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Knowledge Base

Audience: Sales · Management · QA · Support · Engineering · Where in app: Settings → Knowledge Base (also reachable from Settings → AI Agents) · Plan availability: All plans; document/token/page caps are plan-dependent (verify)

The Knowledge Base is the content your company's AI agents read to answer visitor questions. You feed it your website pages, PDFs, FAQs, and free text; the platform turns that into searchable embeddings; and at conversation time the Chat Agent and Call Agent retrieve the most relevant pieces to ground their answers. The more comprehensive and accurate it is, the better your AI performs.

Not the same as Nox's knowledge base. This page is the company KB (what visitors' AI reads). Nox (the in-app admin assistant) has its own, separate product-docs KB. See the comparison table at the end.

What it does​

  • Stores company-specific content (products, services, pricing, policies, FAQs) as embeddings.
  • Powers RAG grounding for the Chat Agent, Call Agent, and Auto-Connect Agent — any content you add benefits all of them.
  • Syncs an e-commerce product catalog (Shopify/WooCommerce) for product lookups during conversations.
  • Auto-seeds itself from the company website on signup so the agent can answer from day one.

How it works​

Source types. Content comes from five source types:

SourceHow it's ingested
Website / URLPaste a URL → scraping / scrapping-bee fetches the page → markdownified → chunked. (Walks the sitemap, capped.)
PDFUpload → pdf-extraction parses → chunked. Needs selectable text (not scanned images). Max 10 MB per file (verify).
TextType/paste custom content directly.
FAQ / Q&AManually entered question+answer pairs (AIConversationModel).
Auto-scrape (signup)On signup the system scrapes the company homepage + sitemap as a starter set.

Ingestion pipeline.

  1. Source ingested into ms-ai (via the relevant queue).
  2. Text extracted (PDF/HTML).
  3. Split into chunks (sliding window with overlap).
  4. Each chunk embedded with OpenAI text-embedding-3-small (1536-dim).
  5. Stored in the ms-ai embeddings pgvector table, keyed by company_id.
  6. Retrievable at chat/call time via semantic search.

Backend tracks uploaded documents on companies.agent_knowledge_base_documents[]:

{
  file: ObjectId (FileModel),
  status: pending | processing | ready | failed,
  job_id: string,
  page_count: number,
  error: string,
  frontmatter: { ... }
}

Retrieval (how agents use it). Per visitor query the agent:

  1. Embeds the question.
  2. Runs an approximate-nearest-neighbour search over the company's chunks — top ~20 candidates by cosine distance (embeddings <=> $vector).
  3. Cohere-reranks down to the top 6 (falls back to vector order if Cohere is unavailable).
  4. Injects those chunks as a "Knowledge" section in the system prompt.
  5. The LLM answers grounded in those chunks.

If retrieval returns nothing relevant, the agent answers from its persona prompt only — less grounded, more likely to hallucinate. Keep the KB tight and current.

FAQs (manual Q&A). AIConversationModel (company_id, optional chat_id, question, answer, question_id, answer_id). Short, high-confidence answers. The agent prefers a strongly-matching FAQ over scraped content — ideal for nailing brand voice on common questions.

Product catalog (RAG). For e-commerce, Shopify and WooCommerce sync the product catalog into rag_products (ms-ai). Each product is embedded for semantic lookup, so the agent can search products by name/description, recommend related items, quote pricing, and link to product pages. Sync state lives on the integration row: sync_status, last_sync_at, product_count.

Configuration & options​

  • Add sources in Settings → Knowledge Base → Add Source (Website / PDF / Text / Q&A). Editable in-dashboard.
  • Refresh / re-train:
    • PDFs — re-upload to replace.
    • URLs — "Refresh" re-scrapes and re-embeds if content changed.
    • FAQs — edit in dashboard.
    • Bulk re-embed via admin endpoint when the embedding model/strategy changes.
  • Preview tool — test agent responses against the KB before going live.

Performance tips for admins (QA/support):

  • Use focused FAQs for high-frequency questions.
  • Trim scraped content — strip nav/footer boilerplate.
  • Add product PDFs (spec sheets, integration guides).
  • Sync your FAQ/help-center page first — it already contains the most-asked Q&A.

Behaviors & edge cases​

  • Status lifecycle. Each document is pending → processing → ready (or failed with an error). A failed doc isn't retrieved; re-upload or re-scrape to retry.
  • Ingestion vs. credits. Embedding new content consumes a small amount of AI Credits per chunk. If credits are exhausted, ingestion stays pending until refilled. See AI Credits.
  • Stale content. URL sources don't auto-refresh — re-scrape when your site changes.
  • Empty retrieval → hallucination risk. Sparse or off-topic KBs leave the agent ungrounded; this is the main lever on answer quality.
  • Scanned PDFs. Image-only PDFs yield no text and won't train the agent.

Plan & limits​

  • Available on all plans.
  • Number of documents, total token count, and page-count caps are plan-dependent — verify under billing/plans.
  • PDF max size 10 MB per file (verify current limit).
  • Embedding/ingestion draws on AI Credits (small per-chunk cost). See AI Credits.

What Nox can tell you​

  • How many docs are in the KB, what sources exist, which are pending/failed.
  • How often the agent has cited each chunk (via session analyses + tool-call logs).

Nox can't add docs — that's a manual admin action in Settings → Knowledge Base.

Technical implementation​

  • Service: ms-ai. Ingestion modules: scraping/ + scrapping-bee/, pdf-extraction/, embedding/, company/ (RAG queries + Cohere rerank), cohere/.
  • Endpoints: POST /context/.../:company_id (RAG context for chatbot), POST /products/.../:company_id (product search), POST /create-embeddings, POST /enqueue-embeddings, POST /fetch-urls, POST /enqueue-scrapping, POST /filter-links, POST /pdf-extraction/extract, GET /pdf-extraction/content/:company_id/:file_id. See ms-ai/API_ENDPOINTS.md.
  • Queues (BullMQ): scrappingQueue, embeddingQueue, pdfExtractionQueue, shopiProductsQueue / wooProductsQueue.
  • Retrieval code: company/company.service.ts — questionEmbeddings() then getUserContext() (top-20 by cosine, CohereService.rerankItems(..., 6)).
  • Embedding model: EMBEDDINGS_MODEL env (default text-embedding-3-small) in constants.ts.
  • Storage: embeddings pgvector table (company KB chunks), rag_products (catalog). The separate Nox product KB uses knox_knowledge_chunks — see Knowledge-Base RAG.

Difference from Nox's own knowledge base​

Company KB (this page)Nox KB
AudienceVisitors talking to the company's AIAdmin asking Nox
ContentCompany-specific (their products, FAQs)Knock Knock product docs
Storageembeddings per companyknox_knowledge_chunks (global)
Retrieved byChat/Call agent at conversation timeNox knox-knowledge-search tool