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After running successfully, you can open `http://localhost:3000` to check out the Flowise AI tool.
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## Build a chatbot for realtime IP lookup
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Step 1: Create a new **Chatflow** from the UI.
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Step 2: On the **Chatflow** canvas, add a node called **ChatLocalAI**.
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Step 3: Configure the **ChatLocalAI** widget to use the Gaia node with tool call support you have created.
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* Base path: `https://YOUR-NODE-ID.gaia.domains/v1`
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* Model name: e.g., `Mistral-7B-Instruct-v0.3.Q5_K_M`
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Step 4: Add a node called **Custom Tool**
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Create a function named `get_ip_address_geo_location`.
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The function requires a `string` parameter called `ip`.
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The **Tool description** field is the "prompt" that tells the LLM when to use this function. In this example,
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if the LLM detects that the user is asking about the city or country of an IP address, it will
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return a tool call response asking FlowiseAI to perform this function call first.
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Now you can add JavaScript code for this function. It looks up the location of the input `ip` parameter.
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```
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const fetch = require("node-fetch")
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const url = "http://ipwho.is/"+$ip
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try {
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const response = await fetch(url)
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const result = await response.text()
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console.log(result)
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return result
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} catch(error) {
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console.error(error)
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}
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```
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Step 5: Add a node called **Buffer Memory** to the canvas.
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Step 6: Add a node called **Tool Agent**.
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Step 7: Connect the nodes.
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Connect the **Custom Tool** and **Buffer Memory** nodes to the appropriate connectors on the
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**Tool Agent** node. Connect the **ChatLocalAI** node to the **Custom Tool**.
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Step 8: Save the **Chatflow**.
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## Give it a try
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From the FlowiseAI UI, you can open a chat window to chat with the **ChatLocalAI** you just created. Let's
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ask a question:
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```
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What's the location of this address 35.222.115.181
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```
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The LLM understands that the request is to find a location for an IP address, and sees that we have a function
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called `get_ip_address_geo_location` in tools, which has a description that matches this task.
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So, it responds with a JSON message to call this function with
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the IP address it extracts from the user query.
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This tool calling JSON message is NOT displayed to the user in the chatbot. Instead, the FlowiseAI
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**Custom Tool** node captures it and executes the JavaScript code associated with this tool call. The result of
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the tool call is then sent back to the LLM together with the original query,
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which is why we need the **Buffer Memory** node BTW,
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and the LLM formulates a human readable response to the original question.
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---
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## FlowiseAI RAG chat
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# FlowiseAI RAG chat
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FlowiseAI is a low-code tool for developers to build customized LLM orchestration flows & AI agents. You can configure the FlowiseAI tool to use Gaia nodes as LLM service providers.
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## Prerequisites
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You will need a Gaia node ready to provide LLM services through a public URL. You can
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* [run your own node](../../getting-started/quick-start)
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* [use a public node](../../nodes/nodes.md)
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In this tutorial, we will use public nodes to power the Continue plugin.
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| Model type | API base URL | Model name |
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|-----|--------|-----|
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