diff --git a/README.md b/README.md
index 5b08fac..7db3882 100755
--- a/README.md
+++ b/README.md
@@ -98,6 +98,27 @@ uv sync
 </details>
 
 
+### 🆕 Using Ollama for Embeddings (Privacy-Focused)
+
+LEANN now supports Ollama for generating embeddings locally, perfect for privacy-sensitive applications:
+
+```bash
+# First, pull an embedding model from Ollama
+ollama pull nomic-embed-text  # or mxbai-embed-large, bge-m3, etc.
+
+# Build an index using Ollama embeddings
+leann build my-project --docs ./documents --embedding-model nomic-embed-text --embedding-mode ollama
+
+# Use with example apps
+python -m apps.document_rag --embedding-model nomic-embed-text --embedding-mode ollama --query "Your question"
+```
+
+**Available Ollama Embedding Models:**
+- `nomic-embed-text`: High-performing 768-dim embeddings
+- `mxbai-embed-large`: Large 1024-dim embeddings
+- `bge-m3`: Multilingual embeddings
+- See [Ollama library](https://ollama.com/library) for more embedding models
+
 ## Quick Start
 
 Our declarative API makes RAG as easy as writing a config file.
@@ -189,8 +210,8 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
 --force-rebuild         # Force rebuild index even if it exists
 
 # Embedding Parameters
---embedding-model MODEL  # e.g., facebook/contriever, text-embedding-3-small or mlx-community/multilingual-e5-base-mlx
---embedding-mode MODE    # sentence-transformers, openai, or mlx
+--embedding-model MODEL  # e.g., facebook/contriever, text-embedding-3-small, nomic-embed-text, or mlx-community/multilingual-e5-base-mlx
+--embedding-mode MODE    # sentence-transformers, openai, mlx, or ollama
 
 # LLM Parameters (Text generation models)
 --llm TYPE              # LLM backend: openai, ollama, or hf (default: openai)
diff --git a/docs/configuration-guide.md b/docs/configuration-guide.md
index 8d910f6..95cb3f0 100644
--- a/docs/configuration-guide.md
+++ b/docs/configuration-guide.md
@@ -49,14 +49,25 @@ Based on our experience developing LEANN, embedding models fall into three categ
 - **Cons**: Slower inference, longer index build times
 - **Use when**: Quality is paramount and you have sufficient compute resources. **Highly recommended** for production use
 
-### Quick Start: OpenAI Embeddings (Fastest Setup)
+### Quick Start: Cloud and Local Embedding Options
 
+**OpenAI Embeddings (Fastest Setup)**
 For immediate testing without local model downloads:
 ```bash
 # Set OpenAI embeddings (requires OPENAI_API_KEY)
 --embedding-mode openai --embedding-model text-embedding-3-small
 ```
 
+**Ollama Embeddings (Privacy-Focused)**
+For local embeddings with complete privacy:
+```bash
+# First, pull an embedding model
+ollama pull nomic-embed-text
+
+# Use Ollama embeddings
+--embedding-mode ollama --embedding-model nomic-embed-text
+```
+
 <details>
 <summary><strong>Cloud vs Local Trade-offs</strong></summary>