src/langraph_rag_backend.py

from __future__ import annotations

import os
import sqlite3
import tempfile
from typing import Annotated, Any, Dict, List, Optional, TypedDict

from dotenv import load_dotenv
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.vectorstores import FAISS
from langchain_core.messages import BaseMessage, SystemMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
import requests

load_dotenv()

# -------------------
# 1. LLM + embeddings
# -------------------
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}}
)

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2",
    model_kwargs={"device": "cpu"},
    encode_kwargs={"normalize_embeddings": True}
)

# -------------------
# 2. Multi-PDF Store (per thread)
# -------------------
# Changed from _THREAD_RETRIEVERS to _THREAD_STORES to keep access to .add_documents()
_THREAD_STORES: Dict[str, FAISS] = {}
_THREAD_METADATA: Dict[str, List[dict]] = {}


def ingest_pdf(file_bytes: bytes, thread_id: str, filename: Optional[str] = None) -> dict:
    """
    Adds a PDF to the existing FAISS index for a thread, or creates a new one.
    """
    if not file_bytes:
        raise ValueError("No bytes received for ingestion.")

    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
        temp_file.write(file_bytes)
        temp_path = temp_file.name

    try:
        loader = PyPDFLoader(temp_path)
        docs = loader.load()

        splitter = RecursiveCharacterTextSplitter(
            chunk_size=500, chunk_overlap=100, separators=["\n\n", "\n", " ", ""]
        )
        chunks = splitter.split_documents(docs)

        thread_key = str(thread_id)
        
        # --- Multi-PDF Logic ---
        if thread_key in _THREAD_STORES:
            # Add to existing vector store
            _THREAD_STORES[thread_key].add_documents(chunks)
        else:
            # Create new vector store
            _THREAD_STORES[thread_key] = FAISS.from_documents(chunks, embeddings)

        # Track metadata as a list of files
        file_info = {
            "filename": filename or os.path.basename(temp_path),
            "documents": len(docs),
            "chunks": len(chunks),
        }
        
        if thread_key not in _THREAD_METADATA:
            _THREAD_METADATA[thread_key] = []
        _THREAD_METADATA[thread_key].append(file_info)

        return file_info
    finally:
        try:
            os.remove(temp_path)
        except OSError:
            pass


# -------------------
# 3. Tools
# -------------------
search_tool = DuckDuckGoSearchRun(region="us-en")

@tool
def calculator(first_num: float, second_num: float, operation: str) -> dict:
    """Perform basic arithmetic: add, sub, mul, div."""
    # ... (same as your previous logic)
    ops = {"add": first_num + second_num, "sub": first_num - second_num, 
           "mul": first_num * second_num, "div": first_num / second_num if second_num != 0 else "Error"}
    return {"result": ops.get(operation, "Unsupported")}

@tool
def get_stock_price(symbol: str) -> dict:
    """Fetch latest stock price for a symbol."""
    url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey=C9PE94QUEW9VWGFM"
    return requests.get(url).json()

@tool
def rag_tool(query: str, thread_id: Optional[str] = None) -> dict:
    """
    Retrieve information from ALL uploaded PDFs for this chat thread.
    """
    thread_key = str(thread_id)
    vector_store = _THREAD_STORES.get(thread_key)
    
    if vector_store is None:
        return {
            "error": "No documents indexed for this chat. Please upload one or more PDFs.",
            "query": query,
        }

    # Search across all documents in the store
    docs = vector_store.similarity_search(query, k=4)
    
    return {
        "query": query,
        "context": [doc.page_content for doc in docs],
        "sources": [doc.metadata for doc in docs],
        "uploaded_files": [f["filename"] for f in _THREAD_METADATA.get(thread_key, [])]
    }

tools = [search_tool, get_stock_price, calculator, rag_tool]
llm_with_tools = llm.bind_tools(tools)

# -------------------
# 4. State & Nodes (Same as previous)
# -------------------
class ChatState(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]

def chat_node(state: ChatState, config=None):
    thread_id = config.get("configurable", {}).get("thread_id") if config else None
    
    system_message = SystemMessage(
        content=(
            "You are a helpful assistant. You have access to multiple PDFs uploaded by the user. "
            f"To search them, use `rag_tool` with thread_id `{thread_id}`. "
            "You can synthesize info from multiple documents if needed."
        )
    )
    return {"messages": [llm_with_tools.invoke([system_message, *state["messages"]], config=config)]}

# -------------------
# 5. Graph Setup
# -------------------
tool_node = ToolNode(tools)
conn = sqlite3.connect(database="chatbot.db", check_same_thread=False)
checkpointer = SqliteSaver(conn=conn)

builder = StateGraph(ChatState)
builder.add_node("chat_node", chat_node)
builder.add_node("tools", tool_node)
builder.add_edge(START, "chat_node")
builder.add_conditional_edges("chat_node", tools_condition)
builder.add_edge("tools", "chat_node")

chatbot = builder.compile(checkpointer=checkpointer)

# -------------------
# 6. Helpers
# -------------------
def get_all_uploaded_files(thread_id: str) -> List[dict]:
    """Returns a list of all files uploaded to this thread."""
    return _THREAD_METADATA.get(str(thread_id), [])