Knowledge Base
The Knowledge Base allows you to provide your agents with domain-specific information. During a conversation, the agent automatically retrieves the most relevant content to answer caller questions accurately — instead of relying on the LLM’s general training data.How It Works (RAG)
Callem Studio uses Retrieval-Augmented Generation (RAG) to give your agent real-time access to your data:Content ingestion
When you create a knowledge item, the text is split into chunks and each chunk is converted into a vector embedding.
Real-time retrieval
During a call, the caller’s question is also converted into a vector. The system finds the most similar chunks from your knowledge base.
Creating a Knowledge Item
- Navigate to Build > Knowledge Base
- Click Add
- Fill in:
- Name: a descriptive label (e.g. “Product Catalog Q3 2025”, “FAQ - Returns Policy”)
- Description: optional summary explaining what the content covers
- Content: the actual text your agent should know
Linking to an Agent
Once created, you need to link the knowledge base to your agent:- Go to Build > Agents and select your agent
- Open the Model tab → Knowledge & Tools section
- Select one or more knowledge bases from the dropdown
- (Optional) Adjust Retrieval Settings
Retrieval Settings
Fine-tune how the agent retrieves content:| Setting | Description | Default | Guidance |
|---|---|---|---|
| Max documents | Maximum text chunks retrieved per query (1-10) | 6 | More chunks = more context but higher token usage. Start with 4-6 and increase if the agent frequently lacks information. |
| Relevance threshold | Minimum similarity score (0.0-1.0) for a chunk to be included | 0.6 | Higher = stricter matching, fewer but more relevant results. Lower = more results but may include loosely related content. |
Content Guidelines
Use clear, factual language
Use clear, factual language
Write content as you’d want the agent to say it. Avoid internal jargon unless the agent should use it with callers. The LLM will rephrase as needed, but clear source text gives better results.
Organize with headings
Organize with headings
Use
## Heading to separate topics. Each heading creates a natural chunk boundary, improving retrieval accuracy. Don’t put unrelated information under the same heading.Keep items focused
Keep items focused
One knowledge item per topic or document. A “Business Hours” item and a “Pricing” item will retrieve better than a single “Everything About Our Company” item.
Be specific with numbers and details
Be specific with numbers and details
Instead of “Our service is affordable”, write “Our Standard plan costs €49/month, Professional is €99/month.” The agent can only relay information it has.
Include Q&A pairs
Include Q&A pairs
Adding common questions and their answers helps the retrieval engine match caller questions to relevant content. Example: “Q: What are your hours? A: We are open Monday to Friday, 9am to 6pm.”
Update regularly
Update regularly
Outdated knowledge leads to wrong answers and erodes caller trust. Delete and recreate items when the underlying information changes — pricing, availability, policies, etc.
Example Content Structure
Multiple Knowledge Bases
You can link multiple knowledge bases to a single agent. The retrieval system searches across all linked knowledge bases simultaneously and returns the most relevant chunks regardless of which knowledge base they belong to. Common patterns:- Product catalog + FAQ + Policy documents — different types of content, all accessible
- Language-specific — separate knowledge bases per language
- Temporal — archive old content in a separate KB, keep current content in the primary one
API Access
Knowledge base items can be managed programmatically via the Public API:GET /v1/knowledge— list all itemsGET /v1/knowledge/:id— get a specific itemPOST /v1/knowledge— create a new item (automatically vectorized)DELETE /v1/knowledge/:id— delete an item