Dify RAG Guide 2026: Answer Questions from Your Own Documents
Retrieval-Augmented Generation (RAG) is the most powerful feature in Dify. This guide shows you exactly how to build an AI that searches your documents before answering — giving accurate, grounded responses instead of hallucinated guesses.
What is Dify RAG?
RAG stands for Retrieval-Augmented Generation. It is a technique that gives your AI access to your own documents, databases, and knowledge sources — so instead of answering from general training data, it searches your content first and then generates a response grounded in what it found.
Standard LLMs like GPT-4 or Claude hallucinate when asked about your internal docs, your product specs, or your company's policies — because they simply don't have that data. Dify RAG solves this by creating a Knowledge Base from your files and injecting the most relevant passages as context before the model responds.
The result: an AI chatbot that answers accurately from your actual data, cites sources, and says "I don't know" instead of making things up when information isn't available.
How Dify RAG Works Under the Hood
Understanding the pipeline helps you configure it better. Here is what happens when you upload a document and ask your chatbot a question:
Document ingestion
You upload a file. Dify extracts the text content from PDFs, Word docs, Markdown, etc.
Chunking
The text is split into smaller chunks (typically 500–1000 tokens each) so retrieval is precise.
Embedding
Each chunk is converted into a vector (a list of numbers) using an embedding model like text-embedding-3-small.
Vector storage
Vectors are stored in a vector database (built-in, pgvector, Qdrant, Weaviate, Milvus, or Pinecone).
Query retrieval
When a user asks a question, it is also embedded, and the most similar chunks are retrieved from the vector store.
Context injection
The retrieved chunks are injected into the LLM prompt as context. The model answers based on this real data.
Setting Up Your Knowledge Base
The Knowledge Base is where you upload and index your documents. Follow these steps:
Open Dify → Knowledge tab
Click "Knowledge" in the top navigation. This is where all your document collections live.
Create a new Knowledge Base
Click "+ Create Knowledge". Give it a descriptive name (e.g., "Product Documentation", "Support FAQ").
Upload your files
Drag and drop or browse to upload. Supported: PDF, Word (.docx), TXT, Markdown (.md), HTML, CSV. Multiple files at once are supported.
Choose chunking strategy
Select "Automatic" for most cases. This splits documents by paragraphs and headings. For dense technical docs, try "Custom" with 800 token chunks and 150 token overlap.
Select embedding model
Choose your embedding model. OpenAI text-embedding-3-small is recommended — fast, cheap, and accurate. If you are self-hosting, nomic-embed-text via Ollama works offline.
Index your documents
Click "Save & Process". Dify chunks and embeds everything. Depending on document size, this takes 1–5 minutes. A progress bar shows status.
Connecting RAG to Your App
Once your Knowledge Base is indexed, attach it to a chatbot or agent app:
Open your Chatbot or Agent app in Studio
In the left panel, find the "Context" section
Click "+ Add Context" and select your Knowledge Base
Set recall mode to "Semantic Search" (recommended) for meaning-based retrieval, or "Full-Text Search" for keyword matching
Set "Top K" to 3–5 (how many chunks to retrieve per query). Start with 3.
Enable "Score Threshold" at 0.5 to filter out low-relevance results
Test in the preview panel with questions from your documents. Verify the bot cites correct info.
Supported Vector Databases
Dify supports multiple vector stores. For most users, the built-in store is all you need. For large-scale deployments (millions of documents), switch to a dedicated vector database:
| Vector Store | Setup | Best For | Cost |
|---|---|---|---|
| Built-in (default) Recommended | None | Most users — up to ~100k documents | Free |
| pgvector | PostgreSQL extension | Existing PostgreSQL users | Free (self-hosted) |
| Qdrant | Docker container | Self-hosted, high performance | Free (self-hosted) |
| Weaviate | Docker or cloud | Multi-modal data (text + images) | Free / paid cloud |
| Milvus | Docker or Zilliz cloud | Billions of vectors, enterprise scale | Free / paid cloud |
| Pinecone | API key only | Fully managed, no infra work | Paid SaaS |
RAG Tips for Best Results
These configuration tips will significantly improve your RAG accuracy:
Optimal chunk size: 500–1000 tokens
Too small = missing context. Too large = diluted relevance. For most docs, 600 tokens with 100 token overlap hits the sweet spot.
Clean your source documents
Remove repeated headers, footers, page numbers, and navigation menus. These add noise that hurts retrieval quality.
Use metadata filtering
Tag documents with categories (e.g., "product: billing", "type: FAQ"). Filters let you retrieve only relevant subsets for each query.
Separate knowledge bases by topic
Do not mix your product manual with your HR policy. Separate datasets give higher-precision retrieval. You can attach multiple bases to one app.
Use Hybrid Search
Dify supports hybrid mode (semantic + keyword search combined). Enable it in your Knowledge Base settings for better coverage on exact terms like product codes or names.
Monitor retrieval in logs
Go to Logs & Annotations in your app to see exactly which chunks were retrieved for each query. Use this to debug poor answers.
Retrieval Modes Explained
Dify offers three retrieval modes. Choose based on your content type:
Semantic Search
Finds conceptually similar content even if exact words differ. Best for natural language questions about complex topics. Uses vector similarity.
Full-Text Search
Keyword-based search like a traditional search engine. Better for exact term matching: product codes, names, IDs. Fast and predictable.
Hybrid Search
Combines semantic and full-text search using a reranker. Best overall accuracy but slower and requires a reranker model (e.g., cohere-rerank).
External Data Sources & Sync
Dify goes beyond file uploads. You can connect external sources that stay in sync automatically:
Notion
Connect your Notion workspace. Dify syncs pages automatically. Great for team wikis and documentation.
Web scraping
Provide a URL and Dify fetches and indexes the page. Good for public documentation sites.
Custom API
Build a retrieval plugin via the External Knowledge Base API. Connect any database or proprietary data source.
File sync via API
Programmatically upload and update documents using the Dataset API. Useful for CMS integrations.
Frequently Asked Questions
What is Dify RAG?
RAG stands for Retrieval-Augmented Generation. Dify RAG lets your AI search through your own documents, PDFs, or databases before generating an answer — dramatically reducing hallucinations and improving accuracy on domain-specific questions.
What file types does Dify RAG support?
Dify Knowledge Base supports PDF, Word (.docx), plain text (.txt), Markdown (.md), HTML, and CSV files. You can also connect external data sources via API or sync with Notion and other platforms.
How does Dify RAG reduce hallucinations?
Instead of relying solely on the LLM's training data, Dify RAG retrieves relevant chunks from your documents and injects them as context. The model then answers based on your actual data, citing specific passages rather than guessing.
Which vector databases work with Dify RAG?
Dify supports multiple vector stores: built-in (default, no setup needed), pgvector (PostgreSQL extension), Qdrant, Weaviate, Milvus, and Pinecone. The built-in store is perfect for most users; switch to an external one for millions of documents.
Ready to Self-Host Dify with RAG?
Self-hosting Dify gives you full control over your data — critical when your Knowledge Base contains sensitive documents. Run Dify on your own server from €3.79/month on Hetzner, or get a fully managed instance on Elestio in under 5 minutes.