How to Integrate with N8N

Learn how to use the SnackPrompt AI Engine API as a knowledge source for AI agents and workflows in N8N.

Overview

N8N offers several ways to integrate external APIs with its AI capabilities:

Method	Use Case	Description
HTTP Request Tool	Agents with tools	Agent decides when to query the API
HTTP Request Node	RAG in workflows	Direct call at a specific point in the workflow
Custom Retriever	Advanced RAG	Replace native vector store with external API

Integration Architecture

┌─────────────────────────────────────────────────────────┐
│                        N8N                              │
│  ┌─────────────┐    ┌─────────────┐    ┌────────────┐   │
│  │   Trigger   │───▶│  AI Agent   │───▶│  Response │   │
│  └─────────────┘    └──────┬──────┘    └────────────┘   │
│                            │                            │
│                     ┌──────▼──────┐                     │
│                     │ HTTP Tool   │                     │
│                     └──────┬──────┘                     │
└────────────────────────────┼────────────────────────────┘
                             │
                             ▼
              ┌──────────────────────────────┐
              │  SnackPrompt AI Engine API   │
              │  /v1/kb/search or /v1/kb/chat│
              └──────────────────────────────┘

---

Method 1: HTTP Request Tool (Recommended)

Use when you want the agent to autonomously decide when to search your knowledge base.

Step 1: Configure the AI Agent

1. Add an AI Agent node to your workflow

2. Configure the LLM model (OpenAI, Anthropic, etc.)

3. Connect an HTTP Request Tool as a tool

Step 2: Configure the HTTP Request Tool

Tool Settings:

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.
Method	POST
URL	https://api-integrations.snackprompt.com/v1/kb/search

Headers:

Content-Type: application/json
x-api-key: YOUR_API_KEY

Body (JSON):

{
  "query": "{{ $fromAI('query', 'The search query') }}",
  "filters": {
    "tenant_id": "YOUR_TENANT_ID"
  },
  "limit": 5
}

Step 3: Optimize the Response

In the HTTP Request Tool Options section:

• Optimize Response: Enabled

• Response Format: JSON

• Limit Response Size: Recommended to reduce tokens

Complete Workflow Example

[Chat Trigger] → [AI Agent] → [Response]
                     │
                     └─── [HTTP Request Tool: search_knowledge_base]

The agent will receive a question, decide if it needs to query the knowledge base, perform the search, and use the results to formulate the response.

---

Method 2: HTTP Request Node (Direct RAG)

Use when you want a deterministic workflow where the search always happens.

Simple RAG Workflow

[Webhook/Chat Trigger]
        │
        ▼
[HTTP Request: Search API]
        │
        ▼
[Set Node: Format Context]
        │
        ▼
[AI Chain: Generate Response]
        │
        ▼
[Respond to Webhook]

HTTP Request Node Configuration

Method: POST

URL:

https://api-integrations.snackprompt.com/v1/kb/search

Headers:

Content-Type: application/json
x-api-key: YOUR_API_KEY

Body:

{
  "query": "{{ $json.chatInput }}",
  "filters": {
    "tenant_id": "YOUR_TENANT_ID"
  },
  "limit": 5
}

Formatting the Context

Use a Set Node or Code Node to format the results:

// Code Node
const results = $input.first().json.items;

const context = results.map((item, index) =>
  `[${index + 1}] ${item.payload.original_text}`
).join('\n\n');

return {
  context: context,
  sources: results.map(r => ({
    id: r.payload.snack_item_id,
    score: r.score
  }))
};

AI Chain Prompt

You are a helpful assistant. Answer the user's question based ONLY on the following context.

Context:
{{ $json.context }}

User Question: {{ $('Webhook').item.json.chatInput }}

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 and return a ready response.

Configuration

URL:

https://api-integrations.snackprompt.com/v1/kb/chat

Headers:

Content-Type: application/json
x-api-key: YOUR_API_KEY

Body:

{
  "query": "{{ $json.chatInput }}",
  "filters": {
    "tenant_id": "YOUR_TENANT_ID",
    "tag_names": ["Support", "FAQ"]
  }
}

Response

The API returns:

• answer: AI-generated response

• sources: Sources used to generate the response

You can use it directly or enrich with additional logic in N8N.

---

Practical Use Cases

1. Support Chatbot

[Chat Trigger] → [HTTP Request: /chat] → [Respond with answer]

Ideal for simple chatbots that need to answer questions about products, policies, etc.

2. Multi-tool Agent

[Chat Trigger] → [AI Agent] → [Response]
                     │
                     ├── [Tool: search_knowledge_base]
                     ├── [Tool: Google Calendar]
                     └── [Tool: Send Email]

The agent can search for information AND execute actions.

3. RAG with Multiple Sources

[Webhook] → [Switch: Route by Topic]
                 │
                 ├── [Search: tag=Sales] → [Merge] → [AI: Generate]
                 └── [Search: tag=Support] ───┘

Search in different categories and combine the results.

4. Response Validation

[Chat] → [AI Agent: Generate Draft]
              │
              ▼
         [HTTP Request: Search for validation]
              │
              ▼
         [AI: Verify and Refine]
              │
              ▼
         [Response]

The agent generates a response, searches for validation in the knowledge base, and refines.

---

Configuration Tips

1. Tool Description is Crucial

The HTTP Request Tool description determines when the agent will use it:

✅ Good description:
"Search the company knowledge base for information about products,
pricing, policies, and procedures. Use when the user asks about
company-specific information."

❌ Bad description:
"Search API"

2. Limit the Results

Too many results = too many tokens = higher cost and possible confusion:

{
  "limit": 3  // Start with few and increase if needed
}

3. Use Filters for Context

Direct the search with tags when you know the context:

{
  "filters": {
    "tenant_id": "...",
    "tag_names": ["{{ $json.detected_topic }}"]
  }
}

4. Handle Errors

Add an Error Trigger or use Continue On Fail to handle API failures.

---

Complete Example: Sales Chatbot

Workflow

1. Chat Trigger: Receives user message

2. AI Agent: Processes with GPT-4

3. HTTP Request Tool: Searches products and prices

4. HTTP Request Tool: Searches discount policies

5. Response: Returns response to user

Agent Configuration

System Prompt:

You are a sales assistant for company X.
Help customers find products and understand pricing.
Use the search_products tool to find product information.
Use the search_policies tool to find discount policies.
Always cite sources when providing information.

Tools:

Tool	Description
search_products	Search for product information, specifications, and pricing
search_policies	Search for discount policies, payment terms, and conditions

---

Troubleshooting

Error: "tenant_id is required"

Make sure tenant_id is inside the filters object:

// ❌ Wrong
{ "query": "...", "tenant_id": "..." }

// ✅ Correct
{ "query": "...", "filters": { "tenant_id": "..." } }

Agent doesn't use the tool

1. Improve the tool description

2. Add examples in the system prompt

3. Verify if the question actually requires the tool

Response too long/truncated

1. Reduce the results limit

2. Enable Optimize Response on the tool

3. Use a Code Node to summarize before passing to the LLM

---

Related

• Endpoints Reference

• Available Filters

• Error Handling

External Resources

• N8N RAG Documentation

• HTTP Request Tool Docs

• Building RAG in 2025 - N8N Community