RAGFlow Tutorial: Build a Document Question-Answering System

RAGFlow combines document processing, retrieval and generation to build question-answering applications over a document collection. Begin with a small, non-sensitive corpus and inspect the extracted passages before trusting the final answer. The system can organise a RAG workflow, but it cannot guarantee that a source is correct or that generation follows it faithfully.

Prepare a supported deployment

Start with the official RAGFlow repository, its Apache 2.0 licence and release instructions. The checked documentation describes a substantial multi-service deployment. It is not merely a Python package that turns any laptop into a production document service.

Review the current operating-system support, storage and memory prerequisites. The checked Docker guidance lists a recommended starting configuration and notes that actual requirements depend on document engines and workloads. Local language and embedding models can require additional capacity beyond the application stack.

For the release documented in the checked README, the deployment sequence includes:

git clone https://github.com/infiniflow/ragflow.git
cd ragflow/docker
git checkout v1.0.0-rc2
docker compose -f docker-compose.yml up -d

These are unexecuted instructional commands from that specific documented release path. A release candidate is not automatically the right production choice. Recheck the selected release, .env settings, passwords, exposed ports and platform support before running anything.

Do not change system-level kernel settings mechanically. Some document-engine options require host configuration; understand the relevant official instructions and apply only those needed for your chosen deployment.

Create a controlled knowledge base

After the services are ready, sign in and configure the required model providers. Identify the generator, embedding model and any reranker separately. Selecting a local generator does not establish that embeddings or document parsing remain local.

Create a knowledge base containing a few documents with known answers. Use public material or clearly labelled synthetic examples. For an initial trial, include one ordinary text PDF and one document with a small table so you can observe extraction differences.

Inspect the parsed text before adjusting retrieval. Check headings, page references, names, amounts and table relationships. If a table was flattened incorrectly, changing the generator will not restore the missing structure reliably.

Choose the parsing and chunking method appropriate to the material rather than selecting the most elaborate option by default. Short policies, scanned invoices and research papers may need different treatment. Record those choices so later results can be explained.

Maintain document identifiers and versions. If two policies conflict, the system needs a rule for which version applies. A semantically similar outdated paragraph can otherwise look like strong evidence for a current question.

Test retrieval, answers and access independently

Create three groups of questions: directly answerable, requiring several passages and unanswerable from the corpus. Record the expected source for each. This exposes whether failure occurs during retrieval or generation.

First inspect the passages returned by search. If the right evidence is absent, adjust extraction, chunking, query handling or retrieval settings. If it is present but the answer is wrong, inspect prompt construction, model behaviour and evidence verification.

Check citations against the exact claim they support. A reference to the right document is insufficient when the quoted section does not contain the answer. Ask the system to acknowledge missing evidence rather than filling gaps with general knowledge.

Test user permissions with separate accounts before shared use. Retrieval must filter to authorised material before generation. Hiding a citation after the model has seen a restricted passage does not prevent disclosure.

Back up configuration and persistent data before upgrades. Review migrations and repeat the small evaluation set afterwards. A service starting successfully does not establish that parsing, retrieval and access rules still work as expected.

Your next step is a documented pilot with source-level checks and a short failure log. Expand the corpus only when you can explain why the first answers are correct, where they came from and who is allowed to see them.

Quick questions, clear answers

Does RAGFlow train a model on my PDFs?

A retrieval workflow processes and indexes documents so relevant passages can be supplied during answering. That is distinct from changing a model's weights through training. Check the actual features and providers enabled in your installation rather than assuming every document upload triggers fine-tuning.

Why are citations present but the answer wrong?

Retrieval and generation can fail separately. A citation may point to a related passage without supporting the particular claim. Inspect the retrieved text, document version and answer sentence together, and include unsupported or conflicting questions in your evaluation set.

Can I use confidential documents immediately?

First verify model endpoints, document-processing components, access boundaries, logs and storage. Use harmless samples while checking the complete data path. Self-hosting the application is useful control, but it does not prove every configured provider or integration operates inside your approved environment.

About this guide

Aniket Thakur prepared this guide from secondary research checked on 22 October 2026. No first-hand software testing or performance benchmarking was conducted. Examples are instructional and require validation in your environment.