LEADS-113: Added support for gemini embedding models

pyproject.toml

@@ -27,6 +27,7 @@ dependencies = [
     "diskcache>=5.6.3",
     "tenacity>=9.1.2",
     "langchain[huggingface]>=0.3.27",
+    "langchain-google-genai>=2.0.0",
     "sentence-transformers>=5.1.0",
 ]
 

src/lightspeed_evaluation/core/embedding/manager.py

@@ -24,6 +24,8 @@ def _validate_config(self) -> None:
         """Validate config and env variables."""
         if self.config.provider == "openai":
             validate_provider_env("openai")
+        elif self.config.provider == "gemini":
+            validate_provider_env("gemini")
         elif self.config.provider == "huggingface":
             pass
         else:

src/lightspeed_evaluation/core/embedding/ragas.py

@@ -1,5 +1,6 @@
 """Ragas Embedding Manager - Ragas specific embedding wrapper."""
 
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_openai import OpenAIEmbeddings
 from ragas.cache import DiskCacheBackend
@@ -19,6 +20,7 @@ def __init__(self, embedding_manager: EmbeddingManager):
         embedding_class = {
             "openai": OpenAIEmbeddings,
             "huggingface": HuggingFaceEmbeddings,
+            "gemini": GoogleGenerativeAIEmbeddings,
         }.get(config.provider)
         if not embedding_class:
             raise RuntimeError(f"Unknown embedding provider {config.provider}")

src/lightspeed_evaluation/core/models/system.py

@@ -106,7 +106,7 @@ class EmbeddingConfig(BaseModel):
     @field_validator("provider")
     @classmethod
     def _validate_provider(cls, v: str) -> str:
-        allowed = {"openai", "huggingface"}
+        allowed = {"openai", "huggingface", "gemini"}
         if v not in allowed:
             raise ValueError(
                 f"Unsupported embedding provider '{v}'. Allowed: {sorted(allowed)}"