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feat: Add swiftide to qdrant docs (#1911)
* feat: Add swiftide to qdrant docs * docs: Add swiftide to frameworks index
This commit is contained in:
Timon Vonk
@@ -36,6 +36,7 @@ aliases: ["/documentation/frameworks/memgpt/"]
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| [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search. |
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| [SmolAgents](/documentation/frameworks/smolagents/) | Barebones library for agents. Agents write python code to call tools and orchestrate other agent. |
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| [Spring AI](/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. |
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| [Swiftide](/documentation/frameworks/swiftide/) | Rust library for building LLM applications. Build fast, streaming indexing and querying pipelines, and composable agents. |
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| [Sycamore](/documentation/frameworks/sycamore/) | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
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| [Testcontainers](/documentation/frameworks/testcontainers/) | Framework for providing throwaway, lightweight instances of systems for testing |
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| [txtai](/documentation/frameworks/txtai/) | Python library for semantic search, LLM orchestration and language model workflows. |
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@@ -0,0 +1,168 @@
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---
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title: Swiftide
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---
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# Swiftide
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Swiftide is a Rust library for building LLM applications. It supports everything from simple prompt completions to fast, streaming indexing and querying pipelines, and building composable agents that use tools or call other agents.
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## High level features
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- Simple primitives for common LLM tasks
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- Streaming indexing and querying pipelines
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- Composable agents and pipelines
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- Modular, extendable API with minimal abstractions
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- Integrations with popular LLMs and storage providers
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- Built-in pipeline transformations (or bring your own)
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- Graph-like workflows with Tasks
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- [Langfuse](https://langfuse.com) support
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---
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## Installation
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Install Swiftide with Qdrant, OpenAI, and Redis support:
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```console
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cargo add swiftide --features=qdrant,openai,redis
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```
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_Note that Swiftide comes barebones by default, so you need to enable features for the integrations you want to use._
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---
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## Indexing Example (Step by Step)
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This example indexes `.rs` files using Swiftide with Qdrant as vector storage.
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```rust
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use swiftide::{
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indexing,
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indexing::LanguageModelWithBackOff,
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indexing::loaders::FileLoader,
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indexing::transformers::{ChunkCode, Embed, MetadataQACode},
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integrations::{self, qdrant::Qdrant, redis::Redis},
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};
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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tracing_subscriber::fmt::init();
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// 1. Set up OpenAI client for embedding and prompt models
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let openai_client = integrations::openai::OpenAI::builder()
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.default_embed_model("text-embedding-3-small")
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.default_prompt_model("gpt-3.5-turbo")
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.build()?;
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// 3. Set up Redis for caching which files/chunks are already processed
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let redis_url = std::env::var("REDIS_URL")
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.as_deref()
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.unwrap_or("redis://localhost:6379")
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.to_owned();
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indexing::Pipeline::from_loader(FileLoader::new(".").with_extensions(&["rs"]))
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// 4. Skip files/chunks already indexed (cached in Redis)
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.filter_cached(Redis::try_from_url(redis_url, "swiftide-examples")?)
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// 5. Generate metadata Q&A for code chunks, using LLM
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.then(MetadataQACode::new(openai_client.clone()))
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// 6. Split code into chunks suitable for embedding
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.then_chunk(ChunkCode::try_for_language_and_chunk_size("rust", 10..2048)?)
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// 7. Embed code+metadata in batches
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.then_in_batch(Embed::new(openai_client.clone()).with_batch_size(10))
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// 8. Store results in a Qdrant collection
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.then_store_with(
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Qdrant::builder()
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.batch_size(50)
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.vector_size(1536)
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.collection_name("swiftide-examples")
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.build()?,
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)
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// 9. Run the pipeline asynchronously
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.run()
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.await?;
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Ok(())
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}
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```
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## Hybrid Search Example
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Below is a streamlined workflow for hybrid dense/sparse search using Qdrant.
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```rust
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use swiftide::{
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indexing::{
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self, EmbeddedField,
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loaders::FileLoader,
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transformers::{self, ChunkCode, MetadataQACode},
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},
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integrations::{fastembed::FastEmbed, openai, qdrant::Qdrant},
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query::{self, answers, query_transformers, search_strategies::HybridSearch},
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};
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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tracing_subscriber::fmt::init();
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// 1. Create fastembed (dense/sparse) clients
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let batch_size = 64;
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let fastembed_sparse = FastEmbed::try_default_sparse().unwrap().to_owned();
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let fastembed = FastEmbed::try_default().unwrap().to_owned();
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// 2. Use a compact OpenAI prompt model for metadata Q&A generation
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let openai = openai::OpenAI::builder()
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.default_prompt_model("gpt-4o-mini")
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.build()
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.unwrap();
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// 3. Set up Qdrant for both dense and sparse vectors
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let qdrant = Qdrant::builder()
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.batch_size(batch_size)
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.vector_size(384)
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.with_vector(EmbeddedField::Combined)
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.with_sparse_vector(EmbeddedField::Combined)
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.collection_name("swiftide-hybrid-example")
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.build()?;
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indexing::Pipeline::from_loader(FileLoader::new("swiftide-core/").with_extensions(&["rs"]))
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.then_chunk(ChunkCode::try_for_language_and_chunk_size("rust", 10..2048)?)
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.then(MetadataQACode::from_client(openai.clone()).build().unwrap())
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.then_in_batch(transformers::SparseEmbed::new(fastembed_sparse.clone()).with_batch_size(batch_size))
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.then_in_batch(transformers::Embed::new(fastembed.clone()).with_batch_size(batch_size))
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.then_store_with(qdrant.clone())
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.run()
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.await?;
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// 4. Run a hybrid search pipeline
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let openai = openai::OpenAI::builder()
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.default_prompt_model("gpt-4o")
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.build()
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.unwrap();
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let query_pipeline = query::Pipeline::from_search_strategy(
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HybridSearch::default()
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.with_top_n(20)
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.with_top_k(20)
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.to_owned(),
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)
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.then_transform_query(query_transformers::GenerateSubquestions::from_client(openai.clone()))
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.then_transform_query(query_transformers::Embed::from_client(fastembed.clone()))
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.then_transform_query(query_transformers::SparseEmbed::from_client(fastembed_sparse.clone()))
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.then_retrieve(qdrant.clone())
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.then_answer(answers::Simple::from_client(openai.clone()));
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let answer = query_pipeline
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.query("What are the different pipelines in Swiftide and how do they work?")
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.await
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.unwrap();
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println!("{}", answer.answer());
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}
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```
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---
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## Further reading
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- [Swiftide Documentation](https://swiftide.rs)
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- [Source Code](https://github.com/bosun-ai/swiftide)
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