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:

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

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:

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:

'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