | 9 | 176 | |
Jerome Choo (jeromechoo) | 1 | 1/1/0 | 1 | 1 | 22 | |
Michael Hunger (jexp) | 2 | 1/1/0 | 2 | 1 | 4 | |
Kain Shu (Kain-90) | 1 | 2/1/1 | 1 | 1 | 2 | |
karanchellani | 1 | 0/0/0 | 1 | 1 | 2 | |
Chunpeng (CpEtoile) | 0 | 3/0/3 | 0 | 0 | 0 | |
Komorebi-r (Komorebi-r) | 0 | 1/0/1 | 0 | 0 | 0 | |
None (buerbaumer) | 0 | 1/0/0 | 0 | 0 | 0 | |
None (ManjuPatel1) | 0 | 0/0/1 | 0 | 0 | 0 | |
None (ShadowOnYOU) | 0 | 1/0/1 | 0 | 0 | 0 | |
Dmitri Marov (DmitriVanGuard) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
Timespan | Opened | Closed | Comments | Labeled | Milestones |
---|---|---|---|---|---|
7 Days | 3 | 3 | 3 | 2 | 1 |
30 Days | 63 | 57 | 78 | 41 | 1 |
90 Days | 183 | 165 | 253 | 92 | 1 |
All Time | 348 | 280 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
The GitHub repository for the LLM Graph Builder has seen a steady stream of activity, with 68 open issues currently logged. Notably, several issues have been created or updated in the past few days, indicating ongoing engagement from contributors and users. A recurring theme among recent issues is the enhancement of functionality related to file processing and error handling, particularly concerning the integration of various data sources and the management of large datasets.
Several issues stand out due to their implications for the project. For instance, #695 addresses a retry option for file processing, which highlights a need for improved resilience in handling failures during data extraction. Meanwhile, issue #691 reveals a critical bug related to attribute errors when processing documents, suggesting potential weaknesses in the robustness of the codebase. Additionally, questions about the use of specific loaders for different file types (#680) indicate ongoing discussions about optimizing data ingestion processes.
Commonalities among issues include inquiries about API integrations and enhancements to user experience, such as better error messaging and UI improvements. The presence of multiple questions and enhancement requests suggests that users are actively seeking to improve their interactions with the application.
Issue #695: Retry option For File Processing
Issue #691: 'str' object has no attribute 'content'
Issue #680: Why use a loader specifically for the pdf type instead of using unstructuredFileLoader?
Issue #678: fireworks_v3p1_405b , ollama_llama3 - File extraction failed
Issue #673: Dev branch local ollama suggestion
Issue #668: Graph Visualization
Issue #666: Remove embeddings from Chunk and entity nodes
Issue #651: Metadata filtering can't be used in combination with a hybrid search approach
Issue #600: Want to keep my PDF data Private
Issue #595: New Feature: allow incremental / resume after when an extraction failed
The recent activity indicates that while there are many enhancements being proposed, there are also significant bugs that need addressing, particularly around file processing and error handling mechanisms. The project's ability to respond to these issues will be crucial for maintaining user trust and satisfaction moving forward.
The dataset contains a total of 10 open pull requests (PRs) and 334 closed PRs for the neo4j-labs/llm-graph-builder
repository. The open PRs focus on enhancements, bug fixes, and feature additions related to the application's functionality, particularly in graph visualization and data processing capabilities.
PR #700: Chatbot changes
PR #699: Search nodes on graph VIz
PR #698: Retry processing
PR #696: Graph enhancements
PR #695: Legend click check
PR #679: 3 Bug fixes for the backend src folder
PR #676: Chatbot status
PR #660: Use env_file in compose_yaml
PR #529: fix: typo for function
PR #678: Add new feature
PR #697: Fix typo: correct 'josn_obj' to 'json_obj'
PR #694: Update Dockerfile missing backslash
PR #690: env changes for VITE
PR #688: Update docker-compose.yml
Several other closed PRs focus on bug fixes, enhancements, and documentation updates, reflecting active maintenance and improvement cycles within the project.
The analysis of the pull requests reveals several key themes and patterns:
The repository exhibits a vibrant development environment characterized by frequent contributions from various developers. The presence of multiple contributors on many PRs indicates a collaborative approach to feature development and bug fixing, which is essential for maintaining high-quality software.
Many open pull requests are directed towards improving user interactions with the application, particularly through enhancements in graph visualization and search functionalities. Features like node highlighting, relationship legends, and chatbot status indicators reflect a strong emphasis on user experience, which is crucial for applications dealing with complex data representations like knowledge graphs.
A significant number of closed pull requests address bugs and issues within the application, showcasing a commitment to maintaining software reliability. This proactive approach helps ensure that users have a stable experience while using the application, which is vital for user retention and satisfaction.
The repository is also focused on expanding its capabilities with new features such as hybrid chat modes and enhanced processing functionalities. This aligns with trends in data processing applications where flexibility and adaptability are key to meeting diverse user needs.
Despite the positive aspects of active collaboration and feature expansion, there are notable challenges reflected in the high number of open issues (78). This could indicate either an ambitious roadmap that exceeds current resource capabilities or a need for better prioritization of tasks within the development team.
In conclusion, while the neo4j-labs/llm-graph-builder
repository demonstrates strong community engagement and ongoing improvements, it also faces challenges typical of rapidly evolving software projects. Continued focus on addressing open issues while fostering collaboration will be essential for its success moving forward.
Kartik Persistent (kartikpersistent)
Prakriti Solankey (prakriti-solankey)
Vasanthasaikalluri (vasanthasaikalluri)
Aashipandya (aashipandya)
Pravesh Kumar (praveshkumar1988)
Michael Hunger (jexp)
Jerome Choo (jeromechoo)
Abhishekkumar-27 (abhishekkumar-27)
DEV
to STAGING
, indicating ongoing development cycles.The development team is actively engaged in enhancing the LLM Graph Builder project through collaborative efforts focused on both backend improvements and frontend usability. The recent activities reflect a commitment to continuous improvement, user-centric design, and robust feature expansion, positioning the project for sustained growth and community engagement.