I just wanna build my own LLM with RAG
Welcome to the my LLM with RAG system! This system is designed for me the ease the learning as a master in HCMUT
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Update vector database
curl -X POST http://localhost:8083/update
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Ask questions with vector data
curl -X POST -H "Content-Type: application/json" -d '{"query": "who is karger"}' http://localhost:8083/query
Link: https://github.com/facebookresearch/nougat
nougat data/web_data/Growth_of_Functions.pdf --markdown --no-skipping -m 0.1.0-base -o data/nougat
Before running the system, follow these steps to set up the environment:
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Clone the Repository:
- Close the Git repository to your local machine:
git clone [repository_url]
- Close the Git repository to your local machine:
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Install Dependencies:
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Navigate to the project directory and install the required packages using the provided
setup.txt
file:pip install -r setup.txt
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To read
.ppt
file we need to run this codeapt update apt install libreoffice
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Get OPENAI_API_KEY Key:
- Google and get OPENAI_API_KEY from OpenAI
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Create .env File:
- Create a new file named
.env
in the project root directory. - Add the following line to the file
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
- Create a new file named
For Linux you must open the port first:
sudo ufw allow 8083
docker:
docker build -t mrzaizai2k/llm_n_rag .
docker run -p 8083:8083 -v data:/app/data -e OPENAI_API_KEY llm_test
Build, run docker compose:
docker-compose up
Test docker on port 8083:
curl -X POST -H "Content-Type: application/json" -d '{"query": "who is karger"}' http://localhost:8083/query
curl -X POST http://localhost:8083/update
Explore practical implementations and demonstrations of the functions in the notebook
folder. These examples showcase real-world scenarios, illustrating how the chatbot can be effectively utilized for stock market monitoring.
- Update some features with langchain
- Build the docker to use with my Telegram bot