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Retrieval-augmented generation (RAG) lets a language model answer using relevant material fetched from a document collection when a question is asked. It can work without an internet connection if the model, document-processing tools, embedding model, retrieval store, and every other required service are available locally or on the isolated network. A locally hosted chat screen by itself is not enough.
What retrieval-augmented generation does
A language model normally generates an answer from its learned parameters and the prompt it receives. RAG adds a retrieval step: the system searches an external collection for passages related to the user’s question, then supplies those passages to the model as context. The model uses that context to compose an answer; RAG does not retrain the model each time you add a document.
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The original RAG research described combining a language model with a dense vector index. In practical systems, the collection may be stored in a vector database or another retrieval store, and retrieval can combine semantic search with keyword matching or reranking.
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How a RAG system works, step by step
- Extract text from documents. The system loads files and converts them into text it can process. The result depends on file format and parser support; a file that cannot be extracted usefully cannot contribute reliable searchable content.
- Split the text into chunks. Long documents are divided into smaller passages so the system can search and supply relevant portions rather than entire files. Chunk boundaries affect what evidence can be retrieved together.
- Turn chunks into embeddings. An embedding model converts each chunk into a numerical representation used to find semantically related text. The chosen embedding model is part of the pipeline, not necessarily the same model that writes the final answer. See Ollama’s embedding-model documentation.
- Store the searchable material. The system keeps the embeddings alongside the text or references to it in a retrieval store. Some configurations persist data locally; deployment and concurrency needs affect which store is suitable.
- Retrieve context for a question. When a user asks something, the system searches the collection for relevant passages. It may combine vector search with keyword search and, optionally, rerank candidate results.
- Generate the response. The retrieved passages are added to the prompt sent to the language model. Open WebUI describes this step as combining retrieved text with a RAG template and prefixing it to the user’s prompt.
Each stage can affect the result. Poor text extraction, unsuitable chunks, weak retrieval, or a model that cannot use the supplied context well can all lead to an incomplete or misleading answer.
Can RAG work with your documents without internet?
Yes, if the complete workflow is local or runs on a network that remains available while internet access is disabled. That includes more than the generator: check document extraction, embeddings, the retrieval store, reranking, authentication, search, optional tools, dependencies, and model files. Any component configured to call a cloud provider or hosted service can break the offline workflow or send data outside the device.
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This distinction matters because a locally accessible interface does not prove that its data processing is local. LlamaIndex notes that its tutorials use hosted OpenAI APIs by default for generation and embeddings; unless reconfigured, documents and queries may be sent to those services. Its privacy and security documentation also describes local options.
Open WebUI’s offline preparation guide recommends setting up and testing the working installation and local inference server, downloading the intended generation and embedding models, configuring local document extraction, and retaining needed dependencies and model caches on persistent storage before disconnecting. Optional reranker or speech models also need to be prepared if the workflow depends on them. The guide identifies itself as a community contribution, not an officially supported Open WebUI team guide.
Prepare and verify an offline setup
- List every component the workflow uses. Include the chat interface, generation model, embedding model, extraction tools, vector or other retrieval store, reranker, search provider, authentication service, and any optional integrations.
- Check where each component runs. Confirm that required services are local or reachable on the isolated network. Look for hosted API defaults, cloud model providers, external authentication, and web search that depends on the public internet.
- Download and retain required files. While connected, obtain the specific models and dependencies the installation needs. Keep caches and persistent data somewhere that will remain available after disconnecting.
- Test the actual document types. Ingest representative files and check that their text is extracted correctly. A successful upload is not proof that the relevant content became searchable.
- Ask questions whose answers are in the documents. Check whether the system retrieves the appropriate passages and whether the generated response reflects them. Open WebUI’s RAG documentation covers its retrieval and knowledge-base behavior.
- Disconnect and repeat the test. A setup is not verified offline until the same ingestion and document-grounded question flow works without relying on internet-hosted services.
What to evaluate when choosing a local RAG setup
Privacy and data flow
Trace each stage that handles document text or user queries. Local generation does not protect data if embeddings, extraction, search, or another step still uses an external provider. The right check is whether all required processing stays on the device or within the network boundary you intend to trust.
Document formats and extraction
Support varies with the extraction tools and parsers installed. Test the exact file types and representative documents you need, especially if they contain complex layouts or scanned content. Retrieval can only find material that was extracted and indexed in a usable form.
Retrieval quality and chunking
Embedding quality and chunk boundaries influence which passages match a question. Keyword search can help with exact names or terms that semantic matching may not prioritize. Open WebUI describes hybrid retrieval as combining BM25 keyword search with vector search, with optional reranking; see its RAG feature documentation. Retrieval settings should be evaluated against the vocabulary and structure of your own collection.
Model capacity and hardware fit
Local generation speed and the amount of context a model can accept depend on the chosen model and local setup. Retrieved passages consume context space alongside the user’s question and instructions. Open WebUI warns that, in the documented configuration, Ollama may select a 4096-token default context on GPUs with less than 24 GiB of VRAM. This is a configuration-specific default warning, not a universal hardware recommendation or performance benchmark.
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Storage and deployment
A single-user setup may be able to use a simpler local store. For multiple users or processes, check persistence and concurrency behavior rather than assuming a development configuration will scale. LlamaIndex documents in-memory persistence and self-hosted storage options; Open WebUI notes limitations in its default ChromaDB/SQLite arrangement for multi-process access.
Maintenance and small collections
Changing the embedding model can make existing vectors incompatible with the new representation, so the indexed collection may need to be rebuilt. Open WebUI’s RAG troubleshooting guide advises reindexing after an embedding-model change. It also notes that for small documents, full-context mode may outperform retrieval; the best approach depends on the size and use of the material.
Quick Recap
What RAG does not guarantee
- It does not guarantee accuracy. The system can retrieve the wrong passage, miss a relevant one, or produce an unsupported interpretation.
- It does not automatically keep data private. That depends on the providers used by every pipeline component, not merely where the interface runs.
- It does not make every file searchable just because it was uploaded. Extraction and indexing must succeed for its content to be retrieved.
- It does not eliminate the need to check evidence. For consequential answers, inspect the retrieved source material and verify that it supports the response.
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