two version of R2R are here HEAD master

1 files changed, 83 insertions, 0 deletions

diff --git a/R2R/r2r/base/providers/embedding_provider.py b/R2R/r2r/base/providers/embedding_provider.py
new file mode 100755
index 00000000..8f3af56f
--- /dev/null
+++ b/R2R/r2r/base/providers/embedding_provider.py
@@ -0,0 +1,83 @@
+import logging
+from abc import abstractmethod
+from enum import Enum
+from typing import Optional
+
+from ..abstractions.search import VectorSearchResult
+from .base_provider import Provider, ProviderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingConfig(ProviderConfig):
+    """A base embedding configuration class"""
+
+    provider: Optional[str] = None
+    base_model: Optional[str] = None
+    base_dimension: Optional[int] = None
+    rerank_model: Optional[str] = None
+    rerank_dimension: Optional[int] = None
+    rerank_transformer_type: Optional[str] = None
+    batch_size: int = 1
+
+    def validate(self) -> None:
+        if self.provider not in self.supported_providers:
+            raise ValueError(f"Provider '{self.provider}' is not supported.")
+
+    @property
+    def supported_providers(self) -> list[str]:
+        return [None, "openai", "ollama", "sentence-transformers"]
+
+
+class EmbeddingProvider(Provider):
+    """An abstract class to provide a common interface for embedding providers."""
+
+    class PipeStage(Enum):
+        BASE = 1
+        RERANK = 2
+
+    def __init__(self, config: EmbeddingConfig):
+        if not isinstance(config, EmbeddingConfig):
+            raise ValueError(
+                "EmbeddingProvider must be initialized with a `EmbeddingConfig`."
+            )
+        logger.info(f"Initializing EmbeddingProvider with config {config}.")
+
+        super().__init__(config)
+
+    @abstractmethod
+    def get_embedding(self, text: str, stage: PipeStage = PipeStage.BASE):
+        pass
+
+    async def async_get_embedding(
+        self, text: str, stage: PipeStage = PipeStage.BASE
+    ):
+        return self.get_embedding(text, stage)
+
+    @abstractmethod
+    def get_embeddings(
+        self, texts: list[str], stage: PipeStage = PipeStage.BASE
+    ):
+        pass
+
+    async def async_get_embeddings(
+        self, texts: list[str], stage: PipeStage = PipeStage.BASE
+    ):
+        return self.get_embeddings(texts, stage)
+
+    @abstractmethod
+    def rerank(
+        self,
+        query: str,
+        results: list[VectorSearchResult],
+        stage: PipeStage = PipeStage.RERANK,
+        limit: int = 10,
+    ):
+        pass
+
+    @abstractmethod
+    def tokenize_string(
+        self, text: str, model: str, stage: PipeStage
+    ) -> list[int]:
+        """Tokenizes the input string."""
+        pass