Briefing
Retrieval-Augmented Generation (RAG) sits underneath most production LLM applications. Dify's RAG pipeline, part of a platform that has gathered roughly 146,000 stars on GitHub (the figure of 136,000 cited in some write-ups is now out of date), is one of the more capable open-source implementations you can pick up today. Here is how it gets from a pile of raw documents to an accurate, cited answer.
The RAG Pipeline Overview
Dify's pipeline runs in five stages:
- Document Ingestion: Accepting dozens of formats
- Chunking: Intelligent text segmentation
- Embedding: Converting text to vectors
- Retrieval: Finding relevant content
- Generation: Producing answers with citations
Every stage is configurable, so you can tune it for the document types and use cases you actually deal with. (Dify Blog: Introducing Knowledge Pipeline)
If you have ever asked an AI tool a question about your own company's documents and watched it confidently make something up, you already understand why RAG matters. The trick is not making the model smarter. It is feeding the model the right paragraph from the right document at the right moment, and then telling it to answer from that and nothing else.
Dify is open-source software that does exactly this plumbing. You point it at your PDFs, spreadsheets, and web pages; it reads them, breaks them into searchable pieces, and stands up a system that can answer questions with a link back to the source. For an Australian business team, the appeal is plain: you can run it yourself, keep your documents in-house, and stop paying per question to a black-box vendor.
The reason it is worth a close look is that the gap between a RAG demo and a RAG system you would trust with customer-facing answers is enormous. Most of that gap lives in the unglamorous middle steps, how you split a document, how you search it, how you stop the model from inventing. Dify exposes those steps as knobs you can turn. The rest of this piece walks through each stage and where the real decisions are.
Stage 1: Document Ingestion
Dify takes in a wide spread of input formats. Official materials cite support for 30-plus formats; some third-party explainers push that number higher, so treat the longer lists as indicative rather than exact. The supported types include:
- Text: Markdown, TXT
- Documents: PDF, DOCX, Word
- Spreadsheets: CSV, XLSX, Excel
- Presentations: PPT
- Web: HTML, URL crawling
- Cloud storage: pulled directly from connected sources
Each format gets a dedicated parser that pulls out the text while keeping its structure intact. PDFs are the hard case. Dify reportedly uses more than one extraction approach, OCR for scanned pages, direct text extraction for digital PDFs, table detection for structured data, and picks whichever gives the best result. (Dify Blog: Introducing Knowledge Pipeline)
Stage 2: Chunking
Chunking is where a lot of RAG systems quietly fall over. Dify's documented modes are General (paragraph and recursive splitting with configurable size and overlap), Parent-Child, and Q&A. Beyond those, the broader RAG toolkit it draws on supports several common strategies, though not all are named as distinct Dify options in the official docs:
Recursive Character Splitting: Splits on natural boundaries, paragraphs, then sentences, with a chunk size and overlap you set. Good for general text.
Semantic Chunking: Uses an embedding model to find topic boundaries and split where the subject shifts. Suits documents that move between clearly different topics. (Reported as a general RAG technique rather than a confirmed standalone Dify mode.)
Fixed-Size Chunking: Cuts the text into equal blocks with overlap. Fast and simple, but it will happily slice a sentence in half.
Markdown Header Splitting: Splits on Markdown headers so the document's hierarchy survives. Useful for structured Markdown. (Also a general technique rather than a documented Dify-specific mode.)
Custom Splitting: Write your own splitting rules with regex.
Parent-Document Retrieval: Stores small chunks for searching but hands back the full parent document for context. The right call when an individual chunk is too thin to make sense on its own.
Chunk size and overlap are the parameters that matter most. Too small and you lose context; too large and irrelevant text dilutes the precision of your retrieval. Dify gives you tools to test different settings rather than guess. (Dify Blog: Introducing Knowledge Pipeline)
Stage 3: Embedding
Dify works with a range of embedding providers (Dify Docs: Model Providers):
- OpenAI: text-embedding-3-small, text-embedding-3-large, ada-002
- Cohere: embed-english-v3, embed-multilingual-v3
- Hugging Face: hundreds of models via the inference API
- Local: run embedding models on your own hardware for privacy and cost control
Which embedding model you choose has a real effect on retrieval quality. Dify provides benchmarks so you can compare models against your own documents instead of taking a vendor's word for it.
Stage 4: Retrieval
Dify uses hybrid retrieval, combining several signals (Dify Blog: Introducing Knowledge Pipeline):
Vector Similarity: Semantic search across the embedding space. Finds related content even when the wording is different.
Keyword Matching (BM25): Old-fashioned text search for exact term matches. Catches the specific terms vector search can miss.
Reranking: A cross-encoder model re-orders the first batch of results for relevance. This second pass lifts quality noticeably.
Metadata Filtering: Narrow results by document source, date, author, or your own custom fields.
Multi-Query Expansion: The system reportedly generates several variations of a query and merges the results, which helps recall on vague questions. This feature was not confirmed in the official knowledge pipeline documentation, so treat it as unconfirmed.
Stage 5: Generation
The last stage writes the answer (Dify Blog: Introducing Knowledge Pipeline):
Context Assembly: The retrieved chunks are stitched into a context window, each tagged with where it came from.
Prompt Engineering: Dify's default prompt tells the LLM to answer only from the supplied context and to cite its sources. You can swap in your own prompt.
Citation Tracking: Claims in the answer link back to the source document and chunk, so a reader can check the work and dig into the original.
Hallucination Guardrails: When the context doesn't hold enough information, the prompt instructs the model to say so rather than invent an answer.
Edge Cases Handled
Dify reportedly copes with the messier realities of real documents, though several of the specifics below are not confirmed in official documentation and read as idealised descriptions:
Tables in PDFs: Pulls out the table structure and keeps the relationships between cells. (PDF and table handling is supported; the exact behaviour is not fully documented.)
Images with Captions: Reads image captions and folds them into the text. (Dify does support multimodal text-plus-image knowledge bases, see Dify Blog: Multimodal retrieval in the knowledge base.)
Multi-language Documents: Reportedly detects the language per chunk and routes each to a suitable embedding model. (Unconfirmed in official docs.)
Duplicate Content: Reportedly strips out repeated boilerplate, headers, footers, that would otherwise muddy retrieval. (Unconfirmed.)
Document Updates: Reportedly re-embeds only the sections that changed when a document is updated, rather than the whole thing. (Unconfirmed.)
Performance Optimisations
Dify performs asynchronous document indexing. Several other optimisations are described in capability write-ups but are not confirmed against official documentation, so the list below mixes confirmed and reported behaviour:
- Async processing: Document ingestion runs asynchronously (confirmed)
- Batch processing: Documents reportedly processed in parallel batches
- Caching: Embedding results reportedly cached to skip re-computation
- Index optimisation: Vector indices reportedly tuned to the specific embedding model
- Query caching: Common queries reportedly cached for instant response
- Partial results: Async ingestion reportedly lets you query partial results before a full document finishes indexing
Evaluation
Dify's pipeline emphasises step-by-step inspection and real-time debugging of each node, so you can see what each stage produced. Some explainers also describe a built-in evaluation suite with named metrics:
- Answer relevance: Does the answer address the question?
- Context precision: Are the retrieved chunks relevant?
- Faithfulness: Does the answer stick to the provided context?
- Citation accuracy: Are the citations correct and helpful?
These metrics mirror RAGAS-style evaluation frameworks. Their presence as native, built-in Dify tools was not confirmed in the documentation reviewed, so don't assume them without checking your own install. (Dify Blog: Introducing Knowledge Pipeline)