Agentic RAG Knowledge Base Interface
search(query)
relevance: 0.94
vector → match
chunk[127]
route → tool
rewrite(q)
Strongly Certified - Intelligent Knowledge Base

AGENTIC
RAG

Upload your documents, ask questions, get cited answers. An AI knowledge base with LLM-powered routing, self-correcting retrieval, and source attribution.

Get Started Explore Capabilities
CRAG
Self-Correcting
BYOM
Bring Your Own Model
Cited
Source Attribution
Step 1
User asks a question
Step 2
LLM routes to correct tool
Step 3
Vector search + relevance grading
Step 4
Self-correction if needed
Step 5
Grounded answer with citations
Core Capabilities

Beyond Simple Retrieval

An agentic RAG system that routes, retrieves, grades, and self-corrects. Every answer grounded in your documents with full source transparency.

LLM-Powered Query Routing

An LLM decides how to handle each query using tool-calling. It routes to document search, lists your documents, or responds directly to greetings and general conversation.

Tool Calling 3 Routes Context-Aware

Batch Relevance Grading

One LLM call scores all retrieved documents at once instead of grading each one individually. Reduces latency from 15 sequential calls down to 1 while maintaining quality.

Single LLM Call Relevance Scoring Low Latency

Self-Correcting Retrieval

When retrieval returns poor results, the system rewrites the query and retries automatically. Bounded to one retry to keep latency predictable. The CRAG pattern in production.

CRAG Pattern Query Rewrite Bounded Retry

Source Attribution

Every answer includes the source documents and relevance scores. Expandable source cards show the filename, match percentage, and content preview for full transparency.

Cited Sources Relevance Scores Content Preview

Document Management

Upload PDFs, text files, Markdown, and Word documents. Drag-and-drop or click to browse. Documents are automatically chunked, embedded, and indexed for semantic search.

PDF TXT Markdown DOCX

Conversation History

Conversations persist across sessions. The router uses recent history to understand follow-up questions and maintain context without re-explaining.

Persistent Threads History-Aware Follow-ups
The Pipeline

How Agentic RAG Works

A control loop, not a linear pipeline. The LLM decides what to do, evaluates the result, and self-corrects when needed.

Step 1
Agent Router

The LLM analyzes the query and conversation history, then selects the right tool: search documents, list documents, or respond directly.

Step 2
Semantic Retrieval

The optimized search query is embedded and matched against your document vectors in Milvus. The top chunks are retrieved for grading.

Step 3
Batch Grading

All retrieved chunks are scored for relevance in a single LLM call. Low-scoring chunks are filtered out before generation.

Step 4
Grounded Generation

The LLM generates an answer using only the graded context. System prompts enforce citation and prevent hallucination beyond the sources.

Step 5
Self-Correction Gate

If the response is empty or poor, the system rewrites the query and retries the workflow once. Bounded to prevent runaway latency.

Step 6
Cited Response

The final answer is returned with source documents, relevance scores, and quality metrics. Everything is saved for conversation continuity.

4
Document Formats
3
Routing Tools
100%
Source Cited
CRAG
Self-Correcting
Document Support

Upload, Index, and Query

Drag-and-drop your documents. They are automatically chunked, embedded, and indexed in a vector database for semantic search.

PDF Documents
.pdf
Plain Text
.txt
Markdown
.md
Word Documents
.docx
Architecture

Built on the Strongly.AI Platform

Deployed from the marketplace with all infrastructure managed. Bring your own LLM, connect your vector store, and start querying.

Agent Router

The LLM-powered brain that decides how to handle each query using tool-calling.

  • LLM tool-calling with 3 tools
  • Conversation history context
  • Optimized search queries
  • Graceful fallback if unavailable

RAG Workflow

A deployed workflow that handles retrieval, grading, and generation as connected nodes.

  • Vector search via Milvus
  • Batch relevance grading
  • Grounded answer generation
  • Source formatting and scoring

Chat Interface

A clean React application with document upload, conversation management, and quality metrics.

  • Drag-and-drop document upload
  • Expandable source cards
  • Relevance and quality scores
  • Persistent conversation threads

Deploy Your Own
Intelligent Knowledge Base

Agentic RAG deploys in minutes from the Strongly.AI Marketplace. Upload your documents, bring your LLM, and start asking questions.

Schedule Demo Explore Marketplace