feat: Use any embedding model with ragas

src/raglite/_eval.py

@@ -210,16 +210,33 @@ def evaluate(
     try:
         from datasets import Dataset
         from langchain_community.chat_models import ChatLiteLLM
-        from langchain_community.embeddings import LlamaCppEmbeddings
         from langchain_community.llms import LlamaCpp
         from ragas import RunConfig
         from ragas import evaluate as ragas_evaluate
+        from ragas.embeddings import BaseRagasEmbeddings
 
+        from raglite._config import RAGLiteConfig
+        from raglite._embed import embed_sentences
         from raglite._litellm import LlamaCppPythonLLM
+
     except ImportError as import_error:
         error_message = "To use the `evaluate` function, please install the `ragas` extra."
         raise ImportError(error_message) from import_error
 
+    class RAGLiteRagasEmbeddings(BaseRagasEmbeddings):
+        def __init__(self, config: RAGLiteConfig):
+            self.config = config or RAGLiteConfig()
+
+        def embed_query(self, text: str) -> list[float]:
+            # Embed the query text using RAGLite's embedding function
+            embeddings = embed_sentences([text], config=self.config)
+            return embeddings[0].tolist()  # type: ignore[no-any-return]
+
+        def embed_documents(self, texts: list[str]) -> list[list[float]]:
+            # Embed a list of documents using RAGLite's embedding function
+            embeddings = embed_sentences(texts, config=self.config)
+            return embeddings.tolist()  # type: ignore[no-any-return]
+
     # Create a set of answered evals if not provided.
     config = config or RAGLiteConfig()
     answered_evals_df = (
@@ -239,23 +256,12 @@ def evaluate(
         )
     else:
         lc_llm = ChatLiteLLM(model=config.llm)  # type: ignore[call-arg]
-    # Load the embedder.
-    if not config.embedder.startswith("llama-cpp-python"):
-        error_message = "Currently, only `llama-cpp-python` embedders are supported."
-        raise NotImplementedError(error_message)
-    embedder = LlamaCppPythonLLM().llm(model=config.embedder, embedding=True)
-    lc_embedder = LlamaCppEmbeddings(  # type: ignore[call-arg]
-        model_path=embedder.model_path,
-        n_batch=embedder.n_batch,
-        n_ctx=embedder.n_ctx(),
-        n_gpu_layers=-1,
-        verbose=embedder.verbose,
-    )
+    embedder = RAGLiteRagasEmbeddings(config=config)
     # Evaluate the answered evals with Ragas.
     evaluation_df = ragas_evaluate(
         dataset=Dataset.from_pandas(answered_evals_df),
         llm=lc_llm,
-        embeddings=lc_embedder,
+        embeddings=embedder,
         run_config=RunConfig(max_workers=1),
     ).to_pandas()
     return evaluation_df