big-rag
Public
Forked from mindstudio/big-rag
Forked from mindstudio/big-rag
Forked from mindstudio/big-rag
QUICKSTART.md
Get up and running with the Big RAG Plugin in 5 minutes.
nomic-ai/nomic-embed-text-v1.5-GGUF)QUICKSTART.md
Get up and running with the Big RAG Plugin in 5 minutes.
nomic-ai/nomic-embed-text-v1.5-GGUF)npm run build
npm run dev
This will start the plugin and register it with LM Studio.
Required Settings:
/Users/yourname/Documents/MyLibrary)/Users/yourname/.lmstudio/big-rag-db)Optional Settings (use defaults for now):
mixedbread-ai/mxbai-embed-large-v1 or text-embedding-mxbai-embed-large-v1 for the same mxbai embedder). Pick one string and keep it so it matches .big-rag-embedding.json after indexing. Default: nomic-ai/nomic-embed-text-v1.5-GGUF. Change only if you intend to reindex.Create a test directory with some documents:
mkdir -p ~/test-docs echo "Artificial intelligence is transforming technology." > ~/test-docs/ai.txt echo "Machine learning is a subset of AI." > ~/test-docs/ml.txt
Set Documents Directory to ~/test-docs in the plugin settings.
First Run: The plugin will automatically:
Subsequent Runs: The plugin will:
Need to force a rebuild? Flip Manual Reindex Trigger ON in the plugin settings. Every chat will then kick off indexing again—full rebuilds if Skip Previously Indexed Files is OFF, or incremental updates when it’s ON. Remember to switch the toggle back OFF when you’re finished so normal incremental behavior resumes.
The LLM will respond using information from your indexed documents, with citations like:
Based on the provided documents: Citation 1 mentions that "Artificial intelligence is transforming technology." Citation 2 explains that "Machine learning is a subset of AI." [Model's response incorporating this information...]
rm -rf ~/.lmstudio/big-rag-db/*
Based on your dataset:
Small Dataset (<100 files, <100MB):
Medium Dataset (100-1000 files, 100MB-1GB):
Large Dataset (1000+ files, 1GB+):
Watch the LM Studio interface for:
Solution: Lower the Affinity Threshold to 0.3-0.4
Solution: Set the Documents Directory in plugin settings
Solution:
Solution:
Documents: ~/Documents/Notes Vector Store: ~/.lmstudio/notes-db Settings: Defaults work well
Documents: ~/Code/project/docs Vector Store: ~/.lmstudio/project-docs-db Chunk Size: 1024 (larger for technical content)
Documents: ~/Research/papers Vector Store: ~/.lmstudio/research-db Retrieval Limit: 10 (more results) Affinity Threshold: 0.6 (higher precision)
Now that you have the plugin running:
Happy querying! 🚀
cd big-rag-plugin
npm install
npm run build
npm run dev
This will start the plugin and register it with LM Studio.
Required Settings:
/Users/yourname/Documents/MyLibrary)/Users/yourname/.lmstudio/big-rag-db)Optional Settings (use defaults for now):
mixedbread-ai/mxbai-embed-large-v1 or text-embedding-mxbai-embed-large-v1 for the same mxbai embedder). Pick one string and keep it so it matches .big-rag-embedding.json after indexing. Default: nomic-ai/nomic-embed-text-v1.5-GGUF. Change only if you intend to reindex.Create a test directory with some documents:
mkdir -p ~/test-docs echo "Artificial intelligence is transforming technology." > ~/test-docs/ai.txt echo "Machine learning is a subset of AI." > ~/test-docs/ml.txt
Set Documents Directory to ~/test-docs in the plugin settings.
First Run: The plugin will automatically:
Subsequent Runs: The plugin will:
Need to force a rebuild? Flip Manual Reindex Trigger ON in the plugin settings. Every chat will then kick off indexing again—full rebuilds if Skip Previously Indexed Files is OFF, or incremental updates when it’s ON. Remember to switch the toggle back OFF when you’re finished so normal incremental behavior resumes.
The LLM will respond using information from your indexed documents, with citations like:
Based on the provided documents: Citation 1 mentions that "Artificial intelligence is transforming technology." Citation 2 explains that "Machine learning is a subset of AI." [Model's response incorporating this information...]
rm -rf ~/.lmstudio/big-rag-db/*
Based on your dataset:
Small Dataset (<100 files, <100MB):
Medium Dataset (100-1000 files, 100MB-1GB):
Large Dataset (1000+ files, 1GB+):
Watch the LM Studio interface for:
Solution: Lower the Affinity Threshold to 0.3-0.4
Solution: Set the Documents Directory in plugin settings
Solution:
Solution:
Documents: ~/Documents/Notes Vector Store: ~/.lmstudio/notes-db Settings: Defaults work well
Documents: ~/Code/project/docs Vector Store: ~/.lmstudio/project-docs-db Chunk Size: 1024 (larger for technical content)
Documents: ~/Research/papers Vector Store: ~/.lmstudio/research-db Retrieval Limit: 10 (more results) Affinity Threshold: 0.6 (higher precision)
Now that you have the plugin running:
Happy querying! 🚀
cd big-rag-plugin
npm install