01ba6fe63d
- Improved error handling and logging in `pr_processing.py` and `github_polling.py` to provide more detailed error information. - Updated AI metadata terminology from "AI-generated file summary" to "AI-generated changes summary" across multiple files for consistency. - Added a placeholder method `publish_file_comments` in `azuredevops_provider.py`. - Refined logging messages in `azuredevops_provider.py` for better clarity.
2.6 KiB
Local and global metadata injection with multi-stage analysis
(1) PR-Agent initially retrieves for each PR the following data:
- PR title and branch name
- PR original description
- Commit messages history
- PR diff patches, in hunk diff format
- The entire content of the files that were modified in the PR
!!! tip "Tip: Organization-level metadata"
In addition to the inputs above, PR-Agent can incorporate supplementary preferences provided by the user, like extra_instructions and organization best practices. This information can be used to enhance the PR analysis.
(2)
By default, the first command that PR-Agent executes is describe, which generates three types of outputs:
- PR Type (e.g. bug fix, feature, refactor, etc)
- PR Description - a bullet points summary of the PR
- Changes walkthrough - going file-by-file, PR-Agent generate a one-line summary and longer bullet points summary of the changes in the file
These AI-generated outputs are now considered as part of the PR metadata, and can be used in subsequent commands like review and improve.
This effectively enables multi-stage chain-of-thought analysis, without doing any additional API calls which will cost time and money.
For example, when generating code suggestions for different files, PR-Agent can inject the AI-generated "Changes walkthrough" file summary in the prompt:
## File: 'src/file1.py'
### AI-generated changes summary:
- edited function `func1` that does X
- Removed function `func2` that was not used
- ....
@@ ... @@ def func1():
__new hunk__
11 unchanged code line0 in the PR
12 unchanged code line1 in the PR
13 +new code line2 added in the PR
14 unchanged code line3 in the PR
__old hunk__
unchanged code line0
unchanged code line1
-old code line2 removed in the PR
unchanged code line3
@@ ... @@ def func2():
__new hunk__
...
__old hunk__
...
(3) The entire PR files that where retrieved are also used to expand and enhance the PR context (see Dynamic Context).
(4) All the metadata described above represents several level of cumulative analysis - ranging from hunk level, to file level, to PR level, to organization level. This comprehensive approach enables PR-Agent AI models to generate more precise and contextually relevant suggestions and feedback.