rag_components.py

import os
import logging
import json
import time
import csv
from typing import List, Dict, Optional, Any

import torch
from sentence_transformers import CrossEncoder

from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_text_splitters import RecursiveCharacterTextSplitter

from config import (
    RAG_RERANKER_MODEL_NAME, RAG_DETAILED_LOGGING,
    RAG_CHUNK_SIZE, RAG_CHUNK_OVERLAP, RAG_CHUNKED_SOURCES_FILENAME,
    RAG_FAISS_INDEX_SUBDIR_NAME, RAG_INITIAL_FETCH_K, RAG_RERANKER_K,
    RAG_MAX_FILES_FOR_INCREMENTAL
)
from utils import FAISS_RAG_SUPPORTED_EXTENSIONS

logger = logging.getLogger(__name__)


class DocumentReranker:
    def __init__(self, model_name: str = RAG_RERANKER_MODEL_NAME):
        self.logger = logging.getLogger(__name__ + ".DocumentReranker")
        self.model_name = model_name
        self.model = None
        
        try:
            self.logger.info(f"[RERANKER_INIT] Loading reranker model: {self.model_name}")
            start_time = time.time()
            self.model = CrossEncoder(model_name, trust_remote_code=True)
            load_time = time.time() - start_time
            self.logger.info(f"[RERANKER_INIT] Reranker model '{self.model_name}' loaded successfully in {load_time:.2f}s")
        except Exception as e:
            self.logger.error(f"[RERANKER_INIT] Failed to load reranker model '{self.model_name}': {e}", exc_info=True)
            raise RuntimeError(f"Could not initialize reranker model: {e}") from e

    def rerank_documents(self, query: str, documents: List[Document], top_k: int) -> List[Document]:
        if not documents or not self.model:
            return documents[:top_k] if documents else []

        try:
            start_time = time.time()
            doc_pairs = [[query, doc.page_content] for doc in documents]
            scores = self.model.predict(doc_pairs)
            rerank_time = time.time() - start_time
            self.logger.info(f"[RERANKER] Computed relevance scores in {rerank_time:.3f}s")
            
            doc_score_pairs = list(zip(documents, scores))
            doc_score_pairs.sort(key=lambda x: x[1], reverse=True)
            
            reranked_docs = []
            for doc, score in doc_score_pairs[:top_k]:
                doc.metadata["reranker_score"] = float(score)
                reranked_docs.append(doc)
            
            return reranked_docs
        except Exception as e:
            self.logger.error(f"[RERANKER] Error during reranking: {e}", exc_info=True)
            return documents[:top_k] if documents else []


class FAISSRetrieverWithScore(BaseRetriever):
    vectorstore: FAISS
    reranker: Optional[DocumentReranker] = None
    initial_fetch_k: int = RAG_INITIAL_FETCH_K
    final_k: int = RAG_RERANKER_K

    def _get_relevant_documents(

        self, query: str, *, run_manager: CallbackManagerForRetrieverRun

    ) -> List[Document]:
        
        start_time = time.time()
        num_to_fetch = self.initial_fetch_k if self.reranker else self.final_k
        
        logger.info(f"[RETRIEVER] Fetching {num_to_fetch} docs (Rerank={self.reranker is not None})")

        docs_and_scores = self.vectorstore.similarity_search_with_score(query, k=num_to_fetch)
        
        relevant_docs = []
        for doc, score in docs_and_scores:
            doc.metadata["retrieval_score"] = float(score)
            relevant_docs.append(doc)
        
        if self.reranker and relevant_docs:
            relevant_docs = self.reranker.rerank_documents(query, relevant_docs, top_k=self.final_k)
        
        total_time = time.time() - start_time
        logger.info(f"[RETRIEVER] Completed in {total_time:.3f}s. Returned {len(relevant_docs)} docs.")
        return relevant_docs


class KnowledgeRAG:
    def __init__(

        self,

        index_storage_dir: str,

        embedding_model_name: str,

        use_gpu_for_embeddings: bool,

        chunk_size: int = RAG_CHUNK_SIZE,

        chunk_overlap: int = RAG_CHUNK_OVERLAP,

        reranker_model_name: Optional[str] = None,

        enable_reranker: bool = True,

    ):
        self.logger = logging.getLogger(__name__ + ".KnowledgeRAG")
        self.logger.info(f"[RAG_INIT] Initializing KnowledgeRAG system")
        
        self.index_storage_dir = index_storage_dir
        os.makedirs(self.index_storage_dir, exist_ok=True)

        self.embedding_model_name = embedding_model_name
        self.use_gpu_for_embeddings = use_gpu_for_embeddings
        self.chunk_size = chunk_size
        self.chunk_overlap = chunk_overlap
        self.reranker_model_name = reranker_model_name or RAG_RERANKER_MODEL_NAME
        self.enable_reranker = enable_reranker
        self.reranker = None

        device = "cpu"
        if self.use_gpu_for_embeddings:
            if torch.cuda.is_available():
                self.logger.info(f"[RAG_INIT] CUDA available. Requesting GPU.")
                device = "cuda"
            else:
                self.logger.warning("[RAG_INIT] CUDA not available. Fallback to CPU.")
        
        self.embeddings = HuggingFaceEmbeddings(
            model_name=self.embedding_model_name,
            model_kwargs={"device": device},
            encode_kwargs={"normalize_embeddings": True}
        )

        if self.enable_reranker:
            try:
                self.reranker = DocumentReranker(self.reranker_model_name)
            except Exception as e:
                self.logger.warning(f"[RAG_INIT] Reranker Init Failed: {e}")
                self.reranker = None

        self.vector_store: Optional[FAISS] = None
        self.retriever: Optional[FAISSRetrieverWithScore] = None
        self.processed_source_files: List[str] = []

    def _save_chunk_config(self):
        faiss_path = os.path.join(self.index_storage_dir, RAG_FAISS_INDEX_SUBDIR_NAME)
        config_file = os.path.join(faiss_path, "chunk_config.json")
        with open(config_file, 'w') as f:
            json.dump({"chunk_size": self.chunk_size, "chunk_overlap": self.chunk_overlap}, f)

    def _load_chunk_config(self) -> Optional[dict]:
        faiss_path = os.path.join(self.index_storage_dir, RAG_FAISS_INDEX_SUBDIR_NAME)
        config_file = os.path.join(faiss_path, "chunk_config.json")
        if os.path.exists(config_file):
            with open(config_file, 'r') as f:
                return json.load(f)
        return None

    def chunk_config_has_changed(self) -> bool:
        saved = self._load_chunk_config()
        if saved is None:
            return False  
        changed = saved.get("chunk_size") != self.chunk_size or saved.get("chunk_overlap") != self.chunk_overlap
        if changed:
            self.logger.warning(
                f"[CONFIG_CHANGE] Chunk config mismatch! "
                f"Saved=(size={saved.get('chunk_size')}, overlap={saved.get('chunk_overlap')}) "
                f"Current=(size={self.chunk_size}, overlap={self.chunk_overlap}). "
                f"Index will be rebuilt."
            )
        return changed

    def build_index_from_source_files(self, source_folder_path: str):
        self.logger.info(f"[INDEX_BUILD] Building from: {source_folder_path}")
        if not os.path.isdir(source_folder_path):
            raise FileNotFoundError(f"Source folder not found: '{source_folder_path}'.")

        all_docs = []
        processed_files = []
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap)

        pre_chunked_path = os.path.join(self.index_storage_dir, RAG_CHUNKED_SOURCES_FILENAME)
        if os.path.exists(pre_chunked_path):
            try:
                with open(pre_chunked_path, 'r', encoding='utf-8') as f:
                    chunk_data_list = json.load(f)
                for chunk in chunk_data_list:
                    doc = Document(page_content=chunk.get("page_content", ""), metadata=chunk.get("metadata", {}))
                    all_docs.append(doc)
                    if 'source_document_name' in doc.metadata:
                        processed_files.append(doc.metadata['source_document_name'])
                processed_files = sorted(list(set(processed_files)))
            except Exception as e:
                self.logger.error(f"[INDEX_BUILD] JSON load failed: {e}")

        if not all_docs:
            for filename in os.listdir(source_folder_path):
                file_path = os.path.join(source_folder_path, filename)
                if not os.path.isfile(file_path): continue
                file_ext = filename.split('.')[-1].lower()
                
                if file_ext in FAISS_RAG_SUPPORTED_EXTENSIONS:
                    # Specific handler for CSV formatting
                    if file_ext == 'csv':
                        try:
                            with open(file_path, mode='r', encoding='utf-8-sig') as f:
                                reader = csv.DictReader(f)
                                for i, row in enumerate(reader):
                                    row_text = "\n".join([f"{k}: {v}" for k, v in row.items() if k and v and str(v).strip()])
                                    meta = {"source_document_name": filename, "chunk_index": i, "source_type": "csv"}
                                    all_docs.append(Document(page_content=row_text, metadata=meta))
                            processed_files.append(filename)
                        except Exception as e:
                            self.logger.error(f"[INDEX_BUILD] Error processing CSV {filename}: {e}")
                    else:
                        text_content = FAISS_RAG_SUPPORTED_EXTENSIONS[file_ext](file_path)
                        if text_content and text_content != "CSV_HANDLED_NATIVELY":
                            chunks = text_splitter.split_text(text_content)
                            for i, chunk_text in enumerate(chunks):
                                meta = {"source_document_name": filename, "chunk_index": i}
                                all_docs.append(Document(page_content=chunk_text, metadata=meta))
                            processed_files.append(filename)

        if not all_docs:
            raise ValueError("No documents to index.")

        self.processed_source_files = processed_files
        self.logger.info(f"[INDEX_BUILD] Creating FAISS index with {len(all_docs)} chunks.")
        
        self.vector_store = FAISS.from_documents(all_docs, self.embeddings)
        faiss_path = os.path.join(self.index_storage_dir, RAG_FAISS_INDEX_SUBDIR_NAME)
        self.vector_store.save_local(faiss_path)
        self._save_chunk_config()
        
        self.retriever = FAISSRetrieverWithScore(
            vectorstore=self.vector_store,
            reranker=self.reranker,
            initial_fetch_k=RAG_INITIAL_FETCH_K,
            final_k=RAG_RERANKER_K
        )

    def load_index_from_disk(self):
        faiss_path = os.path.join(self.index_storage_dir, RAG_FAISS_INDEX_SUBDIR_NAME)
        if not os.path.exists(faiss_path):
            raise FileNotFoundError("Index not found.")

        self.vector_store = FAISS.load_local(
            folder_path=faiss_path,
            embeddings=self.embeddings,
            allow_dangerous_deserialization=True
        )
        self.retriever = FAISSRetrieverWithScore(
            vectorstore=self.vector_store,
            reranker=self.reranker,
            initial_fetch_k=RAG_INITIAL_FETCH_K,
            final_k=RAG_RERANKER_K
        )
        
        meta_file = os.path.join(faiss_path, "processed_files.json")
        if os.path.exists(meta_file):
            with open(meta_file, 'r') as f:
                self.processed_source_files = json.load(f)
        else:
            self.processed_source_files = ["Loaded from disk (unknown sources)"]
            
        self.logger.info("[INDEX_LOAD] Success.")

    def update_index_with_new_files(self, source_folder_path: str, max_files_to_process: Optional[int] = None) -> Dict[str, Any]:
        self.logger.info(f"[INDEX_UPDATE] Checking for new files in: {source_folder_path}")
        
        if not self.vector_store:
            raise RuntimeError("Cannot update: no index loaded.")
        
        processed_set = set(self.processed_source_files)
        all_new_files = []
        for filename in sorted(os.listdir(source_folder_path)):
            if filename not in processed_set:
                file_ext = filename.split('.')[-1].lower()
                if file_ext in FAISS_RAG_SUPPORTED_EXTENSIONS:
                    all_new_files.append(filename)

        if not all_new_files:
            return {"status": "success", "message": "No new files found.", "files_added": []}
            
        limit = max_files_to_process if max_files_to_process is not None else RAG_MAX_FILES_FOR_INCREMENTAL
        files_to_process = all_new_files[:limit]
        
        new_docs = []
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap)
        
        for filename in files_to_process:
            file_path = os.path.join(source_folder_path, filename)
            file_ext = filename.split('.')[-1].lower()

            if file_ext == 'csv':
                try:
                    with open(file_path, mode='r', encoding='utf-8-sig') as f:
                        reader = csv.DictReader(f)
                        for i, row in enumerate(reader):
                            row_text = "\n".join([f"{k}: {v}" for k, v in row.items() if k and v and str(v).strip()])
                            meta = {"source_document_name": filename, "chunk_index": i, "source_type": "csv"}
                            new_docs.append(Document(page_content=row_text, metadata=meta))
                except Exception as e:
                    self.logger.error(f"[INDEX_UPDATE] Error processing CSV {filename}: {e}")
            else:
                text_content = FAISS_RAG_SUPPORTED_EXTENSIONS[file_ext](file_path)
                if text_content and text_content != "CSV_HANDLED_NATIVELY":
                    chunks = text_splitter.split_text(text_content)
                    for i, chunk_text in enumerate(chunks):
                        meta = {"source_document_name": filename, "chunk_index": i}
                        new_docs.append(Document(page_content=chunk_text, metadata=meta))
        
        if not new_docs:
            return {"status": "warning", "message": "New files found but no text extracted.", "files_added": []}
            
        self.vector_store.add_documents(new_docs)
        
        faiss_path = os.path.join(self.index_storage_dir, RAG_FAISS_INDEX_SUBDIR_NAME)
        self.vector_store.save_local(faiss_path)
        
        self.processed_source_files.extend(files_to_process)
        with open(os.path.join(faiss_path, "processed_files.json"), 'w') as f:
            json.dump(sorted(self.processed_source_files), f)

        return {
            "status": "success",
            "message": f"Added {len(files_to_process)} files.",
            "files_added": files_to_process,
            "remaining": len(all_new_files) - len(files_to_process)
        }

    def search_knowledge_base(self, query: str, top_k: Optional[int] = None) -> List[Dict[str, Any]]:
        if not self.retriever:
            raise RuntimeError("Retriever not initialized.")
        
        original_k = self.retriever.final_k
        if top_k:
            self.retriever.final_k = top_k

        try:
            docs = self.retriever.invoke(query)
            results = []
            for doc in docs:
                results.append({
                    "content": doc.page_content,
                    "metadata": doc.metadata,
                    "score": doc.metadata.get("reranker_score") or doc.metadata.get("retrieval_score")
                })
            return results
        finally:
            self.retriever.final_k = original_k