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https://github.com/afaqueumer/DocQA
Question Answering with Custom FIles using LLMs
https://github.com/afaqueumer/DocQA
Last synced: about 1 month ago
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Question Answering with Custom FIles using LLMs
- Host: GitHub
- URL: https://github.com/afaqueumer/DocQA
- Owner: afaqueumer
- Created: 2023-06-24T00:54:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-16T23:18:21.000Z (over 1 year ago)
- Last Synced: 2024-08-03T01:24:35.806Z (4 months ago)
- Language: Python
- Size: 11.7 KB
- Stars: 276
- Watchers: 4
- Forks: 83
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-llm-and-aigc - afaqueumer/DocQA
- awesome-llm-and-aigc - afaqueumer/DocQA
README
# DocQA 🤖
DocQA 🤖 is a web application built using Streamlit 🔥 and the LangChain 🦜🔗 framework, allowing users to leverage the power of LLMs for Generative Question Answering. 🌟
Read More Here 👉
https://ai.plainenglish.io/️-langchain-streamlit-llama-bringing-conversational-ai-to-your-local-machine-a1736252b172
## Installation
To run the LangChain web application locally, follow these steps:
Clone this repository 🔗
```
git clone https://github.com/afaqueumer/DocQA.git
```
Create Virtual Environment and Install the required dependencies ⚙️
```
Run ➡️ setup_env.bat
```
Launch Streamlit App 🚀
```
Run ➡️ run_app.bat
```
## Usage
Once you have the Streamlit web application up and running, you can perform the following steps:
1. Upload the Text File.
2. Once the Text File is loaded as the Vector Store Database it will show a success alert "Document is Loaded".
3. Insert the question in "Ask" textbox and submit your question for LLM to generate the answer.
## Contributing
Contributions to this app are welcome! If you have any ideas, suggestions, or bug fixes, please feel free to open an issue or submit a pull request. We appreciate your contributions.
## License
This project is licensed under the MIT License.
🎉 Thank you 🤗 Happy question answering! 🌟