https://github.com/brucezhao1875/video-chat-transcript-web
An AI-powered chat application that provides intelligent, citable answers by leveraging a knowledge base built from video chat transcripts.
https://github.com/brucezhao1875/video-chat-transcript-web
ai-assistant conversational-ai dify llm nextjs prompt-engineering rag semantic-search vercel
Last synced: 3 months ago
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An AI-powered chat application that provides intelligent, citable answers by leveraging a knowledge base built from video chat transcripts.
- Host: GitHub
- URL: https://github.com/brucezhao1875/video-chat-transcript-web
- Owner: brucezhao1875
- License: mit
- Created: 2025-06-16T08:13:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-16T08:16:46.000Z (about 1 year ago)
- Last Synced: 2025-06-16T08:53:03.138Z (about 1 year ago)
- Topics: ai-assistant, conversational-ai, dify, llm, nextjs, prompt-engineering, rag, semantic-search, vercel
- Language: TypeScript
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Video Chat Transcript AI
An AI-powered chat application that provides intelligent, citable answers by leveraging a knowledge base built from video chat transcripts. This project utilizes a **RAG (Retrieval-Augmented Generation)** architecture to allow a Large Language Model (LLM) to perform deep Q&A on specific, unstructured conversation data, ensuring responses are accurate, context-aware, and verifiable.
*(tbd)*
---
## ✨ Features
* **🤖 Core RAG Architecture**: Implements a full Retrieval-Augmented Generation pipeline, effectively combining an LLM with a private knowledge base of chat transcripts. This solves the problem of knowledge gaps and "hallucinations" in general-purpose models.
* **⚙️ Automated Data Processing**: Includes scripts and methodologies for automated preprocessing of video chat transcript data, transforming raw conversation logs into structured, retrievable knowledge segments.
* **🔍 High-Precision Semantic Search**: Utilizes a vector database and semantic similarity search to efficiently and accurately retrieve the most relevant conversational snippets from the knowledge base in response to user queries.
* **📝 Advanced Prompt Engineering**: Features a sophisticated, persona-driven system prompt with strict output formatting instructions, guiding the LLM to generate responses that are stylistically consistent and include verifiable source citations (e.g., video links with timestamps).
* **🌐 Modern Web Application**: A full-stack application built with Next.js and React, providing a responsive and user-friendly chat interface for seamless interaction.
* **🚀 CI/CD & Global Deployment**: Deployed on Vercel with automated CI/CD pipelines, ensuring seamless updates and high-performance global access for users anywhere.
## 🛠️ Tech Stack
* **Frontend**: [Next.js](https://nextjs.org/), [React](https://react.dev/), [Tailwind CSS](https://tailwindcss.com/)
* **AI Backend & Workflow**: [Dify.ai](https://dify.ai/) (Cloud Version)
* **LLM (Large Language Model)**: [DeepSeek](https://www.deepseek.com/) (or your chosen model)
* **Deployment**: [Vercel](https://vercel.com/)
* **Node.js Version**: 20.x (LTS)
## 🚀 Getting Started
Follow these instructions to set up and run the project locally for development and testing.
### Prerequisites
* [Node.js](https://nodejs.org/) (Version 20.x recommended)
* [npm](https://www.npmjs.com/) or [yarn](https://yarnpkg.com/)
* A Dify.ai account and a configured application with an API key.
### Installation
1. **Clone the repository:**
```bash
git clone [https://github.com/brucezhao1875/video-chat-transcript-web.git](https://github.com/YourUsername/video-chat-transcript-web.git)
cd video-chat-transcript-web
```
2. **Install dependencies:**
```bash
npm install
```
3. **Set up environment variables:**
Create a file named `.env.local` in the root of the project and add the following environment variables. You can get these from your Dify application's "API Access" section.
```env
# Your Dify application's API endpoint
NEXT_PUBLIC_API_URL=[https://api.dify.ai/v1](https://api.dify.ai/v1)
# Your Dify application's API Key
NEXT_PUBLIC_APP_API_KEY=app-xxxxxxxxxxxxxxxxxxxx
```
4. **Run the development server:**
```bash
npm run dev
```
Open [http://localhost:3000](http://localhost:3000) in your browser to see the application running.
## 部署 (Deployment)
This project is optimized for deployment on [Vercel](https://vercel.com/).
1. Push your code to a Git repository (e.g., on GitHub).
2. Import the repository into Vercel.
3. Configure the same environment variables (`NEXT_PUBLIC_API_URL` and `NEXT_PUBLIC_APP_API_KEY`) in the Vercel project settings.
4. Deploy! Vercel will automatically handle the build process and deployment for you.
## 📄 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.