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How the browser RAG pipeline works
Retrieval-augmented generation (RAG) gives a language model selected source passages to use when answering a question. In a browser-local design, the document and query processing, retrieval, and generation can all happen on the user’s device:
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- Load a document. Accept a supported file and extract its text in the browser. The sources cited here do not prescribe a file-parsing library or document formats.
- Split the text into passages. Divide it into chunks suitable for embedding and later inclusion in a prompt. Chunk size and overlap are implementation choices; the cited documentation does not establish universal values.
- Embed the passages. Convert each chunk into a vector representation using an embedding model.
- Embed the question. Use the same embedding model to turn the user’s query into a vector.
- Retrieve context. Rank document passages by relevance to the query and select a useful set. The index, ranking method, and quality need to be chosen and evaluated for the application.
- Generate an answer. Send the question and selected passages to a browser-local language model, asking it to answer from that context. Show which passages support the response so readers can check it against the source document.
WebGPU is a web standard for accelerated graphics and compute, and can be used for machine-learning workloads. Hugging Face’s Transformers.js WebGPU guide demonstrates a feature-extraction pipeline configured with device: "webgpu". Its example uses mixedbread-ai/mxbai-embed-xsmall-v1 with mean pooling and normalization. That demonstrates an embedding building block, not a complete RAG application.
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Run embeddings and generation in the browser
Embeddings with Transformers.js
The Transformers.js example provides a starting point for producing embeddings in a WebGPU-enabled browser. For a document assistant, the application would apply the embedding pipeline to each passage and to each query, then use those vectors for retrieval. The guide does not specify how to divide documents, build an index, rank results, or verify retrieval quality; those choices need to be tested with the documents and questions the app is intended to handle.
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Language-model inference with WebLLM
WebLLM provides browser-based LLM inference using WebGPU, including a chat-completion API and streaming. Its documentation describes worker support, and its paper explains how WebAssembly handles CPU work while workers keep heavy computation away from the main UI thread. This separation matters for an interactive app: inference should not make the document interface unresponsive.
Keep the model’s role narrow and explicit in the prompt: answer using the supplied passages, and indicate when they do not contain enough information. The sources establish the browser inference and embedding capabilities, but do not validate a particular prompt or guarantee answer accuracy. Returning passage references or excerpts alongside an answer makes it easier for users to judge whether the response is grounded.
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Plan for WebGPU compatibility and fallback
WebGPU support varies by browser, version, operating system, and device. Hugging Face’s Transformers.js documentation reported an estimate of around 85% global support as of March 2026, attributing the figure to Can I Use; it is a time-bound global estimate, not a guarantee that WebGPU works on a particular user’s setup. Check the target browser and device rather than treating that percentage as a compatibility promise. MLC’s WebLLM project and getting-started guide also require a WebGPU-compatible browser.
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Check whether the required WebGPU capability is available before initializing the local model. If it is not, explain the limitation and offer a deliberate alternative: let the user try a supported browser or device, provide a non-generative way to search the document, or offer a cloud-backed fallback only with clear disclosure and user consent. Do not imply that every browser can run the local model.
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Explain what “local” means for privacy and connectivity
Local inference means the model runs on the device; it does not, by itself, establish that the entire app is offline or network-free. The application code and model files may need to be downloaded, and analytics or a cloud fallback could also communicate externally. Be precise about which data stays in the browser, what is sent over a network, and when. Do not describe a hypothetical app as audited or fully offline without verifying its actual network behavior.
The WebLLM.io local-inference guide documents browser-side model caching with the Origin Private File System (OPFS), as well as worker execution. Caching can avoid fetching model files again when they are available in storage, but users still need to obtain the app code and model files in the first place. Explain first-run downloads and storage needs in the interface, and handle storage limitations gracefully.
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Choose retrieval settings and measure on target devices
There is no source-established universal chunk size, overlap, vector index, ranking method, or hardware minimum for this application pattern. Treat them as design decisions rather than fixed best practices. Test with representative files and questions: inspect whether the retrieved passages contain the answer, whether unrelated passages crowd out useful context, and whether the final response accurately reflects the retrieved text.
Benchmark the chosen embedding and language models on the browsers and devices you intend to support. Measure retrieval and generation separately, including time to first response and the effect of streaming on perceived latency. The WebLLM paper reports up to 80% of native decoding performance in its authors’ evaluation on an Apple MacBook Pro M3 Max; that result is specific to the paper’s setup and does not predict performance on other hardware, browsers, models, or quantization settings. See WebLLM: A High-Performance In-Browser LLM Inference Engine for the evaluation and architecture.
When deciding between local and cloud-backed generation, assess the trade-offs for your use case instead of assuming one is always better. Compare whether document contents leave the device, browser and device coverage, first-run download and storage demands, latency, answer quality for the selected models, and whether a fallback is available. These are evaluation dimensions, not measured outcomes established by the cited sources.
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