Fetching metadata from the HF Docker repository...
-
cmc
-
cmc
-
cmc
-
cmc
-
cmc
-
cmc
-
cmc
-
clean final: HF Inference API embeddings (no disk)
-
clean final: HF Inference API embeddings (no disk)
-
clean final: HF Inference API embeddings (no disk)
-
clean final: HF Inference API embeddings (no disk)
-
use HuggingFaceInferenceAPIEmbeddings (current wrapper)
-
drop forbidden api_url key (HF Inference default)
-
migrate to HF Inference Providers router (no disk, no cache)
-
use optimum ONNX MiniLM (no disk, no internet)
-
force offline local MiniLM folder (no cache)
-
use pre-installed MiniLM (no cache, no internet)
-
force offline local MiniLM (no internet, no cache)
-
load local MiniLM folder (no optimum, no internet)
-
use optimum ONNX MiniLM via low-level wrapper (no disk)
-
use optimum ONNX MiniLM (no disk, no internet)
-
load local ONNX MiniLM with absolute path (no cache)
-
okay
-
o
-
force local st_model folder (no internet, no cache)
-
add local MiniLM-L6-v2 model (384 KB, no cache, no internet)
-
load ST model once into RAM (no disk, no external API)
-
migrate to HF Inference Providers router (no disk)
-
use in-memory ST model (no disk, no inference API)
-
cmc
-
switch to HuggingFaceInferenceAPIEmbeddings (no disk)
-
use public HF inference endpoint for embeddings (no disk)
-
cmc
-
create writable /code/.cache dir
-
force cache into writable /code/.cache
-
bullet-proof fetch + empty corpus fallback
-
drop build cache scripts – use RAM-only rag
-
cmc
-
switch to text-only dataset for offline build
-
cmc
-
cmc
-
o
-
o
-
cmc
-
cmc
-
o
-
force 0.3+ langchain wheels
-
cmc
-
add base langchain package
