124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
from dotenv import load_dotenv
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from langchain_classic.retrievers import ContextualCompressionRetriever
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnableLambda, RunnablePassthrough
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from langchain_google_community import VertexAISearchRetriever
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from langchain_google_community.vertex_rank import VertexAIRank
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from langchain_google_genai import ChatGoogleGenerativeAI
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load_dotenv()
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PROJECT = "akqa-ita-ai-poc1"
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DATA_STORE = "akern-ds_1771234036654"
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MODEL = "gemini-2.5-flash"
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LOCATION = "eu"
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# LLM CONFIG
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TOP_K = 40
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TOP_P = 1
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TEMPERATURE = 0.0
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MAX_OUTPUT_TOKENS = 65535
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RETRIEVER_MAX_DOCS = 50
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RERANKER_MAX_RESULTS = 25
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with open("prompt.md") as f:
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template = f.read()
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prompt = ChatPromptTemplate.from_template(template)
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def format_docs(question: str) -> str:
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retrieved_docs = base_retriever.invoke(question)
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print("========== RETRIEVER DOCUMENTS ==========")
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for idx, doc in enumerate(retrieved_docs, start=1):
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snippet = doc.page_content[:200].replace("\n", " ")
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print(f"[{idx}] metadata={doc.metadata['source']} | snippet=...{snippet}...")
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reranked_docs = compression_retriever.invoke(question)
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print("========== RERANKED DOCUMENTS ==========")
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for idx, doc in enumerate(reranked_docs, start=1):
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snippet = doc.page_content[:200].replace("\n", " ")
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print(
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f"[{idx}] metadata={doc.metadata['relevance_score']} | snippet=...{snippet}..."
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)
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return "\n\n".join(doc.page_content for doc in reranked_docs)
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llm = ChatGoogleGenerativeAI(
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model=MODEL,
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project=PROJECT,
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vertexai=True,
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top_p=TOP_P,
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top_k=TOP_K,
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temperature=TEMPERATURE,
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max_output_tokens=MAX_OUTPUT_TOKENS,
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)
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base_retriever = VertexAISearchRetriever(
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project_id=PROJECT,
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data_store_id=DATA_STORE,
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max_documents=RETRIEVER_MAX_DOCS,
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location_id=LOCATION,
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beta=True,
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)
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reranker = VertexAIRank(
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project_id=PROJECT,
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location_id="eu",
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ranking_config="default_ranking_config",
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top_n=RERANKER_MAX_RESULTS,
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)
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compression_retriever = ContextualCompressionRetriever(
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base_compressor=reranker, base_retriever=base_retriever
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)
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rag_chain = (
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{"context": RunnableLambda(format_docs), "question": RunnablePassthrough()}
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| prompt
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| llm
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| StrOutputParser()
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)
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# def parse_questions():
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# domande_dir = "domande"
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# risposte_dir = "risposte"
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# os.makedirs(risposte_dir, exist_ok=True)
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# for filename in sorted(os.listdir(domande_dir)):
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# if not filename.lower().endswith(".txt"):
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# continue
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# domanda_path = os.path.join(domande_dir, filename)
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# with open(domanda_path, "r", encoding="utf-8") as f:
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# contents = f.read()
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# print(f"========== DOMANDA ({domanda_path}) ==========")
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# print(contents)
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# response = rag_chain.invoke(contents)
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# print("========== RISPOSTA ==========")
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# print(response)
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# print("\n\n")
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# base_name = os.path.splitext(filename)[0]
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# suffix = "".join(ch for ch in base_name if ch.isdigit()) or base_name
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# risposta_path = os.path.join(risposte_dir, f"risposta{suffix}.txt")
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# with open(risposta_path, "w", encoding="utf-8") as f:
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# f.write(response)
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if __name__ == "__main__":
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response = rag_chain.invoke(
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"""Buongiorno, non so se è la mail specifica ma volevo se possibile dei chiarimenti per l’interpretazione dei parametri BCM /SMM/ASMM. Mi capita a volte di trovare casi in cui la BCM è aumentata ma allo stesso tempo SMM/ASMM hanno subito una piccola flessione in negativo (o viceversa). Se la parte metabolicamente attiva aumenta perchè può succedere che gli altri compartimenti si riducono?? E allo stesso tempo phA e BCM possono essere inversamente proporzionali?? So che il phA correla con massa e struttura + idratazione."""
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)
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print(response)
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