diff --git a/README.md b/README.md
index 866fdbca..7d1d6126 100644
--- a/README.md
+++ b/README.md
@@ -1,194 +1,65 @@
 # Haiku SQLite RAG
 
-A Retrieval-Augmented Generation (RAG) library on SQLite.
+Retrieval-Augmented Generation (RAG) library on SQLite.
 
 ## Features
-- **Local SQLite**: No need to run additional servers
-- **Support for various embedding providers**: You can use Ollama, VoyageAI, OpenAI or add your own
-- **Hybrid Search**: Vector search using `sqlite-vec` combined with full-text search `FTS5`, using Reciprocal Rank Fusion
-- **File monitoring** when run as a server automatically indexing your files
-- **Extended file format Support**: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, audio and more. Or add a url!
-- **MCP server** Exposes functionality as MCP tools.
-- **CLI commands** Access all functionality from your terminal
-- **Python client** Call `haiku.rag` from your own python applications.
 
-## Installation
+- **Local SQLite**: No external servers required
+- **Multiple embedding providers**: Ollama, VoyageAI, OpenAI
+- **Hybrid search**: Vector + full-text search with Reciprocal Rank Fusion
+- **File monitoring**: Auto-index files when run as server
+- **40+ file formats**: PDF, DOCX, HTML, Markdown, audio, URLs
+- **MCP server**: Expose as tools for AI assistants
+- **CLI & Python API**: Use from command line or Python
+
+## Quick Start
 
 ```bash
+# Install
 uv pip install haiku.rag
+
+# Add documents
+haiku-rag add "Your content here"
+haiku-rag add-src document.pdf
+
+# Search
+haiku-rag search "query"
+
+# Start server with file monitoring
+export MONITOR_DIRECTORIES="/path/to/docs"
+haiku-rag serve
 ```
 
-By default Ollama (with the `mxbai-embed-large` model) is used for the embeddings.
-For other providers use:
-
-- **VoyageAI**: `uv pip install haiku.rag --extra voyageai`
-- **OpenAI**: `uv pip install haiku.rag --extra openai`
-
-## Configuration
-
-You can set the directories to monitor using the `MONITOR_DIRECTORIES` environment variable (as comma separated values) :
-
-```bash
-# Monitor single directory
-export MONITOR_DIRECTORIES="/path/to/documents,/another_path/to/documents"
-```
-
-If you want to use an alternative embeddings provider (Ollama being the default) you will need to set the provider details through environment variables:
-
-By default:
-
-```bash
-EMBEDDINGS_PROVIDER="ollama"
-EMBEDDINGS_MODEL="mxbai-embed-large" # or any other model
-EMBEDDINGS_VECTOR_DIM=1024
-```
-
-For VoyageAI:
-```bash
-EMBEDDINGS_PROVIDER="voyageai"
-EMBEDDINGS_MODEL="voyage-3.5" # or any other model
-EMBEDDINGS_VECTOR_DIM=1024
-VOYAGE_API_KEY="your-api-key"
-```
-
-For OpenAI:
-```bash
-EMBEDDINGS_PROVIDER="openai"
-EMBEDDINGS_MODEL="text-embedding-3-small" # or text-embedding-3-large
-EMBEDDINGS_VECTOR_DIM=1536
-OPENAI_API_KEY="your-api-key"
-```
-
-## Command Line Interface
-
-`haiku.rag` includes a CLI application for managing documents and performing searches from the command line:
-
-### Available Commands
-
-```bash
-# List all documents
-haiku-rag list
-
-# Add document from text
-haiku-rag add "Your document content here"
-
-# Add document from file or URL
-haiku-rag add-src /path/to/document.pdf
-haiku-rag add-src https://example.com/article.html
-
-# Get and display a specific document
-haiku-rag get 1
-
-# Delete a document by ID
-haiku-rag delete 1
-
-# Search documents
-haiku-rag search "machine learning"
-
-# Search with custom options
-haiku-rag search "python programming" --limit 10 --k 100
-
-# Start file monitoring & MCP server (default HTTP transport)
-haiku-rag serve # --stdio for stdio transport or --sse for SSE transport
-```
-
-All commands support the `--db` option to specify a custom database path. Run
-```bash
-haiku-rag command -h
-```
-to see additional parameters for a command.
-
-## File Monitoring & MCP server
-
-You can start the server (using Streamble HTTP, stdio or SSE transports) with:
-
-```bash
-# Start with default HTTP transport
-haiku-rag serve # --stdio for stdio transport or --sse for SSE transport
-```
-
-You need to have set the `MONITOR_DIRECTORIES` environment variable for monitoring to take place.
-
-### File monitoring
-
-`haiku.rag` can watch directories for changes and automatically update the document store:
-
-- **Startup**: Scan all monitored directories and add any new files
-- **File Added/Modified**: Automatically parse and add/update the document in the database
-- **File Deleted**: Remove the corresponding document from the database
-
-### MCP Server
-
-`haiku.rag` includes a Model Context Protocol (MCP) server that exposes RAG functionality as tools for AI assistants like Claude Desktop. The MCP server provides the following tools:
-
-- `add_document_from_file` - Add documents from local file paths
-- `add_document_from_url` - Add documents from URLs
-- `add_document_from_text` - Add documents from raw text content
-- `search_documents` - Search documents using hybrid search
-- `get_document` - Retrieve specific documents by ID
-- `list_documents` - List all documents with pagination
-- `delete_document` - Delete documents by ID
-
-## Using `haiku.rag` from python
-
-### Managing documents
+## Python Usage
 
 ```python
-from pathlib import Path
 from haiku.rag.client import HaikuRAG
 
-# Use as async context manager (recommended)
-async with HaikuRAG("path/to/database.db") as client:
-    # Create document from text
-    doc = await client.create_document(
-        content="Your document content here",
-        uri="doc://example",
-        metadata={"source": "manual", "topic": "example"}
-    )
-
-    # Create document from file (auto-parses content)
-    doc = await client.create_document_from_source("path/to/document.pdf")
-
-    # Create document from URL
-    doc = await client.create_document_from_source("https://example.com/article.html")
-
-    # Retrieve documents
-    doc = await client.get_document_by_id(1)
-    doc = await client.get_document_by_uri("file:///path/to/document.pdf")
-
-    # List all documents with pagination
-    docs = await client.list_documents(limit=10, offset=0)
-
-    # Update document content
-    doc.content = "Updated content"
-    await client.update_document(doc)
-
-    # Delete document
-    await client.delete_document(doc.id)
-
-    # Search documents using hybrid search (vector + full-text)
-    results = await client.search("machine learning algorithms", limit=5)
-    for chunk, score in results:
-        print(f"Score: {score:.3f}")
-        print(f"Content: {chunk.content}")
-        print(f"Document ID: {chunk.document_id}")
-        print("---")
-```
-
-## Searching documents
-
-```python
 async with HaikuRAG("database.db") as client:
+    # Add document
+    doc = await client.create_document("Your content")
 
-    results = await client.search(
-        query="machine learning",
-        limit=5,  # Maximum results to return, defaults to 5
-        k=60      # RRF parameter for reciprocal rank fusion, defaults to 60
-    )
-
-    # Process results
-    for chunk, relevance_score in results:
-        print(f"Relevance: {relevance_score:.3f}")
-        print(f"Content: {chunk.content}")
-        print(f"From document: {chunk.document_id}")
+    # Search
+    results = await client.search("query")
+    for chunk, score in results:
+        print(f"{score:.3f}: {chunk.content}")
 ```
+
+## MCP Server
+
+Use with AI assistants like Claude Desktop:
+
+```bash
+haiku-rag serve --stdio
+```
+
+Provides tools for document management and search directly in your AI assistant.
+
+## Documentation
+
+Full documentation at: https://ggozad.github.io/haiku.rag/
+
+- [Installation](https://ggozad.github.io/haiku.rag/installation/) - Provider setup
+- [Configuration](https://ggozad.github.io/haiku.rag/configuration/) - Environment variables
+- [CLI](https://ggozad.github.io/haiku.rag/cli/) - Command reference
+- [Python API](https://ggozad.github.io/haiku.rag/python/) - Complete API docs
diff --git a/docs/configuration.md b/docs/configuration.md
index 6e7c6610..d7cdd593 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -8,14 +8,16 @@ Set directories to monitor for automatic indexing:
 
 ```bash
 # Monitor single directory
-export MONITOR_DIRECTORIES="/path/to/documents"
+MONITOR_DIRECTORIES="/path/to/documents"
 
 # Monitor multiple directories
-export MONITOR_DIRECTORIES="/path/to/documents,/another_path/to/documents"
+MONITOR_DIRECTORIES="/path/to/documents,/another_path/to/documents"
 ```
 
 ## Embedding Providers
 
+If you use Ollama, you can use any pulled model that supports embeddings.
+
 ### Ollama (Default)
 
 ```bash
@@ -25,6 +27,11 @@ EMBEDDINGS_VECTOR_DIM=1024
 ```
 
 ### VoyageAI
+If you want to use VoyageAI embeddings you will need to install `haiku.rag` with the VoyageAI extras,
+
+```bash
+uv pip install haiku.rag --extra voyageai
+```
 
 ```bash
 EMBEDDINGS_PROVIDER="voyageai"
@@ -34,6 +41,13 @@ VOYAGE_API_KEY="your-api-key"
 ```
 
 ### OpenAI
+If you want to use OpenAI embeddings you will need to install `haiku.rag` with the VoyageAI extras,
+
+```bash
+uv pip install haiku.rag --extra openai
+```
+
+and set environment variables.
 
 ```bash
 EMBEDDINGS_PROVIDER="openai"