Delete qa_markdown_qdrant_externalized.py

qa_markdown_qdrant_externalized.py

@@ -1,60 +0,0 @@
-#!/usr/bin/env python3
-from qa_common import parse_args, build_prompt, generate_answer, save_result
-from langchain_qdrant import QdrantVectorStore
-from langchain_huggingface import HuggingFaceEmbeddings
-from llama_cpp import Llama
-# FIX 1: Import the native client to manage its lifecycle
-from qdrant_client import QdrantClient 
-
-# ====================== QDRANT SPECIFIC ======================
-QDRANT_PATH = "qdrant_db"
-COLLECTION_NAME = "articles"
-MODEL_PATH = "articles-Q4_K_M.gguf"
-
-print("Loading embedding model...")
-embeddings = HuggingFaceEmbeddings(
-    model_name="BAAI/bge-small-en-v1.5",
-    encode_kwargs={'normalize_embeddings': True}
-)
-
-print("Loading Qdrant vector store...")
-# FIX 2: Create the client explicitly
-client = QdrantClient(path=QDRANT_PATH)
-
-# Pass the client directly to the vector store
-vectorstore = QdrantVectorStore(
-    client=client,
-    collection_name=COLLECTION_NAME,
-    embedding=embeddings
-)
-
-retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
-
-print("Loading LLM...")
-llm = Llama(model_path=MODEL_PATH, n_ctx=25000, n_threads=8, verbose=False)
-
-
-def get_context(query: str) -> str:
-    docs = retriever.invoke(query)
-    return "\n\n".join([
-        f"[Article: {doc.metadata.get('article_title', 'N/A')}] "
-        f"{doc.page_content}"
-        for doc in docs
-    ])
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    query = args.prompt if args.prompt else input("\nQuestion: ")
-
-    print("Retrieving context and generating answer...\n")
-    
-    context = get_context(query)
-    prompt = build_prompt(query, context)
-    answer = generate_answer(llm, prompt)
-    
-    save_result(query, answer, args.output)
-
-    # FIX 3: Close connection explicitly while Python is still fully intact
-    print("Closing vector store connection...")
-    client.close()