Separate local mode into llms-llama-cpp and embeddings-huggingface for clarity

private_gpt/settings/settings.py

@@ -81,7 +81,7 @@ class DataSettings(BaseModel):
 
 
 class LLMSettings(BaseModel):
-    mode: Literal["local", "openai", "openailike", "sagemaker", "mock", "ollama"]
+    mode: Literal["llamacpp", "openai", "openailike", "sagemaker", "mock", "ollama"]
     max_new_tokens: int = Field(
         256,
         description="The maximum number of token that the LLM is authorized to generate in one completion.",
@@ -104,12 +104,9 @@ class VectorstoreSettings(BaseModel):
     database: Literal["chroma", "qdrant", "pgvector"]
 
 
-class LocalSettings(BaseModel):
+class LlamaCPPSettings(BaseModel):
     llm_hf_repo_id: str
     llm_hf_model_file: str
-    embedding_hf_model_name: str = Field(
-        description="Name of the HuggingFace model to use for embeddings"
-    )
     prompt_style: Literal["default", "llama2", "tag", "mistral", "chatml"] = Field(
         "llama2",
         description=(
@@ -123,8 +120,14 @@ class LocalSettings(BaseModel):
     )
 
 
+class HuggingFaceSettings(BaseModel):
+    embedding_hf_model_name: str = Field(
+        description="Name of the HuggingFace model to use for embeddings"
+    )
+
+
 class EmbeddingSettings(BaseModel):
-    mode: Literal["local", "openai", "sagemaker", "mock"]
+    mode: Literal["huggingface", "openai", "sagemaker", "mock"]
     ingest_mode: Literal["simple", "batch", "parallel"] = Field(
         "simple",
         description=(
@@ -292,7 +295,8 @@ class Settings(BaseModel):
     ui: UISettings
     llm: LLMSettings
     embedding: EmbeddingSettings
-    local: LocalSettings
+    llamacpp: LlamaCPPSettings
+    huggingface: HuggingFaceSettings
     sagemaker: SagemakerSettings
     openai: OpenAISettings
     ollama: OllamaSettings