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๐Ÿ“šCore Feature

Knowledge Base

Centralize your team's documents and web content, then let the AI search, retrieve, and cite them automatically. Knowledge lives in folders you organize, share, and attach to agents โ€” and it's the foundation of every grounded AI answer.

How it works

When you add a document, YOffice runs it through a four-stage pipeline that makes its content searchable by the AI.

๐Ÿ“คUpload docFile or website
โœ‚๏ธExtract & chunkBoundary-aware chunks
๐Ÿง Embedpgvector
๐Ÿ’ฌCited in answersHybrid retrieval
From a raw document to a cited AI answer, automatically
1๐Ÿ“ค
Add a source

Upload a file or link a website. Each source is processed independently.

2โœ‚๏ธ
Extract & chunk

Text is extracted from the file, then split into sized chunks that respect paragraph and sentence boundaries.

3๐Ÿง 
Embed

Each chunk is turned into a vector embedding and written to pgvector alongside its text.

4๐Ÿ’ฌ
Retrieve & cite

When you ask a question, the closest chunks are retrieved and cited in the answer.

Folders
๐Ÿ“All Documents
๐Ÿ“‚Product Specs
๐Ÿ“Engineering
๐Ÿ“Onboarding
๐Ÿ“Legal & Compliance
๏ผ‹New Folder
Product Specs
+ Add
๐Ÿ“„Product Roadmap.pdf2.4 MBReady
๐Ÿ“API Reference.docx840 KBReady
๐Ÿ“ŠPricing Model.xlsx156 KBEmbedding
๐Ÿ“‘Launch Deck.pptx5.1 MBProcessing
๐ŸŒdocs.example.comโ€”Ready
Knowledge folders (left) and a document list with processing-status badges (right)

Supported file types

YOffice extracts text from a broad range of document formats. Spreadsheets are converted into an LLM-friendly structured form (and made queryable), so the AI can reason over tabular data, not just prose.

๐Ÿ“„PDF๐Ÿ“DOCX / DOC / ODT๐Ÿ“‘PPTX๐Ÿ“ŠXLSX / XLS / ODS๐Ÿ”คTXT / MD / RTF๐ŸงพCSV / TSV๐ŸŒHTML๐Ÿ”งJSON / YAML / XMLโœ‰๏ธEML๐ŸชตLOG
โ„น๏ธ
Works best with text-based sources

The converter reads the text already present in a file, so text-based documents give the most reliable retrieval. For a scanned or photographed page (an image-only PDF), supply a text-based version to make its content fully searchable.

Semantic retrieval with pgvector

Chunks are embedded and stored in pgvector. At query time YOffice searches by vector similarity (cosine) to find the most relevant chunks, falling back to keyword matching if a vector search comes back empty, and the AI cites the results inline. Each document has an AI-context toggle, so you control exactly what the AI may draw on.

Embedding modelProvider
text-embedding-3-smallOpenAI (default)
text-embedding-004Google
nomic-embed-textOllama / Ollama Cloud
โ„น๏ธ
Keep one embedding model per organization

Different embedding models produce vectors of different dimensions, so YOffice doesn't silently fail over between providers โ€” your organization's content is embedded with one model across every folder. Pick your embedding provider deliberately at the organization level.

Folders, sharing, and limits

Folders are how you organize and share knowledge. Each folder supports:

1,000 docs
Maximum documents per folder โ€” keeping retrieval fast and relevant
  • Four sharing scopes โ€” Private, Specific people & Workers, Workspaces, and Organisation โ€” each with view or edit permission. See Members & Roles.
  • A 1,000-document limit per folder to keep retrieval fast and relevant.
  • Folder watchers โ€” point a watcher at a local folder and new files are ingested automatically as they arrive.
  1. Open Knowledge

    Click Knowledge in the navigation rail. The Knowledge tab lists your folders.

  2. Create a folder

    Click New Folder and give it a descriptive name like "Engineering Runbooks".

  3. Add sources

    Upload files or link a website. Each source processes independently โ€” a large PDF won't block smaller ones.

  4. Set sharing

    Choose a sharing scope and permission. You can change it any time.

Website as a knowledge source

Beyond files, you can link a website as an AI source. YOffice crawls the site within configurable caps, generates an AI summary at ingest, and indexes the content according to the RAG mode you choose.

Always-on AI summary

At ingest, YOffice produces a real AI summary of the site using a map-reduce pass over the page chunks โ€” it summarises chunks, then summarises the summaries. This is a genuine model-generated overview, not a naive truncation, so even in the lightest mode the AI has a faithful picture of the source.

Three RAG modes

The mode controls what gets indexed for retrieval:

ModeWhat's indexedUse whenโ€ฆ
SummaryDefaultThe AI summary onlyYou want a compact, fast overview of the site.
Summary + fullThe summary and the full page textYou want the overview plus the ability to retrieve specific details.
FullThe full crawled textYou need maximum recall over the entire site's content.

Crawl caps

Crawling is bounded by validated limits โ€” maximum pages, maximum bytes per page, and crawl depth. Each has a hard positive minimum so a website source can't run away with your budget.

Using knowledge in AI chat

Once a folder is indexed, the AI can draw on it in two ways:

  • Scope per chat โ€” open the Knowledge tool in the chat tool strip and pick folders for that session, or add a folder with #.
  • Always-on knowledge โ€” every folder you can see (owned, shared with you, your workspaces, or organization-wide) is searched automatically across sessions, and each document's own AI-context toggle lets you exclude specific files, ideal for a company wiki or product docs.

Attaching folders to agents

Attach one or more knowledge folders to an agent and that agent retrieves from them automatically when it runs โ€” a support agent linked to your "Support Docs" folder answers questions without any manual lookup. Folders can likewise be shared with an agent.

๐Ÿ’ก
Best practices for a useful Knowledge Base

Organize by topic or team rather than by date. Use descriptive folder names. Keep content current โ€” outdated documents mislead the AI. For external docs that change often, link the website source so the content stays fresh, and pick the RAG mode that matches how much detail you actually need to retrieve.