What is Viglet Turing?

Viglet Turing is an open-source enterprise search platform. It helps organizations make their content findable, understandable, and interactive: through keyword search, faceted navigation, and generative AI conversations.

Whether you have thousands of documents on a file server, pages on a CMS, records in a database, or a combination of all of them, Turing indexes everything into a single search experience that your users can explore naturally.

What can you do with Turing?

Search your content

Index content from multiple sources and expose a unified, faceted search experience. Users can filter results by category, date, author, or any attribute you define, and always find what they are looking for.

Ask questions, get answers

Enable generative AI on your search site and let users ask questions in natural language. Turing retrieves the most relevant documents and uses an LLM to generate a grounded, accurate response, not a hallucination.

Build AI Agents

Compose AI Agents that combine a language model with a curated set of tools: search your content, browse the web, run code, query financial data, call external systems via MCP, and more. Each agent appears as a chat tab ready to assist users.

Connect any content source

Turing receives content from Viglet Dumont, a companion application that provides connectors for web crawlers, databases, file systems, AEM, and WordPress. Connectors run independently and push content to Turing through its REST API.

Integrate with any stack

Consume Turing from your application via REST API, GraphQL, the Java SDK (available on Maven Central), or the JavaScript / TypeScript SDK @viglet/turing-sdk (available on npm).

How it works at a glance

%%{init: {'theme': 'base', 'themeVariables': {'fontSize': '13px', 'primaryColor': '#fff', 'primaryBorderColor': '#c0c0c0', 'lineColor': '#888', 'textColor': '#333'}}}%%
graph LR
    A["📡 Your Content\n(CMS, DB, files, web)"]
    B["🔌 Viglet Dumont\n(Connectors)"]
    C["🔍 Turing\n(Index + Search + GenAI)"]
    D["👥 Your Users\n(Search UI, Chat, API)"]

    A --> B --> C --> D

    classDef blue fill:#dbeafe,stroke:#4A90D9,stroke-width:2px,color:#1a1a1a
    classDef green fill:#dcfce7,stroke:#50B86C,stroke-width:2px,color:#1a1a1a
    classDef purple fill:#ede9fe,stroke:#9B6EC5,stroke-width:2px,color:#1a1a1a
    classDef amber fill:#fef3c7,stroke:#E8A838,stroke-width:2px,color:#1a1a1a

    class A amber
    class B purple
    class C blue
    class D green

Content flows from its sources through Dumont connectors into Turing, where it is indexed and made available to users through search interfaces, chat, and APIs.

Key concepts

These are the main building blocks you will work with in Turing. You do not need to understand all of them before getting started: come back to each one as you need it.

Concept	What it is	Learn more
Semantic Navigation Site	The central configuration object. Defines what content is indexed, how it is searched, and how results are presented.	Core Concepts
Connector	A component in Dumont that extracts content from a source and sends it to Turing.	Core Concepts
Facets	Filterable attributes shown alongside search results (e.g., category, date, author).	Core Concepts
Spotlight	Curated results pinned to specific search terms.	Semantic Navigation
Targeting Rules	Rules that show different results to different users based on their profile.	Semantic Navigation
Merge Providers	Rules that combine documents from two different connectors into one enriched result.	Semantic Navigation
RAG	Retrieval-Augmented Generation: finding relevant documents and using them to ground an LLM’s response.	GenAI & LLM
AI Agent	A named assistant that combines an LLM with a set of tools and knowledge sources.	GenAI & LLM
Assets	A file manager backed by MinIO where you upload documents to feed the Knowledge Base, files are automatically indexed as vector embeddings.	Assets
Chat	The conversational AI interface: three tabs for direct LLM chat, Semantic Navigation search, and each configured AI Agent.	Chat
Token Usage	A dashboard showing LLM token consumption by model, day, and month, useful for monitoring AI costs.	Token Usage

Your learning path

The fastest way to understand Turing is to build up one capability at a time. Each step below stands on the previous one (index content → search it → ask it → let an agent act → automate the loop) and links the deep page where you go further. Follow them in order the first time through; come back to any one as a reference later.

Step 1: Understand the model

Before configuring anything, get the mental model: the Semantic Navigation Site (the central object), how content is ingested by connectors, and how search and GenAI fit together. Fifteen minutes here saves hours later.

→ Core Concepts · then, when you want the full picture, Architecture Overview

Step 2: Your first search

Stand up Turing, create an SN Site, point a connector at some content, and run a faceted search. This is the foundation everything else grounds on.

→ Installation Guide → Semantic Navigation → query it via the REST API

Step 3: Your first RAG answer

Turn on Generative AI for that site (or upload files to the Knowledge Base), and ask a question in natural language. Turing retrieves the most relevant documents and an LLM answers grounded in your content, not a hallucination. You’ll wire an LLM Instance and an embedding model + store once, then it just works.

→ What is RAG? → GenAI & LLM Configuration → Assets (Knowledge Base)

Step 4: Your first agent

Compose an AI Agent: an LLM + a curated set of tools (search your content, browse the web, run code, call external systems via MCP) + an optional brand Persona. The agent appears as a chat tab, ready to act, not just answer.

→ AI Agents → Tool Calling → Capabilities

Step 5: Your first automation

Close the loop: schedule an agent to run on a cadence, or design a multi-step Chat Flow that collects information and triggers actions. This is where Turing stops being a search box and becomes part of your operations.

→ Routines → Chat Flow

Other starting points

I want to…	Go to
Monitor LLM usage and costs	Token Usage · Cost Governance
Secure Turing with SSO	Security & Keycloak
Integrate from my own app	REST API · GraphQL · React SDK
Connect a content source	Integration