Empower Agents with Knowledge

Enable context-aware responses with RAG-powered retrieval.

Overview

Knowledge connects your agents to data sources through Knowledge Tools — intelligent components that use Search AI’s Retrieval-Augmented Generation (RAG) capabilities. Instead of relying solely on the LLM’s training data, agents retrieve relevant information from your knowledge bases and incorporate it into responses. Integrated through the AI for Service platform, Knowledge Tools enable agents to understand user intent, retrieve relevant content, and generate accurate, business-aware responses in real time.

User Question → Semantic Search → Retrieve Relevant Chunks → Augment Prompt → Generate Response

How It Works

The RAG Pipeline

┌─────────────────────────────────────────────────────────────────┐ │ User Query │ │ “What’s your return policy for electronics?” │ └───────────────────────────────┬─────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Query Embedding │ │ Convert question to vector representation │ └───────────────────────────────┬─────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Semantic Search │ │ Search knowledge base for similar content │ └───────────────────────────────┬─────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Chunk Retrieval │ │ Return top-k relevant passages from documents │ └───────────────────────────────┬─────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Prompt Augmentation │ │ Inject retrieved context into agent’s prompt │ └───────────────────────────────┬─────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Response Generation │ │ Agent generates grounded, accurate response │ └─────────────────────────────────────────────────────────────────┘

Key Capabilities

Capability	Details
Extensive Connectors	Connect to 100+ data sources: Cloud Storage (Google Drive, OneDrive, Dropbox), Collaboration (Confluence, SharePoint, Notion), Business Apps (Salesforce, Jira, HubSpot), Communication (Slack, Microsoft Teams), Databases (SQL, NoSQL, custom APIs)
Intelligent Search	Hybrid search (keyword + semantic matching), Multi-vector (weighted embedding strategies), Programmable extraction (custom chunking pipelines)
Enterprise Security	Access control integration, permission-aware retrieval, audit logging

Setting Up Knowledge

The Knowledge page of your Agentic App provides a structured view of all connected knowledge bases, including their total count, associated agents, and management options from a central location.

Step 1: Create a Knowledge Tool

Navigate to Knowledge in your app and click the Create Knowledge Base drop-down. Choose one of the following:

Create from existing sources — Link the Agentic App to an existing Search AI application in the same workspace.
Create from scratch — Create a new Search AI application.

Create from Existing Sources

Configure the following details:

Knowledge Base Name — Unique name to identify the knowledge tool in the Agentic App.
Description — Brief summary of the content included. Helps agents determine when to use this knowledge base.
Knowledge Sources — Click Link Knowledge Source to select an existing Search AI application from the same workspace.

After selecting the app, set the retrieval configuration:

Use Knowledge Base to respond with:
- Chunks — Returns the top relevant passages from the knowledge base.
- Answer — Returns an answer generated by Search AI.
- Selecting both returns the generated answer along with the supporting chunks.

Meta Filters (Optional) — Filter retrieved content by metadata. For example, to retrieve only web content:

[
  {
    "condition": "AND",
    "rules": [
      {
        "fieldName": "sourceType",
        "fieldValue": ["web"],
        "operator": "contains"
      }
    ]
  }
]

Click Create to complete the integration.

Create from Scratch

Enter a Knowledge Base Name and Description, then click Create. This creates a new Search AI application in the same workspace. Next, navigate to Search AI to:

Set extraction strategies
Ingest content sources
Set retrieval strategies
Configure answer generation

Step 2: Test the Knowledge Tool

Click Test your knowledge base and provide input parameters to validate the tool before deployment. The response includes output and detailed logs for troubleshooting, performance analysis, and refining query behavior.

Step 3: Link to Agent

Open your agent configuration.
Navigate to Knowledge.
Select the knowledge tool.
Configure retrieval settings.

Other Operations

Delete — Remove a knowledge tool from the app via the Knowledge page. The underlying Search AI application remains available in the workspace and can be linked to other Agentic Apps.
Manage Content — Use the Manage Content option to navigate to the Search AI app and update indexed content or configurations.

Accessing and Managing Search AI Applications

Click Search AI to view and manage all Search AI applications in your workspace. These can be integrated into Agentic Apps as Knowledge Tools. The Linked Apps column shows which Agentic Apps use each integration. To create a new Search AI application from this page:

Click + New Integration.
Provide a name and description.
Click Manage Content to open the app in AI for Service and configure it.

To include an integration in your Agentic App, go to the app’s Knowledge page and add it.

Example Scenario

An agent designed to answer questions about a company’s products can configure a Knowledge Tool that indexes the company’s website using Search AI. When a user asks “What AI solutions does the company offer?”, the agent retrieves information directly from the indexed content and delivers an accurate, up-to-date answer.

Query ───────► ┌──────────┐ ┌─────────────┐ ┌──────────┐ │ │ AI Agent │◄──────►│ Knowledge │◄──────►│ Search │ ◄─────── │ │ │ Tool │ │ AI │ Response └──────────┘ └─────────────┘ └──────────┘ │ │ │ ▼ │ ┌──────────────────────┐ │ │ Company Resources │ │ │ ┌──┐ ┌───┐ ┌──┐ │ │ │ │DB│ │Web│ │ /│ │ │ └──┴──┴──┴───┴──┴──┴───┘

Answer Generation Examples

Extractive

Pull exact quotes from source documents.

Question: "What's the return window?"

Retrieved: "Items may be returned within 30 days of purchase."

Answer: "Items may be returned within 30 days of purchase."
        [Source: Return Policy, Section 2.1]

Generative

Synthesize answers using retrieved context.

Question: "Can I return opened electronics?"

Retrieved:
- "Electronics have a 15-day return window."
- "Opened items may be subject to restocking fee."
- "Defective items can be returned within warranty period."

Answer: "Yes, you can return opened electronics within 15 days,
        though a restocking fee may apply. If the item is
        defective, you can return it within the warranty period
        without a fee."

Best Practices

Quality Content

Keep documentation up-to-date
Use clear, structured writing
Include relevant metadata

Appropriate Chunking

Content Type	Strategy	Chunk Size
FAQs	Question-answer pairs	Small (200-300 tokens)
Documentation	Sections/headers	Medium (400-600 tokens)
Long articles	Semantic breaks	Variable

Retrieval Tuning

Start with top_k=5, adjust based on results
Use score thresholds to filter low-quality matches
Enable reranking for better precision

Grounding Instructions

Tell agents how to use knowledge:

Instructions:
- Base your answers on the retrieved knowledge
- If the answer isn't in the knowledge base, say so
- Cite sources when providing specific information
- Don't make up information not present in the context

Monitoring

Track knowledge retrieval performance:

Hit rate: How often relevant content is found
Latency: Search response times
Relevance: User satisfaction with answers
Coverage: Questions without good matches

Building Agents

Platform Services

Administration

References

Empower Agents with Knowledge

Overview

How It Works

The RAG Pipeline

Key Capabilities

Setting Up Knowledge

Step 1: Create a Knowledge Tool

Create from Existing Sources

Create from Scratch

Step 2: Test the Knowledge Tool

Step 3: Link to Agent

Other Operations

Accessing and Managing Search AI Applications

Example Scenario

Answer Generation Examples

Extractive

Generative

Best Practices

Quality Content

Appropriate Chunking

Retrieval Tuning

Grounding Instructions

Monitoring

Building Agents

Platform Services

Administration

References

​Overview

​How It Works

​The RAG Pipeline

​Key Capabilities

​Setting Up Knowledge

​Step 1: Create a Knowledge Tool

​Create from Existing Sources

​Create from Scratch

​Step 2: Test the Knowledge Tool

​Step 3: Link to Agent

​Other Operations

​Accessing and Managing Search AI Applications

​Example Scenario

​Answer Generation Examples

​Extractive

​Generative

​Best Practices

​Quality Content

​Appropriate Chunking

​Retrieval Tuning

​Grounding Instructions

​Monitoring

Overview

How It Works

The RAG Pipeline

Key Capabilities

Setting Up Knowledge

Step 1: Create a Knowledge Tool

Create from Existing Sources

Create from Scratch

Step 2: Test the Knowledge Tool

Step 3: Link to Agent

Other Operations

Accessing and Managing Search AI Applications

Example Scenario

Answer Generation Examples

Extractive

Generative

Best Practices

Quality Content

Appropriate Chunking

Retrieval Tuning

Grounding Instructions

Monitoring