https://github.com/beacoder/llm
A bunch of experiments using Large Language Models
https://github.com/beacoder/llm
agentic-ai graphrag langchain langgraph llm ollama qwen2 rag
Last synced: about 2 months ago
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A bunch of experiments using Large Language Models
- Host: GitHub
- URL: https://github.com/beacoder/llm
- Owner: beacoder
- License: mit
- Created: 2024-10-12T13:07:05.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-04-09T17:29:02.000Z (about 2 months ago)
- Last Synced: 2025-04-09T17:42:57.241Z (about 2 months ago)
- Topics: agentic-ai, graphrag, langchain, langgraph, llm, ollama, qwen2, rag
- Language: Python
- Homepage:
- Size: 11.8 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# A bunch of experiments with LLMs
## Build RAG/TOOL application with local LLMs
### Install and run Ollama:
```bash
# 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$ ./ollama/bin/run_ollama
```
### Run AgenticRAG to analyze and search for JinPingMei
```bash
# make sure your python3 version >= 3.10.12
~/workspace/ai$ python3 -m venv rag_env
~/workspace/ai$ source rag_env/bin/activate
~/workspace/ai$ pip install -r rag/requirements.txt
~/workspace/ai$ python ./rag/bin/agentic_rag.py
# NOTE: to be able to handle org file, you have to run "pip install pypandoc-binary"
```
### Run GraphRAG to analyze and search for JinPingMei
```bash
# make sure your python3 version >= 3.10.12
~/workspace/ai$ python3 -m venv graphrag_env
~/workspace/ai$ source graphrag_env/bin/activate
~/workspace/ai$ pip install -r graphrag/requirements.txt
~/workspace/ai$ mkdir ~/workspace/ai/ragtest
~/workspace/ai$ cp -rf 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$ ./graphrag/bin/graphrag_index
~/workspace/ai$ ./graphrag/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 AgenticTool to explore AI-Agent
```bash
# make sure your python3 version >= 3.10.12
~/workspace/ai$ python3 -m venv tool_env
~/workspace/ai$ source tool_env/bin/activate
~/workspace/ai$ pip install -r tool/requirements.txt
# support both local_tools and docker_tools.
# for local_tools, tools will be executed on your file system.
# for docker_tools, tools will be executed on docker instance for each user (identified by ip)
# note: for docker_tools to work, #1 install docker, #2 run "docker pull python:3.10.12"
~/workspace/ai$ streamlit run ./tool/bin/agentic_tool.py
```
### Results
```bash
# The model used for these images is qwen2.5, as it's good at Chinese.
# NOTE: The result shows AgenticRAG beats GraphRAG most of the time, strange...
```
```bash
# TOOL task:
# 1.Create a minesweeper game using html,css,js in directory "~/workspace/ai/minesweeper"
# 2.list all the content of these new files.
```
