docs: Improve main lib docs

rig-core/src/lib.rs

@@ -1,7 +1,6 @@
 //! Rig is a Rust library for building LLM-powered applications that focuses on ergonomics and modularity.
 //!
 //! # Table of contents
-//!
 //! - [High-level features](#high-level-features)
 //! - [Simple Example](#simple-example)
 //! - [Core Concepts](#core-concepts)
@@ -18,7 +17,7 @@
 //!
 //! #[tokio::main]
 //! async fn main() {
-//!     // Create OpenAI client and model.
+//!     // Create OpenAI client and agent.
 //!     // This requires the `OPENAI_API_KEY` environment variable to be set.
 //!     let openai_client = openai::Client::from_env();
 //!
@@ -39,35 +38,45 @@
 //! # Core concepts
 //! ## Completion and embedding models
 //! Rig provides a consistent API for working with LLMs and embeddings. Specifically,
-//! each provider (e.g. OpenAI, Cohere) has a `Client` struct that can be used to create completion
+//! each provider (e.g. OpenAI, Cohere) has a `Client` struct that can be used to initialize completion
 //! and embedding models. These models implement the [CompletionModel](crate::completion::CompletionModel)
 //! and [EmbeddingModel](crate::embeddings::EmbeddingModel) traits respectively, which provide a common,
 //! low-level interface for creating completion and embedding requests and executing them.
 //!
 //! ## Agents
-//! Rig provides high-level abstractions over LLMs in the form of the [Agent](crate::agent::Agent) type.
+//! Rig also provides high-level abstractions over LLMs in the form of the [Agent](crate::agent::Agent) type.
 //!
 //! The [Agent](crate::agent::Agent) type can be used to create anything from simple agents that use vanilla models to full blown
 //! RAG systems that can be used to answer questions using a knowledge base.
 //!
 //! ## Vector stores and indexes
-//! Rig provides a common interface for working with vector stores and indexes. Specifically, the library
+//! Rig defines a common interface for working with vector stores and indexes. Specifically, the library
 //! provides the [VectorStore](crate::vector_store::VectorStore) and [VectorStoreIndex](crate::vector_store::VectorStoreIndex)
-//! traits, which can be implemented to define vector stores and indices respectively.
-//! Those can then be used as the knowledgebase for a RAG enabled [Agent](crate::agent::Agent), or
+//! traits, which can be implemented on a given type to define vector stores and indices respectively.
+//! Those can then be used as the knowledge base for a RAG enabled [Agent](crate::agent::Agent), or
 //! as a source of context documents in a custom architecture that use multiple LLMs or agents.
 //!
 //! # Integrations
-//! Rig natively supports the following completion and embedding model providers:
+//! ## Model Providers
+//! Rig natively supports the following completion and embedding model provider integrations:
 //! - OpenAI
 //! - Cohere
 //! - Anthropic
 //! - Perplexity
+//! - Gemini
+//! 
+//! You can also implement your own model provider integration by defining types that 
+//! implement the [CompletionModel](crate::completion::CompletionModel) and [EmbeddingModel](crate::embeddings::EmbeddingModel) traits.
 //!
-//! Rig currently has the following integration companion crates:
+//! ## Vector Stores
+//! Rig currently supports the following vector store integrations via companion crates:
 //! - `rig-mongodb`: Vector store implementation for MongoDB
 //! - `rig-lancedb`: Vector store implementation for LanceDB
 //! - `rig-neo4j`: Vector store implementation for Neo4j
+//! - `rig-qdrant`: Vector store implementation for Qdrant
+//! 
+//! You can also implement your own vector store integration by defining types that 
+//! implement the [VectorStoreIndex](crate::vector_store::VectorStoreIndex) trait.
 
 pub mod agent;
 pub mod cli_chatbot;