Enhance answer generation with dynamic prompts and NLP techniques

[Ncode01]

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[Copilot]

Pull Request Overview

This PR enhances answer generation by integrating advanced NLP techniques for improved context filtering and dynamic feedback handling.

• Introduces spaCy model loading and advanced NLP-based filtering in chunk_utils.py.
• Adds enhanced context relevance checks and feedback loops in generator.py.
• Improves context expansion logic in context_expander.py using NLP for identifying related chunks.

Reviewed Changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated no comments.

File	Description
utils/chunk_utils.py	Adds enhanced redundant chunk filtering and feedback updating using spaCy.
agents/generator.py	Implements advanced context relevance checks and integrates feedback updates.
agents/context_expander.py	Enhances context assessment and adds NLP-based chunk retrieval; updates feedback.

Comments suppressed due to low confidence (2)

agents/context_expander.py:21

• The defaultdict is used without being imported; please add 'from collections import defaultdict' at the top of the file.

self.feedback = defaultdict(list)

agents/context_expander.py:141

• The variable 'section_chunks' is used without prior initialization; please initialize it (e.g., section_chunks = []) before appending elements.

section_chunks.append({