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

import google.generativeai as genai
import os
import google.generativeai as genai

# Initialize Gemini
genai.configure(api_key=os.environ.get("GEMINI_API_KEY"))  # Replace this with actual key or environment-safe config



model = genai.GenerativeModel("gemini-1.5-flash")



# 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)

    system_message = os.environ.get("SYSTEM_MESSAGE", 
        "You are a Game of Thrones measter. Answer the given question strictly based on the provided context. If you don't know, say 'I don't know'. Do not guess.")

    prompt = f"""{system_message}

Context:
{context}

Question:
{message}

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"""

    response = model.generate_content(prompt)
    return response.text



__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