Merge pull request #21 from ggozad/feat/external-chunks

create_document now optionally accepts pre-computed chunks

docs/python.md

@@ -27,6 +27,31 @@ doc = await client.create_document(
 )
 ```
 
+With custom externally generated chunks:
+```python
+from haiku.rag.store.models.chunk import Chunk
+
+# Create custom chunks with optional embeddings
+chunks = [
+    Chunk(
+        content="This is the first chunk",
+        metadata={"section": "intro"}
+    ),
+    Chunk(
+        content="This is the second chunk",
+        metadata={"section": "body"},
+        embedding=[0.1] * 1024  # Optional pre-computed embedding
+    ),
+]
+
+doc = await client.create_document(
+    content="Full document content",
+    uri="doc://custom",
+    metadata={"source": "manual"},
+    chunks=chunks  # Use provided chunks instead of auto-generating
+)
+```
+
 From file:
 ```python
 doc = await client.create_document_from_source("path/to/document.pdf")

src/haiku/rag/client.py

@@ -50,7 +50,11 @@ class HaikuRAG:
         return False
 
     async def create_document(
-        self, content: str, uri: str | None = None, metadata: dict | None = None
+        self,
+        content: str,
+        uri: str | None = None,
+        metadata: dict | None = None,
+        chunks: list[Chunk] | None = None,
     ) -> Document:
         """Create a new document with optional URI and metadata.
 
@@ -58,6 +62,7 @@ class HaikuRAG:
             content: The text content of the document.
             uri: Optional URI identifier for the document.
             metadata: Optional metadata dictionary.
+            chunks: Optional list of pre-created chunks to use instead of generating new ones.
 
         Returns:
             The created Document instance.
@@ -67,7 +72,7 @@ class HaikuRAG:
             uri=uri,
             metadata=metadata or {},
         )
-        return await self.document_repository.create(document)
+        return await self.document_repository.create(document, chunks)
 
     async def create_document_from_source(
         self, source: str | Path, metadata: dict = {}

src/haiku/rag/store/models/chunk.py

@@ -7,8 +7,9 @@ class Chunk(BaseModel):
     """
 
     id: int | None = None
-    document_id: int
+    document_id: int | None = None
     content: str
     metadata: dict = {}
     document_uri: str | None = None
     document_meta: dict = {}
+    embedding: list[float] | None = None

src/haiku/rag/store/repositories/chunk.py

@@ -18,6 +18,8 @@ class ChunkRepository(BaseRepository[Chunk]):
         """Create a chunk in the database."""
         if self.store._connection is None:
             raise ValueError("Store connection is not available")
+        if entity.document_id is None:
+            raise ValueError("Chunk must have a document_id to be created")
 
         cursor = self.store._connection.cursor()
         cursor.execute(
@@ -34,9 +36,15 @@ class ChunkRepository(BaseRepository[Chunk]):
 
         entity.id = cursor.lastrowid
 
-        # Generate and store embedding
+        # Generate and store embedding - use existing one if provided
-        embedding = await self.embedder.embed(entity.content)
+        if entity.embedding is not None:
-        serialized_embedding = self.store.serialize_embedding(embedding)
+            # Use the provided embedding
+            serialized_embedding = self.store.serialize_embedding(entity.embedding)
+        else:
+            # Generate embedding from content
+            embedding = await self.embedder.embed(entity.content)
+            serialized_embedding = self.store.serialize_embedding(embedding)
+
         cursor.execute(
             """
             INSERT INTO chunk_embeddings (chunk_id, embedding)

src/haiku/rag/store/repositories/document.py

@@ -1,8 +1,12 @@
 import json
+from typing import TYPE_CHECKING
 
 from haiku.rag.store.models.document import Document
 from haiku.rag.store.repositories.base import BaseRepository
 
+if TYPE_CHECKING:
+    from haiku.rag.store.models.chunk import Chunk
+
 
 class DocumentRepository(BaseRepository[Document]):
     """Repository for Document database operations."""
@@ -16,7 +20,9 @@ class DocumentRepository(BaseRepository[Document]):
             chunk_repository = ChunkRepository(store)
         self.chunk_repository = chunk_repository
 
-    async def create(self, entity: Document) -> Document:
+    async def create(
+        self, entity: Document, chunks: list["Chunk"] | None = None
+    ) -> Document:
         """Create a document with its chunks and embeddings."""
         if self.store._connection is None:
             raise ValueError("Store connection is not available")
@@ -46,10 +52,20 @@ class DocumentRepository(BaseRepository[Document]):
             assert document_id is not None, "Failed to create document in database"
             entity.id = document_id
 
-            # Create chunks and embeddings using ChunkRepository
+            # Create chunks - either use provided chunks or generate from content
-            await self.chunk_repository.create_chunks_for_document(
+            if chunks is not None:
-                document_id, entity.content, commit=False
+                # Use provided chunks, but update their document_id and set order from list position
-            )
+                for order, chunk in enumerate(chunks):
+                    chunk.document_id = document_id
+                    # Ensure order is set from list position
+                    chunk.metadata = chunk.metadata.copy() if chunk.metadata else {}
+                    chunk.metadata["order"] = order
+                    await self.chunk_repository.create(chunk, commit=False)
+            else:
+                # Create chunks and embeddings using ChunkRepository
+                await self.chunk_repository.create_chunks_for_document(
+                    document_id, entity.content, commit=False
+                )
 
             cursor.execute("COMMIT")
             return entity

tests/test_client.py

@@ -7,6 +7,7 @@ import pytest
 from datasets import Dataset
 
 from haiku.rag.client import HaikuRAG
+from haiku.rag.store.models.chunk import Chunk
 
 
 @pytest.mark.asyncio
@@ -449,3 +450,42 @@ async def test_client_async_context_manager():
     # Context manager should have automatically closed the connection
     # We can't easily test that the connection is closed without accessing internals,
     # but the test passing means the context manager methods work correctly
+
+
+@pytest.mark.asyncio
+async def test_client_create_document_with_custom_chunks():
+    """Test creating a document with pre-created chunks."""
+    async with HaikuRAG(":memory:") as client:
+        # Create some custom chunks with and without embeddings
+        chunks = [
+            Chunk(content="This is the first chunk", metadata={"custom": "metadata1"}),
+            Chunk(
+                content="This is the second chunk",
+                metadata={"custom": "metadata2"},
+                embedding=[0.1] * 1024,
+            ),  # With embedding
+            Chunk(content="This is the third chunk", metadata={"custom": "metadata3"}),
+        ]
+
+        # Create document with custom chunks
+        document = await client.create_document(
+            content="Full document content", chunks=chunks
+        )
+
+        assert document.id is not None
+        assert document.content == "Full document content"
+
+        # Verify the chunks were created correctly
+        doc_chunks = await client.chunk_repository.get_by_document_id(document.id)
+        assert len(doc_chunks) == 3
+
+        # Check chunks have correct content, document_id, and order from list position
+        for i, chunk in enumerate(doc_chunks):
+            assert chunk.document_id == document.id
+            assert chunk.content == chunks[i].content
+            assert (
+                chunk.metadata["order"] == i
+            )  # Order should be set from list position
+            assert (
+                chunk.metadata["custom"] == f"metadata{i + 1}"
+            )  # Original metadata preserved