⚠️ MEDIUM: Performance Issues in Text Processing

[parmarmanojkumar]

⚠️ MEDIUM FUNCTIONAL ISSUE

Severity: Medium
Component: Text Processing Performance
Files: Multiple service classes in service.py

Issue Description

Multiple performance bottlenecks in text processing that can impact system scalability and response times.

Problems Identified

1. Inefficient Text Chunking

Lines 573-600, 878-940: Repeated chunking logic

• Token counting logic inefficient
• No caching of tokenized results
• Repeated tokenization of same text

2. Synchronous Network Calls

Multiple locations: Mixed async/sync HTTP requests

• Blocking requests.post() calls mixed with async code
• No request timeout configurations
• No connection reuse

3. Memory Inefficient Operations

• Large text processing without streaming
• Multiple copies of text data during processing
• No cleanup of temporary data structures

4. No Caching Strategy

• Repeated API calls for similar content
• No memoization of expensive operations
• No result caching for common inputs

Impact

• Slow response times for large text inputs
• High memory usage during processing
• Poor scalability under load
• Resource waste from repeated operations

Recommended Solution

1. Implement Efficient Chunking

• Create reusable ChunkingService
• Cache tokenization results
• Use streaming for large texts

2. Optimize Network Operations

• Standardize on async HTTP client
• Implement connection pooling
• Add request timeouts and retries

3. Add Caching Layer

• Redis cache for API results
• Memory cache for tokenization
• TTL-based cache invalidation

Priority: Medium - Affects performance but not critical functionality

[InfosysResponsibleAI]

Hi Manoj

Thank you for sharing the issues and recommendation. We are analyzing the issue at our end and will revert back to you as early as possible.