Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jw782cn/repochat-200k
⚡Chat with GitHub Repo Using 200k context window of Claude instead of RAG!⚡
https://github.com/jw782cn/repochat-200k
chatbot claude llm openai openrouter streamlit
Last synced: 8 days ago
JSON representation
⚡Chat with GitHub Repo Using 200k context window of Claude instead of RAG!⚡
- Host: GitHub
- URL: https://github.com/jw782cn/repochat-200k
- Owner: jw782cn
- License: mit
- Created: 2024-03-15T11:12:12.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-03-30T07:18:48.000Z (8 months ago)
- Last Synced: 2024-10-10T09:23:12.546Z (about 1 month ago)
- Topics: chatbot, claude, llm, openai, openrouter, streamlit
- Language: Python
- Homepage:
- Size: 3.74 MB
- Stars: 146
- Watchers: 1
- Forks: 23
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# RepoChat in 200k - No RAG!
⚡Chat with GitHub Repo Using 200k context window of Claude instead of RAG!⚡
## Why RepoChat - No RAG?
Take the advantage of Claude 200k! Put all examples and codes to the contexts!
**We need copilot rather than agent sometimes!**
### 1. Not just Chat, But Write Codes with the Lastest Documents
Have troubles memorizing all the apis in llama-index or langchain?
No worries, just include the components examples and the documents from the repo and let Claude Opus - the strongest model and long context window of 200k to write your agent for you!
### 2. No copy paste, Just Select
Download/Clone your Repo from Github then just select the files you'd like, I got you covered on constructing the prompt.
### 3. No RAG, You decide
I've seen many Chat with Repo projects, they all have the painpoints:
`Which files do this query need?`
They use **embedding search** in Code database but most of the time I already knew what documents I'm refering... So make your own choices each time when you are coding.
- Coding Frontend? Just select components and examples.
- Coding Agents? Just select Jupyter Notebook of langgraph.
- Coding RAG? Just select Jupyter Notebook of llamaindex.
### 4. More Use Cases!
#### Use llamaindex doc
select llamaindex example of pipeline to write RAG graph.
#### Use Shadcn doc to write frontend
select examples and components definition.
## Suggestions!
1. You can use Haiku for most of the case.
2. Change models based on tasks.
3. Change files based on tasks.
4. Clone Repos You like!
5. Usually I will include README of repo to help Claude Understand better
6. USE `COUNT TOKENS` on the sidebar to see how many tokens you will send!!!
## Features
1. **Repository Download**: Users can provide a GitHub repository URL, and the application will automatically download and analyze the repository.
2. **File and Folder Selection**: Users can select specific files or folders from the repository to include in the LLM's input.
3. **Language Filtering**: Users can filter the files by programming language to focus the LLM's understanding on specific parts of the codebase.
4. **Token Limit**: Users can set a token limit to control the amount of information sent to the LLM, which can be useful for performance or cost considerations.
5. **Chat Interface**: Users can interact with the LLM through a chat-style interface, allowing them to ask questions or request code generation based on the repository contents.
6. **Streaming Output**: The LLM's responses are displayed in a streaming fashion, providing a more engaging and real-time user experience.
Currently I only supported Openrouter. Planing to add more and refactor someday.
## Get Started
1. **Environment Settings**: Run `pip install -r requirements.txt` to set up environment.
2. **Create a .env file**: Create a `.env` file in the root directory of the project and add your OpenRouter API key (Recommended):
```bash
OPENROUTER_API_KEY=your_openrouter_api_key_here
```
I recommend [OpenRouter](https://openrouter.ai/) because it has all models!
If you want to use OpenAI GPT models, add your `openai api key` as well.
```bash
OPENAI_API_KEY=your_openai_api_key_here
```
3. **Run the application**: Run the `app.py` script using Streamlit:
```bash
streamlit run app.py
```
4. **Use the application**: Follow the instructions in the application to download a GitHub repository, select files and folders, and chat with the LLM.
If you encounter some issues with repo, you can always delete the repo dir in ./repos dir and download it again.
## Configuration
The application's behavior can be customized through the following configuration options:
- **Model**: The specific LLM model to use (e.g., "anthropic/claude-3-haiku", "anthropic/claude-3-opus").
- **Temperature**: The temperature parameter that controls the "creativity" of the LLM's responses.
- **System Prompt**: The initial prompt given to the LLM to set the desired behavior.
These settings can be adjusted in the sidebar of the Streamlit application.
## Contributing
If you'd like to contribute to the RepoChat-200k project, please feel free to submit issues or pull requests on the [GitHub repository](https://github.com/jw782cn/RepoChat-200k).
## License
This project is licensed under the [MIT License](LICENSE).