Cognita (RAG Framework) Connector for AI agents.

Q: How do I check if my documents are being chunked correctly with Cognita?

You can use the search tool to pull raw document chunks from your vector store. This lets you see the exact text segments your agent uses to answer questions.

Q: Can my agent perform semantic RAG queries against my collections?

Yes. The 'ragquery' tool allows you to ask questions in natural language. The agent queries your vector store via Cognita and uses an LLM to synthesize a final answer based explicitly on the retrieved context.

7 live capabilities

Manage your RAG pipelines and vector store data with natural conversation.

Live agent request Cognita (RAG Framework) / Connector

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AI Agent

Why people use Cognita (RAG Framework)

Cognita RAG Framework for Ending Manual Data Syncing

Cognita changes that by putting the entire lifecycle of your knowledge base into a single chat interface. You can force a sync from a remote API or audit the specific text segments your agent is seeing without ever leaving your AI client.

What Vinkius changes

You get a direct command line for your RAG infrastructure inside your favorite AI chat interface.

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

One account · 5,900+ Connectors

Real-world use case 01

Verifying chunking logic

A data scientist needs to check if a new PDF was correctly chunked.
Real-world use case 02

Checking model availability

An AI engineer wants to see which models are currently live in production.
Real-world use case 03

Auditing data sources

A product manager wants to know what data is being fed into the Technical Docs collection.

Complete set · 7capabilities

The complete Cognita (RAG Framework) capability set.

These are the exact actions your AI can choose when you ask it to work with Cognita (RAG Framework).

Capability set01 / 02

01—04

4 capabilities in this set.

Part of 7 available through Cognita (RAG Framework).

01 Capability
List data sources
Use this to see all the external buckets and APIs mapped to your AI workflows. It confirms your data pipelines are connected.
02 Capability
Ingest data
Use this to trigger a sync to pull new files into your vector space. It handles the heavy lifting of generating new resource directories.
03 Capability
Rag query
Use this to ask a question and get a synthesized answer from your vector store. It pulls relevant context to give you a grounded response.
04 Capability
Search chunks
Use this to perform a search to find specific text segments in your vector store. It lets you verify exactly what your agent knows.

Capability set02 / 02

05—07

3 capabilities in this set.

Part of 7 available through Cognita (RAG Framework).

05 Capability
List models
Use this to see every LLM and embedding model registered in your Cognita instance. It helps you verify which models are ready for production.
06 Capability
List collections
Use this to see all your RAG collections and their specific configurations. It helps you keep track of different knowledge domains.
07 Capability
Get collection
Use this to pull specific logging and payload IDs for a single collection. It is great for deep-diving into specific data sets.

Set up in minutes

One URL. Then ask Cognita (RAG Framework) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Cognita (RAG Framework) 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_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/mcp

Step 01

Open Connectors

In Claude Web or Claude Desktop, open Settings and choose Connectors.
Step 02

Add the URL

Choose Add custom connector, name it Cognita (RAG Framework), and paste the URL above.
Step 03

Turn it on in chat

Select +, open Connectors, and enable Cognita (RAG Framework) for the conversation.

ChatGPT · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/mcp

Step 01

Open MCP settings

On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
Step 02

Add the URL

Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Cognita (RAG Framework) URL.
Step 03

Save and start

Save the connection and enable Cognita (RAG Framework) in your conversation. Desktop may ask you to restart once.

Cursor · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cognita-rag-framework": {
      "url": "https://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/mcp"
    }
  }
}

Step 01

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Step 02

Add the server config

Paste the JSON configuration above into the mcp.json file that opens
Step 03

Save the file

Cursor will automatically detect the new Connector
Step 04

Start using Cognita (RAG Framework)

Open Agent mode in chat and ask: "Using Cognita (RAG Framework), help me...". 7 tools available

VS Code Copilot · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cognita-rag-framework": {
      "url": "https://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/mcp"
    }
  }
}

Step 01

Create MCP config

Create a .vscode/mcp.json file in your project root
Step 02

Add the server config

Paste the JSON configuration above
Step 03

Enable Agent mode

Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Step 04

Start using Cognita (RAG Framework)

Ask Copilot: "Using Cognita (RAG Framework), help me...". 7 tools available

Windsurf · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cognita-rag-framework": {
      "url": "https://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/mcp"
    }
  }
}

Step 01

Open MCP Settings

Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
Step 02

Add the server

Paste the JSON configuration above into mcp_config.json
Step 03

Save and reload

Windsurf will detect the new server automatically
Step 04

Start using Cognita (RAG Framework)

Open Cascade and ask: "Using Cognita (RAG Framework), help me...". 7 tools available

Cline · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cognita-rag-framework": {
      "url": "https://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/mcp"
    }
  }
}

Step 01

Open Cline MCP Settings

Click the Connectors icon in the Cline sidebar panel
Step 02

Add remote server

Click "Add Connector" and paste the configuration above
Step 03

Enable the server

Toggle the server switch to ON
Step 04

Start using Cognita (RAG Framework)

Ask Cline: "Using Cognita (RAG Framework), help me...". 7 tools available

Claude Code · Terminal command

Official guide ↗

Advanced setup

claude mcp add cognita-rag-framework --transport http "https://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/mcp"

Step 01

Install Claude Code

Run npm install -g @anthropic-ai/claude-code if not already installed
Step 02

Add the Connector

Run the command above in your terminal
Step 03

Verify the connection

Run claude mcp to list connected servers, or type /mcp inside a session
Step 04

Start using Cognita (RAG Framework)

Ask Claude: "Using Cognita (RAG Framework), show me...". 7 tools are ready

Where the request belongs

Work Cognita can move forward.

Built around the request

This is for the AI engineer who's tired of manually checking sync logs at 2am or the data scientist who needs to verify chunking logic without writing a single line of Python.

AI Engineer

Testing and debugging RAG retrieval logic to ensure the agent is pulling the right context.

Data Scientist

Monitoring ingestion pipelines and verifying document chunking consistency across different collections.

DevOps Engineer

Checking Cognita model registries to ensure all LLM endpoints are active and reachable.

Product Manager

Auditing the knowledge base content during the prototyping phase to see what the agent actually knows.

Build the capability set

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

Browse Connectors

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.

View Connector

02 12 capabilities

FastGPT

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

View Connector

03 7 capabilities

Haystack (deepset Cloud)

Build and manage AI-powered search and RAG pipelines via deepset Cloud. search documents, run pipelines, and manage workspaces.

View Connector

04 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

05 6 capabilities

Amazon Bedrock KB

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

View Connector

06 6 capabilities

LlamaIndex (AI Data Framework & RAG)

Query and manage RAG pipelines via LlamaIndex. execute natural language searches, audit indexed files, and monitor data pipelines.

View Connector

Bring your own AI

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

Claude
ChatGPT
Gemini
Cursor
VS Code
Windsurf
ZCode
Cline
Zed
Continue
Kiro
Roo Code
Zencoder
Goose
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 Cognita.

The practical details behind the request, access and result.

Can the Cognita MCP sync my SQL data automatically?

Yes, it can. The Connector allows your agent to trigger data ingestion from various sources, including SQL databases, cloud storage, and remote APIs, to keep your knowledge base updated.

How do I check if my documents are being chunked correctly with Cognita?

You can use the search capability to pull raw document chunks from your vector store. This lets you see the exact text segments your agent uses to answer questions.

Can I see which LLMs are active in my Cognita instance?

Yes, you can ask your agent to list the models. This shows you every LLM and embedding model currently registered in your Cognita setup.

Does Cognita work with my existing data sources?

It works with many common sources like Cloud Storage, SQL, and APIs. You can list your connected sources to verify which ones are mapped into your workflows.

How does Cognita help with RAG debugging?

It lets you audit your collections and search specific chunks. By seeing the raw data and the model registry in one place, you can quickly find where a retrieval pipeline is failing.

Can I query my knowledge base directly through the Cognita MCP?

Yes, you can. Your agent can perform RAG queries that search your vector store and synthesize accurate answers based on your stored context.

Can my agent perform semantic RAG queries against my collections?

Yes. The 'rag_query' capability allows you to ask questions in natural language. The agent queries your vector store via Cognita and uses an LLM to synthesize a final answer based explicitly on the retrieved context.

How can I trigger a data ingestion pipeline through the agent?

Provide the collection name and the data source FQN (Fully Qualified Name). The 'ingest_data' capability will command the Cognita backend to start a sync, updating your RAG vector space with the latest remote documents.

Can I audit the raw document chunks before LLM generation?

Absolutely. Use the 'search_chunks' capability to perform vector searches that return raw text segments and metadata without LLM synthesis. This is the perfect way to verify that your retrieval logic is pulling the correct data boundaries.

One connection away

Give your agent a direct line to Cognita.

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

Explore every Connector No credit card required · Free tier available

Cognita RAG Framework for Ending Manual Data Syncing

You get a direct command line for your RAG infrastructure inside your favorite AI chat interface.

Verifying chunking logic

Checking model availability

Auditing data sources

List data sources

Ingest data

Rag query

Search chunks

List models

List collections

Get collection

Open Connectors

Add the URL

Turn it on in chat

Open MCP settings

Add the URL

Save and start

Open MCP Settings

Add the server config

Save the file

Start using Cognita (RAG Framework)

Create MCP config

Add the server config

Enable Agent mode

Start using Cognita (RAG Framework)

Open MCP Settings

Add the server

Save and reload

Start using Cognita (RAG Framework)

Open Cline MCP Settings

Add remote server

Enable the server

Start using Cognita (RAG Framework)

Install Claude Code

Add the Connector

Verify the connection

Start using Cognita (RAG Framework)

AI Engineer

Data Scientist

DevOps Engineer

Product Manager

R2R

FastGPT

Haystack (deepset Cloud)

VectorShift (AI Workflow & RAG Automation)

Amazon Bedrock KB

LlamaIndex (AI Data Framework & RAG)

Give your agent a direct line to Cognita.