N8N Tutorial: Creating a RAG Agent in n8n for Beginners! (Full Guide No Code)

Retrieval Augmented Generation (RAG) is a powerful AI technique that lets language models fetch answers from your own knowledge base instead of hallucinating. In this no-code n8n tutorial, we’ll show you how to build a simple RAG agent step-by-step. We’ll use n8n’s visual workflow nodes to index documents (e.g. YouTube transcripts) into a Pinecone vector database, then handle user queries by retrieving relevant content and answering with ChatGPT. This guide covers everything from what is RAG and why it matters, to setting up Pinecone, to preparing documents and splitting text, to querying with OpenAI embeddings. It’s beginner-friendly, with clear headings, bullet lists, and best-practice tips.

What is a RAG Agent and Why It Matters

RAG stands for Retrieval Augmented Generation. In simple terms, it means combining a large language model (LLM) like ChatGPT with an external knowledge base. Instead of relying solely on the LLM’s training data, a RAG agent searches a document database for relevant information and then uses that content to generate an answer. The result is more accurate, up-to-date responses than a standalone chatbot. As n8n’s official guide explains, RAG chatbots “go beyond the limitations of typical chatbot interactions” by using external sources to give “precise and informative answers to complex queries”. In practice, this means your RAG agent can answer questions about your own documents, company data, or other specific sources that the LLM wouldn’t otherwise know.

A key component of RAG is a vector database, which stores numerical embeddings of your documents for semantic search. A vector database “stores mathematical representations of information” that the AI can quickly query. In this tutorial we use pinecone.io – a popular managed vector DB designed “for scale in production”. Pinecone lets you insert text embeddings and later retrieve the most similar chunks to a query. n8n has a built-in Pinecone Vector Store node for this purpose: it “allows you to insert documents into a vector database”. By the end of this guide, your RAG agent will use Pinecone + OpenAI embeddings to augment ChatGPT’s answers with your own content.

Pro Tip: Use your own non-sensitive documents as the knowledge base. Never upload private or confidential data to the RAG system. Only index information you’re comfortable sharing with the AI.

Step 1: Set Up a Pinecone Vector Database

Before building workflows, create a Pinecone account and index for your knowledge base. Pinecone is a fully managed vector database tailored for AI search. It lets you index document embeddings and quickly query them.

• Create an index: In the Pinecone console, make a new index (e.g. “my-rag-index”). Note its name and dimensions.
• Get API credentials: Generate an API key in Pinecone (Project > API Keys) and copy it. You’ll use this in n8n.
• Choose a namespace (optional): Pinecone lets you segment data by namespace (e.g. one for “YouTube transcripts” vs “Company docs”). You can set this up now or later in n8n.

Now you have a vector DB ready. We’ll connect to Pinecone from n8n using the Pinecone Vector Store node. Keep the API key handy – you’ll paste it into the node’s credentials.

Tip: A vector database stores dense embeddings of your content. Pinecone is built for high performance and easy scaling, so it’s ideal for production RAG workflows.

Step 2: Prepare and Index Documents with n8n (No Code)

Next, we build an n8n workflow to load your documents (e.g. PDFs or text transcripts) into Pinecone. The workflow will: fetch the files, split the text into chunks, embed them with OpenAI, and insert them into Pinecone.

2A. Fetch Your Documents

1. Trigger: Start with a Manual Trigger node (for testing). In production you might trigger on a schedule or a file upload event.
2. Locate files: If your transcripts are in Google Drive (or another service), add a Google Drive (or HTTP/File) node. For Google Drive, use “Search Files and Folders” and filter by the folder containing your docs. This yields file IDs for all your documents.
3. Download files: Chain a Google Drive: Download File node (or similar) to actually fetch each file’s content. Map the file ID from the previous node. Test this step – you should see your files (e.g. five transcripts) flow through n8n.

2B. Index into Pinecone with Embeddings

4. Pinecone Vector Store (Insert): Add the Pinecone Vector Store node (set Operation to “Insert Documents”). In its settings, paste your Pinecone API key and select the index you created. Optionally add a namespace (e.g. “knowledge_base”) to group these docs.
5. OpenAI Embeddings: Inside the Pinecone node (sub-node), choose the Embeddings (OpenAI) model and enter your OpenAI API credentials. Select a text embedding model (e.g. text-embedding-ada-002). This will convert each document chunk into a vector.
6. Default Data Loader: Also inside Pinecone, add the Default Data Loader. Set Type of data to “Binary” if you have PDFs (or JSON for text files). Usually you can leave it on “Load all input data (automatically detect)”.
7. Metadata (optional): Click Add Option > metadata. For example, add a field like fileName mapped to your file’s name. This helps later if you want to filter by document.
8. Text Splitter: Finally, add a Text Splitter node (sub-node of Pinecone). Choose Recursive Character Text Splitter (it preserves context well) and set Chunk Size ~1000 characters and Chunk Overlap ~100 characters. This breaks each long transcript into smaller pieces. The example used 1000/100 as a good starting point.

Run the workflow. n8n will upload your files to Pinecone. In our test (5 transcripts), we got 84 text chunks indexed (5 files → 84 chunks). You can verify in Pinecone’s console that the vectors are stored (check the index and namespace). Now your documents are in the vector database and ready for retrieval.

Table: Indexing Workflow Nodes

Node	Purpose
Manual Trigger	Starts the indexing workflow for testing
Google Drive “Search Files”	Finds documents/transcripts in your Google Drive folder
Google Drive “Download File”	Downloads each file’s content
Pinecone Vector Store (Insert)	Inserts docs into Pinecone (with API key & index)
• OpenAI Embeddings	Generates vector embeddings for each chunk
• Default Data Loader	Reads file content (PDF/text) into Pinecone
• Recursive Text Splitter	Splits long text into ~1500-char chunks

Best Practice: Keep chunks around 500–1500 characters. Too small and you lose context; too large and retrieval becomes slow. The Recursive Char splitter with chunk size ~1000 and overlap ~100 is a good starting point. Adjust as needed for your documents.

Step 3: Build the Query Workflow (User Q&A)

With the knowledge base indexed, build a second n8n workflow for handling user questions. This “RAG chatbot” workflow will: trigger on a question, retrieve relevant chunks from Pinecone, and answer using ChatGPT.

3A. Trigger and Paraphrase the Question

1. Trigger: Use a Chat Trigger (or HTTP Request/Webhook) node to receive user input. This node starts the workflow when a new message arrives. (n8n’s docs note you can also use an AI Agent node directly, but we’ll do retrieval manually.)

2. Paraphrase (LLM Chain): It’s helpful to paraphrase the user’s question into a few variants. Add a Basic LLM Chain node with ChatGPT (e.g. GPT-4 or DeepSeek Chat) to generate 3 rephrasings of the question. This improves recall: even if a user’s phrasing doesn’t match the indexed text exactly, some paraphrase likely will. As one expert noted, generating multiple versions “helps the model look at the query from different perspectives”. Configure the LLM Chain with your OpenAI key and a system prompt like “Provide 3 paraphrases of the following question.”

3. Split Paraphrases: The LLM Chain will output one item containing three paraphrased queries. Use a Split In/Out node to break this into separate items (one per paraphrase). Set Fields to split out to the output field containing the paraphrases. Now each paraphrase is its own item in the flow.

Tip: Paraphrasing the query (into 2–3 forms) can significantly boost search accuracy. The model will then search Pinecone for each version of the question.

3B. Retrieve Relevant Chunks from Pinecone

4. Pinecone Vector Store (Get Many): For each paraphrased query, add another Pinecone Vector Store node, this time with Operation = Get Many. Connect your Pinecone credentials and select the same index/namespace you used before. Map the query text to the Query field. Set a Limit (e.g. 4) for top matches. Also enable Include Metadata if you need it.

   Each query item now fetches the top similar document chunks (by vector similarity). In our example, 3 paraphrases × 4 results each = 12 items returned.

5. Filter by Score: Pipe the results into a Filter node. In the Filter, require that the similarity score (0–1) exceeds a threshold (e.g. > 0.4). This weeds out very weak matches. The transcript filtered out anything under 0.4, though you may choose 0.5 or higher for stricter relevance.

6. Remove Duplicates: Often different paraphrases find the same chunks. Add a Remove Duplicates node (operation: Remove items repeated within current input). In Fields to compare, select the document text field (e.g. document.pageContent). This eliminates duplicate context fragments. In our example, filtering 12 items left 7 unique chunks.

Best Practice: Discard low-score results to keep only meaningful matches, and remove duplicates so the AI isn’t fed the same info multiple times.

3C. Aggregate Context and Generate Answer

7. Aggregate Chunks: Now combine all remaining chunks into one context. Add an Aggregate node: set Mode to “Merge items” (concatenate). Choose the field with the document text (e.g. pageContent) to merge. After this, you’ll have a single item whose content is the combined retrieved text. This way, the LLM sees all relevant info at once.

8. Answer (LLM Chain): Finally, add a Basic LLM Chain node to craft the answer. Use ChatGPT (e.g. GPT-4 or DeepSeek) as the model. In the prompt, include: (a) the user’s original question, and (b) the aggregated “context” from Pinecone. You can label them in a system/user prompt (for example: System: “You are a helpful assistant. Answer using only the provided context.” User: “Question: [user’s query]” Assistant: “Context: [retrieved chunks]” Assistant (final): [Answer to generate]).

   Provide clear instructions to only use the retrieved data when answering, to avoid hallucinations. The n8n LLM Chain node lets you set this system/user prompt easily.

9. Output: The LLM Chain will output the final AI-generated answer string. You can send this back through chat, email, or any channel you want.

Table: Query Workflow Nodes

Node	Purpose
Chat Trigger (or Webhook)	Receives the user’s query (starts workflow)
LLM Chain (Paraphrase)	Generates multiple paraphrases of the question (ChatGPT)
Split In/Out (Split Paraphrases)	Splits paraphrases into separate items
Pinecone Vector Store (Get Many)	Retrieves top matching chunks from Pinecone for each query
Filter	Keeps only chunks with score above threshold
Remove Duplicates	Drops duplicate chunks so each context is unique
Aggregate	Merges all chunks into one combined context item
LLM Chain (Final Answer)	Generates the final answer using ChatGPT and the context

Tip: Test step-by-step. First check that paraphrases look reasonable, then that Pinecone returns relevant text, then that the LLM Chain produces a good answer. Adjust chunk sizes, thresholds, and prompts as needed.

Tips, Best Practices, and Keywords

• Chunking Text: Aim for moderate chunk sizes (around 1000–5000 chars). In our example we used 1000 chars with 100 overlap. This is like splitting a document into small paragraphs. Too large and search is slow; too small and context is lost.
• Prompt Engineering: In the final LLM Chain, be explicit. For example, start the system prompt with something like: “Answer the question using ONLY the following information:” and then list your Context. The n8n chain node can include an output parser or format if you want structured output.
• Filter Score: A threshold around 0.5 works for many cases. You can experiment (0.3 for broad recall, 0.7 for precision). The demo used 0.4.
• Metadata Tagging: If you added metadata (like file names), you can later filter searches by that. For instance, restrict searches to one namespace or tag to avoid mixing unrelated docs.
• Sensitive Data: Again, do not index private information. Treat the RAG base like public info.
• n8n Nodes: Note that n8n also has a combined AI Agent node, but using separate LLM Chain and Pinecone nodes gives more flexibility. The built-in ChatGPT integration nodes make it no-code.
• Keywords: This guide covers n8n RAG tutorial, no-code AI agent, vector database Pinecone, ChatGPT integration n8n, and how to create RAG workflow. Using these concepts will help you find more resources and templates.

By following these steps and using n8n’s built‑in nodes for Pinecone, OpenAI Embeddings, and ChatGPT or DeepSeek (e.g. Chat Model or LLM Chain), you can create a fully no-code RAG agent. You’ll end up with an automated chatbot that reads your documents and answers questions with current, relevant info – without writing a single line of code. Enjoy your RAG-powered n8n agent!

Sources: This tutorial is based on practical examples from n8n’s documentation and community blogs. It also cites n8n’s official docs for Pinecone and LLM nodes to ensure accuracy.