wehub-resource-sync
57 lines
2.1 KiB
Markdown
57 lines
2.1 KiB
Markdown
# Chat with Code using Qwen3-Coder
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Enhance your experience with GitHub repositories through a natural language interface. We are developing a Streamlit app that enables users to communicate with code using the Qwen3-Coder model. This app offers a user-friendly interface for querying code and receiving responses, along with the additional advantage of validating those responses using Cleanlab Codex.
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We use:
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- [Llama_Index](https://docs.llamaindex.ai/en/stable/) for orchestration
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- [Milvus](https://milvus.io/) to self-host a VectorDB
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- [Cleanlab](https://help.cleanlab.ai/codex/) codex to validate the response
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- [OpenRouterAI](https://openrouter.ai/docs/quick-start) to access Alibaba's Qwen3-Coder
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## Set Up
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Follow these steps one by one:
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### Setup Milvus VectorDB
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Milvus provides an installation script to install it as a docker container.
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To install Milvus in Docker, you can use the following command:
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```bash
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curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh -o standalone_embed.sh
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bash standalone_embed.sh start
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```
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### Install Dependencies
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```bash
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uv sync
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```
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## Run the Notebook
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You can run the `notebook.ipynb` file to test the functionality of the code in a Jupyter Notebook environment. This notebook will guide you through the process of querying code and validating responses.
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## Run the Application
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To run the Streamlit app, use the following command:
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```bash
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streamlit run app.py
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```
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Open your browser and navigate to `http://localhost:8501` to access the app.
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## 📬 Stay Updated with Our Newsletter!
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**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
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[](https://join.dailydoseofds.com)
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## Contribution
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Contributions are welcome! Feel free to fork this repository and submit pull requests with your improvements.
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