Initial commit: RAG demo with build and query scripts

query.py

@@ -0,0 +1,110 @@
+# query.py
+#
+# Run a query on a vector store
+#
+# August 2025
+# E. M. Furst
+
+from llama_index.core import (
+    load_index_from_storage,
+    StorageContext,
+    Settings,
+)
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.llms.ollama import Ollama
+from llama_index.core.prompts import PromptTemplate
+import os, time
+
+#
+# Globals
+#
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# Embedding model used in vector store (this should match the one in build.py)
+embed_model = HuggingFaceEmbedding(cache_folder="./models",
+    model_name="BAAI/bge-large-en-v1.5")
+
+# LLM model to use in query transform and generation
+llm = "command-r7b"
+
+#
+# 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 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,
+        request_timeout=360.0,
+    )
+
+    # 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")
+    index = load_index_from_storage(storage_context)
+
+    # Build regular query engine with custom prompt
+    query_engine = index.as_query_engine(
+        similarity_top_k=15,
+        text_qa_template=PROMPT,
+    )
+
+    # Query
+    while True:
+        q = input("\nEnter a search topic or question (or 'exit'): ").strip()
+        if q.lower() in ("exit", "quit"):
+            break
+        print()
+
+        # Generate the response by querying the engine
+        start_time = time.time()
+        response = query_engine.query(q)
+        end_time = time.time()
+
+        # 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(f"  {meta.get('file_name')}  {getattr(node, 'score', None)}")
+
+        print(f"\nElapsed time: {(end_time-start_time):.1f} seconds")
+
+if __name__ == "__main__":
+    main()