Table of Contents
- [About The Project](#about-the-project)
- [Latest updates on our blog :fire:](#latest-updates-on-our-blog-fire)
- [Example](#example)
- [Vision](#vision)
- [Features](#features)
- [In detail](#in-detail)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage and concepts](#usage-and-concepts)
- [Roadmap](#roadmap)
- [Contributing](#contributing)
- [License](#license)
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## 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. Swiftide's goal is to offer a fully fledged retrieval augmented generation library, that is fast, easy-to-use, reliable and easy-to-extend.
Part of the [bosun.ai](https://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 or contact us on [discord](https://discord.gg/3jjXYen9UY).
**Great starting points are this readme, [swiftide.rs](https://swiftide.rs), [the examples folder](https://github.com/bosun-ai/swiftide/tree/master/examples), our blog at [bosun.ai](https://bosun.ai), and in depth tutorials at [swiftide-tutorial](https://github.com/bosun-ai/swiftide-tutorial).**
> [!CAUTION]
> Swiftide is under heavy development and can have breaking changes while we work towards 1.0. Documentation here might fall short of all features, and despite our efforts be slightly outdated. Expect bugs. We recommend to always keep an eye on our [github](https://github.com/bosun-ai/swiftide) and [api documentation](https://docs.rs/swiftide/latest/swiftide/). If you found an issue or have any kind of feedback we'd love to hear from you in an issue.
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## Latest updates on our blog :fire:
- [Evaluate Swiftide pipelines with Ragas](https://bosun.ai/posts/evaluating-swiftide-with-ragas/) (2024-09-15)
- [Release - Swiftide 0.12](https://bosun.ai/posts/swiftide-0-12/) (2024-09-13)
- [Local code intel with Ollama, FastEmbed and OpenTelemetry](https://bosun.ai/posts/ollama-and-telemetry/) (2024-09-04
- [Release - Swiftide 0.9](https://bosun.ai/posts/swiftide-0-9/) (2024-09-02)
- [Bring your own transformers](https://bosun.ai/posts/bring-your-own-transformers-in-swiftide/) (2024-08-13)
- [Release - Swiftide 0.8](https://bosun.ai/posts/swiftide-0-8/) (2024-08-12)
- [Release - Swiftide 0.7](https://bosun.ai/posts/swiftide-0-7/) (2024-07-28)
- [Building a code question answering pipeline](https://bosun.ai/posts/indexing-and-querying-code-with-swiftide/) (2024-07-13)
- [Release - Swiftide 0.6](https://bosun.ai/posts/swiftide-0-6/) (2024-07-12)
- [Release - Swiftide 0.5](https://bosun.ai/posts/swiftide-0-5/) (2024-07-1)
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## Examples
Indexing a local code project, chunking into smaller pieces, enriching the nodes with metadata, and persisting into [Qdrant](https://qdrant.tech):
```rust
indexing::Pipeline::from_loader(FileLoader::new(".").with_extensions(&["rs"]))
.with_default_llm_client(openai_client.clone())
.filter_cached(Redis::try_from_url(
redis_url,
"swiftide-examples",
)?)
.then_chunk(ChunkCode::try_for_language_and_chunk_size(
"rust",
10..2048,
)?)
.then(MetadataQACode::default())
.then(move |node| my_own_thing(node))
.then_in_batch(Embed::new(openai_client.clone()))
.then_store_with(
Qdrant::builder()
.batch_size(50)
.vector_size(1536)
.build()?,
)
.run()
.await?;
```
Querying for an example on how to use the query pipeline:
```rust
query::Pipeline::default()
.then_transform_query(GenerateSubquestions::from_client(
openai_client.clone(),
))
.then_transform_query(Embed::from_client(
openai_client.clone(),
))
.then_retrieve(qdrant.clone())
.then_answer(Simple::from_client(openai_client.clone()))
.query("How can I use the query pipeline in Swiftide?")
.await?;
```
_You can find more examples in [/examples](https://github.com/bosun-ai/swiftide/tree/master/examples)_
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## Vision
Our goal is to create a fast, extendable platform for Retrieval Augmented Generation to further the development of automated AI applications, with an easy-to-use and easy-to-extend api.
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## Features
- Fast, modular streaming indexing pipeline with async, parallel processing
- Experimental query pipeline
- A variety of loaders, transformers, semantic chunkers, embedders, and more
- Bring your own transformers by extending straightforward traits or use a closure
- Splitting and merging pipelines
- Jinja-like templating for prompts
- Store into multiple backends
- Integrations with OpenAI, Groq, Redis, Qdrant, Ollama, FastEmbed-rs, Fluvio, LanceDB, and Treesitter
- Evaluate pipelines with RAGAS
- Sparse vector support for hybrid search
- `tracing` supported for logging and tracing, see /examples and the `tracing` crate for more information.
### In detail
| **Feature** | **Details** |
| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Supported Large Language Model providers** | OpenAI (and Azure) - All models and embeddings
AWS Bedrock - Anthropic and Titan
Groq - All models
Ollama - All models |
| **Loading data** | Files
Scraping
Fluvio
Parquet
Other pipelines and streams |
| **Transformers and metadata generation** | Generate Question and answerers for both text and code (Hyde)
Summaries, titles and queries via an LLM
Extract definitions and references with tree-sitter |
| **Splitting and chunking** | Markdown
Text (text_splitter)
Code (with tree-sitter) |
| **Storage** | Qdrant
Redis
LanceDB |
| **Query pipeline** | Similarity and hybrid search, query and response transformations, and evaluation |
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## Getting Started
### Prerequisites
Make sure you have the rust toolchain installed. [rustup](https://rustup.rs) 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 might have their own requirements.
### Installation
1. Set up a new Rust project
2. Add swiftide
```sh
cargo add swiftide
```
3. Enable the features of integrations you would like to use in your `Cargo.toml`
4. Write a pipeline (see our examples and documentation)
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## Usage and concepts
Before building your streams, you need to enable and configure any integrations required. See /examples.
_We have a lot of examples, please refer to /examples and the [Documentation](https://docs.rs/swiftide/latest/swiftide/)_
> [!NOTE]
> No integrations are enabled by default as some are code heavy. We recommend you to cherry-pick the integrations you need. By convention flags have the same name as the integration they represent.
### Indexing
An indexing stream starts with a Loader that emits Nodes. 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.
Nodes 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 Nodes
- **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.
> [!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.
### Querying
A query stream starts with a search strategy. In the query pipeline a `Query` goes through several stages. Transformers and retrievers work together to get the right context into a prompt, before generating an answer. Transformers and Retrievers operate on different stages of the Query via a generic statemachine. Additionally, the search strategy is generic over the pipeline and Retrievers need to implement specifically for each strategy.
That sounds like a lot but, tl&dr; the query pipeline is _fully and strongly typed_.
- **Pending** The query has not been executed, and can be further transformed with transformers
- **Retrieved** Documents have been retrieved, and can be further transformed to provide context for an answer
- **Answered** The query is done
Additionally, query pipelines can also be evaluated. I.e. by [Ragas](https://ragas.io).
Similar to the indexing pipeline each step is governed by simple Traits and closures implement these traits as well.
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## Roadmap
See the [open issues](https://github.com/bosun-ai/swiftide/issues) for a full list of proposed features (and known issues).
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## Community
If you want to get more involved with Swiftide, have questions or want to chat, you can find us on [discord](https://discord.gg/3jjXYen9UY).
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## Contributing
Swiftide is in a very early stage and we are aware that we 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](https://github.com/bosun-ai/swiftide/issues) or join us on Discord.
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
See [CONTRIBUTING](https://github.com/bosun-ai/swiftide/blob/master/CONTRIBUTING.md) for more
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## License
Distributed under the MIT License. See `LICENSE` for more information.
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[contributors-shield]: https://img.shields.io/github/contributors/bosun-ai/swiftide.svg?style=flat-square
[contributors-url]: https://github.com/bosun-ai/swiftide/graphs/contributors
[stars-shield]: https://img.shields.io/github/stars/bosun-ai/swiftide.svg?style=flat-square
[stars-url]: https://github.com/bosun-ai/swiftide/stargazers
[license-shield]: https://img.shields.io/github/license/bosun-ai/swiftide.svg?style=flat-square
[license-url]: https://github.com/bosun-ai/swiftide/blob/master/LICENSE.txt
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/company/bosun-ai
[Crate Badge]: https://img.shields.io/crates/v/swiftide?logo=rust&style=flat-square&logoColor=E05D44&color=E05D44
[Crate]: https://crates.io/crates/swiftide
[Docs Badge]: https://img.shields.io/docsrs/swiftide?logo=rust&style=flat-square&logoColor=E05D44
[API Docs]: https://docs.rs/swiftide
