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_prompt_engine.py

@@ -0,0 +1,123 @@
+# query_topk_prompt_engine.py
+# Run a querry on a vector store
+# 
+# This version is query_topk_prompt.py but the query is passed though the command line.
+#
+# Implements a prompt and uses the build_exp.py vector store with BAAI/bge-large-en-v1.5
+# Based on query_exp.py->query_topk.py
+#
+# E.M.F. August 2025
+
+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
+from llama_index.core.prompts import PromptTemplate
+import os
+import sys
+
+#
+# Globals
+#
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# Embedding model used in vector store (this should match the one in build_exp.py or equivalent)
+# embed_model = HuggingFaceEmbedding(model_name="all-mpnet-base-v2")
+embed_model = HuggingFaceEmbedding(cache_folder="./models",model_name="BAAI/bge-large-en-v1.5",local_files_only=True)
+
+# LLM model to use in query transform and generation
+# command-r7b generates about as quickly as llama3.1:8B, but provides results that stick better
+# to the provided context
+llm="command-r7b"
+# Other models tried:
+#llm="llama3.1:8B"
+#llm="deepseek-r1:8B"
+#llm="gemma3:1b"
+
+#
+# Custom prompt for the query engine
+#
+PROMPT = PromptTemplate(
+"""You are an expert research assistant. You are given top-ranked writing excerpts (CONTEXT) and a user's QUERY.
+
+Instructions:
+- Base your response *only* on the CONTEXT.
+- The snippets are ordered from most to least relevant—prioritize insights from earlier (higher-ranked) snippets.
+- Aim to reference *as many distinct* relevant files as possible (up to 10).
+- Do not invent or generalize; refer to specific passages or facts only.
+- If a passage only loosely matches, deprioritize it.
+
+Format your answer in two parts:
+
+1. **Summary Theme**  
+   Summarize the dominant theme from the relevant context in a few sentences.
+
+2. **Matching Files**  
+   Make a list of 10 matching files. The format for each should be:  
+   <filename> - 
+   <rationale tied to content. Include date or section hints if available.>
+
+CONTEXT:
+{context_str}
+
+QUERY:
+{query_str}
+
+Now provide the theme and list of matching files."""
+)
+
+#
+# Main program routine
+#
+
+def main():
+    # Use a local model to generate -- in this case using Ollama
+    Settings.llm = Ollama(
+        model=llm,    # First model tested
+        request_timeout=360.0,
+        context_window=8000
+        )
+   
+    # Load embedding model (same as used for vector store)
+    Settings.embed_model = embed_model
+
+    # Load persisted vector store + metadata
+    storage_context = StorageContext.from_defaults(persist_dir="./storage_exp")
+    index = load_index_from_storage(storage_context)
+
+    # Build regular query engine with custom prompt
+    query_engine = index.as_query_engine(
+        similarity_top_k=15,            # pull wide 
+        #response_mode="compact"        # concise synthesis
+        text_qa_template=PROMPT,        # custom prompt
+        # node_postprocessors=[
+        #     SimilarityPostprocessor(similarity_cutoff=0.75)  # keep strong hits; makes result count flexible
+        # ],
+    )  
+
+    # Query
+    if len(sys.argv) < 2:
+        print("Usage: python query.py QUERY_TEXT")
+        sys.exit(1)
+    q = " ".join(sys.argv[1:])    
+
+    # Generate the response by querying the engine
+    # This performes the similarity search and then applies the prompt
+    response = query_engine.query(q)
+
+    # Return the query response and source documents
+    print("\nResponse:\n")
+    print(response.response) 
+
+    print("\nSource documents:")
+    for node in response.source_nodes:
+        meta = getattr(node, "metadata", None) or node.node.metadata
+        print(f"{meta.get('file_name')}  {meta.get('file_path')}  {getattr(node, 'score', None):.3f}")
+
+
+if __name__ == "__main__":
+    main()