Delete qa_markdown_chroma_externalized.py

qa_markdown_chroma_externalized.py

@@ -1,55 +0,0 @@
-#!/usr/bin/env python3
-from qa_common import parse_args, build_prompt, generate_answer, save_result
-# REVISED: Imported from the dedicated langchain_chroma package
-from langchain_chroma import Chroma
-from langchain_huggingface import HuggingFaceEmbeddings
-from llama_cpp import Llama
-
-# ====================== CHROMA SPECIFIC ======================
-VECTORSTORE_PATH = "chroma_db"
-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 Chroma vector store...")
-vectorstore = Chroma(
-    persist_directory=VECTORSTORE_PATH,
-    embedding_function=embeddings
-)
-
-retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
-
-print("Loading LLM...")
-llm = Llama(
-    model_path=MODEL_PATH,
-    n_ctx=65000,
-    n_threads=8,
-    verbose=False,
-)
-
-
-def get_context(query: str) -> str:
-    """Retrieve context using Chroma"""
-    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)