swiftide

Table of Contents

- [About The Project](#about-the-project) - [Example](#example) - [Features](#features) - [Vision](#vision) - [Getting Started](#getting-started) - [Prerequisites](#prerequisites) - [Installation](#installation) - [Usage and concepts](#usage-and-concepts) - [Roadmap](#roadmap) - [Contributing](#contributing) - [License](#license)

Swiftide

Blazing fast data pipelines for Retrieval Augmented Generation written in Rust
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About The Project

Swiftide is a straightforward, easy-to-use, easy-to-extend asynchronous data ingestion and processing library. It is designed to be used in a RAG (Retrieval Augmented Generation) system. It is built to be fast and efficient, with a focus on parallel processing and asynchronous operations.

While working with other Python-based tooling, frustrations arose around performance, stability, and ease of use. Thus, Swiftide was born. Ingestion performance went from multiple tens of minutes to a few seconds.

Part of the bosun.ai project. An upcoming platform for autonomous code improvement.

We <3 feedback: project ideas, suggestions, and complaints are very welcome. Feel free to open an issue.

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Example

IngestionPipeline::from_loader(FileLoader::new(".").with_extensions(&["rs"]))
        .filter_cached(Redis::try_from_url(
            redis_url,
            "swiftide-examples",
        )?)
        .then(MetadataQACode::new(openai_client.clone()))
        .then_chunk(ChunkCode::try_for_language_and_chunk_size(
            "rust",
            10..2048,
        )?)
        .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?;

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Features

• Extremely fast streaming ingestion pipeline with async, parallel processing
• Integrations with OpenAI, Redis, Qdrant, FastEmbed, and Treesitter
• A variety of loaders, transformers, and embedders and other common, generic tools
• Bring your own transformers by extending straightforward traits
• Store into multiple backends
• tracing supported for logging and tracing, see /examples and the tracing crate for more information.

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Vision

Our goal is to create an extremely fast, extendable platform for data ingestion and querying to further the development of automated LLM applications, with an easy-to-use and easy-to-extend api.

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Getting Started

Prerequisites

Make sure you have the rust toolchain installed. rustup Is the recommended approach.

To use OpenAI, an API key is required. Note that by default async_openai uses the OPENAI_API_KEY environment variables.

Other integrations will need to be installed accordingly.

Installation

1. Set up a new Rust project
2. Add swiftide

   cargo add swiftide
   

3. Enable the features of integrations you would like to have or use ‘all’ in your Cargo.toml
4. Write a pipeline (see our examples and documentation)

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Usage and concepts

Before building your stream, you need to enable and configure any integrations required. See /examples.

A stream starts with a Loader that emits IngestionNodes. For instance, with the Fileloader each file is a Node.

You can then slice and dice, augment, and filter nodes. Each different kind of step in the pipeline requires different traits. This enables extension.

IngestionNodes have a path, chunk and metadata. Currently metadata is copied over when chunking and always embedded when using the OpenAIEmbed transformer.

• from_loader (impl Loader) starting point of the stream, creates and emits IngestionNodes
• filter_cached (impl NodeCache) filters cached nodes
• then (impl Transformer) transforms the node and puts it on the stream
• then_in_batch (impl BatchTransformer) transforms multiple nodes and puts them on the stream
• then_chunk (impl ChunkerTransformer) transforms a single node and emits multiple nodes
• then_store_with (impl Storage) stores the nodes in a storage backend, this can be chained

Additionally, several generic transformers are implemented. They take implementers of SimplePrompt and EmbedModel to do their things.

[!NOTE] No integrations are enabled by default as some are code heavy. Either cherry-pick the integrations you need or use the “all” feature flag.

[!WARNING] Due to the performance, chunking before adding metadata gives rate limit errors on OpenAI very fast, especially with faster models like 3.5-turbo. Be aware.

For more examples, please refer to /examples and the Documentation

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Roadmap

See the open issues for a full list of proposed features (and known issues).

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Contributing

Swiftide is in a very early stage and we are aware that we do lack features for the wider community. Contributions are very welcome. :tada:

If you have a great idea, please fork the repo and create a pull request. You can also simply open an issue with the tag “enhancement”. Don’t forget to give the project a star! Thanks again!

If you just want to contribute (bless you!), see our issues.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'feat: Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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