Format files by pre-commit run -a

Signed-off-by: Yu Ishikawa <yu-iskw@users.noreply.github.com>

docs/docs/core-abilities/code_oriented_yaml.md

@@ -1,2 +1,2 @@
 ## Overview
-TBD
+TBD

docs/docs/core-abilities/compression_strategy.md

@@ -12,9 +12,9 @@ We prioritize the languages of the repo based on the following criteria:
 
 1. Exclude binary files and non code files (e.g. images, pdfs, etc)
 2. Given the main languages used in the repo
-3. We sort the PR files by the most common languages in the repo (in descending order): 
+3. We sort the PR files by the most common languages in the repo (in descending order):
    * ```[[file.py, file2.py],[file3.js, file4.jsx],[readme.md]]```
-   
+
 
 ### Small PR
 In this case, we can fit the entire PR in a single prompt:

docs/docs/core-abilities/dynamic_context.md

@@ -1,7 +1,7 @@
 ## TL;DR
 
-Qodo Merge uses an **asymmetric and dynamic context strategy** to improve AI analysis of code changes in pull requests. 
-It provides more context before changes than after, and dynamically adjusts the context based on code structure (e.g., enclosing functions or classes). 
+Qodo Merge uses an **asymmetric and dynamic context strategy** to improve AI analysis of code changes in pull requests.
+It provides more context before changes than after, and dynamically adjusts the context based on code structure (e.g., enclosing functions or classes).
 This approach balances providing sufficient context for accurate analysis, while avoiding needle-in-the-haystack information overload that could degrade AI performance or exceed token limits.
 
 ## Introduction
@@ -17,12 +17,12 @@ Pull request code changes are retrieved in a unified diff format, showing three
  code line that already existed in the file...
  code line that already existed in the file...
  code line that already existed in the file...
- 
+
 @@ -26,2 +26,4 @@ def func2():
 ...
 ```
 
-This unified diff format can be challenging for AI models to interpret accurately, as it provides limited context for understanding the full scope of code changes. 
+This unified diff format can be challenging for AI models to interpret accurately, as it provides limited context for understanding the full scope of code changes.
 The presentation of code using '+', '-', and ' ' symbols to indicate additions, deletions, and unchanged lines respectively also differs from the standard code formatting typically used to train AI models.
 
 
@@ -37,7 +37,7 @@ Pros:
 Cons:
 
 - Excessive context may overwhelm the model with extraneous information, creating a "needle in a haystack" scenario where focusing on the relevant details (the code that actually changed) becomes challenging.
-LLM quality is known to degrade when the context gets larger. 
+LLM quality is known to degrade when the context gets larger.
 Pull requests often encompass multiple changes across many files, potentially spanning hundreds of lines of modified code. This complexity presents a genuine risk of overwhelming the model with excessive context.
 
 - Increased context expands the token count, increasing processing time and cost, and may prevent the model from processing the entire pull request in a single pass.
@@ -47,18 +47,18 @@ To address these challenges, Qodo Merge employs an **asymmetric** and **dynamic*
 
 **Asymmetric:**
 
-We start by recognizing that the context preceding a code change is typically more crucial for understanding the modification than the context following it. 
+We start by recognizing that the context preceding a code change is typically more crucial for understanding the modification than the context following it.
 Consequently, Qodo Merge implements an asymmetric context policy, decoupling the context window into two distinct segments: one for the code before the change and another for the code after.
 
-By independently adjusting each context window, Qodo Merge can supply the model with a more tailored and pertinent context for individual code changes. 
+By independently adjusting each context window, Qodo Merge can supply the model with a more tailored and pertinent context for individual code changes.
 
 **Dynamic:**
 
 We also employ a "dynamic" context strategy.
-We start by recognizing that the optimal context for a code change often corresponds to its enclosing code component (e.g., function, class), rather than a fixed number of lines. 
+We start by recognizing that the optimal context for a code change often corresponds to its enclosing code component (e.g., function, class), rather than a fixed number of lines.
 Consequently, we dynamically adjust the context window based on the code's structure, ensuring the model receives the most pertinent information for each modification.
 
-To prevent overwhelming the model with excessive context, we impose a limit on the number of lines searched when identifying the enclosing component. 
+To prevent overwhelming the model with excessive context, we impose a limit on the number of lines searched when identifying the enclosing component.
 This balance allows for comprehensive understanding while maintaining efficiency and limiting context token usage.
 
 ## Appendix - relevant configuration options
@@ -69,4 +69,4 @@ allow_dynamic_context=true                  # Allow dynamic context extension
 max_extra_lines_before_dynamic_context = 8  # will try to include up to X extra lines before the hunk in the patch, until we reach an enclosing function or class
 patch_extra_lines_before = 3                # Number of extra lines (+3 default ones) to include before each hunk in the patch
 patch_extra_lines_after = 1                 # Number of extra lines (+3 default ones) to include after each hunk in the patch
-```
+```

docs/docs/core-abilities/impact_evaluation.md

@@ -41,4 +41,4 @@ Here are key metrics that the dashboard tracks:
 
 #### Suggestion Score Distribution
 ![Impacted_Suggestion_Score](https://codium.ai/images/pr_agent/impacted_score_dist.png){width=512}
-> Explanation: The distribution of the suggestion score for the implemented suggestions, ensuring that higher-scored suggestions truly represent more significant improvements. 
+> Explanation: The distribution of the suggestion score for the implemented suggestions, ensuring that higher-scored suggestions truly represent more significant improvements.

docs/docs/core-abilities/index.md

@@ -13,7 +13,7 @@ Qodo Merge utilizes a variety of core abilities to provide a comprehensive and e
 
 ## Blogs
 
-Here are some additional technical blogs from Qodo, that delve deeper into the core capabilities and features of Large Language Models (LLMs) when applied to coding tasks. 
+Here are some additional technical blogs from Qodo, that delve deeper into the core capabilities and features of Large Language Models (LLMs) when applied to coding tasks.
 These resources provide more comprehensive insights into leveraging LLMs for software development.
 
 ### Code Generation and LLMs
@@ -25,4 +25,4 @@ These resources provide more comprehensive insights into leveraging LLMs for sof
 - [Introduction to Code Coverage Testing](https://www.qodo.ai/blog/introduction-to-code-coverage-testing/)
 
 ### Cost Optimization
-- [Reduce Your Costs by 30% When Using GPT for Python Code](https://www.qodo.ai/blog/reduce-your-costs-by-30-when-using-gpt-3-for-python-code/)
+- [Reduce Your Costs by 30% When Using GPT for Python Code](https://www.qodo.ai/blog/reduce-your-costs-by-30-when-using-gpt-3-for-python-code/)

docs/docs/core-abilities/interactivity.md

@@ -1,2 +1,2 @@
 ## Interactive invocation 💎
-TBD
+TBD

docs/docs/core-abilities/metadata.md

@@ -53,4 +53,4 @@ __old hunk__
 
 
 (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 Qodo Merge AI models to generate more precise and contextually relevant suggestions and feedback.
+This comprehensive approach enables Qodo Merge AI models to generate more precise and contextually relevant suggestions and feedback.

docs/docs/core-abilities/self_reflection.md

@@ -1,7 +1,7 @@
 ## TL;DR
 
-Qodo Merge implements a **self-reflection** process where the AI model reflects, scores, and re-ranks its own suggestions, eliminating irrelevant or incorrect ones. 
-This approach improves the quality and relevance of suggestions, saving users time and enhancing their experience. 
+Qodo Merge implements a **self-reflection** process where the AI model reflects, scores, and re-ranks its own suggestions, eliminating irrelevant or incorrect ones.
+This approach improves the quality and relevance of suggestions, saving users time and enhancing their experience.
 Configuration options allow users to set a score threshold for further filtering out suggestions.
 
 ## Introduction - Efficient Review with Hierarchical Presentation
@@ -24,7 +24,7 @@ The AI model is initially tasked with generating suggestions, and outputting the
 However, in practice we observe that models often struggle to simultaneously generate high-quality code suggestions and rank them well in a single pass.
 Furthermore, the initial set of generated suggestions sometimes contains easily identifiable errors.
 
-To address these issues, we implemented a "self-reflection" process that refines suggestion ranking and eliminates irrelevant or incorrect proposals. 
+To address these issues, we implemented a "self-reflection" process that refines suggestion ranking and eliminates irrelevant or incorrect proposals.
 This process consists of the following steps:
 
 1. Presenting the generated suggestions to the model in a follow-up call.
@@ -48,4 +48,4 @@ This results in a more refined and valuable set of suggestions for the user, sav
 [pr_code_suggestions]
 self_reflect_on_suggestions	= true # Enable self-reflection on code suggestions
 suggestions_score_threshold	= 0 # Filter out suggestions with a score below this threshold (0-10)
-```
+```