🧠 IncarnaMind

👀 In a Nutshell

IncarnaMind enables you to chat with your personal documents 📁 (PDF, TXT) using Large Language Models (LLMs) like GPT (architecture overview). 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 mechanism and Emsemble Retriever enable efficient querying of both fine-grained and coarse-grained information within your ground truth documents to augment the LLMs.

Please feel free to use it and welcome any feedback and new feature suggestions 🙌.

💻 Demo

Demo.mp4

💡 Challenges Addressed

Fixed Chunking: Our Sliding Window Chunking technique provides a balanced solution in terms of time, computing power, and performance.
Precision vs. Semantics: Small chunks enable fine-grained information retrieval, while large chunks focus on coarse-grained data. We leverage both embedding-based and BM25 methods for a hybrid search approach.
Single-Document Limitation: IncarnaMind supports multi-document querying, breaking the one-document-at-a-time barrier.
Stability: We use Chains instead of Agent to ensure stable parsing across different LLMs.

🎯 Key Features

Adaptive Chunking: Dynamically adjust the size and position of text chunks to improve retrieval augmented generation (RAG).
Multi-Document Conversational QA: Perform simmple and multi-hop queries across multiple documents simultaneously.
File Compatibility: Supports both PDF and TXT file formats.
LLM Model Compatibility: Supports both OpenAI GPT and Anthropic Claude models.

🏗 Architecture

High Level Architecture

Sliding Window Chunking

🚀 Getting Started

1. Installation

The installation is simple, you just need run few commands.

1.0. Prerequisites

3.8 ≤ Python < 3.11 with Conda
OpenAI API Key or Anthropic Claude API Key
And of course, your own documents.

1.1. Clone the repository

git clone https://github.com/junruxiong/IncarnaMind
cd IncarnaMind

1.2. Setup

Create Conda virtual environment

conda create -n IncarnaMind python=3.10

Activate

conda activate IncarnaMind

Install all requirements

pip install -r requirements.txt

Setup your API keys in configparser.ini file

[tokens]
OPENAI_API_KEY = sk-(replace_me)
and/or
ANTHROPIC_API_KEY = sk-(replace_me)

(Optional) Setup your custom parameters in configparser.ini file

[parameters]
PARAMETERS 1 = (replace_me)
PARAMETERS 2 = (replace_me)
...
PARAMETERS n = (replace_me)

2. Usage

2.1. Upload and process your files

Put all your files (please name each file correctly to maximize the performance) into the /data directory and run the following command to ingest all data: (You can delete example files in the /data directory before running the command)

python docs2db.py

2.2. Run

In order to start the conversation, run a command like:

python main.py

2.3. Chat and ask any questions

Wait for the script to require your input like the below.

Human:

2.4. Others

When you start a chat, the system will automatically generate a IncarnaMind.log file. If you want to edit the logging, please edit in the configparser.ini file.

[logging]
enabled = True
level = INFO
filename = IncarnaMind.log
format = %(asctime)s [%(levelname)s] %(name)s: %(message)s

🚫 Limitations

Citation is not supported for current version, but will release soon.
Limited asynchronous capabilities.

📝 Upcoming Features

Frontend UI interface
OCR support
Asynchronous optimization
Support open source LLMs
Support more document formats

📑 License

Apache 2.0 License

🖋 Citation

If you want to cite our work, please use the following bibtex entry:

@misc{IncarnaMind2023,
  author = {Junru Xiong},
  title = {IncarnaMind},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/junruxiong/IncarnaMind}}
}

devtanna/IncarnaMind