added max_length for embedding models. Some models crash if not provided, see https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer/discussions/7#65ce1ca0102df4e9e00ff823

private_gpt/components/embedding/embedding_component.py

@@ -31,6 +31,7 @@ class EmbeddingComponent:
                 self.embedding_model = HuggingFaceEmbedding(
                     model_name=settings.huggingface.embedding_hf_model_name,
                     cache_folder=str(models_cache_path),
+                    max_length=settings.huggingface.embedding_hf_max_length,
                 )
             case "sagemaker":
                 try:

private_gpt/settings/settings.py

@@ -151,6 +151,10 @@ class HuggingFaceSettings(BaseModel):
     embedding_hf_model_name: str = Field(
         description="Name of the HuggingFace model to use for embeddings"
     )
+    embedding_hf_max_length: int = Field(
+        None,
+        description="Some embedding models have a maximum length for input, provide here for not crashing"
+    )
     access_token: str = Field(
         None,
         description="Huggingface access token, required to download some models",

settings.yaml

@@ -69,6 +69,7 @@ embedding:
 
 huggingface:
   embedding_hf_model_name: BAAI/bge-small-en-v1.5
+  embedding_hf_max_length: 512 # some models have a maximum length for input
   access_token: ${HUGGINGFACE_TOKEN:}
 
 vectorstore: