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
from langchain_community.llms import Ollama
from langchain_text_splitters import RecursiveCharacterTextSplitter

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from langchain_community.llms import HuggingFacePipeline
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.document_loaders import DirectoryLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.retrievers import BM25Retriever

# Load Zephyr model
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta")

# Create HF pipeline
hf_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=128,
    temperature=0.8,
    pad_token_id=tokenizer.eos_token_id,
)

# Wrap in LangChain LLM
llm = HuggingFacePipeline(pipeline=hf_pipeline)

# Define your RAG response function


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": 5})

# Keyword Retriever (BM25)
bm25_retriever = BM25Retriever.from_documents(texts)
bm25_retriever.k = 5

# Combine both
def ensemble_retriever(query):
    vector_docs = vector_retriever.get_relevant_documents(query)
    bm25_docs = bm25_retriever.get_relevant_documents(query)

    # Optionally weight them
    combined_docs = vector_docs+ bm25_docs  # or use custom weighting
    return combined_docs

# Use in ask_question()



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


 def respond_rag_huggingface(message: str):
    docs = ensemble_retriever(message)
    context = "\n\n".join(doc.page_content for doc in docs)

    prompt_template = ChatPromptTemplate.from_messages([
        ("system", "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"),
        ("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""")
    ])

    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