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| # RAG Knowledge Base | |
| Drop your own neuroscience knowledge files here to improve LLM interpretations. | |
| ## Supported formats | |
| ### JSON files (`*.json`) | |
| Each JSON file should be a flat object mapping region names to descriptions: | |
| ```json | |
| { | |
| "Amygdala (AMY)": "The amygdala processes threat detection and fear conditioning. It receives rapid subcortical visual input via the superior colliculus and pulvinar for fast threat assessment...", | |
| "Hippocampal Formation (HPF)": "The hippocampus is essential for episodic memory encoding and spatial navigation. Place cells in CA1 fire at specific locations..." | |
| } | |
| ``` | |
| Region names do not need to match the Allen atlas names exactly -- the system uses semantic similarity (embedding search) to find the most relevant knowledge for any query. | |
| ### Text files (`*.txt`) | |
| Use `## Region Name` headers to separate entries: | |
| ``` | |
| ## Amygdala (AMY) | |
| The amygdala processes threat detection and fear conditioning... | |
| ## Hippocampal Formation (HPF) | |
| The hippocampus is essential for episodic memory encoding... | |
| ``` | |
| ## Partial coverage is fine | |
| You do not need to cover every brain region. The system merges your files with a built-in fallback knowledge base (21 regions). For any region not covered by your files or the fallback, the LLM uses its own training knowledge. | |
| Priority order: | |
| 1. Your files in this directory (highest priority) | |
| 2. Built-in fallback knowledge base (`REGION_KNOWLEDGE` in `src/region_analyzer.py`) | |
| 3. LLM's own neuroscience training knowledge | |
| ## Tips | |
| - More detail is better -- include connectivity, cell types, clinical relevance | |
| - Multiple JSON/TXT files are merged automatically | |
| - If two files define the same region, the last one loaded (alphabetical order) wins | |
| - Changes take effect on the next app startup (no need to rebuild anything) | |