Knowledge Base

A knowledge base is a store of documents your agents can search by meaning. You upload files, Sim splits them into chunks and indexes them, and a Knowledge block retrieves the chunks most relevant to a query. This is how an agent answers from your own content instead of the model's general training.

How a document becomes searchable

When you upload a document, Sim processes it in the background:

1. Extract the text, with a parser for each file type and OCR for scanned PDFs.
2. Chunk it into passages, with a size and overlap you can tune.
3. Embed each chunk as a vector so it can be matched by meaning, not just keywords.

A document is searchable once its status reads completed. Open any document to view, edit, merge, or split its chunks.

Every knowledge base records the embedding model and the vector width it was built with, and keeps them for its lifetime. Two bases built differently cannot be searched in one request — vectors are only comparable when they come from the same model at the same size — so search them separately, or rebuild one to match. On Sim Cloud every base uses text-embedding-3-small at 1,536 dimensions; self-hosted deployments choose both with KB_EMBEDDING_MODEL and EMBEDDING_OUTPUT_DIMS, including models on their own Ollama.

What you can upload

Sim accepts PDF, Word, text, Markdown, HTML, Excel, PowerPoint, CSV, JSON, and YAML files, up to 100 MB each (best under 50 MB). Scanned PDFs work too: with Azure or Mistral OCR configured, Sim extracts text from image-based pages.

Shaping what a search returns

Two things control retrieval quality, and each has its own page:

• Chunking decides how a document is split. Smaller chunks are more precise; larger ones keep more context. See chunking strategies.
• Tags label documents so a search can filter to a subset. See tags and filtering.

To keep a base in sync with an outside source like Google Drive, use a connector.

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