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Vinkius

Verba Connector for AI agents.

6 live capabilities

Query and manage your Weaviate knowledge base with semantic search.

Live agent request Verba / Connector

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AI Agent

Why people use Verba

Verba for Weaviate RAG knowledge base

With this Connector, you just talk to your agent. You can add new documents or query your entire manual in one sentence. You get summarized answers with citations instead of a dozen open tabs.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a chat-based interface for your entire Weaviate RAG stack.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Finding specific steps in a massive technical manual

    An engineer asks for the deployment steps from a 200-page PDF.

  2. Real-world use case 02

    Cleaning up an old knowledge base

    A manager realizes some docs are outdated.

  3. Real-world use case 03

    Verifying embedding model status

    A developer wants to know if the local model is active.

Complete set · 6capabilities

The complete Verba capability set.

These are the exact actions your AI can choose when you ask it to work with Verba.

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through Verba.

  1. 01 Capability

    Delete knowledge document

    Remove a specific document from your index. Use this to prune dead knowledge permanently.

  2. 02 Capability

    Get document details

    Show the full content and metadata for a single document. This helps you verify what the agent actually sees.

  3. 03 Capability

    List knowledge documents

    List every document your base currently holds. It's the fastest way to get an overview of your indexed files.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through Verba.

  1. 04 Capability

    Perform rag query

    Execute a RAG query against your knowledge base. It returns summarized answers with citations.

  2. 05 Capability

    Add knowledge document

    Add new content into your knowledge base. You can include metadata to keep your data organized.

  3. 06 Capability

    Get system config

    Check your current Verba setup details. This lets you see which embedding models are active and how the system is performing.

Set up in minutes

One URL. Then ask Verba to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Verba from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_mosNtker0LQyevIfUh7gGdgJqJOKEbbiUG4YbxIE/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Verba, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Verba for the conversation.

Where the request belongs

Work Verba can move forward.

Built around the request

This is for people who are tired of manual data entry and want to interact with their private knowledge base using natural language. It's for the person who needs to find facts in a 500-page manual without scrolling.

01

RAG Developer

Testing embedding fidelity by adding and deleting text chunks during a coding session.

02

Knowledge Manager

Querying technical manuals to find verified snippets for internal wikis.

03

Data Engineer

Monitoring cluster health and system configurations via chat instead of logs.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

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R2R logo
01 6 capabilities

R2R

Equip your AI with direct access to your R2R engine. execute vector searches, run precise RAG queries, and manage your documents.

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Amazon Bedrock KB logo
02 6 capabilities

Amazon Bedrock KB

Connect your AI agent to AWS Bedrock Knowledge Bases. execute semantic searches, managed RAG, and sync vector datasources natively.

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Linkup (AI Search & RAG) logo
03 2 capabilities

Linkup (AI Search & RAG)

Power your AI agents with real-time web search via Linkup. execute semantic queries and extract RAG-ready content.

View Connector
Cognita (RAG Framework) logo
04 7 capabilities

Cognita (RAG Framework)

Manage modular RAG via Cognita. list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.

View Connector
FastGPT logo
05 12 capabilities

FastGPT

Manage FastGPT Knowledge Bases. automate dataset creation, document ingestion, and RAG search directly from any AI agent.

View Connector
VectorShift (AI Workflow & RAG Automation) logo
06 29 capabilities

VectorShift (AI Workflow & RAG Automation)

Automate AI workflows and RAG via VectorShift. manage pipelines, query knowledge bases, and deploy chatbots directly from any AI agent.

View Connector

Bring your own AI

Change the model, client or framework. Keep Verba connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
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  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Verba.

The practical details behind the request, access and result.

Can Verba help me search my own private documents?

Yes, it connects your agent to your private Verba RAG platform. You can search your own files and get answers based only on that data.

Does Verba work with my existing Weaviate database?

Yes, it's designed to work with your Verba instance which runs on Weaviate. It lets your agent interact with that specific knowledge base.

Can I add new info to my knowledge base through chat?

You can use the add_knowledge_document capability. Just tell your agent what to add and it will handle the ingestion for you.

How do I know if the agent is giving me real info?

The perform_rag_query capability provides citations. This means your agent will show you exactly which document it used to generate the answer.

Can I remove old documents from the search results?

Yes, you can use the delete_knowledge_document capability. This allows you to keep your knowledge base clean by removing outdated information.

Is it hard to set up Verba for my AI agent?

It's straightforward. You just need a running Verba instance, an API URL, and an API Key to get started.

Can I query my local Verba instance directly through Cursor?

Yes! Once you configure VERBA_API_URL to point to http://localhost:8000 (or your host port), you can prompt your AI assistant to execute rigorous perform_rag_query instructions without ever breaking your developer focus.

How do I insert fresh text data into Verba completely using conversational chat?

Provide the agent with your desired context directly. For example: Add this chunk of markdown as a new document to Verba: '# Title Content...'. The agent leverages addDocumentTool, serializes the payload, and commits it into Verba's vector store immutably.

Are the query answers backed by citations from its embedded documents?

Absolutely. That's the primary benefit of the integration. When you run perform_rag_query, Verba utilizes Weaviate's hybrid search mechanics. The output explicitly includes natural language synthesis backed by the unique document IDs and snippet texts it referenced.

One connection away

Give your agent a direct line to Verba.

Connect Verba once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available