Local RAG
Docs/Local RAG

Local RAG

Import your datasheets and documentation. Neuro Input uses this knowledge for hardware-specific commands.

What is RAG?

RAG (Retrieval-Augmented Generation) enhances AI responses with your own documents. When you ask a question, NeuroTerm searches your imported docs and includes relevant context in the AI prompt.

@"what's the GPIO pin for the status LED?"
Answer:
GPIO5_IO19 (pin 147) is STATUS_LED
Source: imx8mp-pinmux.md

Importing Documents

1

Open the Context sidebar

Click the Context icon in the activity bar

2

Go to Knowledge Base

Find the document import section

3

Add documents

Click "Import" and select your files

Supported Formats

.pdf.docx.txt.md.markdown

How It Works

1. Chunking

Documents are split into smaller chunks for efficient searching.

2. Embedding

Each chunk is converted to a vector using local LLM's embedding model. Stored locally in SQLite-vec.

3. Retrieval

When you ask a question, similar chunks are found via vector search.

4. Generation

Retrieved context is included in the prompt. AI generates answers using your docs.

Best Practices

Use focused documents

Import specific datasheets, not entire manuals. Pinmux tables, register maps, and command references work best.

Include context in filenames

Name files descriptively: imx8mp-gpio-pinmux.md instead of notes.txt

Keep documents current

Re-import updated docs to refresh the knowledge base. Old versions can be removed.

100% Local

All documents stay on your machine. Embeddings are generated locally via local LLM. Nothing is uploaded to any server.

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