Simplifying RAG with Gemini File Search API in n8n

I've been playing around with the new Gemini File Search API lately, and honestly, it's a game-changer for building retrieval-augmented generation agents in n8n. What makes it so cool? It's dead simple to use and won't break the bank.

Here's the deal - you just upload a document, and Gemini automatically chunks and embeds it for you. Then you can start chatting with it right away. No fuss. This skips all that complicated stuff normally needed for RAG systems - you know, figuring out file types, adding context, splitting files, running embeddings, managing vector stores... ugh. The API handles everything, making integration way faster than traditional setups with Pinecone or Superbase.

The pricing is what really caught my attention. It costs just 15 cents per million tokens for indexing. I uploaded a massive 121-page PDF that was about 95,000 tokens - that's less than a tenth of the threshold! At the time I made my video, Gemini wasn't charging anything for storage (pretty sweet), and you only pay when running queries. To put it in perspective, if you're storing 100GB with a million monthly queries, you'd pay nothing for storage, under $12 for indexing, and around $35 for queries in the first month. Compare that to Pinecone Assistant or OpenAI Vector store, which charge way more for similar services. Pinecone even adds 5 cents per hour just to keep things running!

Setting up the workflow in n8n takes just four HTTP requests. First, create a file store (think of it as a folder). Second, upload your file to Google Cloud. Third, move that file into your folder. Fourth, run your query. That's it! I've made my workflow template available for free through my school community - check the link in the description if you want it.

But there are some limitations to keep in mind. The system isn't magical - it doesn't handle duplicates if you re-upload files, which can mess with answer quality over time. And remember: garbage in, garbage out. If your documents are poorly scanned or formatted, don't expect great responses.

Also, since everything's processed on Google's servers, be careful with sensitive data. Make sure you understand regulatory requirements like GDPR or HIPAA before uploading anything confidential.

Have you tried building semantic search into your workflows before? The chunk-based retrieval in Gemini File Search works great for finding specific info but struggles when you need context from an entire document. For instance, asking "how many total rules are in this PDF?" might confuse it since it can't easily aggregate across all chunks.

Anyway, if you're interested in RAG, vector search, or document chunking techniques, join my plus community for more in-depth resources. I run weekly Q&As and have built a community of over 2,500 members working with n8n and AI automation.

Drop a like if this helped! I'll be back with more on embeddings generation and cost-effective indexing soon.