> For the complete documentation index, see [llms.txt](https://docs.snackprompt.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snackprompt.com/bring-your-data-into-ai/how-to/get-started-with-integrations/how-to-integrate-with-flowise.md).
# How to Integrate with Flowise
Learn how to use the SnackPrompt AI Engine API as an external knowledge source in Flowise chatbots and AI workflows.
### Overview
Flowise is a visual tool for building LLM applications. It offers several ways to integrate external APIs:
| Method | Use Case | Description |
| --------------------- | -------------------- | ---------------------------------------------- |
| **Custom Tool** | Agent with tools | Agent decides when to query the API |
| **HTTP Request Node** | Direct RAG | Call API at specific point in the flow |
| **Custom Retriever** | Replace vector store | Use external API instead of built-in retriever |
| **API Chain** | Sequential calls | Chain multiple API calls together |
### Integration Architecture
```
┌─────────────────────────────────────────────────────────┐
│ Flowise │
│ ┌─────────────┐ ┌─────────────┐ ┌────────────┐ │
│ │ Chat │───▶│ LLM Chain │───▶│ Response │ │
│ │ Input │ │ or Agent │ │ │ │
│ └─────────────┘ └──────┬──────┘ └────────────┘ │
│ │ │
│ ┌──────▼──────┐ │
│ │ Custom Tool │ │
│ │ or Chain │ │
│ └──────┬──────┘ │
└────────────────────────────┼────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ SnackPrompt AI Engine API │
│ /v1/kb/search or /v1/kb/chat│
└──────────────────────────────┘
```
***
### Method 1: Custom Tool (Recommended for Agents)
Use when you want the **agent to autonomously decide** when to search your knowledge base.
#### Step 1: Create a Custom Tool
1. In Flowise, go to **Tools** in the sidebar
2. Click **Add New**
3. Configure the tool:
**Tool Configuration:**
| Field | Value |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Name | `search_knowledge_base` |
| Description | `Use this tool to search for information in the company knowledge base. Send a natural language query to find relevant documents about products, policies, procedures, etc. Returns relevant text snippets.` |
#### Step 2: Configure the Tool Code
```javascript
const fetch = require('node-fetch');
const searchKnowledgeBase = async (query) => {
const response = await fetch('https://api-integrations.snackprompt.com/v1/kb/search', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'x-api-key': process.env.SNACKPROMPT_API_KEY
},
body: JSON.stringify({
query: query,
filters: {
tenant_id: process.env.SNACKPROMPT_TENANT_ID
},
limit: 5
})
});
const data = await response.json();
if (!data.items || data.items.length === 0) {
return 'No relevant information found in the knowledge base.';
}
return data.items
.map((item, index) => `[${index + 1}] ${item.payload.original_text}`)
.join('\n\n');
};
module.exports = { searchKnowledgeBase };
```
#### Step 3: Build the Agent Flow
1. Add **Chat Trigger** node
2. Add **Tool Agent** or **OpenAI Function Agent** node
3. Connect your **Custom Tool**
4. Add **Chat Model** (OpenAI, Anthropic, etc.)
```
[Chat Trigger] → [Tool Agent] → [Response]
│
├── [Chat Model: GPT-4]
└── [Custom Tool: search_knowledge_base]
```
***
### Method 2: HTTP Request Chain
Use when you want a **deterministic flow** where the search always happens.
#### Step 1: Create the Flow
1. Add **Chat Trigger** node
2. Add **HTTP Request** node
3. Add **LLM Chain** node for response generation
#### Step 2: Configure HTTP Request Node
**Request Configuration:**
**Headers:**
```json
{
"Content-Type": "application/json",
"x-api-key": "{{$env.SNACKPROMPT_API_KEY}}"
}
```
**Body:**
```json
{
"query": "{{$input}}",
"filters": {
"tenant_id": "{{$env.SNACKPROMPT_TENANT_ID}}"
},
"limit": 5
}
```
#### Step 3: Process and Format Results
Add a **JavaScript Function** node to format the results:
```javascript
function formatResults(data) {
if (!data.items || data.items.length === 0) {
return 'No relevant information found.';
}
return data.items
.map((item, index) => `[${index + 1}] ${item.payload.original_text}`)
.join('\n\n');
}
return formatResults($input);
```
#### Step 4: Generate Response with LLM
Add **LLM Chain** with prompt:
```
You are a helpful assistant. Answer the user's question based ONLY on the following context.
Context:
{context}
User Question: {question}
If the context doesn't contain relevant information, say "I don't have information about that."
```
***
### Method 3: Chat Endpoint for Complete Responses
Use the `/v1/kb/chat` endpoint when you want the **API to handle all the RAG**.
#### Simple Chatbot Flow
```
[Chat Trigger] → [HTTP Request: /chat] → [Parse Response] → [Output]
```
#### HTTP Request Configuration