Testing

.github/workflows/docker-build-push-backend-container-on-tag.yml

@@ -31,14 +31,5 @@ jobs:
  push: true
  tags: |
  danswer/danswer-backend:${{ github.ref_name }}
- danswer/danswer-backend:latest
  build-args: |
  DANSWER_VERSION=${{ github.ref_name }}
-
- - name: Run Trivy vulnerability scanner
- uses: aquasecurity/trivy-action@master
- with:
- # To run locally: trivy image --severity HIGH,CRITICAL danswer/danswer-backend
- image-ref: docker.io/danswer/danswer-backend:${{ github.ref_name }}
- severity: 'CRITICAL,HIGH'
- trivyignores: ./backend/.trivyignore

.github/workflows/docker-build-push-model-server-container-on-tag.yml

@@ -31,12 +31,5 @@ jobs:
  push: true
  tags: |
  danswer/danswer-model-server:${{ github.ref_name }}
- danswer/danswer-model-server:latest
  build-args: |
  DANSWER_VERSION=${{ github.ref_name }}
-
- - name: Run Trivy vulnerability scanner
- uses: aquasecurity/trivy-action@master
- with:
- image-ref: docker.io/danswer/danswer-model-server:${{ github.ref_name }}
- severity: 'CRITICAL,HIGH'

.github/workflows/docker-build-push-web-container-on-tag.yml

@@ -31,12 +31,5 @@ jobs:
  push: true
  tags: |
  danswer/danswer-web-server:${{ github.ref_name }}
- danswer/danswer-web-server:latest
  build-args: |
  DANSWER_VERSION=${{ github.ref_name }}
-
- - name: Run Trivy vulnerability scanner
- uses: aquasecurity/trivy-action@master
- with:
- image-ref: docker.io/danswer/danswer-web-server:${{ github.ref_name }}
- severity: 'CRITICAL,HIGH'

backend/danswer/search/search_nlp_models.py

@@ -5,7 +5,9 @@
 from typing import TYPE_CHECKING
 
 import requests
+from huggingface_hub.constants import HF_HOME
 from transformers import logging as transformer_logging # type:ignore
+from transformers.utils import TRANSFORMERS_CACHE
 
 from danswer.configs.model_configs import DOC_EMBEDDING_CONTEXT_SIZE
 from danswer.configs.model_configs import DOCUMENT_ENCODER_MODEL
@@ -58,6 +60,17 @@ def get_default_tokenizer(model_name: str | None = None) -> "AutoTokenizer":
  # This could be inaccurate
  model_name = DOCUMENT_ENCODER_MODEL
 
+ print(f"HF Home: {HF_HOME}")
+ print(f"Cache dir: {TRANSFORMERS_CACHE}")
+
+ from model_server.hf import hf_hub_download
+ from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
+
+ hf_file_path = hf_hub_download(
+ model_name, TOKENIZER_CONFIG_FILE, local_files_only=True
+ )
+ print("hf_file_path", hf_file_path)
+
  _TOKENIZER = (AutoTokenizer.from_pretrained(model_name), model_name)
 
  if hasattr(_TOKENIZER[0], "is_fast") and _TOKENIZER[0].is_fast:

backend/model_server/custom_models.py

@@ -3,8 +3,10 @@
 import numpy as np
 import tensorflow as tf # type: ignore
 from fastapi import APIRouter
+from huggingface_hub.constants import HF_HOME
 from transformers import AutoTokenizer # type: ignore
 from transformers import TFDistilBertForSequenceClassification
+from transformers.utils import TRANSFORMERS_CACHE
 
 from model_server.constants import MODEL_WARM_UP_STRING
 from model_server.utils import simple_log_function_time
@@ -24,6 +26,17 @@
 def get_intent_model_tokenizer(
  model_name: str = INTENT_MODEL_VERSION,
 ) -> "AutoTokenizer":
+ print(f"HF Home: {HF_HOME}")
+ print(f"Cache dir: {TRANSFORMERS_CACHE}")
+
+ from model_server.hf import hf_hub_download
+ from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
+
+ hf_file_path = hf_hub_download(
+ model_name, TOKENIZER_CONFIG_FILE, local_files_only=True
+ )
+ print("hf_file_path", hf_file_path)
+
  global _INTENT_TOKENIZER
  if _INTENT_TOKENIZER is None:
  _INTENT_TOKENIZER = AutoTokenizer.from_pretrained(model_name)
@@ -36,6 +49,8 @@ def get_local_intent_model(
 ) -> TFDistilBertForSequenceClassification:
  global _INTENT_MODEL
  if _INTENT_MODEL is None or max_context_length != _INTENT_MODEL.max_seq_length:
+ print(f"HF Home: {HF_HOME}")
+ print(f"Cache dir: {TRANSFORMERS_CACHE}")
  _INTENT_MODEL = TFDistilBertForSequenceClassification.from_pretrained(
  model_name
  )