@@ -2,6 +2,10 @@
 
 Thank you for your interest in contributing to Selectools! We welcome contributions from the community.
 
+**Current Version:** v0.8.0 (Embeddings & RAG Support)
+**Test Status:** ✅ 463 tests passing (100%)
+**Next Release:** v0.9.0 (Hybrid Search, Reranking)
+
 ## Getting Started
 
 ### Development Setup
@@ -49,9 +53,18 @@ The hooks will automatically run on staged files when you commit. They include:
 5. **Set up API keys for testing**
 
 ```bash
+# LLM Providers
 export OPENAI_API_KEY="your-key-here"
 export ANTHROPIC_API_KEY="your-key-here"  # Optional
 export GEMINI_API_KEY="your-key-here"     # Optional
+
+# Embedding Providers (for RAG features)
+export VOYAGE_API_KEY="your-key-here"     # Optional (Anthropic embeddings)
+export COHERE_API_KEY="your-key-here"     # Optional
+
+# Vector Stores (for RAG features)
+export PINECONE_API_KEY="your-key-here"   # Optional
+export PINECONE_ENV="your-env-here"       # Optional
 ```
 
 ## Available Scripts
@@ -61,7 +74,7 @@ Similar to `npm run` scripts, here are the common commands for this project:
 ### Testing
 
 ```bash
-# Run all tests
+# Run all tests (463 tests)
 pytest tests/ -v
 
 # Run tests quietly (summary only)
@@ -70,11 +83,20 @@ pytest tests/ -q
 # Run specific test file
 pytest tests/test_framework.py -v
 
+# Run RAG-specific tests
+pytest tests/test_embedding_providers.py -v
+pytest tests/test_vector_stores_crud.py -v
+pytest tests/test_rag_workflow.py -v
+
 # Run tests matching a pattern
 pytest tests/ -k "test_tool" -v
+pytest tests/ -k "rag" -v
 
 # Run with coverage (if installed)
 pytest tests/ --cov=src/selectools --cov-report=html
+
+# Skip end-to-end tests (require real API keys)
+pytest tests/ -m "not e2e" -v
 ```
 
 ### Code Quality
@@ -111,6 +133,11 @@ python examples/customer_support_bot.py
 
 # Data analysis agent example
 python examples/data_analysis_agent.py
+
+# RAG (Retrieval-Augmented Generation) examples
+python examples/rag_basic_demo.py           # Basic RAG with in-memory store
+python examples/rag_advanced_demo.py        # Advanced RAG with PDF + SQLite
+python examples/semantic_search_demo.py     # Multi-provider semantic search
 ```
 
 ### Development Scripts
@@ -220,31 +247,66 @@ selectools/
 │   ├── cli.py                  # CLI interface
 │   ├── env.py                  # Environment variable loading
 │   ├── exceptions.py           # Custom exception classes
+│   ├── models.py               # Model registry (120+ models)
 │   ├── parser.py               # TOOL_CALL parser
 │   ├── pricing.py              # LLM pricing data and cost calculation
 │   ├── prompt.py               # Prompt builder
 │   ├── tools.py                # Tool definitions and registry
 │   ├── types.py                # Core types (Message, Role, etc.)
-│   ├── usage.py                # Usage tracking (tokens, costs)
-│   └── providers/              # LLM provider adapters
-│       ├── base.py             # Provider interface
-│       ├── openai_provider.py  # OpenAI implementation
-│       ├── anthropic_provider.py # Anthropic implementation
-│       ├── gemini_provider.py  # Google Gemini implementation
-│       └── stubs.py            # Local/test providers
-├── tests/                      # Test suite
+│   ├── usage.py                # Usage tracking (tokens, costs, analytics)
+│   ├── providers/              # LLM provider adapters
+│   │   ├── base.py             # Provider interface
+│   │   ├── openai_provider.py  # OpenAI implementation
+│   │   ├── anthropic_provider.py # Anthropic implementation
+│   │   ├── gemini_provider.py  # Google Gemini implementation
+│   │   ├── ollama_provider.py  # Ollama local models
+│   │   └── stubs.py            # Local/test providers
+│   ├── embeddings/             # Embedding providers (v0.8.0)
+│   │   ├── provider.py         # EmbeddingProvider interface
+│   │   ├── openai.py           # OpenAI embeddings
+│   │   ├── anthropic.py        # Anthropic/Voyage embeddings
+│   │   ├── gemini.py           # Google Gemini embeddings
+│   │   └── cohere.py           # Cohere embeddings
+│   ├── rag/                    # RAG components (v0.8.0)
+│   │   ├── __init__.py         # RAGAgent high-level API
+│   │   ├── vector_store.py     # VectorStore interface
+│   │   ├── chunking.py         # Text splitting strategies
+│   │   ├── loaders.py          # Document loaders
+│   │   ├── tools.py            # RAGTool & SemanticSearchTool
+│   │   └── stores/             # Vector store implementations
+│   │       ├── memory.py       # In-memory (NumPy)
+│   │       ├── sqlite.py       # SQLite persistent storage
+│   │       ├── chroma.py       # ChromaDB integration
+│   │       └── pinecone.py     # Pinecone cloud storage
+│   └── toolbox/                # Pre-built tools (v0.5.1)
+│       ├── file.py             # File operations
+│       ├── web.py              # Web scraping & HTTP
+│       ├── data.py             # Data processing
+│       ├── datetime_tools.py   # Date/time utilities
+│       └── text.py             # Text manipulation
+├── tests/                      # Test suite (463 tests)
 │   ├── conftest.py             # Pytest configuration
 │   ├── test_framework.py       # Core framework tests
 │   ├── test_better_errors.py   # Error handling tests
 │   ├── test_cost_tracking.py   # Cost tracking tests
-│   └── test_e2e_providers.py   # End-to-end provider tests
+│   ├── test_e2e_providers.py   # End-to-end provider tests
+│   ├── test_models.py          # Model registry tests
+│   ├── test_embedding_providers.py  # Embedding provider tests
+│   ├── test_vector_stores_crud.py   # Vector store CRUD tests
+│   ├── test_document_loaders.py     # Document loader tests
+│   ├── test_text_chunking.py       # Chunking strategy tests
+│   ├── test_rag_workflow.py        # RAG integration tests
+│   └── test_sqlite_integration.py  # SQLite persistence tests
 ├── examples/                   # Usage examples
 │   ├── search_weather.py       # Basic tool usage
 │   ├── async_agent_demo.py     # Async operations
 │   ├── conversation_memory_demo.py
 │   ├── cost_tracking_demo.py
 │   ├── customer_support_bot.py
-│   └── data_analysis_agent.py
+│   ├── data_analysis_agent.py
+│   ├── rag_basic_demo.py       # Basic RAG with in-memory store
+│   ├── rag_advanced_demo.py    # Advanced RAG with PDF + SQLite
+│   └── semantic_search_demo.py # Multi-provider semantic search
 └── scripts/                    # Development scripts
     ├── README.md               # Release documentation
     ├── release.py              # Python release script
@@ -325,21 +387,31 @@ We especially welcome contributions in these areas:
 - Add support for new LLM providers (Cohere, AI21, etc.)
 - Improve existing provider implementations
 - Add vision support to more providers
+- Add new embedding providers
 
 ### 🛠️ **New Tools**
 - Pre-built tools for common use cases
 - Integration with popular APIs and services
 - Example tools demonstrating best practices
 
+### 🗄️ **RAG & Vector Stores (New in v0.8.0!)**
+- Add new vector store integrations (Weaviate, Qdrant, Milvus)
+- Implement reranking support (Cohere, Jina)
+- Add hybrid search (vector + BM25 keyword)
+- Improve chunking strategies (contextual, agentic)
+- Create advanced RAG examples and tutorials
+
 ### 📚 **Documentation**
 - Improve README examples
-- Add tutorials and guides
+- Add tutorials and guides (especially for RAG features!)
 - Fix typos and clarify confusing sections
+- Add comparison guides (vs LangChain, LlamaIndex)
 
 ### 🧪 **Testing**
-- Increase test coverage
+- Increase test coverage (currently 463 tests passing!)
 - Add integration tests
 - Add performance benchmarks
+- Test RAG workflows with different vector stores
 
 ### 🐛 **Bug Fixes**
 - Fix reported issues
@@ -448,6 +520,122 @@ def create_your_tool() -> Tool:
 
 3. **Update documentation**
 
+## Adding RAG Features (New in v0.8.0!)
+
+### Adding a New Vector Store
+
+To add support for a new vector database:
+
+1. **Create a new vector store implementation**
+
+```python
+# src/selectools/rag/stores/your_store.py
+
+from typing import List, Optional
+from ..vector_store import VectorStore, Document, SearchResult
+
+class YourVectorStore(VectorStore):
+    """Your vector database implementation."""
+
+    def __init__(self, embedder, **config):
+        self.embedder = embedder
+        # Initialize your vector DB client
+        # self.client = your_db.Client(**config)
+
+    def add_documents(
+        self,
+        documents: List[Document],
+        embeddings: Optional[List[List[float]]] = None
+    ) -> List[str]:
+        """Add documents to the store."""
+        # Generate embeddings if not provided
+        if embeddings is None:
+            texts = [doc.text for doc in documents]
+            embeddings = self.embedder.embed_texts(texts)
+
+        # Insert into your vector DB
+        # ids = self.client.insert(...)
+        return ids
+
+    def search(
+        self,
+        query_embedding: List[float],
+        top_k: int = 5,
+        filter: Optional[dict] = None
+    ) -> List[SearchResult]:
+        """Search for similar documents."""
+        # results = self.client.search(query_embedding, top_k, filter)
+        # return [SearchResult(document=..., score=...) for r in results]
+        pass
+
+    def delete(self, ids: List[str]) -> None:
+        """Delete documents by ID."""
+        # self.client.delete(ids)
+        pass
+```
+
+2. **Register in the factory**
+
+```python
+# src/selectools/rag/vector_store.py
+
+@staticmethod
+def create(store_type: str, embedder, **kwargs) -> "VectorStore":
+    if store_type == "your_store":
+        from .stores.your_store import YourVectorStore
+        return YourVectorStore(embedder, **kwargs)
+    # ... existing stores
+```
+
+3. **Add tests**
+
+```python
+# tests/test_vector_stores_crud.py
+
+class TestYourVectorStore:
+    def test_add_and_search(self, mock_embedder):
+        store = VectorStore.create("your_store", embedder=mock_embedder)
+        docs = [Document(text="test", metadata={})]
+        ids = store.add_documents(docs)
+
+        query_emb = mock_embedder.embed_query("test")
+        results = store.search(query_emb, top_k=1)
+
+        assert len(results) == 1
+        assert results[0].document.text == "test"
+```
+
+### Adding a New Embedding Provider
+
+1. **Create the provider**
+
+```python
+# src/selectools/embeddings/your_provider.py
+
+from typing import List
+from .provider import EmbeddingProvider
+
+class YourEmbeddingProvider(EmbeddingProvider):
+    def __init__(self, model: str = "default-model", api_key: str = None):
+        self.model = model
+        self.api_key = api_key or os.getenv("YOUR_API_KEY")
+        # self.client = your_sdk.Client(api_key=self.api_key)
+
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
+        """Embed multiple texts."""
+        # response = self.client.embeddings.create(input=texts, model=self.model)
+        # return [emb.embedding for emb in response.data]
+        pass
+
+    def embed_query(self, query: str) -> List[float]:
+        """Embed a single query."""
+        return self.embed_texts([query])[0]
+```
+
+2. **Add model definitions to `models.py`**
+
+3. **Add tests and update documentation**
+
 ## Code Review Process
 
 1. A maintainer will review your PR