How to build RAG AI workflows on n8n

Create a new workflow in n8n and add a Webhook node as your trigger. Configure the webhook to accept POST requests with JSON data containing user queries. Set the webhook path to something like /rag-query and enable Return Response option to send results back to the caller.

Insert a Code node after your trigger to process incoming documents. Use JavaScript to split large documents into smaller chunks:

const text = $input.first().json.document;
const chunkSize = 1000;
const chunks = [];

for (let i = 0; i < text.length; i += chunkSize) {
  chunks.push({
    chunk: text.slice(i, i + chunkSize),
    index: Math.floor(i / chunkSize)
  });
}

return chunks.map(chunk => ({ json: chunk }));

Add an OpenAI node and select Embeddings operation. Configure it to use the text-embedding-3-small model for cost efficiency. Map the chunk text from the previous node to the Input Text field. Enable Execute Once for All Items to process multiple chunks efficiently.

Connect a Pinecone or HTTP Request node (for other vector DBs) to store your embeddings. For Pinecone, use the Insert operation and map:

• ID: Generate unique identifier using {{$json.index}}-{{Date.now()}}
• Values: The embedding array from OpenAI
• Metadata: Include original text and any relevant metadata

Create a separate branch for handling user queries. Add another OpenAI node to generate embeddings for the incoming question. Then add a Pinecone node with Query operation to find similar content. Set Top K to 5-10 results and include metadata in the response. Use the query embedding as the search vector.

Add a Code node to combine search results into a coherent context. Process the vector search results:

const searchResults = $input.all();
const context = searchResults
  .map(item => item.json.metadata.text)
  .join('\n\n');

const userQuery = $('Webhook').first().json.query;

return [{
  json: {
    context: context,
    query: userQuery,
    prompt: `Context: ${context}\n\nQuestion: ${userQuery}\n\nAnswer:`
  }
}];

Add a final OpenAI node with Chat operation using gpt-4 or gpt-3.5-turbo. Create a system message: You are a helpful assistant. Answer questions based only on the provided context. Use the constructed prompt from the previous step as the user message. Set temperature to 0.1 for more consistent responses.

Add a final Code node to format the response for your application. Structure the output with the AI answer, source information, and confidence indicators:

const aiResponse = $input.first().json.choices[0].message.content;
const sources = $('Pinecone Query').all().map(item => item.json.metadata);

return [{
  json: {
    answer: aiResponse,
    sources: sources,
    timestamp: new Date().toISOString()
  }
}];