[CHORE] integrations: core: update readme, make clippy happy

Author: Shifta-Robel

README.md

@@ -83,12 +83,13 @@ async fn main() -> Result<()> {
 ```rust
 use seedframe::prelude::*;
 use seedframe::providers::{completions::OpenAI, embeddings::OpenAIEmbedding};
+use seedframe::vector_store::InMemoryVectorStore;
 
 // Declare file loader that doesnt check for updates, loading files that match the glob pattern
 #[loader(kind = "FileOnceLoader", path = "/tmp/data/**/*.txt")]
 pub struct MyLoader;
 
-#[vector_store(kind = "InMemoryVectorStore")]
+#[vector_store(store = "InMemoryVectorStore")]
 pub struct MyVectorStore;
 
 #[embedder(provider = "OpenAIEmbedding")]
@@ -115,11 +116,47 @@ async fn main() {
     let response = client.prompt("Explain quantum computing").send().await.unwrap();
 }
 ```
+## Built-in Components
 
+**Loaders**
+- [`FileOnceLoader`](https://github.com/Shifta-Robel/SeedFrame/blob/main/core/src/loader/builtins/file_loaders/file_once_loader.rs) - Load files once using glob patterns
+- [`FileUpdatingLoader`](https://github.com/Shifta-Robel/SeedFrame/blob/main/core/src/loader/builtins/file_loaders/file_updating_loader.rs)  - Load files and watch for changes
+
+**Vector Stores**
+- [`InMemoryVectorStore`](https://github.com/Shifta-Robel/SeedFrame/blob/main/core/src/vector_store/in_memory_vec_store.rs)  - Simple in-memory vector storage implementation
+
+**Completion Providers**
+- [`OpenAI`](https://github.com/Shifta-Robel/SeedFrame/blob/main/core/src/providers/completions/openai.rs) - [OpenAI](https://openai.com)) API integration
+- [`Deepseek`](https://github.com/Shifta-Robel/SeedFrame/blob/main/core/src/providers/completions/deepseek.rs)  - (Deepseek](https://deepseek.com) API integration
+- [`Xai`](https://github.com/Shifta-Robel/SeedFrame/blob/main/core/src/providers/completions/xai.rs)  - [Xai](https://x.ai)'s API integration
+
+**Embeddings**
+- [`OpenAI`](https://github.com/Shifta-Robel/SeedFrame/blob/main/core/src/providers/embeddings/openai.rs) - [OpenAI](https://openai.com) embeddings API integration
+
+---
+
+## Integrations
+
+SeedFrame supports extending functionality through external crates. To create an integration all thats needed is to provide a type that implements the relevant trait from seedframe (`Loader`, `CompletionModel`, etc.). You can use the following crates as inspiration if you want to write an integration crate of your own.
+
+**Completion Providers**
+- [`seedframe_anthropic`](https://github.com/Shifta-Robel/SeedFrame/tree/main/integrations/completion_providers/seedframe_anthropic)  - [Anthropic](https://anthropic.com) API integration
+
+**Embedding Providers**
+- [`seedframe_voyageai`](https://github.com/Shifta-Robel/SeedFrame/tree/main/integrations/embedding_providers/seedframe_voyageai)  - [VoyageAI](https://voyageai.com) embeddings
+
+**Vector Stores**
+- [`seedframe_pinecone`](https://github.com/Shifta-Robel/SeedFrame/tree/main/integrations/vector_stores/seedframe_pinecone)  - [Pinecone](https://pinecone.io) vector database integration
+
+**Loaders**
+- [`seedframe_webscraper`](https://github.com/Shifta-Robel/SeedFrame/tree/main/integrations/seedframe_webscraper)  - Web scraping using [scraper-rs](https://docs.rs/scraper)
+
+---
+
+If you wrote an integration crate, please update this list and [submit a PR](https://github.com/Shifta-Robel/SeedFrame/compare).
 ## Contributing
 
-All contributions as welcome! Writing integrations for LLM providers and Embedders is some what trivial, use the implementations for the already supported providers as inspiration.
-This library could use support for more loaders, vector stores... so don't shy away from helping!
+All contributions as welcome! This library could use support for more loaders, vector stores, providers ... so don't shy away from helping!
 
 
 #### ⭐ Leave a Star!

core/src/lib.rs

@@ -19,15 +19,16 @@
 //!
 //! ### Building a simple RAG
 //!
-//! ```rust,no_run
+//! ```rust,ignore
 //! use seedframe::prelude::*;
 //! use seedframe::providers::{completions::OpenAI, embeddings::OpenAIEmbedding};
+//! use seedframe::vector_store::InMemoryVectorStore;
 //!
 //! // Declare file loader that doesnt check for updates, loading files that match the glob pattern
 //! #[loader(kind = "FileOnceLoader", path = "/tmp/data/**/*.txt")]
 //! pub struct MyLoader;
 //!
-//! #[vector_store(kind = "InMemoryVectorStore")]
+//! #[vector_store(store = "InMemoryVectorStore")]
 //! pub struct MyVectorStore;
 //!
 //! #[embedder(provider = "OpenAIEmbedding")]
@@ -57,7 +58,7 @@
 //!
 //! ### Tool calls and Extractors
 //!
-//! ```rust,no_run
+//! ```rust,ignore
 //! use seedframe::{providers::completions::OpenAI, prelude::*};
 //!
 //! #[client(provider = "OpenAI", tools("analyze"))]
@@ -88,6 +89,8 @@
 //!
 //!     // Tool call
 //!     client.prompt("Analyze this: 'I love Rust!' (en)")
+//!         .append_tool_response(true) // append result of tool execution to the message history
+//!         // more prompt modifiers if you want
 //!         .send()
 //!         .await?;
 //!
@@ -102,7 +105,7 @@
 //!
 //! You can pass state to tools by adding arguments of type `State<_>` to them, the only catch is that there can only be one type of State\<T\> attached to the client.
 //!
-//! ```rust,no_run
+//! ```rust,ignore
 //! use seedframe::{providers::completions::OpenAI, prelude::*};
 //!
 //! #[client(provider = "OpenAI", tools("greet"))]
@@ -135,7 +138,7 @@
 //!
 //! To share mutable state you can use types with interior mutablity, eg `Mutex`s
 //!
-//! ```rust,no_run
+//! ```rust,ignore
 //! /// Greets a user
 //! /// # Arguments
 //! /// * `name`: name of the user

integrations/seedframe_webscraper/src/lib.rs

@@ -63,6 +63,7 @@ pub struct WebScraper {
 }
 
 impl WebScraper {
+    #[allow(clippy::missing_panics_doc)]
     /// Creates a new `WebScraper` from a JSON configuration string
     ///
     /// # Errors

integrations/vector_stores/seedframe_pinecone/src/lib.rs

@@ -127,6 +127,7 @@ impl VectorStore for PineconeVectorStore {
         }
         Ok(())
     }
+    #[allow(clippy::cast_possible_truncation)]
     async fn top_n(&self, query: &[f64], n: usize) -> Result<Vec<Embedding>, VectorStoreError> {
         let mut index_guard = self.index.lock().await;
         let resp = index_guard
@@ -149,6 +150,7 @@ fn value_from_str(value: String) -> Value {
     Value { kind }
 }
 
+#[allow(clippy::cast_possible_truncation)]
 fn vector_from_embedding(embedding: Embedding) -> Vector {
     let id = embedding.id;
     let values = embedding.embedded_data.iter().map(|&v| v as f32).collect();
@@ -189,6 +191,7 @@ impl TryFrom<QueryResponse> for Embeddings {
     }
 }
 
+#[allow(clippy::needless_pass_by_value)]
 fn into_vec_store_error(e: PineconeError) -> VectorStoreError {
     VectorStoreError::Provider(e.to_string())
 }