https://github.com/jokerdii/knowledge-management-chatbot
A personal knowledge management chatbot for efficient retrieval of information from a custom knowledge base.
https://github.com/jokerdii/knowledge-management-chatbot
chatbot gradio langchain llm rag
Last synced: 2 months ago
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A personal knowledge management chatbot for efficient retrieval of information from a custom knowledge base.
- Host: GitHub
- URL: https://github.com/jokerdii/knowledge-management-chatbot
- Owner: JoKerDii
- Created: 2024-12-23T03:17:12.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-26T05:27:07.000Z (over 1 year ago)
- Last Synced: 2025-02-17T21:46:27.779Z (over 1 year ago)
- Topics: chatbot, gradio, langchain, llm, rag
- Language: Python
- Homepage: https://jokerdii.github.io/knowledge-management-chatbot/
- Size: 104 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Personal Knowledge Management Chatbot
## **Introduction**
This project focuses on creating a personal knowledge management chatbot to enable efficient retrieval of information from a custom knowledge base. The knowledge base (https://jokerdii.github.io/di-blog) consists of markdown files documenting monthly readings, including article titles, links, and personal comments. The chatbot uses **Retrieval-Augmented Generation (RAG)** to combine vector-based retrieval with generative AI, ensuring precise and contextually relevant answers to user queries. The application is built with **Gradio** for a user-friendly interactive interface.
## **Data Processing Pipeline**
1. **Document Loading**: The chatbot uses **LangChain's DirectoryLoader** to read and process markdown files stored in a hierarchical folder structure. Each document is enriched with metadata, including its document type, to facilitate organized processing.
2. **Text Splitting**: Content is split into manageable chunks using **LangChain's CharacterTextSplitter**, ensuring that documents can be processed efficiently and relevant segments are retrieved during queries.
3. **Vector Embedding and Storage**: Chunks are embedded using **OpenAI Embeddings**, converting text into high-dimensional vectors that capture semantic relationships. These embeddings are stored in a **FAISS vector database**, enabling fast and accurate retrieval.
## **Retrieval-Augmented Generation (RAG)**
The chatbot employs **RAG** by integrating a retriever and a generative language model. The **retriever** queries the **FAISS vector database** for relevant content, while the **GPT-4o-mini** model generates responses based on retrieved data. This ensures contextually relevant answers, even for complex or nuanced queries.
## **Conversation Management**
To maintain a coherent and context-aware interaction, the chatbot incorporates a **Conversational Retrieval Chain** from LangChain. This chain combines the **GPT-4o-mini** model, the **retriever**, and a **ConversationBufferMemory**, which retains chat history to provide context across multiple exchanges.
## **User Interface**
The chatbot is deployed using **Gradio**, offering a simple and intuitive chat interface. Users can input natural language queries and receive dynamically generated responses that reference relevant articles from their knowledge base.
## **Technical Highlights**
- **Retrieval-Augmented Generation (RAG)** ensures precise and relevant responses by combining retrieval and generation.
- **FAISS Vector Database** provides scalable and efficient storage for high-dimensional embeddings, allowing for rapid retrieval of content.
- **LangChain Framework** streamlines document loading, text splitting, and conversational chain management.
- **Gradio Interface** enhances accessibility and usability through an interactive chat platform.
## **Applications**
The chatbot enables users to revisit and synthesize past readings by querying the knowledge base using natural language prompts. This makes it a valuable tool for knowledge organization and review, facilitating efficient retrieval without manual searches.
## Usage
Clone this repo (https://github.com/JoKerDii/knowledge-management-chatbot.git):
```bash
git clone https://github.com/JoKerDii/knowledge-management-chatbot.git
cd knowledge-management-chatbot
```
Create a virtual environment:
```
python -m venv llm_venv
```
Activate the virtual environment:
```bash
source llm_venv/bin/activate
```
Download necessary dependencies.
```bash
pip install -r requirements.txt
```
Create `.env` file and add OpenAI API Key:
```bash
echo "OPENAI_API_KEY=sk-*******" | cat > .env
```
Run the application locally:
```bash
python3 app.py
```