swiftide 0.3.3 - Docs.rs

About The Project

Swiftide is a straightforward, easy-to-use, easy-to-extend asynchronous file ingestion and processing system. 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.

Example

IngestionPipeline::from_loader(FileLoader::new(".").with_extensions(&["rs"]))
        .filter_cached(RedisNodeCache::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, OpenAIEmbed::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?;

Features

Extremely fast streaming pipeline with parallel processing
Integrations with OpenAI, Redis, Qdrant and Treesitter
Bring your own transformers by extending straightforward traits.
Store into multiple backends
tracing supported

Vision

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

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

Set up a new Rust project
Add swiftide
```
cargo add swiftide
```
Write a pipeline (see our examples and documentation)

Usage and concepts

Before building your stream, you need to configure any integrations required. See /examples for a full example.

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 Embed to do their things.

All integrations are enabled by default but can be disabled with feature flags.

note: 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

Roadmap

Python / Nodejs bindings
Multiple storage and sparse vector support
Query pipeline

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

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!

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

License

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