Hugging Face's logo

Spaces:
chelleboyer
/
15_Open_Source_Endpoints
Runtime error

App Files Community
15_Open_Source_Endpoints
File size: 2,130 Bytes 
8f31f1c
63813e0
 
 
 
8f31f1c
 
16bf509
8f31f1c
 
63813e0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
 
---
title: Paul Graham Essay Explorer
emoji: 📚
colorFrom: purple
colorTo: indigo
sdk: docker
pinned: false
app_port: 7860
---

# Paul Graham Essay Explorer 🤖

An AI-powered interface for exploring and understanding Paul Graham's essays. This tool uses advanced language models to help you dive deep into PG's insights and ideas.

## Features 🌟

- **Smart Search**: Ask questions about any of Paul Graham's essays and get AI-powered responses
- **Context-Aware**: The AI understands the full context of PG's writing and can reference specific essays
- **Interactive Chat**: Have natural conversations about PG's ideas and get detailed explanations
- **Essay Exploration**: Discover connections between different essays and themes

## How to Use 🚀

1. **Ask Questions**: Type any question about Paul Graham's essays
   - "What does PG think about startups?"
   - "Explain the concept of 'taste' from his essays"
   - "What are his views on wealth creation?"

2. **Get Insights**: The AI will provide detailed responses with relevant context from the essays

3. **Follow-up**: Ask follow-up questions to dive deeper into any topic

## Example Questions 💡

- "What are PG's main arguments about wealth inequality?"
- "How does he define a good startup idea?"
- "What does he say about the importance of writing?"
- "Explain his concept of 'makers vs managers'"
- "What are his thoughts on programming languages?"

## Technical Details 🔧

- Built with Chainlit and LangChain
- Uses Hugging Face's advanced language models
- Implements RAG (Retrieval Augmented Generation) for accurate responses
- Dockerized for easy deployment

## Running Locally 🛠️

```bash
# Clone the repository
git clone [your-repo-url]

# Build and run with Docker
docker build -t pg-explorer .
docker run -p 7860:7860 pg-explorer
```

Visit `http://localhost:7860` to start exploring!

## Contributing 🤝

Contributions are welcome! Feel free to:
- Add new features
- Improve the UI/UX
- Enhance the response quality
- Add more essays to the database

## License 📄

MIT License - feel free to use this project for your own purposes!