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Overview

CrewForm’s Knowledge Base enables Retrieval-Augmented Generation (RAG) — upload documents, and agents automatically search relevant content when answering questions or completing tasks.

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How It Works

Upload Document  →  Text Extraction  →  Chunking  →  Embedding (1536-dim)
                                                             ↓
Agent Task  →  knowledge_search tool  →  Cosine Similarity  →  Top-K Results
  1. Upload documents to the Knowledge Base
  2. CrewForm automatically chunks the text and generates vector embeddings
  3. Enable the knowledge_search tool on your agents
  4. During task execution, agents semantically search the knowledge base for relevant context

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Supported File Types

FormatExtensionDescription
Plain Text.txtRaw text files
Markdown.mdMarkdown documents
CSV.csvTabular data (rows become chunks)
JSON.jsonStructured data

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Uploading Documents

  1. Navigate to Knowledge Base from the sidebar
  2. Click Upload Document
  3. Select your file — upload begins automatically
  4. The document status progresses: pending → processing → ready
During processing, CrewForm:
  • Extracts text content from the file
  • Splits into chunks (optimized for retrieval quality)
  • Generates vector embeddings using OpenAI’s text-embedding-3-small model (1536 dimensions)
  • Stores chunks with embeddings in pgvector for fast similarity search

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Enabling Knowledge Search on Agents

  1. Open the agent’s configuration
  2. In the Tools section, enable knowledge_search
  3. Optionally restrict to specific documents via Knowledge Base IDs in the agent config
  4. Save — the agent can now search your documents during task execution

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How Agents Use It

When an agent has knowledge_search enabled, it can call: 
knowledge_search(query: "What is our refund policy?")
This returns the top-K most semantically similar chunks from your uploaded documents, which the agent uses as context for its response. CrewForm uses pgvector with cosine similarity for semantic search:
  • Embedding model: OpenAI text-embedding-3-small (1536 dimensions)
  • Index type: IVFFlat (lists = 100) for fast approximate nearest-neighbor search
  • Default top-K: 5 results
  • Scope: Workspace-level (all documents in the workspace, or filtered by document IDs)

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Managing Documents

From the Knowledge Base page you can:
  • View — See all uploaded documents with status, file size, and chunk count
  • Delete — Remove a document and all its chunks (cascading delete)
  • Monitor — Real-time status updates during processing

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Database

The Knowledge Base uses two tables:
TableDescription
knowledge_documentsUploaded file metadata (name, size, status, chunk count)
knowledge_chunksEmbedded text chunks with 1536-dim vectors
Both tables have workspace-scoped RLS. The match_knowledge_chunks function performs cosine similarity search.