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Recall is a local-first personal search system designed to help you find saved material by what you remember about it—not only by its filename or exact wording. Its builder, Soham Maity, describes it as a way to search across webpages, screenshots, PDFs, notes, images, and other files, including with queries such as “the screenshot where GitHub could not resolve the host.”
What Recall is designed to do
Recall targets a familiar problem: collecting useful material is easy, but finding it again can be difficult. Maity puts it this way: “The problem was not storing those things. It was finding them again.” Rather than requiring you to remember a filename or the precise phrase in a document, Recall is meant to retrieve an item from a description of its subject or context.
For example, someone looking for an old screenshot might search for “that screenshot about the GitHub DNS problem.” A person trying to locate a saved technical article could search for “the article about FAISS.” These are examples of the intended search experience, not published measurements of how reliably the software returns results.
What you can search
The project description says Recall can ingest text, webpages, images, screenshots, PDFs, and other files. For images, it uses OCR and image captioning to make text and visual content available to search. That means an image may be findable by what it depicts or by text recognized inside it, rather than only by its filename.
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- The available storage capacity may vary.
The description does not give a complete file-format list or explain the handling of every kind of file. If a particular format matters to you, confirm that the current version supports it before relying on Recall as its catalog.
How search combines meaning and exact wording
According to Maity, Recall extracts content and metadata when material is added, then stores the resulting data in SQLite. SQLite FTS5 supplies lexical search, while FAISS provides vector retrieval. The system combines BM25 lexical results and vector results using reciprocal rank fusion; metadata, recency, and exact-match signals are also described as ranking inputs.
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- To get set up, connect the portable hard drive to a computer for automatic recognition software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
When semantic retrieval helps
Vector retrieval is intended to connect related ideas even when a query uses different words from the saved item. A remembered description such as “the GitHub DNS problem” may point toward a screenshot whose text or filename does not use that exact phrase.
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Lexical search can be useful when you remember an exact error message, filename, or identifier. Combining lexical and vector results is intended to support both kinds of recall: searching by the idea you remember and searching by a literal string.
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- Designed to work with Windows or Mac computers, this external hard drive makes backup a snap just drag and drop. Reformatting may be required for Mac
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- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
The project author says this processing happens during ingestion so ordinary retrieval can be deterministic and need not run a generative AI model in the background. That is a description of the design and its rationale, not an independent benchmark of result quality or speed.
What “local-first” means here—and what is not verified
Maity says Recall keeps data on the user’s machine, needs no account or cloud API, and uses open models to perform image captioning, OCR, and embedding generation locally. If implemented as described, that design avoids sending saved material to a remote embedding API for processing.
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- Easily store and access 4TB of content on the go with the Seagate Portable Drive, a USB external hard drive.Specific uses: Personal
- Designed to work with Windows or Mac computers, this external hard drive makes backup a snap just drag and drop
- To get set up, connect the portable hard drive to a computer for automatic recognition no software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
These are first-party project claims. The available project description does not independently audit network traffic, data handling, or security, so it is not enough on its own to establish that no data ever leaves a device or to assess the security of a particular installation. Anyone handling sensitive files should review the software and its current documentation before importing them.
Models and software components named by the project
The project description identifies Google’s EmbeddingGemma 300M as the default embedding model, with all-MiniLM-L6-v2 and deterministic hash vectors as fallbacks. For image processing, it names BLIP-base for captioning and RapidOCR for OCR. The application stack is described as FastAPI and Python on the backend, SQLite for storage, FAISS for vector retrieval, and React with Vite for the frontend.
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Those component names describe the implementation reported by the builder; they do not establish which versions are currently bundled or whether every component is required in every setup. Implementation details may change as the project evolves.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Hardware, requirements, and operational design
The builder says Recall is intended to run on a normal laptop and does not require a dedicated GPU. The description provides no supported operating-system list, minimum memory or storage requirement, search-latency measurement, or hardware benchmark. “Normal laptop” is therefore a design claim, not a defined minimum specification.
The described operational choices include treating the database as the source of truth and making the FAISS index rebuildable. Ingestion uses content hashes to avoid duplicate processing, and optional AI model failures are not supposed to stop the ingestion pipeline. These design details indicate how the project is intended to recover from some failures, but the description does not document a complete backup, export, or recovery procedure.
Who may find this approach useful
Recall’s approach is most relevant to people who save a mix of personal material and later remember its meaning or circumstances better than its name. Its design also matters to readers who want local processing rather than a remote embedding API, provided they verify the privacy and operational details that matter for their own files.
Before choosing it, check the current project information for the file formats you need, installation steps, operating-system support, data export or backup options, and how the application behaves on your hardware. The available project description does not include an independent comparison with other personal search tools, nor does it establish comparative performance, accuracy, or security.
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