Initial commit: RAG pipeline for semantic search over personal journal archive

Vector search with cross-encoder re-ranking, hybrid BM25+vector retrieval,
incremental index updates, and multiple LLM backends (Ollama local, OpenAI API).

archived/query_topk.py

@@ -0,0 +1,58 @@
+# query_topk.py
+# Run a querry on a vector store
+#
+# E.M.F. July 2025
+# August 2025 - updated for nd ssearch
+# this version uses top-k similarity
+
+from llama_index.core import (
+    StorageContext,
+    load_index_from_storage,
+    ServiceContext,
+    Settings,
+)
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.llms.ollama import Ollama
+
+# Use a local model to generate
+Settings.llm = Ollama(
+    model="llama3.1:8B",    # First model tested
+#    model="deepseek-r1:8B", # This model shows its reasoning
+#    model="gemma3:1b",
+    request_timeout=360.0,
+    context_window=8000
+    )
+
+def main():
+    # Load embedding model (same as used for vector store)
+    embed_model = HuggingFaceEmbedding(model_name="all-mpnet-base-v2")
+    Settings.embed_model = embed_model
+
+    # Load persisted vector store + metadata
+    storage_context = StorageContext.from_defaults(persist_dir="./storage")
+    index = load_index_from_storage(storage_context)
+
+    query_engine = index.as_query_engine(similarity_top_k=5)
+
+    # Query
+    while True:
+        q = input("\nEnter your question (or 'exit'): ").strip()
+        if q.lower() in ("exit", "quit"):
+            break
+        print()
+        response = query_engine.query(q)
+
+        # Return the query response and source documents
+        print(response.response) 
+        print("\nSource documents:")
+        for node in response.source_nodes:
+            meta = getattr(node, "metadata", None) or node.node.metadata
+            print(meta.get("file_name"), "---", meta.get("file_path"), getattr(node, "score", None))
+
+
+if __name__ == "__main__":
+    main()
+
+
+    
+