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Knowledge bases enable agents and gateways to search through documents using semantic (meaning-based) search, not just keywords.

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What is a Knowledge Base?

A knowledge base stores:
  • Documents (PDF, Word, text, etc.)
  • Converted to vector embeddings
  • Indexed for fast semantic search
  • Retrieved for RAG augmentation
Documents automatically chunked, embedded, and searchable.

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Creating a Knowledge Base

1

Navigate to Knowledge

Knowledge > Create Knowledge Base
2

Basic Info

  • Name: “Product Docs”
  • Description: “Official product documentation”
  • Click Create
3

Upload Documents

Click Upload Documents:
  • Select files (PDF, DOCX, TXT, etc.)
  • Set metadata if needed
  • Documents processed automatically
4

Configure

  • Choose embedding model
  • Set search parameters
  • Enable reranking (optional)
5

Test

Use Test Search to try queries before using in agents.
6

Attach to Agent/Gateway

Open Knowledge Retrieval capability, link knowledge base.

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Knowledge Base Features

  • Semantic Search - Understand meaning, not just keywords
  • Multi-document - Search across many files
  • Automatic Chunking - Documents split intelligently
  • Vector Embeddings - Math-based document representation
  • Reranking - LLM refines search results
  • Metadata - Tags, dates, sources attached

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Supported Document Types

FormatSupport
PDF✓
DOCX✓
TXT✓
Markdown✓
JSON✓
XLSX✓
HTML✓

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Usage in Agents

When agent has Knowledge Retrieval attached: Agent: “What’s our return policy?” Process:
  1. Query embedded to vectors
  2. Similar documents retrieved
  3. Top matches injected into prompt
  4. Agent responds with context
Result: Accurate, sourced answers

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Embedding Models

Choose model based on needs:
ModelDimensionsBest For
small1536General
large3072Nuanced
customVariableSpecialized

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Search Modes

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Semantic

Find by meaning: 
Query: "How do I reset password?"
Matches: password reset, account access, authentication

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Keyword

Find by exact words: 
Query: "password"
Matches: pages containing "password"

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Hybrid

Combination of both.

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Vector Database

Vectors stored in the vector database:
  • Fast similarity search
  • Distributed storage
  • Automatic indexing
No manual configuration needed.

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Limits

LimitValue
Docs per knowledge base10,000
Total embeddings1M per account
Doc size100MB
Search results20 (top)

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Cost

For current pricing details, see Pricing.

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How Agents Access Knowledge

There are two ways to connect agents to your knowledge bases:

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Attach Knowledge Base Directly (Automatic RAG)

Configure knowledge_base_ids on your agent. The platform automatically retrieves relevant documents at the start of each conversation turn and injects them into the agent’s context. Best for: Agents that always need access to specific documentation (support bots, FAQ assistants, domain experts).

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Attach Knowledge Retrieval Capability (On-Demand Tool)

Attach the Knowledge Retrieval built-in capability to your agent. This gives the agent search, get_by_id, and list tools that it calls when it decides it needs to look something up. Best for: General-purpose agents that only sometimes need knowledge lookups, or agents that should control when and what they search. You can combine both approaches — automatic context from attached KBs plus on-demand searches via the capability tool. See Knowledge Bases & RAG for a detailed conceptual overview.

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Next Steps