> For the complete documentation index, see [llms.txt](https://docs.ixhello.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ixhello.com/ixhc2/integrations/unified-rag-retrieval-augmented-generation-integration.md). # Unified RAG (Retrieval-Augmented Generation) Integration ### Overview Unified RAG provides a built-in knowledge base system that enables agents to retrieve and respond using structured enterprise data. It enhances conversational intelligence by grounding responses in domain-specific content. ### Key Benefits * Knowledge-Driven Responses: Improves accuracy and relevance * Centralized Knowledge Management: Upload and manage documents easily * Multi-Document Retrieval: Query across multiple documents in a single knowledge base ### Core Capabilities * Create and manage knowledge bases * Upload and index multiple documents * Attach knowledge bases to agents * Retrieve contextual responses based on user queries ### Functional Behavior Agent queries: * Knowledge Base (primary source) * LLM fallback (if enabled) Supports: * Cross-document search within a knowledge base * Prompt-based control to restrict or allow LLM fallback ### Setup Workflow 1. Create a knowledge base “Dentist Data” as example: * Click on “New Knowledge Base”:

* Upload documents

Provide: 1. Knowledge Base name 2. Description 3. Option to share organization-wide 4. Advanced settings: The Advanced Settings control how your knowledge base is indexed and searched, directly impacting the quality and efficiency of AI responses. * Embedding: Embedding section defines how the platform converts your uploaded documents into a format that can be searched and retrieved efficiently during RAG (Retrieval-Augmented Generation). Embedding is a critical step in knowledge base creation, as it transforms text into vector representations that enable semantic search. * Provider name: example: azure-openai indicates that embeddings are generated using Azure-hosted OpenAI models * Model ID: Specifies the embedding model used to convert textual content Upload knowledge sources by clicking on “Add Data Source” button and selecting the file from folders: * View documents in KB “Dentist Data”:

• Select data sources to delete or re-index:

• Option to edit Knowledge Base details and Indexing Settings

* Indexing Settings:

* Chunk Size: This controls how big each “piece” of your document is when the system reads it. * Chunk Overlap: This controls how much content overlaps between two chunks so nothing important is missed. * Batch Size: This controls how many chunks are processed at the same time during setup. * Enable LLM Moderation: This checks content for unsafe, inappropriate, or restricted information. * Enable Redaction: This automatically removes or hides sensitive information (like names, phone numbers, etc.) from your data. User can define define what entities need to be hidden:

2. Attach knowledge base “Dentist Data” to an agent in flow

3. Configure agent instructions Notes / Limitations * Only one knowledge base per agent (current release) * Multiple documents allowed within a knowledge base * Multi-tool support (KB + APIs) is not yet available --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.ixhello.com/ixhc2/integrations/unified-rag-retrieval-augmented-generation-integration.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.