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# My GPU is "NVIDIA GeForce RTX 4070 Laptop GPU with 8G VRAM", so I downloaded 7B version models.~$ mkdir ~/workspace/ai/
~/workspace/ai$ curl -fsSL https://ollama.com/install.sh | sh
~/workspace/ai$ ollama pull mistral
~/workspace/ai$ ollama pull qwen2.5
~/workspace/ai$ ollama pull nomic-embed-text
~/workspace/ai$ mkdir bin
# make sure you have all the scripts placed in ~/workspace/ai/bin~/workspace/ai$ ./bin/run_ollama
Create a virtual environment and install the required packages:
~/workspace/ai$ mkdir ~/workspace/ai/ragtest
~/workspace/ai$ cp -rf llm/graphrag/ragtest ~/workspace/ai/ragtest
# apply changes in modified_graphrag to installed graphrag for mistral/qwen2.5 accordingly# NOTE: finetuned prompt has been provided, if you wanna do it yourself, run ./bin/prompt_tuning~/workspace/ai$ ./bin/graphrag_index
Search GraphRAG for JinPingMei questions:
~/workspace/ai$ ./bin/local_query "这个章节中,西门庆有几个老婆,他们的关系如何?"# NOTE: global_query is not working due to graphrag code broken
Run baseline RAG to analyze and search for JinPingMei:
~/workspace/ai$ python3 -m venv test_env2
~/workspace/ai$ source test_env2/bin/activate
~/workspace/ai$ pip install -r llm/rag/requirements.txt
~/workspace/ai$ python bin/langchain_rag.py
~/workspace/ai$ python bin/langchain_rag.py "西门庆参加了哪些聚会?都有哪些人参加了?"# NOTE: to be able to handle org file, you have to run "pip install pypandoc-binary"
Results
# The model used for these images is qwen2.5, as it's good at Chinese.# NOTE: The result shows baseline RAG beats GraphRAG most of the time, strange...