প্রকল্পের স্ক্রিনশট
ওভারভিউ
Infino optimizes for speed per dollar by enabling search engine performance on object-storage economics. It allows users to run multi-modal queries—keyword (BM25), vector, and SQL—over the same rows without duplicating data. Data remains in spec-compliant Parquet format on S3, Azure, GCS, or local disk, ensuring snapshot-isolated reads, atomic commits, and no vendor lock-in for read operations. The system uses a 'superfile' format, which is a valid Parquet file with embedded full-text and vector indexes spliced before the standard footer.
প্রধান বৈশিষ্ট্যসমূহ
- Multi-modal queries: BM25, vector, and SQL over a single data copy.
- Object-storage-native: Data lives on S3, Azure, GCS, or local disk.
- Open format: Data stored as spec-compliant Parquet, readable by any Parquet-compatible tool.
- Hybrid search: Native support for combining keyword and vector search with reciprocal-rank fusion.
- SQL joins across tables: Ability to join retrieval table functions with standard tables.
- Snapshot-isolated reads and atomic commits.
- Pushdown filters: Vector search restricted by keyword matches.
প্রয়োজনীয়তা, ইনস্টলেশন ও দ্রুত শুরু
Node.js: `npm install @infino-ai/infino`
Rust: `cargo add infino` (or add `infino = "0.1"` to `Cargo.toml` dependencies). Note: Infino installs the mimalloc global allocator by default; disable it with `default-features = false` if embedding in a process that already sets one.
ব্যবহার
মডেলের সামঞ্জস্যতা ও ব্যবহারের ক্ষেত্র
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লাইসেন্স ও ঝুঁকি-সংক্রান্ত নোট
Apache-2.0
Editorial verification 2026-08-02: repository URL, owner, description, license and repository statistics were reviewed. License metadata: Apache-2.0. README was fetched for the channel draft; re-check repository dependencies, releases and model terms before production use.
রিলিজ ও রক্ষণাবেক্ষণ
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