Add search to client

README.md

@@ -66,10 +66,44 @@ 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("---")
+
 # Clean up
 client.close()
 ```
 
+## Search Functionality
+
+`haiku.rag` provides hybrid search combining vector similarity and full-text search:
+1. **Vector Search**: Uses embeddings to find semantically similar content
+2. **Full-text Search**: Uses SQLite FTS5 for exact keyword matching
+3. **Hybrid Ranking**: Combines both using Reciprocal Rank Fusion (RRF)
+4. **Chunked Results**: Returns relevant document chunks with scores
+
+```python
+# Basic search
+results = await client.search("your query here")
+
+# Search with custom parameters
+results = await client.search(
+    query="machine learning",
+    limit=10,  # Maximum results to return
+    k=60       # RRF parameter for reciprocal rank fusion
+)
+
+# 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}")
+```
+
 ## Smart Document Updates
 
 The system automatically tracks file changes using MD5 hashes:
@@ -119,6 +153,5 @@ print(doc.metadata)
 2. Create a feature branch
 3. Add tests for new functionality
 4. Ensure all tests pass: `pytest`
-5. Run type checking: `pyright`
-6. Run linting: `ruff check`
-7. Submit a pull request
+5. Run type checking & linting with `pyright` & `ruff check`
+6. Submit a pull request

src/haiku/rag/client.py

@@ -9,7 +9,9 @@ import httpx
 
 from haiku.rag.reader import FileReader
 from haiku.rag.store.engine import Store
+from haiku.rag.store.models.chunk import Chunk
 from haiku.rag.store.models.document import Document
+from haiku.rag.store.repositories.chunk import ChunkRepository
 from haiku.rag.store.repositories.document import DocumentRepository
 
 
@@ -20,6 +22,7 @@ class HaikuRAG:
         """Initialize the RAG client with a database path."""
         self.store = Store(db_path)
         self.document_repository = DocumentRepository(self.store)
+        self.chunk_repository = ChunkRepository(self.store)
 
     async def create_document(
         self, content: str, uri: str | None = None, metadata: dict | None = None
@@ -221,6 +224,21 @@ class HaikuRAG:
         """List all documents with optional pagination."""
         return await self.document_repository.list_all(limit=limit, offset=offset)
 
+    async def search(
+        self, query: str, limit: int = 5, k: int = 60
+    ) -> list[tuple[Chunk, float]]:
+        """Search for relevant chunks using hybrid search (vector similarity + full-text search).
+
+        Args:
+            query: The search query string
+            limit: Maximum number of results to return
+            k: Parameter for Reciprocal Rank Fusion (default: 60)
+
+        Returns:
+            List of (chunk, score) tuples ordered by relevance
+        """
+        return await self.chunk_repository.search_chunks_hybrid(query, limit, k)
+
     def close(self):
         """Close the underlying store connection."""
         self.store.close()

tests/test_client.py

@@ -426,3 +426,49 @@ async def test_client_url_create_update_no_op_behavior():
         assert doc3.content == updated_content.decode()  # Updated content
 
     client.close()
+
+
+@pytest.mark.asyncio
+async def test_client_search():
+    """Test HaikuRAG search functionality."""
+    client = HaikuRAG(":memory:")
+
+    # Add multiple documents to search from
+    doc1_text = "Python is a high-level programming language known for its simplicity and readability."
+    doc2_text = "Machine learning algorithms help computers learn patterns from data without explicit programming."
+    doc3_text = "Data science combines statistics, programming, and domain expertise to extract insights."
+
+    # Create documents
+    doc1 = await client.create_document(
+        content=doc1_text, uri="doc1.txt", metadata={"topic": "python"}
+    )
+    doc2 = await client.create_document(
+        content=doc2_text, uri="doc2.txt", metadata={"topic": "ml"}
+    )
+    await client.create_document(
+        content=doc3_text, uri="doc3.txt", metadata={"topic": "data_science"}
+    )
+
+    # Test search with keyword that should match doc1
+    results = await client.search("Python programming", limit=3)
+
+    assert len(results) > 0
+    assert all(len(result) == 2 for result in results)
+
+    # Verify first result is from the Python document (doc1)
+    first_chunk, _ = results[0]
+    assert first_chunk.document_id == doc1.id
+
+    # Test search with different query
+    ml_results = await client.search("machine learning data", limit=2)
+    assert len(ml_results) > 0
+
+    # Verify first result is from the machine learning document (doc2)
+    first_ml_chunk, _ = ml_results[0]
+    assert first_ml_chunk.document_id == doc2.id
+
+    # Test search with limit parameter
+    limited_results = await client.search("programming", limit=1)
+    assert len(limited_results) <= 1
+
+    client.close()