mirror of
https://github.com/MindWorkAI/AI-Studio.git
synced 2026-11-03 23:47:21 +00:00
193 lines
7.2 KiB
TOML
193 lines
7.2 KiB
TOML
[package]
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name = "mindwork-ai-studio"
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version = "26.10.1"
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edition = "2024"
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description = "MindWork AI Studio"
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authors = ["Thorsten Sommer"]
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[build-dependencies]
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tauri-build = { version = "2.6.3", features = [] }
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[dependencies]
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tauri = { version = "2.11.5", features = [] }
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tauri-plugin-window-state = { version = "2.4.1" }
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tauri-plugin-shell = "2.3.5"
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tauri-plugin-dialog = "2.7.1"
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tauri-plugin-opener = "2.5.4"
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tauri-plugin-single-instance = "2"
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serde = { version = "1.0.228", features = ["derive"] }
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serde_json = "1.0.150"
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keyring-core = "1.0.0"
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arboard = { version = "3.6.1", features = ["wayland-data-control"] }
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tokio = { version = "1.52.3", features = ["rt", "rt-multi-thread", "macros", "process"] }
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tokio-stream = { version = "0.1.18", features = ["sync"] }
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futures = "0.3.32"
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async-stream = "0.3.6"
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flexi_logger = "0.31.9"
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chrono = "0.4.44"
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dirs = "6.0.0"
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log = { version = "0.4.33", features = ["kv"] }
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once_cell = "1.21.4"
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axum = { version = "0.8.9", features = ["http2", "json", "query", "tokio"] }
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axum-server = { version = "0.8.0", features = ["tls-rustls"] }
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rustls = { version = "0.23.46", default-features = false, features = ["aws_lc_rs"] }
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rand = "0.10.2"
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rand_chacha = "0.10.0"
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subtle = "2.6.1"
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base64 = "0.22.1"
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aes = "0.9.1"
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cbc = "0.2.1"
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pbkdf2 = "0.13.0"
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hmac = "0.13.0"
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sha2 = "0.11.0"
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rcgen = { version = "0.14.8", features = ["pem"] }
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# The readers are needed to identify a file by its content instead of its extension: zip covers
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# OOXML and ODF, cfb the legacy Office formats, txt tells actual text from binary data, and exe
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# recognizes executables which carry a harmless extension. Without them, every ZIP-based document
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# is only detected as a plain archive.
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file-format = { version = "0.29.0", features = ["reader-zip", "reader-cfb", "reader-txt", "reader-exe"] }
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# Text files are not always UTF-8: on Windows they are frequently encoded in Windows-1252, whose
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# umlauts are single bytes and therefore invalid UTF-8. chardetng guesses the encoding, encoding_rs
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# decodes it.
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chardetng = "1.0.0"
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encoding_rs = "0.8.35"
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symphonia = { version = "0.6", default-features = false, features = ["aac", "aiff", "alac", "caf", "flac", "isomp4", "mkv", "mp1", "mp2", "mp3", "ogg", "pcm", "vorbis", "wav"] }
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ropus = "=0.12.18"
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rubato = { version = "4", default-features = false, features = ["fft_resampler"] }
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webm-iterable = "0.6.4"
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calamine = "0.36.0"
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pdfium-render = "0.9.1"
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sys-locale = "0.3.2"
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whoami = "2.1.2"
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cfg-if = "1.0.4"
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pptx-to-md = "1.0.0"
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docx-to-md = "0.1.0"
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tempfile = "3.27.0"
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strum_macros = "0.28.0"
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sysinfo = "0.39.6"
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bytes = "1.12.1"
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qdrant-edge = "0.8.0"
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# Prompt-injection detection. `regex` gives us linear-time matching without backtracking, so
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# a hostile document cannot make a scan blow up, and `aho-corasick` matches the ~1600 fixed
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# phrases in one pass no matter how long that list grows.
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regex = "1.13.1"
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aho-corasick = "1.1.5"
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toml = "1.1.4"
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image = { version = "0.25.10", default-features = false, features = ["jpeg", "png", "webp", "gif", "bmp", "tiff"] }
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tokenizers = "0.23.1"
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[dev-dependencies]
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# Sends requests through the runtime API router in tests, without starting a server.
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tower = { version = "0.5.2", features = ["util"] }
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[patch.crates-io]
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# Issue: This repo was not updated since 2020. The rand crate was outdated. We patched it to use a newer version of rand.
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# State: There is a PR for a long time, but it was not merged. We use the git version for now. Qdrant Edge still depends
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# on this crate in version 0.1.2, so the patch stays relevant: https://github.com/asimihsan/permutation-iterator-rs/pull/14
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permutation_iterator = { git = "https://github.com/SommerEngineering/permutation-iterator-rs.git", rev = "76836ed316d18dfef530ba908f58481c343e80d7" }
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[target.'cfg(target_os = "windows")'.dependencies]
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windows-registry = "0.6.1"
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windows-native-keyring-store = "1.1.0"
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windows = { version = "=0.61.3", features = ["ApplicationModel_DataTransfer", "Foundation", "Foundation_Collections", "Storage", "Storage_Streams", "Win32_Foundation", "Win32_System_WinRT", "Win32_UI_Shell"] }
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windows-collections = "=0.2.0"
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[target.'cfg(target_os = "macos")'.dependencies]
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apple-native-keyring-store = { version = "1.0.0", features = ["keychain"] }
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objc2 = "0.6.3"
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objc2-app-kit = { version = "0.3.2", features = ["NSResponder", "NSSharingService", "NSView", "NSWorkspace"] }
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objc2-foundation = { version = "0.3.2", features = ["NSArray", "NSBundle", "NSGeometry", "NSString", "NSURL"] }
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[target.'cfg(target_os = "linux")'.dependencies]
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ashpd = { version = "0.13.12", default-features = false, features = ["tokio", "open_uri", "global_shortcuts"] }
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dbus-secret-service-keyring-store = { version = "1.0.0", features = ["crypto-rust"] }
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dbus-secret-service = "4.1.0"
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webkit2gtk = { version = "2.0.2", features = ["v2_8"] }
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[target.'cfg(not(any(target_os = "android", target_os = "ios")))'.dependencies]
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tauri-plugin-global-shortcut = "2"
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tauri-plugin-updater = "2.10.1"
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[features]
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custom-protocol = ["tauri/custom-protocol"]
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# Media normalization is CPU-heavy even when the application itself is built for development.
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# Keep release settings untouched while optimizing the hot decoder/resampler/container crates.
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[profile.dev.package.symphonia-core]
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opt-level = 3
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[profile.dev.package.symphonia-format-mkv]
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opt-level = 3
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[profile.dev.package.ropus]
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opt-level = 3
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[profile.dev.package.rubato]
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opt-level = 3
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[profile.dev.package.webm-iterable]
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opt-level = 3
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# Scanning a document for prompt injections is just as CPU-heavy, and unoptimized matching
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# engines dominate it completely: a 1580-page PDF takes about 3 seconds to scan when these
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# crates are optimized and over a minute when they are not. Without this, every developer
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# measuring the app against a large document measures the build profile instead of the scan.
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# The engines are `regex-automata` and `aho-corasick`; `regex` is the wrapper around the
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# former, `regex-syntax` compiles the ~1600 phrases once at startup, and `memchr` provides
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# the SIMD prefilters both engines rely on.
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[profile.dev.package.regex]
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opt-level = 3
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[profile.dev.package.regex-automata]
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opt-level = 3
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[profile.dev.package.regex-syntax]
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opt-level = 3
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[profile.dev.package.aho-corasick]
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opt-level = 3
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[profile.dev.package.memchr]
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opt-level = 3
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# Preparing images is CPU-heavy as well: AI Studio decodes a picture, scales it down to 2560 pixels,
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# and encodes it again, both for the AI and for the slides of the Slide Assistant. A photo of
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# 2912 × 1632 pixels saved as PNG takes about 0.12 seconds in a release build, 6.6 seconds in a
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# development build without this, and about 0.5 seconds with it; almost all of that is scaling and
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# encoding. `image` does the scaling; `png`, `fdeflate`, `flate2`, `miniz_oxide`, `crc32fast`,
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# and `simd-adler32` read and write PNG files; `zune-jpeg` and `zune-core` read JPEG files; and
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# `image-webp` reads and writes WebP files.
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[profile.dev.package.image]
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opt-level = 3
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[profile.dev.package.png]
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opt-level = 3
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[profile.dev.package.fdeflate]
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opt-level = 3
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[profile.dev.package.flate2]
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opt-level = 3
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[profile.dev.package.miniz_oxide]
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opt-level = 3
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[profile.dev.package.crc32fast]
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opt-level = 3
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[profile.dev.package.simd-adler32]
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opt-level = 3
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[profile.dev.package.zune-jpeg]
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opt-level = 3
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[profile.dev.package.zune-core]
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opt-level = 3
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[profile.dev.package.image-webp]
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opt-level = 3
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