feat: Part 1 - Add RAG Embedding Interface (#628)

**Reason for Change**:
This series of PR will integrate llamaindex RAG service for Kaito. 

This first PR introduces the Embedding Model Interface and two classes
that implement it `huggingface_local` and `huggingface_remote`. These
allows users to specify both local and remote HuggingFace models for
text embeddings.

ragengine/embedding/base.py

@@ -0,0 +1,13 @@
+from abc import ABC, abstractmethod
+
+
+class BaseEmbeddingModel(ABC):
+    @abstractmethod
+    def get_text_embedding(self, text: str):
+        """Returns the text embedding for a given input string."""
+        pass
+
+    @abstractmethod
+    def get_embedding_dimension(self) -> int:
+        """Returns the embedding dimension for the model."""
+        pass

ragengine/embedding/huggingface_local.py

@@ -0,0 +1,19 @@
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+from .base import BaseEmbeddingModel
+
+
+class LocalHuggingFaceEmbedding(BaseEmbeddingModel):
+    def __init__(self, model_name: str):
+        self.model = HuggingFaceEmbedding(model_name=model_name) # TODO: Ensure/test loads on GPU (when available)
+
+    def get_text_embedding(self, text: str):
+        """Returns the text embedding for a given input string."""
+        return self.model.get_text_embedding(text)
+
+    def get_embedding_dimension(self) -> int:
+        """Infers the embedding dimension by making a local call to get the embedding of a dummy text."""
+        dummy_input = "This is a dummy sentence."
+        embedding = self.get_text_embedding(dummy_input)
+
+        return len(embedding)

ragengine/embedding/huggingface_remote.py

@@ -0,0 +1,20 @@
+from llama_index.embeddings.huggingface_api import \
+    HuggingFaceInferenceAPIEmbedding
+
+from .base import BaseEmbeddingModel
+
+
+class RemoteHuggingFaceEmbedding(BaseEmbeddingModel):
+    def __init__(self, model_name: str, api_key: str):
+        self.model = HuggingFaceInferenceAPIEmbedding(model_name=model_name, token=api_key)
+
+    def get_text_embedding(self, text: str):
+        """Returns the text embedding for a given input string."""
+        return self.model.get_text_embedding(text)
+
+    def get_embedding_dimension(self) -> int:
+        """Infers the embedding dimension by making a remote call to get the embedding of a dummy text."""
+        dummy_input = "This is a dummy sentence."
+        embedding = self.get_text_embedding(dummy_input)
+
+        return len(embedding)

ragengine/requirements.txt

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+# RAG Library Requirements
+llama-index
+llama-index-embeddings-huggingface
+fastapi
+faiss-cpu
+llama-index-vector-stores-faiss
+uvicorn