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IncarnaMind introduces a new way to interact with your papers and extract information from personal documents through natural language processing and unique extraction methods.
IncarnaMind is a “Notion-like notebook connected to your personal knowledge,” made to fill a specific gap in artificial intelligence (AI). While large language models (LLMs) have demonstrated remarkable capabilities in generating human-like text, IncarnaMind aims to provide a better way to extract information from personal documents, which can be uploaded in PDF or TXT format.
The core system comprises two key components: the Sliding Window and an Ensemble Retriever mechanism. Together, these mechanisms provide a considerable improvement in accuracy and context management—a task that can be difficult even for OpenAI‘s GPT models.
The Sliding Window Chunking technique sets it apart from conventional Retrieval-Augmented Generation (RAG) methods. Instead of depending on fixed chunk sizes, the window’s size and position will be dynamic to information complexity and user queries. This maintains thorough, context-rich information, making it excellent for organizing large documents.
On the other hand, the Ensemble Retriever integrates multiple retrieval strategies to enhance query responses. It can provide precise and pertinent information by filtering through “both coarse—and fine-grained” data within the user’s documents, reducing the possibility of hallucinations commonly seen in large language models.
IncarnaMind could be a significant player in the AI landscape, but it faces competition from established solutions like Notion and Roam Research, which have been developing ways to utilize AI in note-taking and research.
Other applications, such as Obsidian and Evernote, also offer powerful tools for organizing documents, though with less emphasis on AI-driven features.
Still, IncarnaMind stands out from its competitors by allowing users to input queries spanning multiple documents in one session. This is an essential feature for researchers dealing with hundreds of documents when performing a semantic search for very specific information.
Furthermore, IncarnaMind is compatible with numerous large language models, including the Llama2 series, Anthropic Claude, and OpenAI GPT.
When testing for performance, several models were compared inside the system. GPT-4 exhibited superior reasoning capabilities but moderate speed, while Claude 2.0 demonstrated strong reasoning and average speed. The Llama 2 models, while offering varying degrees of safety, generally required substantial GPU RAM and experienced lower speeds. These comparisons and other technical information can be found in its GitHub repository.
IncarnaMind is specifically optimized for the Llama2-70b-chat model. While it has alternatives, the fact that the tool relies on the quantized and resource-intensive version of the said model can prove to be quite a challenge.
However, the Together.ai API, which supports llama2-70b-chat and other open-source models, offers a viable solution in such cases.
The potential applications of IncarnaMind are vast. Students can extract key points from research papers, professionals can quickly find certain information from long legal documents, and individuals can easily manage and organize their personal files.
As the tool continues to develop and refine its capabilities, it has the potential to significantly impact how we interact with and derive insights from our digital documents in the future.
