Load bge-m3 as safetensors for x86; update README, add EINSTIEG.md

- EMBEDDING_REVISION pins the official safetensors conversion of bge-m3, so the x86
  env (torch 2.2.2) can load it; identical vectors, no index rebuild
- README rewritten for the current state (personas, providers, retrieval, environments)
- EINSTIEG.md: short German guide to running and configuring the chat

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

local_rag.py

@@ -28,6 +28,10 @@ CHUNK_OVERLAP = 400
 # Multilingual embedding model with a long input window (8192 tokens), so chunks
 # are never truncated. Changing it requires rebuilding the index.
 EMBEDDING_MODEL = "BAAI/bge-m3"
+# Same weights as safetensors (the official Hugging Face conversion, refs/pr/130). The main
+# branch only has pytorch_model.bin, which transformers refuses to load with torch < 2.6;
+# macOS x86 (conda env local_rag) is stuck at torch 2.2.2. Identical vectors, no index rebuild.
+EMBEDDING_REVISION = "9a0624b896d81da7492a910ffa53731274b6cf3d"
 
 # How PDFs are read: "pypdf" (PyPDFLoader, one unit per page) or "docling"
 # (structured output from prepare_corpus.py, cleaned by docling_loader.py).
@@ -105,6 +109,7 @@ def load_embeddings(model_name):
         # Normalized vectors: FAISS's L2 distance then ranks exactly like cosine similarity.
         _EMBEDDINGS[model_name] = HuggingFaceEmbeddings(
             model_name=model_name,
+            model_kwargs={"revision": EMBEDDING_REVISION} if model_name == EMBEDDING_MODEL else {},
             encode_kwargs={"normalize_embeddings": True},
             query_encode_kwargs={"normalize_embeddings": True},
         )