Update dependencies

2026-01-23 11:57:02 +02:00 · 2026-01-23 11:57:02 +02:00 · 47d355ed0a
commit 47d355ed0a
parent 35ebdd67e2
8 changed files with 98 additions and 282 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -1,6 +1,17 @@
 # Changelog
 ## [Unreleased]

+### Changed
+
+- **Dependencies**: Updated core dependencies
+  - `pydantic-ai-slim`: 1.44.0 → 1.46.0
+  - `lancedb`: 0.26.1 → 0.27.0
+  - `docling`: 2.68.0 → 2.69.1
+  - `docling-core`: 2.59.0 → 2.60.1
+- **VoyageAI Embeddings**: Now uses pydantic-ai-slim's native VoyageAI support instead of custom implementation
+  - Removed `haiku.rag.embeddings.voyageai` module
+  - The `voyageai` extra now delegates to `pydantic-ai-slim[voyageai]`
+
 ## [0.26.9] - 2026-01-22

 ### Fixed
--- a/app/backend/pyproject.toml
+++ b/app/backend/pyproject.toml
@ -6,9 +6,9 @@ requires-python = ">=3.12"
 dependencies = [
    "starlette>=0.50.0",
    "uvicorn[standard]>=0.40.0",
-    "pydantic-ai-slim[ag-ui,anthropic,openai]>=1.39.0",
+    "pydantic-ai-slim[ag-ui,anthropic,openai]>=1.46.0",
    "python-dotenv>=1.2.1",
-    "haiku.rag-slim[agui]>=0.26.9",
+    "haiku.rag-slim>=0.26.9",
    "logfire[pydantic-ai]>=3.17.0",
 ]

--- a/evaluations/pyproject.toml
+++ b/evaluations/pyproject.toml
@ -9,7 +9,7 @@ requires-python = ">=3.12"

 dependencies = [
    "haiku.rag-slim",
-    "pydantic-ai-slim[evals,logfire]>=1.44.0",
+    "pydantic-ai-slim[evals,logfire]>=1.46.0",
    "datasets>=4.5.0",
    "typer>=0.19.2,<0.20.0",
    "python-dotenv>=1.2.1",
--- a/haiku_rag_slim/haiku/rag/client.py
+++ b/haiku_rag_slim/haiku/rag/client.py
@ -514,7 +514,9 @@ class HaikuRAG:
            raise ValueError(f"File does not exist: {source_path}")

        uri = source_path.absolute().as_uri()
-        md5_hash = hashlib.md5(source_path.read_bytes(), usedforsecurity=False).hexdigest()
+        md5_hash = hashlib.md5(
+            source_path.read_bytes(), usedforsecurity=False
+        ).hexdigest()

        # Get content type from file extension (do before early return)
        content_type, _ = mimetypes.guess_type(str(source_path))
--- a/haiku_rag_slim/haiku/rag/embeddings/init.py
+++ b/haiku_rag_slim/haiku/rag/embeddings/init.py
@ -126,16 +126,7 @@ def get_embedder(config: AppConfig = Config) -> EmbedderWrapper:
        return EmbedderWrapper(Embedder(f"openai:{model_name}"), vector_dim)

    if provider == "voyageai":
-        try:
-            from haiku.rag.embeddings.voyageai import VoyageAIEmbeddingModel
-        except ImportError:  # pragma: no cover
-            raise ImportError(
-                "VoyageAI embedder requires the 'voyageai' package. "
-                "Please install haiku.rag with the 'voyageai' extra: "
-                "uv pip install haiku.rag[voyageai]"
-            )
-        model = VoyageAIEmbeddingModel(model_name)
-        return EmbedderWrapper(Embedder(model), vector_dim)
+        return EmbedderWrapper(Embedder(f"voyageai:{model_name}"), vector_dim)

    if provider == "cohere":
        return EmbedderWrapper(Embedder(f"cohere:{model_name}"), vector_dim)
--- a/haiku_rag_slim/haiku/rag/embeddings/voyageai.py
+++ b/haiku_rag_slim/haiku/rag/embeddings/voyageai.py
@ -1,182 +0,0 @@
-from collections.abc import Sequence
-from dataclasses import dataclass, field
-from typing import Literal, cast
-
-from pydantic_ai.embeddings.base import EmbeddingModel
-from pydantic_ai.embeddings.result import EmbeddingResult, EmbedInputType
-from pydantic_ai.embeddings.settings import EmbeddingSettings
-from pydantic_ai.exceptions import ModelAPIError
-from pydantic_ai.usage import RequestUsage
-
-try:
-    from voyageai.client_async import AsyncClient
-    from voyageai.error import VoyageError
-except ImportError as _import_error:
-    raise ImportError(
-        "Please install `voyageai` to use the VoyageAI embeddings model, "
-        "you can use — `pip install voyageai`"
-    ) from _import_error
-
-LatestVoyageAIEmbeddingModelNames = Literal[
-    "voyage-3-large",
-    "voyage-3.5",
-    "voyage-3.5-lite",
-    "voyage-code-3",
-    "voyage-finance-2",
-    "voyage-law-2",
-    "voyage-code-2",
-]
-"""Latest VoyageAI embedding models.
-
-See [VoyageAI Embeddings](https://docs.voyageai.com/docs/embeddings)
-for available models and their capabilities.
-"""
-
-VoyageAIEmbeddingModelName = str | LatestVoyageAIEmbeddingModelNames
-"""Possible VoyageAI embedding model names."""
-
-
-class VoyageAIEmbeddingSettings(EmbeddingSettings, total=False):
-    """Settings used for a VoyageAI embedding model request.
-
-    All fields from [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings] are supported,
-    plus VoyageAI-specific settings prefixed with `voyageai_`.
-    """
-
-    # ALL FIELDS MUST BE `voyageai_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.
-
-    voyageai_truncation: bool
-    """Whether to truncate inputs that exceed the model's context length.
-
-    Defaults to True. If False, an error is raised for inputs that are too long.
-    """
-
-    voyageai_output_dtype: Literal["float", "int8", "uint8", "binary", "ubinary"]
-    """The output data type for embeddings.
-
-    - `'float'` (default): 32-bit floats
-    - `'int8'`: Signed 8-bit integers (quantized)
-    - `'uint8'`: Unsigned 8-bit integers (quantized)
-    - `'binary'`: Binary embeddings
-    - `'ubinary'`: Unsigned binary embeddings
-    """
-
-
-_MAX_INPUT_TOKENS: dict[VoyageAIEmbeddingModelName, int] = {
-    "voyage-3-large": 32000,
-    "voyage-3.5": 32000,
-    "voyage-3.5-lite": 32000,
-    "voyage-code-3": 32000,
-    "voyage-finance-2": 32000,
-    "voyage-law-2": 16000,
-    "voyage-code-2": 16000,
-}
-
-
-@dataclass(init=False)
-class VoyageAIEmbeddingModel(EmbeddingModel):
-    """VoyageAI embedding model implementation.
-
-    VoyageAI provides state-of-the-art embedding models optimized for
-    retrieval, with specialized models for code, finance, and legal domains.
-
-    Example:
-    ```python
-    from pydantic_ai.embeddings.voyageai import VoyageAIEmbeddingModel
-
-    model = VoyageAIEmbeddingModel('voyage-3.5')
-    ```
-    """
-
-    _model_name: VoyageAIEmbeddingModelName = field(repr=False)
-    _client: AsyncClient = field(repr=False)
-
-    def __init__(
-        self,
-        model_name: VoyageAIEmbeddingModelName,
-        *,
-        api_key: str | None = None,
-        max_retries: int = 0,
-        timeout: int | None = None,
-        settings: EmbeddingSettings | None = None,
-    ):
-        """Initialize a VoyageAI embedding model.
-
-        Args:
-            model_name: The name of the VoyageAI model to use.
-                See [VoyageAI models](https://docs.voyageai.com/docs/embeddings)
-                for available options.
-            api_key: The VoyageAI API key. If not provided, uses the
-                `VOYAGE_API_KEY` environment variable.
-            max_retries: Maximum number of retries for failed requests.
-            timeout: Request timeout in seconds.
-            settings: Model-specific [`EmbeddingSettings`][pydantic_ai.embeddings.EmbeddingSettings]
-                to use as defaults for this model.
-        """
-        self._model_name = model_name
-        self._client = AsyncClient(
-            api_key=api_key,
-            max_retries=max_retries,
-            timeout=timeout,
-        )
-
-        super().__init__(settings=settings)
-
-    @property
-    def model_name(self) -> VoyageAIEmbeddingModelName:
-        """The embedding model name."""
-        return self._model_name
-
-    @property
-    def system(self) -> str:
-        """The embedding model provider."""
-        return "voyageai"
-
-    async def embed(
-        self,
-        inputs: str | Sequence[str],
-        *,
-        input_type: EmbedInputType,
-        settings: EmbeddingSettings | None = None,
-    ) -> EmbeddingResult:
-        inputs, settings = self.prepare_embed(inputs, settings)
-        settings = cast(VoyageAIEmbeddingSettings, settings)
-
-        voyageai_input_type = "document" if input_type == "document" else "query"
-
-        try:
-            response = await self._client.embed(
-                texts=list(inputs),
-                model=self.model_name,
-                input_type=voyageai_input_type,
-                truncation=settings.get("voyageai_truncation", True),
-                output_dtype=settings.get("voyageai_output_dtype", "float"),
-                output_dimension=settings.get("dimensions"),
-            )
-        except VoyageError as e:
-            raise ModelAPIError(model_name=self.model_name, message=str(e)) from e
-
-        return EmbeddingResult(
-            embeddings=response.embeddings,
-            inputs=inputs,
-            input_type=input_type,
-            usage=_map_usage(response.total_tokens, self.model_name),
-            model_name=self.model_name,
-            provider_name=self.system,
-        )
-
-    async def max_input_tokens(self) -> int | None:
-        return _MAX_INPUT_TOKENS.get(self.model_name)
-
-
-def _map_usage(total_tokens: int, model: str) -> RequestUsage:
-    usage_data = {"total_tokens": total_tokens}
-    response_data = {"model": model, "usage": usage_data}
-
-    return RequestUsage.extract(
-        response_data,
-        provider="voyageai",
-        provider_url="https://api.voyageai.com",
-        provider_fallback="voyageai",
-        api_flavor="embeddings",
-    )
--- a/haiku_rag_slim/pyproject.toml
+++ b/haiku_rag_slim/pyproject.toml
@ -22,12 +22,12 @@ classifiers = [
 ]

 dependencies = [
-    "docling-core==2.59.0",
+    "docling-core==2.60.1",
    "httpx>=0.28.1",
-    "lancedb==0.26.1",
+    "lancedb==0.27.0",
    "pathspec>=1.0.3",
    "pydantic>=2.12.5",
-    "pydantic-ai-slim[openai,fastmcp,logfire,ag-ui]>=1.44.0",
+    "pydantic-ai-slim[openai,fastmcp,logfire,ag-ui]>=1.46.0",
    "python-dotenv>=1.2.1",
    "pyyaml>=6.0.3",
    "rich>=14.2.0",
@ -37,9 +37,9 @@ dependencies = [

 [project.optional-dependencies]
 # Document processing
-docling = ["docling==2.68.0", "opencv-python-headless>=4.13.0.90"]
+docling = ["docling==2.69.1", "opencv-python-headless>=4.13.0.90"]
 # Embedding providers
-voyageai = ["voyageai>=0.3.7"]
+voyageai = ["pydantic-ai-slim[voyageai]"]
 # Rerankers
 mxbai = ["mxbai-rerank>=0.1.6"]
 cohere = ["cohere>=5.20.1"]
--- a/uv.lock
+++ b/uv.lock
@ -741,7 +741,7 @@ wheels = [

 [[package]]
 name = "docling"
-version = "2.68.0"
+version = "2.69.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "accelerate" },
@ -773,14 +773,14 @@ dependencies = [
    { name = "tqdm" },
    { name = "typer" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f4/cb/5dcda48a739e18c3d087de55be064d1fe9dfa802132aba274d55466ced01/docling-2.68.0.tar.gz", hash = "sha256:fabadc7aa807b96ac9bf85cc78f4fe343b5df658857386bc23541a9a6c116132", size = 265952, upload-time = "2026-01-13T10:47:35.925Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/90/eb/d42bd7f9a781189f6ba2f8f6a9c5b80ca85cfde1a6052a9a41c5c48c6d8b/docling-2.69.1.tar.gz", hash = "sha256:b4a91a7874def5a4080b04fbe7b1148686b6215c4fd75d26070986dbedb8c47b", size = 267941, upload-time = "2026-01-21T12:49:20.463Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d5/a5/08716dcbf93e350f0595b58ece138b3b78606ef2f6a5a5058b1642cfe142/docling-2.68.0-py3-none-any.whl", hash = "sha256:97b2a4957e221973c69f1706da326fda8e973707d041778df12c01a5279e3e7e", size = 282854, upload-time = "2026-01-13T10:47:34.325Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/b2/54ed8c9ebc2571d5058c6ec435f1819eb58bfcab8ef95a99fa263b4fbc60/docling-2.69.1-py3-none-any.whl", hash = "sha256:75526d2c208bb2571c685763c10d728feecb4adc453c0600877888fa8021c432", size = 286583, upload-time = "2026-01-21T12:49:19.115Z" },
 ]

 [[package]]
 name = "docling-core"
-version = "2.59.0"
+version = "2.60.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonref" },
@ -794,9 +794,9 @@ dependencies = [
    { name = "typer" },
    { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f6/ae/b1216598ab2f2a0fbe5fe974bcd65d9407f26fff0ba89cf6d8d760601a0a/docling_core-2.59.0.tar.gz", hash = "sha256:08900edbaf86a0acdd3d1fb5d47284f3826f68638245ba4a03aba1558d0067d9", size = 231580, upload-time = "2026-01-12T16:40:21.321Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/42/7e/a71caa528ea01b7ebdb0c861e63dcb525fdce8d7c7c86232c3606cd7c8bf/docling_core-2.60.1.tar.gz", hash = "sha256:64bd71dee243bd11b25f216fec219e046a130b851b8e1d0c0dd362a4aac0e994", size = 231877, upload-time = "2026-01-22T18:28:47.151Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e3/e7/27ba60d253b65415e6e709fda5ddfc1baf82fa9f8fbd8ae14b9bbbba5474/docling_core-2.59.0-py3-none-any.whl", hash = "sha256:014e1075c41fdac0932f25226d6c0972cb9772d866efcf8d04ca2abe246797c1", size = 223304, upload-time = "2026-01-12T16:40:19.488Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/36/2261f921001fcf04025145bee1cc020b61a51139a6e7a0a5d208cb9c6ebd/docling_core-2.60.1-py3-none-any.whl", hash = "sha256:45390e50cb4d83a70e2384c70a46e6e64acb15e69674d9d2c67315155f252aef", size = 222155, upload-time = "2026-01-22T18:28:44.031Z" },
 ]

 [package.optional-dependencies]
@ -1354,7 +1354,7 @@ dependencies = [
 requires-dist = [
    { name = "datasets", specifier = ">=4.5.0" },
    { name = "haiku-rag-slim", editable = "haiku_rag_slim" },
-    { name = "pydantic-ai-slim", extras = ["evals", "logfire"], specifier = ">=1.44.0" },
+    { name = "pydantic-ai-slim", extras = ["evals", "logfire"], specifier = ">=1.46.0" },
    { name = "python-dotenv", specifier = ">=1.2.1" },
    { name = "typer", specifier = ">=0.19.2,<0.20.0" },
 ]
@ -1415,7 +1415,7 @@ vertexai = [
    { name = "pydantic-ai-slim", extra = ["vertexai"] },
 ]
 voyageai = [
-    { name = "voyageai" },
+    { name = "pydantic-ai-slim", extra = ["voyageai"] },
 ]
 zeroentropy = [
    { name = "zeroentropy" },
@ -1424,10 +1424,10 @@ zeroentropy = [
 [package.metadata]
 requires-dist = [
    { name = "cohere", marker = "extra == 'cohere'", specifier = ">=5.20.1" },
-    { name = "docling", marker = "extra == 'docling'", specifier = "==2.68.0" },
-    { name = "docling-core", specifier = "==2.59.0" },
+    { name = "docling", marker = "extra == 'docling'", specifier = "==2.69.1" },
+    { name = "docling-core", specifier = "==2.60.1" },
    { name = "httpx", specifier = ">=0.28.1" },
-    { name = "lancedb", specifier = "==0.26.1" },
+    { name = "lancedb", specifier = "==0.27.0" },
    { name = "mxbai-rerank", marker = "extra == 'mxbai'", specifier = ">=0.1.6" },
    { name = "opencv-python-headless", marker = "extra == 'docling'", specifier = ">=4.13.0.90" },
    { name = "pathspec", specifier = ">=1.0.3" },
@ -1437,8 +1437,9 @@ requires-dist = [
    { name = "pydantic-ai-slim", extras = ["google"], marker = "extra == 'google'" },
    { name = "pydantic-ai-slim", extras = ["groq"], marker = "extra == 'groq'" },
    { name = "pydantic-ai-slim", extras = ["mistral"], marker = "extra == 'mistral'" },
-    { name = "pydantic-ai-slim", extras = ["openai", "fastmcp", "logfire", "ag-ui"], specifier = ">=1.44.0" },
+    { name = "pydantic-ai-slim", extras = ["openai", "fastmcp", "logfire", "ag-ui"], specifier = ">=1.46.0" },
    { name = "pydantic-ai-slim", extras = ["vertexai"], marker = "extra == 'vertexai'" },
+    { name = "pydantic-ai-slim", extras = ["voyageai"], marker = "extra == 'voyageai'" },
    { name = "python-dotenv", specifier = ">=1.2.1" },
    { name = "pyyaml", specifier = ">=6.0.3" },
    { name = "rich", specifier = ">=14.2.0" },
@ -1447,7 +1448,6 @@ requires-dist = [
    { name = "torch", marker = "extra == 'jina'", specifier = ">=2.0.0" },
    { name = "transformers", marker = "extra == 'jina'", specifier = ">=4.40.0" },
    { name = "typer", specifier = ">=0.19.2,<0.20.0" },
-    { name = "voyageai", marker = "extra == 'voyageai'", specifier = ">=0.3.7" },
    { name = "watchfiles", specifier = ">=1.1.1" },
    { name = "zeroentropy", marker = "extra == 'zeroentropy'", specifier = ">=0.1.0a7" },
 ]
@ -1847,7 +1847,7 @@ wheels = [

 [[package]]
 name = "lancedb"
-version = "0.26.1"
+version = "0.27.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "deprecation" },
@ -1859,12 +1859,12 @@ dependencies = [
    { name = "tqdm" },
 ]
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