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from __future__ import annotations
from typing import List, Any
import os
class SimpleDummyEmbeddings:
def __init__(self, dim: int = 384):
self.dimension = dim
def embed_documents(self, texts: List[str]) -> List[List[float]]:
vecs: List[List[float]] = []
for t in texts:
h = abs(hash(t.lower()))
v = [(float((h >> i) & 1)) for i in range(self.dimension)]
norm = sum(x * x for x in v) ** 0.5 or 1.0
vecs.append([x / norm for x in v])
return vecs
def embed_query(self, text: str) -> List[float]:
return self.embed_documents([text])[0]
def build_embeddings() -> Any:
# Allow forcing lightweight embeddings to speed up cold starts (e.g., on Spaces)
if os.environ.get("USE_DUMMY_EMBEDDINGS", "").lower() in ("1", "true", "yes"): # pragma: no cover
return SimpleDummyEmbeddings()
try:
from langchain_huggingface import HuggingFaceEmbeddings # type: ignore
except Exception:
try:
from langchain_community.embeddings import HuggingFaceEmbeddings # type: ignore
except Exception:
HuggingFaceEmbeddings = None # type: ignore
if "HuggingFaceEmbeddings" in locals() and HuggingFaceEmbeddings is not None: # type: ignore
try:
# Set cache directory to a writable location
cache_dir = "/tmp/sentence_transformers"
if os.path.exists("/data"):
cache_dir = "/data/sentence_transformers"
# Set environment variable for sentence-transformers cache
os.environ['SENTENCE_TRANSFORMERS_HOME'] = cache_dir
return HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={"device": "cpu"},
encode_kwargs={"normalize_embeddings": True},
cache_folder=cache_dir,
)
except Exception as e:
print(f"⚠️ Could not load HuggingFace embeddings: {e}")
print("🔄 Using dummy embeddings fallback")
return SimpleDummyEmbeddings()
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