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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Connect and chat with your multiple documents (pdf and txt) through GPT 3.5, GPT-4 Turbo, Claude and Local Open-Source LLMs
| Date | Stars |
|---|---|
| 2026-07-31 | 800 |
| 2026-08-02 | 800 |
| 2026-08-06 | 800 |
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# 🧠 IncarnaMind ## 👀 In a Nutshell IncarnaMind enables you to chat with your personal documents 📁 (PDF, TXT) using Large Language Models (LLMs) like GPT ([architecture overview](#high-level-architecture)). While OpenAI has recently launched a fine-tuning API for GPT models, it doesn't enable the base pretrained models to learn new data, and the responses can be prone to factual hallucinations. Utilize our [Sliding Window Chunking](#sliding-window-chunking) mechanism and Ensemble Retriever enables efficient querying of both fine-grained and coarse-grained information within your ground truth documents to augment the LLMs. Feel free to use it and we welcome any feedback and new feature suggestions 🙌. ## ✨ New Updates ### Open-Source and Local LLMs Support - **Recommended Model:** We've primarily tested with the Llama2 series models and recommend using [llama2-70b-chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) (either full or GGUF version) for optimal performance. Feel free to experiment with other LLMs. - **System Requirements:** It requires more than 35GB of GPU RAM to run the GGUF quantized version. ### Alternative Open-Source LLMs Options - **Insufficient RAM:** If you're limited by GPU RAM, consider using the [Together.ai](https://api.together.xyz/playground) API. It supports llama2-70b-chat and most other open-source LLMs. Plus, you get $25 in free usage. - **Upcoming:** Smaller and cost-effecitive, fine-tuned models will be released in the future. ### How to use GGUF models - For instructions on acquiring and using quantized GGUF LLM (similar to GGML), please refer to this [video](https://www.youtube.com/watch?v=lbFmceo4D5E) (from 10:45 to 12:30).. Here is a comparison table of the different models I tested, for reference only: | Metrics | GPT-4 | GPT-3.5 | Claude 2.0 | Llama2-70b | Llama2-70b-gguf | Llama2-70b-api | |-----------|--------|---------|------------|------------|-----------------|----------------| | Reasoning | High | Medium | High | Medium | Medium | Medium | | Speed | Medium | High | Medium | Very Low | Low | Medium | | GPU RAM | N/A | N/A | N/A | Very High | High | N/A | | Safety | Low | Low | Low | High | High | Low | ## 💻 Demo https://github.com/junruxiong/IncarnaMind/assets/44308338/89d479fb-de90-4f7c-b166-e54f7bc7344c ## 💡 Challenges Addressed - **Fixed Chunking**: Traditional RAG tools rely on fixed chunk sizes, limiting their adaptability in handling varying data complexity and context. - **Precision vs. Semantics**: Current retrieval methods usually focus either on semantic understanding or precise retrieval, but rarely both. - **Single-Document Limitation**: Many solutions can only query one document at a time, restricting multi-document information retrieval. - **Stability**: IncarnaMind is compatible with OpenAI GPT, Anthropic Claude, Llama2, and other open-source LLMs, ensuring stable parsing. ## 🎯 Key Features - **Adaptive Chunking**: Our Sliding Window Chunking technique dynamically adjusts window size and position for RAG, balancing fine-grained and coarse-grained data access based on data complexity and context. - **Multi-Document Conversational QA**: Supports simple and multi-hop queries across multiple documents simultaneously, breaking the single-document limitation. - **File Compatibility**: Supports both PDF and TXT file formats. - **LLM Model Compatibility**: Supports OpenAI GPT, Anthropic Claude, Llama2 and other open-source LLMs. ## 🏗 Architecture ### High Level Architecture  ### Sliding Window Chunking  ## 🚀 Getting Started ### 1. Installation The installation is simple, you just need to run few commands. #### 1.0. Prerequisites - 3.8 ≤ Python < 3.11 with [Conda](https://www.anaconda.com/download)
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:210f1838f8a66de5, topic:llm, topic:gpt
matched fp:210f1838f8a66de5, topic:nlp
matched fp:210f1838f8a66de5, topic:chatbot