Stop Searching Your Documents Manually: Build Your Own AI Knowledge Base with n8n, Gemini, and Qdrant

Description and use

An automated RAG pipeline using n8n, Gemini, and Qdrant eliminates manual document search, grounding answers in indexed content and reducing hallucinations.

Problem

Finding answers in scattered documents becomes slower than re‑deriving the answer, and keyword search fails as collections grow.

Outcome

The system reduces manual search time, improves answer relevance, and avoids hallucinations by grounding responses in indexed documents.

Workflow

• Receive document uploads via webhook
• Clean and extract text from PDFs or web URLs
• Split text into overlapping chunks
• Embed chunks with Gemini embedding model
• Store vectors and metadata in Qdrant
• When a query arrives, embed query with Gemini
• Search Qdrant for nearest chunks above similarity threshold
• Filter out low‑similarity chunks
• Retrieve recent conversation history
• Combine context, history, and query into prompt
• Send prompt to Gemini chat model with low temperature
• Return answer to user

Evidence

A basic search bar handles small document collections fine. Ten files, no problem. A hundred files, and it starts missing things constantly, not because the answer isn’t there, but because the words don’t match.

Source

Instead of asking a model to answer from memory, you first search a database of your own content for the passages most relevant to the question, then hand those passages to the model along with the question itself.

Source

Text that clears a basic quality check gets broken into overlapping chunks rather than one long block. The overlap matters more than it sounds like it would: without it, a sentence that spans a chunk boundary loses context on one side or the other, and retrieval quality suffers.

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Each chunk then gets converted into a vector, a numerical representation of its meaning, using Google’s Gemini embedding model. Those vectors are stored in Qdrant, a vector database built specifically for similarity search at scale, alongside metadata that ties each chunk back to its source document.

Source

If nothing in the vector database clears the similarity threshold, the system should say so plainly rather than letting the model fall back on general knowledge and produce a confident‑sounding guess.

Source