Knowledge Overview
Knowledge allows BindAI agents to retrieve information from external sources instead of relying only on the language model’s built-in knowledge. This enables agents to answer questions using your own documents, manuals, APIs, databases, or business information. Knowledge is the foundation of Retrieval-Augmented Generation (RAG).What is Knowledge?
Knowledge represents information that exists outside the language model. Examples include:- company documentation
- PDF files
- product manuals
- knowledge bases
- internal wikis
- databases
- websites
Why Use Knowledge?
Language models have limitations:- training data becomes outdated
- they do not know your private information
- they cannot access proprietary documentation by default
Knowledge Architecture
A typical knowledge pipeline looks like this:Example
Suppose your company has an internal policy document. User:Knowledge Sources
Knowledge can originate from many places. Common sources include:- Markdown documents
- PDFs
- Word documents
- databases
- REST APIs
- vector databases
- cloud storage
- custom repositories
Retrieval
Knowledge is typically retrieved rather than loaded entirely. Instead of sending hundreds of documents to the model:Knowledge vs Memory
Knowledge and Memory serve different purposes.
Use Knowledge to answer factual questions.
Use Memory to remember previous conversations.
Knowledge vs Variables
Workflow variables exist only during execution. Knowledge exists independently of any workflow.Knowledge in Agents
Agents can be connected to a knowledge source. Conceptually:Knowledge in Workflows
Knowledge is also available inside workflows.Common Use Cases
Knowledge is commonly used for:- customer support assistants
- documentation search
- internal company chatbots
- legal assistants
- product support
- technical manuals
- enterprise knowledge bases
Benefits
Using external knowledge provides:- up-to-date responses
- private information access
- fewer hallucinations
- domain-specific expertise
- improved factual accuracy
- scalable information retrieval
Best Practices
- Keep documents well organized.
- Use retrieval instead of loading everything.
- Remove outdated information.
- Separate Knowledge from Memory.
- Use embeddings for semantic search.
- Keep retrieved context concise.
