k

backend/danswer/configs/model_configs.py

@@ -12,17 +12,13 @@ import os
 # The useable models configured as below must be SentenceTransformer compatible
 # NOTE: DO NOT CHANGE SET THESE UNLESS YOU KNOW WHAT YOU ARE DOING
 # IDEALLY, YOU SHOULD CHANGE EMBEDDING MODELS VIA THE UI
-DEFAULT_DOCUMENT_ENCODER_MODEL = "intfloat/e5-base-v2"
-DOCUMENT_ENCODER_MODEL = (
-    os.environ.get("DOCUMENT_ENCODER_MODEL") or DEFAULT_DOCUMENT_ENCODER_MODEL
-)
+DEFAULT_DOCUMENT_ENCODER_MODEL = "nomic-ai/nomic-embed-text-v1"
+DOCUMENT_ENCODER_MODEL = "nomic-ai/nomic-embed-text-v1"
 # If the below is changed, Vespa deployment must also be changed
 DOC_EMBEDDING_DIM = int(os.environ.get("DOC_EMBEDDING_DIM") or 768)
 # Model should be chosen with 512 context size, ideally don't change this
 DOC_EMBEDDING_CONTEXT_SIZE = 512
-NORMALIZE_EMBEDDINGS = (
-    os.environ.get("NORMALIZE_EMBEDDINGS") or "true"
-).lower() == "true"
+NORMALIZE_EMBEDDINGS = False
 
 # Old default model settings, which are needed for an automatic easy upgrade
 OLD_DEFAULT_DOCUMENT_ENCODER_MODEL = "thenlper/gte-small"
@@ -34,8 +30,8 @@ OLD_DEFAULT_MODEL_NORMALIZE_EMBEDDINGS = False
 SIM_SCORE_RANGE_LOW = float(os.environ.get("SIM_SCORE_RANGE_LOW") or 0.0)
 SIM_SCORE_RANGE_HIGH = float(os.environ.get("SIM_SCORE_RANGE_HIGH") or 1.0)
 # Certain models like e5, BGE, etc use a prefix for asymmetric retrievals (query generally shorter than docs)
-ASYM_QUERY_PREFIX = os.environ.get("ASYM_QUERY_PREFIX", "query: ")
-ASYM_PASSAGE_PREFIX = os.environ.get("ASYM_PASSAGE_PREFIX", "passage: ")
+ASYM_QUERY_PREFIX = "search_query: "
+ASYM_PASSAGE_PREFIX = "search_document: "
 # Purely an optimization, memory limitation consideration
 BATCH_SIZE_ENCODE_CHUNKS = 8
 # For score display purposes, only way is to know the expected ranges

backend/model_server/encoders.py

@@ -190,7 +190,7 @@ def get_embedding_model(
 
     if model_name not in _GLOBAL_MODELS_DICT:
         logger.info(f"Loading {model_name}")
-        model = SentenceTransformer(model_name)
+        model = SentenceTransformer(model_name, trust_remote_code=True)
         model.max_seq_length = max_context_length
         _GLOBAL_MODELS_DICT[model_name] = model
     elif max_context_length != _GLOBAL_MODELS_DICT[model_name].max_seq_length:

backend/requirements/model_server.txt

@@ -1,3 +1,4 @@
+einops==0.8.0
 fastapi==0.109.2
 h5py==3.9.0
 pydantic==1.10.13