Reorganize project: rename scripts, archive superseded, add clippings_search/

- Rename build_exp_claude.py → build_store.py
- Rename query_hybrid_bm25_v4.py → query_hybrid.py
- Rename retrieve_hybrid_raw.py → retrieve.py
- Archive query_topk_prompt_engine_v3.py (superseded by hybrid)
- Archive retrieve_raw.py (superseded by hybrid)
- Move build_clippings.py, retrieve_clippings.py → clippings_search/
- Update run_query.sh, README.md, CLAUDE.md for new names

archived/retrieve_raw.py

@@ -0,0 +1,97 @@
+# retrieve_raw.py
+# Verbatim chunk retrieval: vector search + cross-encoder re-ranking, no LLM.
+#
+# Returns the top re-ranked chunks with their full text, file metadata, and
+# scores. Useful for browsing source material directly and verifying what
+# the RAG pipeline retrieves before LLM synthesis.
+#
+# Uses the same vector store, embedding model, and re-ranker as
+# query_topk_prompt_engine_v3.py, but skips the LLM step entirely.
+#
+# E.M.F. February 2026
+
+# Environment vars must be set before importing huggingface/transformers
+# libraries, because huggingface_hub.constants evaluates HF_HUB_OFFLINE
+# at import time.
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+os.environ["SENTENCE_TRANSFORMERS_HOME"] = "./models"
+os.environ["HF_HUB_OFFLINE"] = "1"
+
+from llama_index.core import (
+    StorageContext,
+    load_index_from_storage,
+    Settings,
+)
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core.postprocessor import SentenceTransformerRerank
+import sys
+import textwrap
+
+#
+# Globals
+#
+
+# Embedding model (must match build_exp_claude.py)
+EMBED_MODEL = HuggingFaceEmbedding(cache_folder="./models", model_name="BAAI/bge-large-en-v1.5", local_files_only=True)
+
+# Cross-encoder model for re-ranking (cached in ./models/)
+RERANK_MODEL = "cross-encoder/ms-marco-MiniLM-L-12-v2"
+RERANK_TOP_N = 15
+RETRIEVE_TOP_K = 30
+
+# Output formatting
+WRAP_WIDTH = 80
+
+
+def main():
+    # No LLM needed -- set embed model only
+    Settings.embed_model = EMBED_MODEL
+
+    # Load persisted vector store
+    storage_context = StorageContext.from_defaults(persist_dir="./storage_exp")
+    index = load_index_from_storage(storage_context)
+
+    # Build retriever (vector search only, no query engine / LLM)
+    retriever = index.as_retriever(similarity_top_k=RETRIEVE_TOP_K)
+
+    # Cross-encoder re-ranker
+    reranker = SentenceTransformerRerank(
+        model=RERANK_MODEL,
+        top_n=RERANK_TOP_N,
+    )
+
+    # Query
+    if len(sys.argv) < 2:
+        print("Usage: python retrieve_raw.py QUERY_TEXT")
+        sys.exit(1)
+    q = " ".join(sys.argv[1:])
+
+    # Retrieve and re-rank
+    nodes = retriever.retrieve(q)
+    reranked = reranker.postprocess_nodes(nodes, query_str=q)
+
+    # Output
+    print(f"\nQuery: {q}")
+    print(f"Retrieved {len(nodes)} chunks, re-ranked to top {len(reranked)}\n")
+
+    for i, node in enumerate(reranked, 1):
+        meta = getattr(node, "metadata", None) or node.node.metadata
+        score = getattr(node, "score", None)
+        file_name = meta.get("file_name", "unknown")
+        text = node.get_content()
+
+        print("="*WRAP_WIDTH)
+        print(f"=== [{i}] {file_name}  (score: {score:.3f}) ")
+        print("="*WRAP_WIDTH)
+        # Wrap text for readability
+        for line in text.splitlines():
+            if line.strip():
+                print(textwrap.fill(line, width=WRAP_WIDTH))
+            else:
+                print()
+        print()
+
+
+if __name__ == "__main__":
+    main()