Pre-release
This release is a pre-release and may not be stable for production use.
A minimal, local-first AI workbench and retrieval-augmented generation (RAG) platform powered by SQLite.
Notice: Pre-Alpha
This package is currently in active pre-alpha development (0.0.0a0). The public API, storage layers, and local-first container runtimes are being actively stabilized.
- Repository: github.com/mesotron-dev/velites
- Author: mesotron.dev
Architecture Highlights
- Hypermedia Frontend: Server-rendered UI using FastAPI, Jinja2, and HTMX, eliminating client-side JavaScript build pipelines and heavy SPA frameworks.
- Dual In-Process Vector Engine: Leverages SQLite C-extensions directly—sqlite-vector for immediate linear scans and vec1 (IVFADC + OPQ) for scalable approximate nearest neighbor search.
- Embedded Storage & Caching: Utilizes local database primitives and embedded key-value caching to manage document chunks, scraping caches, and conversational state without external database servers.
- Minimal Runtime Footprint: Designed around a single-process model packaged on a minimal distroless container base, eliminating background runtime daemons.
Anticipated Use Cases & Applications
- Privacy-First Document Intelligence: Query local PDF archives, private research notes, and internal documentation entirely in-process, ensuring zero data leaves your local host.
- Resource-Constrained Environments: Deploy embedded semantic retrieval to lightweight VPS nodes, developer containers, and local edge hardware where multi-container RAG stacks cannot easily run.
- Semantic Codebase Search: Index repository source trees and API documentation for fast, in-process symbol and context retrieval.
- Embedded Domain Workbenches: Function as an embeddable vector and hybrid retrieval engine for downstream tools, automated pipelines, and specialized local assistants.
License
Licensed under the Apache License, Version 2.0. See LICENSE for details.
Release files for velites 0.0.0a0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| velites-0.0.0a0.tar.gz | 9.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| velites-0.0.0a0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 20.4 kB
Release files / velites-0.0.0a0.tar.gz
| Download URL | velites-0.0.0a0.tar.gz |
|---|---|
| Size | 9.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Release files / velites-0.0.0a0-py3-none-any.whl
| Download URL | velites-0.0.0a0-py3-none-any.whl |
|---|---|
| Size | 10.5 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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uv/0.11.28 {"installer":{"name":"uv","version":"0.11.28","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Fedora Linux","version":"44","id":"","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
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