IMPROVEMENTS.md

# Paperbot Improvements Summary

## Overview
Enhanced the AI Research Paper Chatbot to better connect filenames from the abstracts database to the corresponding full text files in the Papers folder, enabling retrieval of comprehensive information from complete papers rather than just abstracts.

## Key Improvements

### 1. Enhanced Filename Matching System
- **Added `normalize_filename()` function**: Normalizes filenames by removing special characters, converting to lowercase, and standardizing spacing
- **Added `find_matching_paper_file()` function**: Uses sophisticated matching algorithms to find the best matching paper file based on query terms
- **Improved search relevance**: Better scoring system that prioritizes exact matches and word overlaps

### 2. Improved Paper Content Retrieval
- **Enhanced `search_papers()` function**: Now returns up to 5 most relevant papers instead of 3, with better relevance scoring
- **Better content truncation**: Provides more content (up to 12,000 characters) for full paper requests
- **Improved title extraction**: Properly handles filename to title conversion

### 3. New Paper Summary Functionality
- **Added `get_paper_summary()` function**: Extracts and structures key sections of papers including:
  - Abstract
  - Introduction
  - Methodology
  - Results
  - Conclusions
- **Structured output**: Provides organized summaries when users request paper summaries

### 4. Enhanced System Prompt
- **Updated instructions**: Added guidance for using full paper content instead of just abstracts
- **Better context**: System now prioritizes full paper content when available
- **New capabilities**: Supports both full paper retrieval and structured summaries

### 5. Improved User Experience
- **Better example queries**: Added new example button for testing summary functionality
- **Enhanced error handling**: Better handling of missing files and edge cases
- **More informative responses**: Shows relevance scores and better content organization

## Technical Details

### Filename Matching Algorithm
The system now uses a multi-stage matching approach:
1. **Exact matching**: Checks for exact substring matches in normalized filenames
2. **Word overlap**: Counts overlapping words between query and filename
3. **Partial matching**: Checks for partial word matches
4. **Scoring**: Assigns scores based on match quality and prioritizes best matches

### Content Retrieval Strategy
- **Full paper requests**: Provides up to 12,000 characters of content
- **Summary requests**: Extracts and structures key sections
- **General queries**: Provides up to 3,000 characters for context
- **Conclusion requests**: Extracts conclusion sections specifically

### Data Structure
- **Papers dictionary**: Now uses filenames as keys for better matching
- **Content mapping**: Direct connection between abstracts filenames and full text files
- **Metadata preservation**: Maintains original filenames while providing clean titles

## Testing Results
The system successfully:
- Loads 54 papers from the Papers folder
- Correctly matches queries to appropriate paper files
- Normalizes filenames for better matching
- Provides relevant content based on user queries

## Usage Examples
Users can now:
1. **Search for specific papers**: "Show me the paper about pigs"
2. **Request full content**: "Show me the full paper about AI companions"
3. **Get structured summaries**: "Summarize the paper about labor market effects"
4. **Find conclusions**: "What's the conclusion of the pig disease paper?"
5. **General queries**: "Find papers about transformer architecture"

## Benefits
1. **More comprehensive information**: Access to full paper content instead of just abstracts
2. **Better accuracy**: Direct quotes and detailed information from complete papers
3. **Improved relevance**: Better matching between user queries and paper content
4. **Structured responses**: Organized summaries with key sections clearly identified
5. **Enhanced user experience**: More informative and helpful responses