update docs

README.md

@@ -1,15 +1,15 @@
-# Haiku SQLite RAG
+# Haiku LanceDB RAG
 
-Retrieval-Augmented Generation (RAG) library on SQLite.
+Retrieval-Augmented Generation (RAG) library built on LanceDB.
 
-`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work on SQLite alone without the need for external vector databases. It uses [sqlite-vec](https://github.com/asg017/sqlite-vec) for storing the embeddings and performs semantic (vector) search as well as full-text search combined through Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
+`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work with LanceDB as a local vector database. It uses LanceDB for storing embeddings and performs semantic (vector) search as well as full-text search combined through native hybrid search with Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
 
 ## Features
 
-- **Local SQLite**: No external servers required
+- **Local LanceDB**: No external servers required
 - **Multiple embedding providers**: Ollama, VoyageAI, OpenAI
 - **Multiple QA providers**: Any provider/model supported by Pydantic AI
-- **Hybrid search**: Vector + full-text search with Reciprocal Rank Fusion
+- **Native hybrid search**: Vector + full-text search with native LanceDB RRF reranking
 - **Reranking**: Default search result reranking with MixedBread AI or Cohere
 - **Question answering**: Built-in QA agents on your documents
 - **File monitoring**: Auto-index files when run as server
@@ -49,7 +49,7 @@ haiku-rag serve
 ```python
 from haiku.rag.client import HaikuRAG
 
-async with HaikuRAG("database.db") as client:
+async with HaikuRAG("database.lancedb") as client:
     # Add document
     doc = await client.create_document("Your content")
 

docs/cli.md

@@ -54,7 +54,7 @@ haiku-rag search "machine learning"
 
 With options:
 ```bash
-haiku-rag search "python programming" --limit 10 --k 100
+haiku-rag search "python programming" --limit 10
 ```
 
 ## Question Answering

docs/index.md

@@ -1,12 +1,12 @@
 # haiku.rag
 
-`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work on SQLite alone without the need for external vector databases. It uses [sqlite-vec](https://github.com/asg017/sqlite-vec) for storing the embeddings and performs semantic (vector) search as well as full-text search combined through Reciprocal Rank Fusion. Both open-source (Ollama, MixedBread AI) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
+`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work with LanceDB as a local vector database. It uses LanceDB for storing embeddings and performs semantic (vector) search as well as full-text search combined through native hybrid search with Reciprocal Rank Fusion. Both open-source (Ollama, MixedBread AI) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
 
 ## Features
 
-- **Local SQLite**: No need to run additional servers
+- **Local LanceDB**: No need to run additional servers
 - **Support for various embedding providers**: Ollama, VoyageAI, OpenAI or add your own
-- **Hybrid Search**: Vector search using `sqlite-vec` combined with full-text search `FTS5`, using Reciprocal Rank Fusion
+- **Native Hybrid Search**: Vector search combined with full-text search using native LanceDB RRF reranking
 - **Reranking**: Optional result reranking with MixedBread AI or Cohere
 - **Question Answering**: Built-in QA agents using Ollama, OpenAI, or Anthropic.
 - **File monitoring**: Automatically index files when run as a server
@@ -26,7 +26,7 @@ Use from Python:
 ```python
 from haiku.rag.client import HaikuRAG
 
-async with HaikuRAG("database.db") as client:
+async with HaikuRAG("database.lancedb") as client:
     # Add a document
     doc = await client.create_document("Your content here")
 
@@ -34,7 +34,7 @@ async with HaikuRAG("database.db") as client:
     results = await client.search("query")
 
     # Ask questions
-    answer = await client.ask("Who is the author of haiku.rag?", rerank=False)
+    answer = await client.ask("Who is the author of haiku.rag?")
 ```
 
 Or use the CLI:

docs/python.md

@@ -9,7 +9,7 @@ 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:
+async with HaikuRAG("path/to/database.lancedb") as client:
     # Your code here
     pass
 ```
@@ -101,9 +101,9 @@ async for doc_id in client.rebuild_database():
 
 ## Searching Documents
 
-The search method performs hybrid search (vector + full-text) with **reranking enabled by default** for improved relevance:
+The search method performs native hybrid search (vector + full-text) using LanceDB with **reranking enabled by default** for improved relevance:
 
-Basic search (with reranking):
+Basic hybrid search (default, with reranking):
 ```python
 results = await client.search("machine learning algorithms", limit=5)
 for chunk, score in results:
@@ -112,13 +112,27 @@ for chunk, score in results:
     print(f"Document ID: {chunk.document_id}")
 ```
 
-With options:
+Search with different search types:
 ```python
+# Vector search only
 results = await client.search(
     query="machine learning",
-    limit=5,  # Maximum results to return
-    k=60,     # RRF parameter for reciprocal rank fusion
-    rerank=False  # Disable reranking for faster search
+    limit=5,
+    search_type="vector"
+)
+
+# Full-text search only
+results = await client.search(
+    query="machine learning",
+    limit=5,
+    search_type="fts"
+)
+
+# Hybrid search (default - combines vector + fts with native LanceDB RRF)
+results = await client.search(
+    query="machine learning",
+    limit=5,
+    search_type="hybrid"
 )
 
 # Process results