Merge pull request #18 from Shifta-Robel/adaptor_crate_support

support components from external crates with the macros, a few more adaptor crates in the workspace, more docs ...

Author: Shifta-Robel

Cargo.lock

@@ -4,4 +4,8 @@ resolver="2"
 members = [
   "core/",
   "proc_macros/",
+  "integrations/completion_providers/seedframe_anthropic/",
+  "integrations/embedding_providers/seedframe_voyageai/",
+  "integrations/seedframe_webscraper/",
+  "integrations/vector_stores/seedframe_pinecone/"
 ]

Cargo.toml

@@ -38,7 +38,10 @@ You could also extract structured output from the llms, the target types need to
 Like the tools the description for the type and for it's fields will get extracted from the docs and get passed to the llm, but its not an error to leave them undocumented.
 
 ```rust
-#[client(provider = "openai", model = "gpt-4o-mini", tools("analyze"))]
+use seedframe::prelude::*;
+use seedframe::providers::completions::OpenAI;
+
+#[client(provider = "OpenAI", tools("analyze"))]
 struct ToolClient;
 
 /// Perform sentiment analysis on text
@@ -79,6 +82,7 @@ async fn main() -> Result<()> {
 ### Building a simple RAG
 ```rust
 use seedframe::prelude::*;
+use seedframe::providers::{completions::OpenAI, embeddings::OpenAIEmbedding};
 
 // Declare file loader that doesnt check for updates, loading files that match the glob pattern
 #[loader(kind = "FileOnceLoader", path = "/tmp/data/**/*.txt")]
@@ -87,15 +91,15 @@ pub struct MyLoader;
 #[vector_store(kind = "InMemoryVectorStore")]
 pub struct MyVectorStore;
 
-#[embedder(provider = "openai", model = "text-embedding-3-small")]
+#[embedder(provider = "OpenAIEmbedding")]
 struct MyEmbedder {
     #[vector_store]
     my_vector_store: MyVectorStore,
     #[loader]
     my_loader: MyLoader,
 }
 
-#[client(provider = "openai", model = "gpt-4o-mini")]
+#[client(provider = "OpenAI", config = r#"{"model": "gpt-4o-mini"}"#)]
 struct MyClient {
     #[embedder]
     my_embedder: MyEmbedder,

README.md

@@ -16,7 +16,6 @@ walkdir = "2.5.0"
 seedframe_macros =  { path = "../proc_macros"}
 notify = "8.0.0"
 tracing = "0.1.41"
-pinecone-sdk = {version ="0.1.2", optional = true}
 schemars = "0.8.22"
 thiserror = "2.0.12"
 dashmap = "6.1.0"
@@ -26,10 +25,4 @@ tempfile = "3.16.0"
 tracing-subscriber = "0.3.19"
 
 [features]
-pinecone = ["pinecone-sdk"]
 pdf = ["pdf-extract"]
-
-[[example]]
-name = "agent_with_pinecone"
-path = "examples/agent_with_pinecone.rs"
-required-features = ["pinecone"]

core/Cargo.toml

@@ -1,11 +1,13 @@
-use schemars::JsonSchema;
 use seedframe::prelude::*;
+use seedframe::providers::completions::OpenAI;
+
+use schemars::JsonSchema;
 use serde::{Deserialize, Serialize};
 use std::fmt::Display;
 
 #[client(
-    provider = "openai",
-    model = "gpt-4o-mini",
+    provider = "OpenAI",
+    config = "{\"model\": \"gpt-4o-mini\"}",
     tools("schedule_meeting", "convert_temperature")
 )]
 struct AdvancedClient;

core/examples/advanced_tool_calls.rs

@@ -1,8 +1,9 @@
+use schemars::JsonSchema;
 use seedframe::prelude::*;
+use seedframe::providers::completions::OpenAI;
 use serde::Deserialize;
-use schemars::JsonSchema;
 
-#[client(provider = "openai",model = "gpt-4o-mini")]
+#[client(provider = "OpenAI")]
 struct ExtractorClient;
 
 #[allow(unused)]
@@ -33,20 +34,19 @@ struct MeetingDetails {
 
 #[tokio::main]
 async fn main() -> Result<(), seedframe::error::Error> {
-    let mut client = ExtractorClient::build("You are a helpful assistant".to_string()).await;
+    let mut client = ExtractorClient::build("You are a helpful assistant").await;
 
     let person_text = "My colleague John Doe is 28 years old. He works as a software engineer and enjoys hiking, reading, and playing chess, his email is [email protected]";
-    let person = client.prompt(person_text)
-        .extract::<Person>()
-        .await?;
+    let person = client.prompt(person_text).extract::<Person>().await?;
     println!("Extracted Person:\n{person:#?}\n");
 
     let meeting_text = "We have a team meeting scheduled for 2026-2-15 at 11:30. \
                        Purpose is quarterly planning. Attendees include: \
                        John Doe (35, Product Manager, hobbies: golf), \
                        Alice Smith (29, hobbies: painting, yoga), \
                        and Bob Johnson (42, CTO).";
-    let meeting = client.prompt(meeting_text)
+    let meeting = client
+        .prompt(meeting_text)
         .extract::<MeetingDetails>()
         .await?;
     println!("Extracted Meeting:\n{meeting:#?}\n");

core/examples/agent_with_pinecone.rs

@@ -1,20 +1,22 @@
 use seedframe::prelude::*;
+use seedframe::providers::{completions::OpenAI, embeddings::OpenAIEmbedding};
+use seedframe::vector_store::InMemoryVectorStore;
 
 #[loader(kind = "FileOnceLoader", path = "/tmp/data/**/*.txt")]
 pub struct MyLoader;
 
-#[vector_store(kind = "InMemoryVectorStore")]
+#[vector_store(store = "InMemoryVectorStore")]
 pub struct MyVectorStore;
 
-#[embedder(provider = "openai", model = "text-embedding-3-small")]
+#[embedder(provider = "OpenAIEmbedding")]
 struct MyEmbedder {
     #[vector_store]
     my_vector_store: MyVectorStore,
     #[loader]
     my_loader: MyLoader,
 }
 
-#[client(provider = "openai", model = "gpt-4o-mini")]
+#[client(provider = "OpenAI")]
 struct MyClient {
     #[embedder]
     my_embedder: MyEmbedder,
@@ -23,7 +25,7 @@ struct MyClient {
 #[tokio::main]
 async fn main() {
     let mut c = MyClient::build(
-        "Respond with the definition and language of origin for the word the user prompts you with, you'll be given a context to use for the words, if you cant get the meaning for the word from the context reply with a \"I dont know\"".to_string(),
+        "Respond with the definition and language of origin for the word the user prompts you with, you'll be given a context to use for the words, if you cant get the meaning for the word from the context reply with a \"I dont know\"",
     )
     .await;
     // delay for the vector store to finish upserting the loaded resource before the first prompt

core/examples/extractors.rs

@@ -1,8 +1,9 @@
 use seedframe::prelude::*;
+use seedframe::providers::completions::OpenAI;
 
 #[client(
-    provider = "openai",
-    model = "gpt-4o-mini",
+    provider = "OpenAI",
+    config = "{\"model\": \"gpt-4o-mini\"}",
     tools("capitalize", "greet")
 )]
 struct SimpleClient;
@@ -35,7 +36,7 @@ struct AppState {
 
 #[tokio::main]
 async fn main() -> Result<(), seedframe::error::Error> {
-    let mut client = SimpleClient::build("You are a helpful assistant".to_string())
+    let mut client = SimpleClient::build("You are a helpful assistant")
         .await
         .with_state(AppState { some_number: 3 })?;