# Knowledge

### **What is Knowledge in Agent?**

In the context of AI agents on UPTIQ, **Knowledge** refers to the **repository of structured and unstructured information** that the agent relies on to better understand user queries and provide accurate responses. This knowledge is typically stored in a **RAG (Retrieval-Augmented Generation)** container, which combines traditional information retrieval techniques with generative AI to enhance the agent's reasoning and intent classification capabilities.

* **RAG Container**: A hybrid system where relevant data is retrieved from the knowledge base and passed to the reasoning engine for context-aware responses.
* **Role in Reasoning**: Knowledge is not just static data but a dynamic resource that informs the **Reasoning Engine** when interpreting or classifying user intents. It allows the agent to understand nuanced queries, connect them with relevant information, and generate meaningful responses.

### **How Does it Work?**

1. **Data Storage**:
   * Knowledge is preloaded into the RAG container.
   * It may consist of documents, FAQs, databases, product manuals, or any domain-specific resources.
   * Information can be structured (e.g., tabular data) or unstructured (e.g., natural language text).
2. **Query Interpretation**:
   * When a user query is received, the **Reasoning Engine** first attempts to classify the intent.
   * To refine the classification or respond accurately, it retrieves contextually relevant information from the RAG container.
   * This ensures that responses are both intent-driven and knowledge-informed.
3. **Retrieval-Augmented Generation**:
   * Relevant data is fetched from the knowledge base using sophisticated search algorithms, embeddings, or semantic similarity techniques.
   * The retrieved data is used to inform the generative AI model, ensuring responses are precise and fact-based.
4. **Real-Time Execution**:
   * Knowledge is dynamically accessed during the workflow execution to enrich outputs, adapt responses, or resolve ambiguities in user queries.
   * For example, a workflow node might explicitly call for retrieving data from the RAG container as part of its process.

**Examples**

1. **Customer Support Agent**:
   * **Knowledge**: A collection of FAQs, product manuals, and troubleshooting guides.
   * **Functionality**: When a customer asks about a specific product feature, the RAG container retrieves the relevant section of the manual, which the reasoning engine uses to generate a response.
2. **Loan Origination AI Agent**:
   * **Knowledge**: Bank policies, loan application criteria, and documentation templates.
   * **Functionality**: If a user asks about the eligibility criteria for a loan, the agent retrieves the relevant policy details and provides a tailored explanation.
3. **Healthcare Assistant**:
   * **Knowledge**: Medical guidelines, patient records, and drug interaction databases.
   * **Functionality**: When a user inquires about a possible drug interaction, the agent pulls information from its knowledge repository and advises accordingly.
4. **Document QA Workflow**
   * **Knowledge**: A repository of documents (e.g., PDFs) stored in the RAG container.
   * **Functionality**: A query like "What is the interest rate on this loan?" would trigger the workflow to:
     * Fetch the document using the external database node.
     * Extract relevant sections using the reasoning engine.
     * Deliver an accurate response informed by the document's content.

### **Key takeaway for developers**

In UPTIQ, Knowledge serves as the **foundation for intelligent reasoning** in AI agents. It equips agents with the ability to:

✅ Contextualize user queries.

✅ Refine intent classification.

✅ Provide factually correct and relevant responses.

Developers should focus on curating high-quality, domain-specific knowledge bases to maximize the accuracy and utility of their AI agents.