IncarnaMind: Your Personal Document Conversational Partner
Overview
IncarnaMind is an innovative platform that allows users to have interactive conversations with their personal documents, such as PDF and TXT files, using Large Language Models (LLMs) like GPT. These models, developed by OpenAI and others, provide advanced language processing capabilities, although they can sometimes give inaccurate results due to limitations in learning new data. IncarnaMind addresses these challenges through a sophisticated querying system that efficiently retrieves both detailed and general information from your documents.
Latest Features
Open-Source and Local LLMs Support
- Recommended Model: IncarnaMind is optimized for the Llama2 series, particularly the llama2-70b-chat model, available in both standard and GGUF versions. Users are encouraged to try other LLMs if desired.
- System Requirements: The tool requires GPUs with more than 35GB of RAM to run the quantized GGUF version effectively.
Alternatives for Limited Resources
- Insufficient RAM: For those with less GPU capacity, using the Together.ai API is a feasible option. It supports a variety of open-source LLMs and provides free usage credits.
Using GGUF Models
- Interested users can learn how to implement quantized GGUF LLMs by watching a helpful video tutorial.
Key Advantages
- Adaptive Chunking: Unlike traditional methods that use fixed text segments, IncarnaMind's Sliding Window Chunking adjusts dynamically to manage varying data complexities and contexts.
- Multi-Document Queries: The platform enables simultaneous querying across multiple documents, overcoming the limitations of single-document queries.
- Broad File Support: IncarnaMind works with both PDF and TXT file formats.
- Wide LLM Compatibility: It supports models from OpenAI GPT, Anthropic Claude, and other open-source developers.
Challenges Addressed
IncarnaMind tackles several common issues in document querying:
- Fixed Chunking: Many retrieval tools are limited by set text chunks, which can hinder adaptable data processing.
- Precision vs. Semantics: Standard retrieval methods often focus on either precise information or understanding context, but not both.
- Limited Document Querying: IncarnaMind's ability to query multiple documents simultaneously sets it apart from solutions limited to single-document interactions.
- Stability: The platform is stable across various models, ensuring reliable performance.
Getting Started
Installation Steps
- Prerequisites: Ensure you have the necessary software, including Python and API keys from your preferred LLM provider.
- Clone Repository: Download the IncarnaMind repository from GitHub.
- Set Up Environment: Create a Python virtual environment, activate it, and install all required packages.
- Configure API Keys: Enter your API keys in the configuration file to access LLM services.
Usage
- Upload Files: Place your documents in the designated directory and process them with a simple script command.
- Start Conversation: Run another script to begin chatting with your documents.
- Ask Questions: Interact naturally with the system to discover content from your files.
Limitations and Future Developments
- Current Limitations: Citation support and asynchronous operations are limited but will be addressed in future updates.
- Upcoming Enhancements: Expect new features like a frontend interface, fine-tuned smaller models, OCR support, and more document format compatibility.
Acknowledgements & Licensing
IncarnaMind appreciates the contributions from various open-source projects such as Langchain and Chroma DB, which have played a crucial role in its development. The project is open-source under the Apache 2.0 License.
This comprehensive guide should help new users understand and appreciate the capabilities of the IncarnaMind project, encouraging more efficient and interactive use of document-based information.