src/rag_engine.py

import os
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough, RunnableParallel
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_openai import ChatOpenAI
from langchain_community.chat_models import ChatLiteLLM
from langchain_core.messages import HumanMessage, AIMessage

class ProjectRAGEngine:
    def __init__(self):
        # ✅ Hugging Face Embeddings (LOCAL / FREE)
        self.embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2",
            model_kwargs={"device": "cpu"},   # change to "cuda" if GPU available
            encode_kwargs={"normalize_embeddings": True}
        )
        # ✅ OpenRouter LLM (Chat only)
        self.llm = ChatOpenAI(
                model="openai/gpt-oss-120b:free",
                base_url="https://openrouter.ai/api/v1",
                api_key=os.getenv("OPENROUTER_API_KEY"),
                extra_body={"reasoning": {"enabled": True}})
        self.vector_store = None

    def process_documents(self, pdf_paths):
        all_docs = []

        for path in pdf_paths:
            loader = PyPDFLoader(path)
            all_docs.extend(loader.load())

        splitter = RecursiveCharacterTextSplitter(
            chunk_size=500,
            chunk_overlap=50
        )

        splits = splitter.split_documents(all_docs)

        # ✅ FAISS with HuggingFace embeddings
        self.vector_store = FAISS.from_documents(
            splits, self.embeddings
        )

    def _format_docs(self, docs):
        return "\n\n".join(d.page_content for d in docs)

    def get_answer(self, query):
        if not self.vector_store:
            return "Please upload documents first.", []

        template = """
        You are a professional Project Analyst.
        Answer strictly using the context.
        If unknown, say you don't know.
        Cite document names and page numbers.
        Context:
        {context}
        Question:
        {question}
        """

        prompt = ChatPromptTemplate.from_template(template)
        retriever = self.vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 5, "lambda_mult":0.25})

        rag_chain = (
            RunnablePassthrough.assign(
                context=lambda x: self._format_docs(x["context"])
            )
            | prompt
            | self.llm
            | StrOutputParser()
        )

        chain = RunnableParallel(
            {"context": retriever, "question": RunnablePassthrough()}
        ).assign(answer=rag_chain)

        result = chain.invoke(query)

        sources = [
            {"content": d.page_content, "metadata": d.metadata}
            for d in result["context"]
        ]

        return result["answer"], sources