init

main.py

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+import os
+
+from dotenv import load_dotenv
+from langchain_classic.retrievers import ContextualCompressionRetriever
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_google_community import VertexAISearchRetriever
+from langchain_google_community.vertex_rank import VertexAIRank
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+load_dotenv()
+
+PROJECT = "akqa-ita-ai-poc1"
+DATA_STORE = "akern-ds_1771234036654"
+MODEL = "gemini-2.5-flash"
+LOCATION = "eu"
+
+with open("prompt.md") as f:
+    template = f.read()
+
+prompt = ChatPromptTemplate.from_template(template)
+
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+
+llm = ChatGoogleGenerativeAI(
+    model=MODEL,
+    project=PROJECT,
+    vertexai=True,
+    top_p=0.95,
+    top_k=40,
+    temperature=0.0,
+    max_output_tokens=65535,
+)
+
+base_retriever = VertexAISearchRetriever(
+    project_id=PROJECT,
+    data_store_id=DATA_STORE,
+    max_documents=50,
+    location_id=LOCATION,
+    beta=True,
+)
+
+reranker = VertexAIRank(
+    project_id=PROJECT,
+    location_id="global",
+    ranking_config="default_ranking_config",
+    top_n=5,
+)
+
+compression_retriever = ContextualCompressionRetriever(
+    base_compressor=reranker, base_retriever=base_retriever
+)
+
+rag_chain = (
+    {"context": compression_retriever | format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+
+def answer_questions() -> None:
+    QUESTIONS_DIR = "domande"
+
+    if not os.path.exists(QUESTIONS_DIR):
+        print(f"Errore: la directory '{QUESTIONS_DIR}' non esiste.")
+        return
+
+    files = sorted([f for f in os.listdir(QUESTIONS_DIR) if f.endswith(".txt")])
+
+    for filename in files:
+        filepath = os.path.join(QUESTIONS_DIR, filename)
+
+        with open(filepath, "r", encoding="utf-8") as f:
+            question_content = f.read()
+
+        print(f"Elaborazione: {filename}...")
+
+        try:
+            response = rag_chain.invoke(question_content)
+
+            # Genera il nome del file di risposta (es. domanda1.txt -> risposta1.txt)
+            output_filename = filename.replace("domanda", "risposta")
+
+            with open(output_filename, "w", encoding="utf-8") as f:
+                f.write(response)
+
+            print(f"Risposta salvata in: {output_filename}")
+        except Exception as e:
+            print(f"Errore durante l'elaborazione di {filename}: {e}")
+
+
+if __name__ == "__main__":
+    response = rag_chain.invoke("come si calcola il rapporto sodio potassio?")
+    print(response)