Merge branch 'main' into lt/better_no_default_class_error

examples/document-search/chroma.py

@@ -35,7 +35,8 @@
 
 from chromadb import EphemeralClient
 
-from ragbits.core.embeddings.litellm import LiteLLMEmbeddings
+from ragbits.core.embeddings.litellm import LiteLLMEmbeddings, LiteLLMEmbeddingsOptions
+from ragbits.core.vector_stores import VectorStoreOptions
 from ragbits.core.vector_stores.chroma import ChromaVectorStore
 from ragbits.document_search import DocumentSearch, SearchConfig
 from ragbits.document_search.documents.document import DocumentMeta
@@ -70,10 +71,18 @@ async def main() -> None:
     """
     embedder = LiteLLMEmbeddings(
         model="text-embedding-3-small",
+        default_options=LiteLLMEmbeddingsOptions(
+            dimensions=1024,
+            timeout=1000,
+        ),
     )
     vector_store = ChromaVectorStore(
         client=EphemeralClient(),
         index_name="jokes",
+        default_options=VectorStoreOptions(
+            k=10,
+            max_distance=0.22,
+        ),
     )
     document_search = DocumentSearch(
         embedder=embedder,

packages/ragbits-core/CHANGELOG.md

@@ -2,6 +2,10 @@
 
 ## Unreleased
 
+### Changed
+
+- Feat: Implement generic Options class (#248).
+
 ## 0.5.1 (2024-12-09)
 
 ### Changed

packages/ragbits-core/src/ragbits/core/embeddings/__init__.py

@@ -1,5 +1,5 @@
-from .base import Embeddings, EmbeddingType
+from .base import Embeddings, EmbeddingsOptionsT, EmbeddingType
 from .litellm import LiteLLMEmbeddings
 from .noop import NoopEmbeddings
 
-__all__ = ["EmbeddingType", "Embeddings", "LiteLLMEmbeddings", "NoopEmbeddings"]
+__all__ = ["EmbeddingType", "Embeddings", "EmbeddingsOptionsT", "LiteLLMEmbeddings", "NoopEmbeddings"]

packages/ragbits-core/src/ragbits/core/embeddings/base.py

@@ -1,9 +1,12 @@
 from abc import ABC, abstractmethod
 from enum import Enum
-from typing import ClassVar
+from typing import ClassVar, TypeVar
 
 from ragbits.core import embeddings
-from ragbits.core.utils.config_handling import WithConstructionConfig
+from ragbits.core.options import Options
+from ragbits.core.utils.config_handling import ConfigurableComponent
+
+EmbeddingsOptionsT = TypeVar("EmbeddingsOptionsT", bound=Options)
 
 
 class EmbeddingType(Enum):
@@ -17,25 +20,27 @@ class EmbeddingType(Enum):
     allowing for the creation of different embeddings for the same element.
     """
 
-    TEXT: str = "text"
-    IMAGE: str = "image"
+    TEXT = "text"
+    IMAGE = "image"
 
 
-class Embeddings(WithConstructionConfig, ABC):
+class Embeddings(ConfigurableComponent[EmbeddingsOptionsT], ABC):
     """
     Abstract client for communication with embedding models.
     """
 
+    options_cls: type[EmbeddingsOptionsT]
     default_module: ClassVar = embeddings
     configuration_key: ClassVar = "embedder"
 
     @abstractmethod
-    async def embed_text(self, data: list[str]) -> list[list[float]]:
+    async def embed_text(self, data: list[str], options: EmbeddingsOptionsT | None = None) -> list[list[float]]:
         """
         Creates embeddings for the given strings.
 
         Args:
             data: List of strings to get embeddings for.
+            options: Additional settings used by the Embeddings model.
 
         Returns:
             List of embeddings for the given strings.
@@ -50,12 +55,13 @@ def image_support(self) -> bool:  # noqa: PLR6301
         """
         return False
 
-    async def embed_image(self, images: list[bytes]) -> list[list[float]]:
+    async def embed_image(self, images: list[bytes], options: EmbeddingsOptionsT | None = None) -> list[list[float]]:
         """
         Creates embeddings for the given images.
 
         Args:
             images: List of images to get embeddings for.
+            options: Additional settings used by the Embeddings model.
 
         Returns:
             List of embeddings for the given images.

packages/ragbits-core/src/ragbits/core/embeddings/litellm.py

@@ -8,17 +8,33 @@
     EmbeddingResponseError,
     EmbeddingStatusError,
 )
+from ragbits.core.options import Options
+from ragbits.core.types import NOT_GIVEN, NotGiven
 
 
-class LiteLLMEmbeddings(Embeddings):
+class LiteLLMEmbeddingsOptions(Options):
+    """
+    Dataclass that represents available call options for the LiteLLMEmbeddingClient client.
+    Each of them is described in the [LiteLLM documentation](https://docs.litellm.ai/docs/embedding/supported_embedding#optional-litellm-fields).
+    """
+
+    dimensions: int | None | NotGiven = NOT_GIVEN
+    timeout: int | None | NotGiven = NOT_GIVEN
+    user: str | None | NotGiven = NOT_GIVEN
+    encoding_format: str | None | NotGiven = NOT_GIVEN
+
+
+class LiteLLMEmbeddings(Embeddings[LiteLLMEmbeddingsOptions]):
     """
     Client for creating text embeddings using LiteLLM API.
     """
 
+    options_cls = LiteLLMEmbeddingsOptions
+
     def __init__(
         self,
         model: str = "text-embedding-3-small",
-        options: dict | None = None,
+        default_options: LiteLLMEmbeddingsOptions | None = None,
         api_base: str | None = None,
         api_key: str | None = None,
         api_version: str | None = None,
@@ -29,26 +45,26 @@ def __init__(
         Args:
             model: Name of the [LiteLLM supported model](https://docs.litellm.ai/docs/embedding/supported_embedding)\
                 to be used. Default is "text-embedding-3-small".
-            options: Additional options to pass to the LiteLLM API.
+            default_options: Defualt options to pass to the LiteLLM API.
             api_base: The API endpoint you want to call the model with.
             api_key: API key to be used. API key to be used. If not specified, an environment variable will be used,
                 for more information, follow the instructions for your specific vendor in the\
                 [LiteLLM documentation](https://docs.litellm.ai/docs/embedding/supported_embedding).
             api_version: The API version for the call.
         """
-        super().__init__()
+        super().__init__(default_options=default_options)
         self.model = model
-        self.options = options or {}
         self.api_base = api_base
         self.api_key = api_key
         self.api_version = api_version
 
-    async def embed_text(self, data: list[str]) -> list[list[float]]:
+    async def embed_text(self, data: list[str], options: LiteLLMEmbeddingsOptions | None = None) -> list[list[float]]:
         """
         Creates embeddings for the given strings.
 
         Args:
             data: List of strings to get embeddings for.
+            options: Additional options to pass to the Lite LLM API.
 
         Returns:
             List of embeddings for the given strings.
@@ -59,12 +75,14 @@ async def embed_text(self, data: list[str]) -> list[list[float]]:
             EmbeddingStatusError: If the embedding API returns an error status code.
             EmbeddingResponseError: If the embedding API response is invalid.
         """
+        merged_options = (self.default_options | options) if options else self.default_options
+
         with trace(
             data=data,
             model=self.model,
             api_base=self.api_base,
             api_version=self.api_version,
-            options=self.options,
+            options=merged_options.dict(),
         ) as outputs:
             try:
                 response = await litellm.aembedding(
@@ -73,7 +91,7 @@ async def embed_text(self, data: list[str]) -> list[list[float]]:
                     api_base=self.api_base,
                     api_key=self.api_key,
                     api_version=self.api_version,
-                    **self.options,
+                    **merged_options.dict(),
                 )
             except litellm.openai.APIConnectionError as exc:
                 raise EmbeddingConnectionError() from exc

packages/ragbits-core/src/ragbits/core/embeddings/local.py

@@ -1,5 +1,8 @@
 from collections.abc import Iterator
 
+from ragbits.core.embeddings import Embeddings
+from ragbits.core.options import Options
+
 try:
     import torch
     import torch.nn.functional as F
@@ -9,32 +12,42 @@
 except ImportError:
     HAS_LOCAL_EMBEDDINGS = False
 
-from ragbits.core.embeddings import Embeddings
 
+class LocalEmbeddingsOptions(Options):
+    """
+    Dataclass that represents available call options for the LocalEmbeddings client.
+    """
 
-class LocalEmbeddings(Embeddings):
+    batch_size: int = 1
+
+
+class LocalEmbeddings(Embeddings[LocalEmbeddingsOptions]):
     """
     Class for interaction with any encoder available in HuggingFace.
     """
 
+    options_cls = LocalEmbeddingsOptions
+
     def __init__(
         self,
         model_name: str,
         api_key: str | None = None,
+        default_options: LocalEmbeddingsOptions | None = None,
     ) -> None:
         """Constructs a new local LLM instance.
 
         Args:
             model_name: Name of the model to use.
             api_key: The API key for Hugging Face authentication.
+            default_options: Default options for the embedding model.
 
         Raises:
             ImportError: If the 'local' extra requirements are not installed.
         """
         if not HAS_LOCAL_EMBEDDINGS:
             raise ImportError("You need to install the 'local' extra requirements to use local embeddings models")
 
-        super().__init__()
+        super().__init__(default_options=default_options)
 
         self.hf_api_key = api_key
         self.model_name = model_name
@@ -43,18 +56,20 @@ def __init__(
         self.model = AutoModel.from_pretrained(self.model_name, token=self.hf_api_key).to(self.device)
         self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, token=self.hf_api_key)
 
-    async def embed_text(self, data: list[str], batch_size: int = 1) -> list[list[float]]:
+    async def embed_text(self, data: list[str], options: LocalEmbeddingsOptions | None = None) -> list[list[float]]:
         """Calls the appropriate encoder endpoint with the given data and options.
 
         Args:
             data: List of strings to get embeddings for.
-            batch_size: Batch size.
+            options: Additional options to pass to the embedding model.
 
         Returns:
             List of embeddings for the given strings.
         """
+        merged_options = (self.default_options | options) if options else self.default_options
+
         embeddings = []
-        for batch in self._batch(data, batch_size):
+        for batch in self._batch(data, merged_options.batch_size):
             batch_dict = self.tokenizer(
                 batch,
                 max_length=self.tokenizer.model_max_length,

packages/ragbits-core/src/ragbits/core/embeddings/noop.py

@@ -1,8 +1,9 @@
 from ragbits.core.audit import traceable
 from ragbits.core.embeddings.base import Embeddings
+from ragbits.core.options import Options
 
 
-class NoopEmbeddings(Embeddings):
+class NoopEmbeddings(Embeddings[Options]):
     """
     A no-op implementation of the Embeddings class.
 
@@ -11,13 +12,16 @@ class NoopEmbeddings(Embeddings):
     or as a placeholder when an actual embedding model is not required.
     """
 
+    options_cls = Options
+
     @traceable
-    async def embed_text(self, data: list[str]) -> list[list[float]]:  # noqa: PLR6301
+    async def embed_text(self, data: list[str], options: Options | None = None) -> list[list[float]]:  # noqa: PLR6301
         """
         Embeds a list of strings into a list of vectors.
 
         Args:
             data: A list of input text strings to embed.
+            options: Additional settings used by the Embeddings model.
 
         Returns:
             A list of embedding vectors, where each vector