Comprehensive Document Reference System with LLM Integration

Executive Summary

I propose the development of a persistent knowledge base system with direct LLM integration that allows users to upload comprehensive documents that remain fully accessible throughout conversations. This addresses a fundamental limitation in how complex, information-dense projects are handled within the Venice.ai platform.

Current Limitations

The current document upload system is constrained by both file size limits and context window restrictions. For complex projects requiring detailed documentation, this forces users to:

• Split documents into arbitrary chunks

• Lose nuanced connections between different sections

• Repeatedly re-establish context that was previously discussed

• Accept oversimplification of complex concepts to fit within constraints

Proposed Solution

Implement a persistent document reference system with the following capabilities:

1. Extended Document Support

• Support for larger documents (initial target: 10-20 MB)

• Efficient storage and retrieval of document contents

• Support for multiple document formats (text, PDF, etc.)

2. LLM Integration Component

• Dynamic Context Retrieval: The LLM automatically queries the knowledge base during response generation

• Thread-Specific Document Association: Documents linked to conversation threads for automatic reference

• Intelligent Content Prioritization: Most relevant passages identified and incorporated into context window

• Seamless Cross-Referencing: Identification of connections between different parts of documents

3. Semantic Search Functionality

• Ability to search across entire document contents

• Contextual retrieval of relevant passages based on conversational queries

• Cross-document reference capabilities

4. Persistent Knowledge Base

• Documents remain accessible throughout extended conversations

• Ability to reference specific passages without re-uploading

• Version control for iterative document development

5. Visual Document Interface

• Sidebar navigation of uploaded documents

• Highlighting of referenced passages during conversation

• Ability to add annotations or notes to documents

Technical Implementation Considerations

• Vector-based document indexing for efficient semantic search

• Tiered access system (potentially a Pro feature)

• Privacy-focused design (documents stored only in user's browser)

• Efficient chunking algorithm for processing large documents

• Critical Integration: Direct API connection between knowledge base and LLM processing pipeline

Benefits

• Elimination of artificial context constraints

• Preservation of nuance and detail in complex discussions

• More efficient workflow for knowledge-intensive projects

• Enhanced ability to develop and refine complex ideas over time

• Reduced cognitive load for users managing detailed projects

• Technical Advantage: LLM can reference comprehensive knowledge without exceeding context limitations

This feature would represent a significant advancement in how AI assistants can support complex intellectual work, transforming Venice.ai from a conversational tool into a comprehensive knowledge management system.