Knowledge & RAG Overview

AI systems are only as useful as the context they can access.

While modern language models are powerful, they do not understand your organization’s policies, documents, databases, or business processes by default.

Retrieval-Augmented Generation (RAG) solves this problem by allowing AI systems to retrieve relevant information from enterprise data during execution.

In Karyam, RAG is designed as an observable, production-ready retrieval pipeline rather than a simple document upload feature.

Why RAG Matters

Without retrieval, AI systems rely entirely on their training data.

This leads to:

Hallucinations
Outdated information
Missing business context
Inability to reference internal systems

With RAG, AI systems can:

Retrieve company policies
Search internal documentation
Access operational procedures
Reference historical records
Ground responses using enterprise context

The Karyam Approach

Many AI platforms expose RAG as:

Upload Documents
        ↓
Knowledge Base
        ↓
Chatbot

Karyam takes a more production-oriented approach:

Vector Database
        +
Embedding Model
        +
Embedding Listener
        +
File Upload
        ↓
Embedding Pipeline
        ↓
Retrieval
        ↓
AI Agent

This architecture provides:

Flexible vector storage
Multiple embedding providers
Event-driven ingestion
Full observability
Enterprise scalability

Core Components

Vector Databases

Vector databases store embeddings generated from enterprise content.

Example providers:

Qdrant
PGVector

Embedding Models

Embedding models convert documents into numerical representations that enable semantic search.

Examples include:

OpenAI Embeddings
Gemini Embeddings
Voyage AI
Ollama Embeddings

Embedding Listeners

Embedding Listeners monitor folders for uploaded files and automatically trigger ingestion pipelines.

This removes the need for manual synchronization.

File Uploads

Users upload files into configured folders.

Supported formats include:

PDF
DOCX
TXT
Markdown
Images
Audio files

Chunking

Large documents are divided into smaller chunks before embeddings are generated.

Chunking improves retrieval quality and relevance.

Retrieval

When an AI Agent receives a request, Karyam performs semantic search against the configured vector database and retrieves the most relevant context.

RAG Runs

Every ingestion pipeline execution is recorded as a RAG Run.

This provides:

Execution history
Processing status
Error visibility
Performance insights

Ingestion Pipeline

When a file is uploaded, Karyam automatically executes the following process:

File Uploaded
      ↓
Embedding Listener Triggered
      ↓
Document Parsing
      ↓
Chunk Generation
      ↓
Embedding Creation
      ↓
Vector Storage
      ↓
RAG Run Created

No manual synchronization is required.

Retrieval Pipeline

When an AI Agent needs information:

User Question
      ↓
Agent Receives Request
      ↓
Semantic Search
      ↓
Relevant Chunks Retrieved
      ↓
Context Added To Prompt
      ↓
Model Generates Response

This allows AI systems to answer using enterprise knowledge instead of relying solely on model training data.

Observability

Production AI requires visibility.

Karyam exposes retrieval operations through:

RAG Runs
Retrieval Logs
Chunk Metadata
Embedding Status
Error Tracking

This makes retrieval pipelines observable and debuggable.

Example Architecture

Employee Request
        ↓
AI Agent
        ↓
Vector Search
        ↓
Retrieved Context
        ↓
Reasoning
        ↓
Response

Example:

Employee:
"My VPN access has expired."

Agent:
→ Retrieves VPN policy
→ Checks approval requirements
→ Executes workflow
→ Creates ticket

Knowledge as Infrastructure

Karyam treats retrieval as infrastructure rather than a feature.

Instead of managing isolated document collections, organizations build reusable retrieval pipelines that can serve multiple agents, workflows, and AI systems.

This approach enables:

Shared enterprise context
Consistent knowledge retrieval
Centralized governance
Production observability

Next Steps

Now that you understand the overall architecture, continue with:

Vector Databases
Embeddings
File Uploads
Chunking
Retrieval
RAG Runs

Together, these components form the foundation of enterprise AI systems in Karyam.