rag/src/main.py

from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
    PromptTemplate,
)
from langchain_ollama import OllamaLLM

from chroma_manager import ChromaManager
from config import OLLAMA_URL

print("Инициализация LLM...")
llm = OllamaLLM(
    model="llama3.1:8b",
    base_url=f"{OLLAMA_URL}",
    temperature=0.15,
    num_predict=1024,
    reasoning=False,
)

print("Инициализация Chroma...")
retriever = ChromaManager().retriever()

contextualize_q_system_prompt = (
    "Given a chat history and the latest user question "
    "which might reference context in the chat history, "
    "formulate a standalone question which can be understood "
    "without the chat history. Do NOT answer the question, just "
    "reformulate it if needed and otherwise return it as is."
)

contextualize_q_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", contextualize_q_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}"),
    ]
)

history_aware_retriever = create_history_aware_retriever(
    llm, retriever, contextualize_q_prompt
)

qa_system_prompt = (
    "You are an assistant for question-answering tasks. Use "
    "the following pieces of retrieved context to answer the "
    "question. If you don't know the answer, just say that you "
    "don't know. Use three sentences maximum and keep the answer "
    "concise."
    "{context}"
)

qa_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", qa_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}"),
    ]
)

question_answer_chain = create_stuff_documents_chain(
    llm, qa_prompt, document_prompt=PromptTemplate.from_template("{answer}")
)
rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)

chat_history = []


def ask(question: str):
    print("=" * 100)
    print("Вопрос пользователя:", question)

    result = {"question": question, "answer": ""}

    print("=" * 100)
    print("Ответ модели:")
    for chunk in rag_chain.stream(
        {"input": question, "chat_history": chat_history},
        config={"callbacks": [langfuse_handler]},
    ):
        if "answer" in chunk:
            print(chunk["answer"], end="", flush=True)
            result["answer"] += chunk["answer"]
    print()

    chat_history.append(("human", result["question"]))
    chat_history.append(("ai", result["answer"]))


def main():
    questions = [
        # 'Какие есть боссы в Террарии?',
        # 'Какой финальный босс?',
        # 'И как его победить?',
        # 'Какую броню на него использовать?',
        "What bosses are there in Terraria?",
        "What is the final boss?",
        "And how to defeat it?",
        "What armor should be used against it?",
    ]

    for question in questions:
        ask(question)


if __name__ == "__main__":
    main()