Reduce ThreadPoolExecutor max_workers to 100

Lowered the maximum number of workers in ThreadPoolExecutor from 200 to 100 in ai_moderator.py and rule_processor.py to optimize resource usage and prevent potential overload during parallel processing.

Author: Bentlybro

app/services/ai/ai_moderator.py

@@ -325,9 +325,8 @@ def moderate_content(self, content, content_type='text', custom_prompt=None):
                         chunks.append(content[i:i + MAX_CHARS_PER_CHUNK])
 
                     # Process all chunks IN PARALLEL for maximum speed
-                    # Increased from 10 -> 50 -> 200 workers for OpenAI API concurrency
                     chunk_results = []
-                    with ThreadPoolExecutor(max_workers=min(len(chunks), 200)) as executor:
+                    with ThreadPoolExecutor(max_workers=min(len(chunks), 100)) as executor:
                         # Submit all chunks at once with context wrapper
                         future_to_chunk = {
                             executor.submit(self._context_wrapper, self._analyze_with_custom_prompt, chunk, custom_prompt): i
@@ -375,9 +374,8 @@ def moderate_content(self, content, content_type='text', custom_prompt=None):
                 chunks = self.split_text_into_chunks(content, max_content_tokens)
 
                 # Process all chunks IN PARALLEL for maximum speed
-                # Increased from 10 -> 50 -> 200 workers for OpenAI API concurrency
                 chunk_results = []
-                with ThreadPoolExecutor(max_workers=min(len(chunks), 200)) as executor:
+                with ThreadPoolExecutor(max_workers=min(len(chunks), 100)) as executor:
                     # Submit all chunks at once with context wrapper
                     future_to_chunk = {
                         executor.submit(self._context_wrapper, self._run_enhanced_default_moderation, chunk): i

app/services/moderation/rule_processor.py

@@ -103,8 +103,7 @@ def process_single_ai_rule(rule):
                 return (rule.id, None)
 
         # Execute in parallel
-        # Increased from 10 -> 50 -> 200 workers for OpenAI API concurrency
-        with ThreadPoolExecutor(max_workers=min(len(ai_rules), 200)) as executor:
+        with ThreadPoolExecutor(max_workers=min(len(ai_rules), 100)) as executor:
             futures = {executor.submit(
                 process_single_ai_rule, rule): rule for rule in ai_rules}