apache/incubator-hugegraph-ai
Nov 2024 – Mar 2025
5 Months active
Languages Used
PythonShellMarkdown
Technical Skills
API IntegrationConfiguration ManagementLLMLLM IntegrationPrompt EngineeringPython Development
HaoJin Yang
PROFILE
Haojin Yang
Over five months, Yuhang Jiang contributed to the apache/incubator-hugegraph-ai repository, focusing on enhancing LLM-driven graph query workflows and developer experience. He built configurable multi-LLM pipelines for chat, extraction, and text-to-Gremlin tasks, leveraging Python, FastAPI, and prompt engineering to improve accuracy and reduce latency. His work included refactoring prompt configurations, integrating fuzzy matching for robust Gremlin query generation, and enabling multilingual RAG support with reranker integration. Jiang also improved onboarding through comprehensive Python client documentation. The depth of his contributions is reflected in the system’s increased flexibility, reliability, and maintainability, addressing both user-facing and backend engineering challenges.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
7
Lines of code
1,732
Activity Months5
Your Network
14 people
Work History
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for apache/incubator-hugegraph-ai: Focused on enhancing LLM integration, parsing robustness, and multilingual RAG capabilities. Delivered two major features with targeted refactors and configuration improvements, and addressed bugs to improve reliability and maintainability. The work enhances business value by delivering more accurate, multilingual supportable responses and a more configurable, scalable RAG/reranker system.
2 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for apache/incubator-hugegraph-ai: Focused on enhancing LLM integration, parsing robustness, and multilingual RAG capabilities. Delivered two major features with targeted refactors and configuration improvements, and addressed bugs to improve reliability and maintainability. The work enhances business value by delivering more accurate, multilingual supportable responses and a more configurable, scalable RAG/reranker system.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for the Apache HugeGraph AI project. Focus was on enhancing the robustness of Gremlin query generation through fuzzy matching, improving the accuracy and reliability of automated graph queries, and delivering business value with a smaller risk of incorrect results. Key features delivered: - Introduced a new fuzzy matching rule to handle cases where user-provided entity names closely resemble vertex IDs, enabling more robust and accurate Gremlin query generation. Major bugs fixed: - Updated the LLM prompt used by the gremlin_generate workflow to apply fuzzy matching, reducing misalignment between natural language inputs and graph queries. Overall impact and accomplishments: - Increased automation reliability for graph queries, reducing manual corrections and improving user satisfaction with generated queries. - Strengthened the product’s ability to handle ambiguous inputs, leading to more consistent results in real-world usage. Technologies/skills demonstrated: - Prompt engineering for LLM workflows - Fuzzy matching techniques and integration with Gremlin query generation - NLP-driven feature enhancement in a graph database context Repository focus: apache/incubator-hugegraph-ai for February 2025.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for the Apache HugeGraph AI project. Focus was on enhancing the robustness of Gremlin query generation through fuzzy matching, improving the accuracy and reliability of automated graph queries, and delivering business value with a smaller risk of incorrect results. Key features delivered: - Introduced a new fuzzy matching rule to handle cases where user-provided entity names closely resemble vertex IDs, enabling more robust and accurate Gremlin query generation. Major bugs fixed: - Updated the LLM prompt used by the gremlin_generate workflow to apply fuzzy matching, reducing misalignment between natural language inputs and graph queries. Overall impact and accomplishments: - Increased automation reliability for graph queries, reducing manual corrections and improving user satisfaction with generated queries. - Strengthened the product’s ability to handle ambiguous inputs, leading to more consistent results in real-world usage. Technologies/skills demonstrated: - Prompt engineering for LLM workflows - Fuzzy matching techniques and integration with Gremlin query generation - NLP-driven feature enhancement in a graph database context Repository focus: apache/incubator-hugegraph-ai for February 2025.
January 2025
2 Commits • 2 Features
Jan 1, 2025January 2025 performance summary for apache/incubator-hugegraph-ai: Focused on developer experience and AI-assisted query tooling. Delivered two features with clear business value: (1) Python client documentation enhancement for onboarding and usage clarity; (2) LLM intent recognition improvements for Gremlin queries, refining prompts and RAG demo. No major bugs fixed this month. Overall impact: improved onboarding, clearer guidance, and stronger LLM-assisted query handling. Technologies demonstrated: Python documentation practices, prompt engineering, LLM/RAG workflows, Gremlin query understanding, and robust commit hygiene.
2 Commits • 2 Features
Jan 1, 2025January 2025 performance summary for apache/incubator-hugegraph-ai: Focused on developer experience and AI-assisted query tooling. Delivered two features with clear business value: (1) Python client documentation enhancement for onboarding and usage clarity; (2) LLM intent recognition improvements for Gremlin queries, refining prompts and RAG demo. No major bugs fixed this month. Overall impact: improved onboarding, clearer guidance, and stronger LLM-assisted query handling. Technologies demonstrated: Python documentation practices, prompt engineering, LLM/RAG workflows, Gremlin query understanding, and robust commit hygiene.
January 2025
December 2024
3 Commits • 1 Features
Dec 1, 2024In December 2024, delivered Graph RAG templating and prompt API enhancements for the Apache HugeGraph AI project, enabling configurable template selection for Gremlin queries and keyword extraction, refactoring prompt configurations and API routing, and simplifying Gremlin template usage with gremlin_tmpl_num and dynamic defaults. This reduced hardcoded prompts, improved flexibility, and laid groundwork for faster experimentation across /rag and /rag/graph endpoints. Commits underpinning this work include: 90182713537fd88946b76ec4a05c9ff44f1549d9 (fix(llm): support choose template in /rag & /rag/graph api #135), d2fcfdb2348baea1d5ebf526d6e687271d5cda03 (fix(llm): update prompt to fit prefix cache #137), and 39e8d486b31f6a2dd99f8a78dbd6402e91e7ce18 (refactor(llm): remove enable_gql logic in api & rag block #148).
December 2024
3 Commits • 1 Features
Dec 1, 2024In December 2024, delivered Graph RAG templating and prompt API enhancements for the Apache HugeGraph AI project, enabling configurable template selection for Gremlin queries and keyword extraction, refactoring prompt configurations and API routing, and simplifying Gremlin template usage with gremlin_tmpl_num and dynamic defaults. This reduced hardcoded prompts, improved flexibility, and laid groundwork for faster experimentation across /rag and /rag/graph endpoints. Commits underpinning this work include: 90182713537fd88946b76ec4a05c9ff44f1549d9 (fix(llm): support choose template in /rag & /rag/graph api #135), d2fcfdb2348baea1d5ebf526d6e687271d5cda03 (fix(llm): update prompt to fit prefix cache #137), and 39e8d486b31f6a2dd99f8a78dbd6402e91e7ce18 (refactor(llm): remove enable_gql logic in api & rag block #148).
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 summary for apache/incubator-hugegraph-ai: Delivered per-task multi-LLM configurations (chat, extraction, text-to-Gremlin), refined keyword extraction prompts to improve RAG accuracy and efficiency, and updated demo interfaces. No major bugs fixed this month. Business impact: more configurable, faster, and more accurate LLM-driven workflows with clearer stakeholder visibility and ready-to-evaluate demos.
2 Commits • 1 Features
Nov 1, 2024November 2024 summary for apache/incubator-hugegraph-ai: Delivered per-task multi-LLM configurations (chat, extraction, text-to-Gremlin), refined keyword extraction prompts to improve RAG accuracy and efficiency, and updated demo interfaces. No major bugs fixed this month. Business impact: more configurable, faster, and more accurate LLM-driven workflows with clearer stakeholder visibility and ready-to-evaluate demos.
November 2024
Activity
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Quality Metrics
Correctness84.0%
Maintainability85.0%
Architecture83.0%
Performance70.0%
AI Usage52.0%
Skills & Technologies
Programming Languages
MarkdownPythonShell
Technical Skills
API DevelopmentAPI IntegrationBackend DevelopmentConfiguration ManagementDocumentationFastAPIGraph DatabasesGremlinLLMLLM IntegrationPrompt EngineeringPydanticPythonPython DevelopmentRAG
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