rag.py

from langchain_community.document_loaders import DirectoryLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.retrievers import BM25Retriever, EnsembleRetriever
from langchain_community.llms import Ollama

loader = DirectoryLoader('.', glob="all_dialogues.txt") 
docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=200
)
texts = text_splitter.split_documents(docs)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")


db = FAISS.load_local(
    folder_path="./",
    embeddings=HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2"),
    allow_dangerous_deserialization=True  
)



# Vector Store Retriever
vector_retriever = db.as_retriever(search_kwargs={"k": 3})

# Keyword Retriever (BM25)
bm25_retriever = BM25Retriever.from_documents(texts)
bm25_retriever.k = 2

# Combine both
ensemble_retriever = EnsembleRetriever(
    retrievers=[vector_retriever, bm25_retriever],
    weights=[0.6, 0.4]  # Tune based on your tests
)

# Use in ask_question()



from langchain_community.llms import HuggingFaceHub
from langchain_core.prompts import ChatPromptTemplate

def respond_rag_huggingface(
    message: str,
    system_message: str = " you are game of thrones measter answer the given question strictly based on the context provived.if u donot know the answer reply i dont know donot give gibberish answers",
    num_predict: int = 128,
    temperature: float = 0.8,
):
    # 1. Retrieve context
    docs = ensemble_retriever.get_relevant_documents(message)
    context = "\n\n".join(doc.page_content for doc in docs)

    # 2. Prompt
    prompt_template = ChatPromptTemplate.from_messages([
        ("system", system_message),
        ("human", """Context: {context}

        Question: {question}

        Rules:
        - If the answer isn't in the context, respond with "I don't know"
        - Keep answers under 5 sentences
        - Include book/season references when possible""")
    ])

    # 3. HuggingFace LLM (e.g., use `HuggingFaceH4/zephyr-7b-beta`)
    llm = HuggingFaceHub(
        repo_id="HuggingFaceH4/zephyr-7b-beta",
        model_kwargs={
            "temperature": temperature,
            "max_new_tokens": num_predict
        }
    )

    # 4. Run chain
    chain = prompt_template | llm
    response = chain.invoke({"context": context, "question": message})

    return response.content

__all__ = ["respond_rag_huggingface"]
# def respond_rag_ollama(
#     message: str,
#     system_message: str = "you are game of thrones measter answer the given question strictly based on the context provived.if u donot know the answer reply i dont know donot give gibberish answers ",
#     num_ctx: int = 2048,
#     num_predict: int = 128,
#     temperature: float = 0.8,
#     top_k: int = 40,
#     repeat_penalty: float = 1.1,
#     stop: list[str] | None = None,
# ):
#     partial_response=""
#     # 1. Retrieve relevant context from your vector DB
#     docs = ensemble_retriever.get_relevant_documents(message)
#     context = "\n\n".join(doc.page_content for doc in docs)

    # # 2. Build a conversational prompt
    # prompt_template = ChatPromptTemplate.from_messages([
    #     ("system", system_message),
    #     ("human", f"""Context: {{context}}

    #     Question: {{question}}

    #     Rules:
    #     - If the answer isn't in the context, respond with "I don't know"
    #     - Keep answers under 5 sentences
    #     - Include book/season references when possible""")
    # ])

    # # 3. Configure the Ollama LLM with adjustable parameters
    # llm = Ollama(
    #     model="llama3:8b-instruct-q4_0",
    #     temperature=temperature,
    #     num_ctx=num_ctx,
    #     num_predict=num_predict,
    #     top_k=top_k,
    #     repeat_penalty=repeat_penalty,
    #     stop= ["<|eot_id|>"],
    # )


    # chain = prompt_template | llm
    # response = chain.invoke({"context": context, "question": message})

    # return response.content