982 Tabular Data Llm Content Inclusion Fix 188fb717

# Tabular Data LLM Content Inclusion Fix

**Version Implemented:** 0.229.001

## Issue Description
When users uploaded tabular data files (CSV, Excel) and asked questions like "add up all the amounts", the LLM would either:
1. Provide incorrect calculations 
2. Generate Python code instead of direct answers
3. Miss data entirely

**Root Cause**: The conversation history sent to the LLM was truncated to only 1000 characters of file content, meaning most of the tabular data was missing from the LLM's context.

## Example Problem
User uploads credit card activity file with total of $440.64:
- **File size**: ~30KB of transaction data
- **LLM receives**: Only first 1000 characters + "..."
- **Result**: LLM sees only headers and 2-3 transactions, cannot calculate correct total

When user pasted the same data directly into chat:
- **LLM receives**: Complete data
- **Result**: Correct calculation of $440.64

## Root Cause Analysis

### File Content Truncation in Conversation History
In `route_backend_chats.py`, line 998:
```python
max_file_content_length_in_history = 1000 # Limit file content directly in history
```

This 1KB limit was designed to prevent token overflow, but it made tabular data analysis impossible.

### The Truncation Logic
```python
display_content = file_content[:max_file_content_length_in_history]
if len(file_content) > max_file_content_length_in_history:
    display_content += "..."
```

For a 30KB credit card file, the LLM only saw:
```
[User uploaded a file named 'activity.csv'. Content preview:
Date,Description,Amount,Category
08/30/2025,PANERA BREAD,12.49,Restaurant
08/29/2025,AMAZON.COM,25.99,Internet Purchase
...]
```

## Solution Implemented

### Smart Content Limits Based on File Type
Enhanced the conversation history logic to use different limits:

```python
max_file_content_length_in_history = 1000 # General files
max_tabular_content_length_in_history = 50000 # Tabular data (50KB)

# Use higher limit for tabular data that needs complete analysis
content_limit = max_tabular_content_length_in_history if is_table else max_file_content_length_in_history
```

### Enhanced LLM Context for Tabular Data
For tabular files, the LLM now receives:
```python
'content': f"[User uploaded a tabular data file named '{filename}'. This is CSV format data for analysis:\n{display_content}]\nThis is complete tabular data in CSV format. You can perform calculations, analysis, and data operations on this dataset."
```

### Key Improvements
1. **50KB Limit for Tabular Data**: Allows complete datasets for analysis
2. **1KB Limit for Other Files**: Prevents token overflow for general files
3. **Enhanced Context Messages**: Clear instructions that tabular data is complete and ready for analysis
4. **Automatic Detection**: Uses existing `is_table` flag to determine file type

## Impact Analysis

### Before Fix
- ❌ **Incomplete Data**: Only first 1000 characters sent to LLM
- ❌ **Wrong Calculations**: LLM working with partial data
- ❌ **Poor UX**: LLM suggests Python code instead of direct answers
- ❌ **Inconsistent Behavior**: Manual paste works, file upload doesn't

### After Fix  
- ✅ **Complete Data**: Up to 50KB of tabular data sent to LLM
- ✅ **Accurate Calculations**: LLM has full dataset for analysis
- ✅ **Direct Answers**: LLM provides immediate results like $440.64
- ✅ **Consistent Behavior**: File upload works same as manual paste

## Testing Validation
Created comprehensive test suite `test_tabular_llm_content_inclusion_fix.py`:
- ✅ Tabular files get 50KB content limit
- ✅ Non-tabular files remain at 1KB limit  
- ✅ Complete content included when under limits
- ✅ Large files properly truncated
- ✅ Enhanced context messages for tabular data

## Files Modified
- `route_backend_chats.py` - Enhanced conversation history logic
- `functional_tests/test_tabular_llm_content_inclusion_fix.py` - Test coverage
- `config.py` - Updated version to 0.226.101

## User Experience Improvement
Now when users upload tabular data and ask "add up all the amounts":
1. **Complete CSV data** is sent to LLM (up to 50KB)
2. **LLM calculates directly** from the full dataset
3. **Accurate results** like $440.64 are provided immediately
4. **No Python code suggestions** - just direct answers

## Version
Fixed in version: **0.226.101**

## Related Fixes
This builds on the CSV storage optimization (v0.226.099) to provide end-to-end tabular data efficiency:
- **Storage**: CSV format instead of HTML (74% size reduction)
- **Display**: Frontend renders CSV as tables
- **Analysis**: LLM receives complete data for calculations