Support batch embedding

src/haiku/rag/embeddings/base.py

@@ -9,7 +9,7 @@ class EmbedderBase:
         self._model = model
         self._vector_dim = vector_dim
 
-    async def embed(self, text: str) -> list[float]:
+    async def embed(self, text: str | list[str]) -> list[float] | list[list[float]]:
         raise NotImplementedError(
             "Embedder is an abstract class. Please implement the embed method in a subclass."
         )

src/haiku/rag/embeddings/ollama.py

@@ -1,11 +1,17 @@
-from ollama import AsyncClient
+from openai import AsyncOpenAI
 
 from haiku.rag.config import Config
 from haiku.rag.embeddings.base import EmbedderBase
 
 
 class Embedder(EmbedderBase):
-    async def embed(self, text: str) -> list[float]:
-        client = AsyncClient(host=Config.OLLAMA_BASE_URL)
-        res = await client.embeddings(model=self._model, prompt=text)
-        return list(res["embedding"])
+    async def embed(self, text: str | list[str]) -> list[float] | list[list[float]]:
+        client = AsyncOpenAI(base_url=f"{Config.OLLAMA_BASE_URL}/v1", api_key="dummy")
+        response = await client.embeddings.create(
+            model=self._model,
+            input=text,
+        )
+        if isinstance(text, str):
+            return response.data[0].embedding
+        else:
+            return [item.embedding for item in response.data]

src/haiku/rag/embeddings/openai.py

@@ -4,10 +4,13 @@ from haiku.rag.embeddings.base import EmbedderBase
 
 
 class Embedder(EmbedderBase):
-    async def embed(self, text: str) -> list[float]:
+    async def embed(self, text: str | list[str]) -> list[float] | list[list[float]]:
         client = AsyncOpenAI()
         response = await client.embeddings.create(
             model=self._model,
             input=text,
         )
-        return response.data[0].embedding
+        if isinstance(text, str):
+            return response.data[0].embedding
+        else:
+            return [item.embedding for item in response.data]

src/haiku/rag/embeddings/vllm.py

@@ -5,7 +5,7 @@ from haiku.rag.embeddings.base import EmbedderBase
 
 
 class Embedder(EmbedderBase):
-    async def embed(self, text: str) -> list[float]:
+    async def embed(self, text: str | list[str]) -> list[float] | list[list[float]]:
         client = AsyncOpenAI(
             base_url=f"{Config.VLLM_EMBEDDINGS_BASE_URL}/v1", api_key="dummy"
         )
@@ -13,4 +13,7 @@ class Embedder(EmbedderBase):
             model=self._model,
             input=text,
         )
-        return response.data[0].embedding
+        if isinstance(text, str):
+            return response.data[0].embedding
+        else:
+            return [item.embedding for item in response.data]

src/haiku/rag/embeddings/voyageai.py

@@ -4,10 +4,14 @@ try:
     from haiku.rag.embeddings.base import EmbedderBase
 
     class Embedder(EmbedderBase):
-        async def embed(self, text: str) -> list[float]:
+        async def embed(self, text: str | list[str]) -> list[float] | list[list[float]]:
             client = Client()
-            res = client.embed([text], model=self._model, output_dtype="float")
-            return res.embeddings[0]  # type: ignore[return-value]
+            if isinstance(text, str):
+                res = client.embed([text], model=self._model, output_dtype="float")
+                return res.embeddings[0]  # type: ignore[return-value]
+            else:
+                res = client.embed(text, model=self._model, output_dtype="float")
+                return res.embeddings  # type: ignore[return-value]
 
 except ImportError:
     pass

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

@@ -154,13 +154,7 @@ class ChunkRepository:
         """Create chunks and embeddings for a document from DoclingDocument."""
         chunk_texts = await chunker.chunk(document)
 
-        # Generate embeddings in parallel for all chunks
-        embeddings_tasks = []
-        for chunk_text in chunk_texts:
-            embeddings_tasks.append(self.embedder.embed(chunk_text))
-
-        # Wait for all embeddings to complete
-        embeddings = await asyncio.gather(*embeddings_tasks)
+        embeddings = await self.embedder.embed(chunk_texts)
 
         # Prepare all chunk records for batch insertion
         chunk_records = []

tests/test_embedder.py

@@ -28,7 +28,13 @@ async def test_ollama_embedder():
         "Python is my favorite programming language.",
         "I love to travel and see new places.",
     ]
-    embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
+
+    # Test batch embedding
+    embeddings = await embedder.embed(phrases)
+    assert isinstance(embeddings, list)
+    assert len(embeddings) == 3
+    assert all(isinstance(emb, list) for emb in embeddings)
+    embeddings = [np.array(emb) for emb in embeddings]
 
     test_phrase = "I am going for a camping trip."
     test_embedding = await embedder.embed(test_phrase)
@@ -58,7 +64,13 @@ async def test_openai_embedder():
         "Python is my favorite programming language.",
         "I love to travel and see new places.",
     ]
-    embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
+
+    # Test batch embedding
+    embeddings = await embedder.embed(phrases)
+    assert isinstance(embeddings, list)
+    assert len(embeddings) == 3
+    assert all(isinstance(emb, list) for emb in embeddings)
+    embeddings = [np.array(emb) for emb in embeddings]
 
     test_phrase = "I am going for a camping trip."
     test_embedding = await embedder.embed(test_phrase)
@@ -91,7 +103,13 @@ async def test_voyageai_embedder():
             "Python is my favorite programming language.",
             "I love to travel and see new places.",
         ]
-        embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
+
+        # Test batch embedding
+        embeddings = await embedder.embed(phrases)
+        assert isinstance(embeddings, list)
+        assert len(embeddings) == 3
+        assert all(isinstance(emb, list) for emb in embeddings)
+        embeddings = [np.array(emb) for emb in embeddings]
 
         test_phrase = "I am going for a camping trip."
         test_embedding = await embedder.embed(test_phrase)
@@ -126,7 +144,13 @@ async def test_vllm_embedder():
         "Python is my favorite programming language.",
         "I love to travel and see new places.",
     ]
-    embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
+
+    # Test batch embedding
+    embeddings = await embedder.embed(phrases)
+    assert isinstance(embeddings, list)
+    assert len(embeddings) == 3
+    assert all(isinstance(emb, list) for emb in embeddings)
+    embeddings = [np.array(emb) for emb in embeddings]
 
     test_phrase = "I am going for a camping trip."
     test_embedding = await embedder.embed(test_phrase)