While working with other Python-based tooling, frustrations arose around performance, stability, and ease of use.
Excited to announce Swiftide, blazing fast data pipelines for Retrieval Augmented Generation written in Rust. Python bindings soon!
Check it out at https://swiftide.rs
IngestionPipeline::from_loader(FileLoader::new(".").with_extensions(&["md"]))
.then_chunk(ChunkMarkdown::with_chunk_range(10..512))
.then(MetadataQACode::new(openai_client.clone()))
.then_in_batch(10, Embed::new(openai_client.clone()))
.then_store_with(
Qdrant::try_from_url(qdrant_url)?
.batch_size(50)
.vector_size(1536)
.collection_name("swiftide-examples".to_string())
.build()?,
)
.run()
.await?;
Questions, feedback, complaints and great ideas are more than welcome in the comments <3
bosun-ai
/
swiftide
Fast, streaming indexing and query library for AI (RAG) applications, written in Rust
Table of Contents
Swiftide
Fast, streaming indexing and query library for AI applications, written in Rust
Read more on swiftide.rs »
<a href="https://docs.rs/swiftide/latest/swiftide/" rel="nofollow">API Docs</a>
·
<a href="https://github.com/bosun-ai/swiftide/issues/new?labels=bug&template=bug_report.md">Report Bug</a>
·
<a href="https://github.com/bosun-ai/swiftide/issues/new?labels=enhancement&template=feature_request.md">Request Feature</a>
·
<a href="https://discord.gg/3jjXYen9UY" rel="nofollow">Discord</a>
About The Project
Swiftide is a Rust native library for building LLM applications. Large language models are amazing, but need context to solve real problems. Swiftide allows you to ingest, transform and index large amounts of data fast, and then query that data so it it can be injected into prompts. This process is called Retrieval Augmented Generation.
With Swiftide, you can build your AI application from idea to production in a few lines of code.
While working with other Python-based tooling, frustrations arose around performance, stability, and ease of use. Thus, Swiftide was born…
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