> 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-n8n.md). # 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):** ```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:** ```json { "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: ```javascript // 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:** ```json { "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: ```json { "limit": 3 // Start with few and increase if needed } ``` #### 3. Use Filters for Context Direct the search with tags when you know the context: ```json { "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: ```json // ❌ 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](/bring-your-data-into-ai/reference/endpoints.md) * [Available Filters](/bring-your-data-into-ai/reference/filters.md) * [Error Handling](/bring-your-data-into-ai/how-to/how-to-handle-errors.md) ### External Resources * [N8N RAG Documentation](https://docs.n8n.io/advanced-ai/rag-in-n8n/) * [HTTP Request Tool Docs](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolhttprequest/) * [Building RAG in 2025 - N8N Community](https://community.n8n.io/t/building-rag-in-2025-vector-stores-as-tools-is-here/75166)