Knowledge Bases
Sema4.ai Knowledge Bases(KBs) enable AI agents to understand, process, and reason over your organization's unstructured data with accuracy and context. They transform static content into a semantic, queryable layer that agents can use to generate accurate, grounded, and business-specific responses. Unlike dynamic data access, which focuses on real-time querying of transactional systems, a KB acts as a long-term memory for agents—optimized for recall, reasoning, and citations.
Why Use a Knowledge Base?
Agents are powerful, but have limitations:
- Short context window: Can’t retain or reason across large datasets
- No persistent memory: Forget previous conversations
- Risk of hallucination: Especially for enterprise-specific information
Without a KB: Agents may provide inconsistent, outdated, or inaccurate answers- especially when grounded in fast-changing or domain-specific knowledge.
A KB addresses these gaps by providing:
- Unlimited knowledge scope: Handles libraries of PDFs, policies, or CRM records.
- Persistent context: Acts as a long-term memory for your agent.
- Grounded responses: Traceable to exact documents or records.
- Enterprise specificity: Includes your own vocabulary, data, and logic.
Key Capabilities
- Semantic Search: Powered by vector embeddings and metadata
- Structured and Unstructured Data: From PDFs, emails, Slack threads to database records
- Citations: Source-level tracing in agent responses
- Metadata Filtering: Query by tag, author, date, region, and more
- Built-in Agent Integration: Available in SDK, Studio, and Control Room
What Can You Store in a KB?
A KB is flexible and can store a wide variety of content, including:
- PDFs & Reports: Policy documents, user manuals, whitepapers
- Office Documents: Word and Excel files with notes or planning
- Email Archives: Customer support threads, internal approvals
- Web Content: Public docs, internal wikis, help centers
- Chat History: Slack, Teams — Product Q&A, discussions
- Spreadsheets & Data Exports: CSVs from CRMs, project trackers, analytics dumps
Architecture at a Glance
KBs are defined as semantic layers that connect to external vector storage like pgvector. When you insert content, embeddings are generated automatically using the model you've specified. The resulting vectors, along with metadata and IDs, are stored in the vector database.
Understanding the underlying architecture helps you see how KBs fit into your workflow:
When to Use KB (vs. Data Access)
| Use Case | Data Access | Knowledge Base |
|---|---|---|
| Document search & Q&A | ❌ No | ✅ Yes |
| Semantic reasoning across sources | ❌ No | ✅ Yes |
| Real-time status checks | ✅ Yes | ❌ No |
| CRM record updates | ✅ Yes | ❌ No |
| Policy & procedure lookup | ❌ No | ✅ Yes |
Common Use Cases
Here are some practical scenarios where KBs add value:
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Support Q&A Pull relevant answers from internal docs, past tickets, or FAQs.
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Product and documentation assistant Answer questions based on README files, help center content, or blog posts.
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Marketing claim validation Match new collateral against a bank of approved claims.
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Legal document assistant Suggest preferred language pulled from legal clause libraries.
What's Next?
Now that you understand what a KB is and when to use it, let’s walk through how to build one.