src/query/executors/tabular.py

"""Executor for tabular document sources (source_type="document", file_type csv/xlsx).

Receives sheet-level RetrievalResults from SchemaRetriever (each result
represents a relevant sheet, with its full column list available via
data.column_names in metadata).

Flow:
  1. Group RetrievalResult chunks by (document_id, sheet_name).
  2. Per group: download Parquet from Azure Blob → pandas DataFrame.
  3. Build schema context from full DataFrame columns + sample values.
  4. LLM decides operation (groupby_sum, filter, top_n, etc.) via structured output.
  5. Pandas runs the operation; retry up to 3x on error with feedback to LLM.
  6. Fallback to raw rows if all retries fail.
  7. Return QueryResult per group.
"""
import asyncio
from typing import Literal, TypedDict

import pandas as pd
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import AzureChatOpenAI
from pydantic import BaseModel
from sqlalchemy.ext.asyncio import AsyncSession

from src.config.settings import settings
from src.knowledge.parquet_service import download_parquet
from src.middlewares.logging import get_logger
from src.query.base import BaseExecutor, QueryResult
from src.rag.base import RetrievalResult

logger = get_logger("tabular_executor")


class _GroupInfo(TypedDict):
    filename: str
    file_type: str


_TABULAR_FILE_TYPES = ("csv", "xlsx")
_MAX_RETRIES = 3

_SYSTEM_PROMPT = """\
You are a data analyst. Given a DataFrame schema and a user question, \
decide which pandas operation to perform.

IMPORTANT rules:
- Use ONLY the exact column names as written in the schema below. Never translate or rename them.
- For top_n: always set value_col to the column to sort by. Do NOT use sort_col for top_n.
- For sort: use sort_col for the column to sort by.
- For filter with comparison (>, <, >=, <=, !=): set filter_operator accordingly (gt, lt, gte, lte, ne). Default is eq (==).
- For multi-condition filters (AND logic), use the filters field as a list of {{"col", "value", "op"}} dicts instead of filter_col/filter_value.
  Example: status=SUCCESS AND amount_paid>200000 → filters=[{{"col":"status","value":"SUCCESS","op":"eq"}},{{"col":"amount_paid","value":"200000","op":"gt"}}]
- IMPORTANT: When the question uses "or" / "atau" between values of the same column, you MUST use or_filters (NOT filters).
  or_filters applies OR logic: rows matching ANY condition are kept.
  filters applies AND logic: rows must match ALL conditions.
  Example: "(status FAILED or REVERSED) AND payment_channel=Tokopedia" →
    or_filters=[{{"col":"status","value":"FAILED","op":"eq"}},{{"col":"status","value":"REVERSED","op":"eq"}}]
    filters=[{{"col":"payment_channel","value":"Tokopedia","op":"eq"}}]
- For groupby with a pre-filter (e.g. count SUCCESS per channel): use filters or or_filters to narrow rows first, then use groupby_count/groupby_sum/groupby_avg on the filtered data by setting both filters and group_col.

Schema:
{schema}

{error_section}"""


class TabularOperation(BaseModel):
    operation: Literal[
        "filter", "groupby_sum", "groupby_avg", "groupby_count",
        "top_n", "sort", "aggregate", "raw"
    ]
    group_col: str | None = None       # for groupby_*
    value_col: str | None = None       # for groupby_*, top_n, aggregate
    filter_col: str | None = None      # for single filter
    filter_value: str | None = None    # for single filter
    filter_operator: Literal["eq", "ne", "gt", "gte", "lt", "lte"] = "eq"  # for single filter
    filters: list[dict] | None = None     # for multi-condition AND: [{"col": ..., "value": ..., "op": ...}]
    or_filters: list[dict] | None = None  # for OR conditions, applied before AND filters
    sort_col: str | None = None        # for sort
    ascending: bool = True             # for sort
    n: int | None = None               # for top_n
    agg_func: Literal["sum", "avg", "min", "max", "count"] | None = None  # for aggregate
    reasoning: str


def _get_filter_mask(df: pd.DataFrame, col: str, value: str, operator: str) -> pd.Series:
    numeric = pd.to_numeric(df[col], errors="coerce")
    coerced_nulls = numeric.isnull() & df[col].notna()
    if coerced_nulls.any():
        logger.warning("numeric coercion introduced NaN", col=col, count=int(coerced_nulls.sum()))
    if operator == "eq":
        return df[col].astype(str) == str(value)
    elif operator == "ne":
        return df[col].astype(str) != str(value)
    elif operator == "gt":
        return numeric > float(value)
    elif operator == "gte":
        return numeric >= float(value)
    elif operator == "lt":
        return numeric < float(value)
    elif operator == "lte":
        return numeric <= float(value)
    raise ValueError(f"Unknown operator: {operator}")


def _apply_single_filter(df: pd.DataFrame, col: str, value: str, operator: str) -> pd.DataFrame:
    numeric = pd.to_numeric(df[col], errors="coerce")
    coerced_nulls = numeric.isnull() & df[col].notna()
    if coerced_nulls.any():
        logger.warning("numeric coercion introduced NaN", col=col, count=int(coerced_nulls.sum()))
    if operator == "eq":
        return df[df[col].astype(str) == str(value)]
    elif operator == "ne":
        return df[df[col].astype(str) != str(value)]
    elif operator == "gt":
        return df[numeric > float(value)]
    elif operator == "gte":
        return df[numeric >= float(value)]
    elif operator == "lt":
        return df[numeric < float(value)]
    elif operator == "lte":
        return df[numeric <= float(value)]
    raise ValueError(f"Unknown operator: {operator}")


def _build_schema_context(df: pd.DataFrame) -> str:
    lines = []
    for col in df.columns:
        sample = df[col].dropna().head(3).tolist()
        lines.append(f"- {col} ({df[col].dtype}): sample values: {sample}")
    return "\n".join(lines)


def _apply_operation(df: pd.DataFrame, op: TabularOperation, limit: int) -> pd.DataFrame:
    if op.operation == "groupby_sum":
        if not op.group_col or not op.value_col:
            raise ValueError(f"groupby_sum requires group_col and value_col, got {op}")
        return df.groupby(op.group_col)[op.value_col].sum().reset_index().nlargest(limit, op.value_col)
    elif op.operation == "groupby_avg":
        if not op.group_col or not op.value_col:
            raise ValueError(f"groupby_avg requires group_col and value_col, got {op}")
        return df.groupby(op.group_col)[op.value_col].mean().reset_index().nlargest(limit, op.value_col)
    elif op.operation == "groupby_count":
        if not op.group_col:
            raise ValueError(f"groupby_count requires group_col, got {op}")
        df_filtered = df.copy()
        if op.or_filters:
            or_mask = pd.Series([False] * len(df_filtered), index=df_filtered.index)
            for f in op.or_filters:
                or_mask = or_mask | _get_filter_mask(df_filtered, f["col"], f["value"], f.get("op", "eq"))
            df_filtered = df_filtered[or_mask]
        if op.filters:
            for f in op.filters:
                df_filtered = _apply_single_filter(df_filtered, f["col"], f["value"], f.get("op", "eq"))
        elif op.filter_col and op.filter_value is not None:
            df_filtered = _apply_single_filter(df_filtered, op.filter_col, op.filter_value, op.filter_operator)
        return df_filtered.groupby(op.group_col).size().reset_index(name="count").nlargest(limit, "count")
    elif op.operation == "filter":
        result = df.copy()
        if op.or_filters:
            or_mask = pd.Series([False] * len(result), index=result.index)
            for f in op.or_filters:
                or_mask = or_mask | _get_filter_mask(result, f["col"], f["value"], f.get("op", "eq"))
            result = result[or_mask]
        if op.filters:
            for f in op.filters:
                result = _apply_single_filter(result, f["col"], f["value"], f.get("op", "eq"))
        elif op.filter_col and op.filter_value is not None and not op.or_filters:
            result = _apply_single_filter(result, op.filter_col, op.filter_value, op.filter_operator)
        elif not op.or_filters and not op.filters and (not op.filter_col or op.filter_value is None):
            raise ValueError(f"filter requires filter_col/filter_value or filters or or_filters, got {op}")
        return result.head(limit)
    elif op.operation == "top_n":
        col = op.value_col
        if not col:
            raise ValueError(f"top_n requires value_col, got {op}")
        n = op.n or limit
        return df.nlargest(n, col)
    elif op.operation == "sort":
        if not op.sort_col:
            raise ValueError(f"sort requires sort_col, got {op}")
        return df.sort_values(op.sort_col, ascending=op.ascending).head(limit)
    elif op.operation == "aggregate":
        if not op.agg_func:
            raise ValueError(f"aggregate requires agg_func, got {op}")
        if op.agg_func == "count":
            if not op.value_col:
                return pd.DataFrame([{"column_name": c, "dtype": str(df[c].dtype)} for c in df.columns])
            return pd.DataFrame([{"count": int(df[op.value_col].count()), "operation": "count"}])
        if not op.value_col:
            raise ValueError(f"aggregate requires value_col for {op.agg_func}, got {op}")
        funcs = {"sum": "sum", "avg": "mean", "min": "min", "max": "max"}
        value = getattr(df[op.value_col], funcs[op.agg_func])()
        return pd.DataFrame([{op.value_col: value, "operation": op.agg_func}])
    else:  # "raw"
        return df.head(limit)


class TabularExecutor(BaseExecutor):
    def __init__(self) -> None:
        self._llm = AzureChatOpenAI(
            azure_deployment=settings.azureai_deployment_name_4o,
            openai_api_version=settings.azureai_api_version_4o,
            azure_endpoint=settings.azureai_endpoint_url_4o,
            api_key=settings.azureai_api_key_4o,
            temperature=0,
        )
        self._prompt = ChatPromptTemplate.from_messages([
            ("system", _SYSTEM_PROMPT),
            ("human", "{question}"),
        ])
        self._chain = self._prompt | self._llm.with_structured_output(TabularOperation)

    async def execute(
        self,
        results: list[RetrievalResult],
        user_id: str,
        _db: AsyncSession,
        question: str,
        limit: int = 100,
    ) -> list[QueryResult]:
        tabular = [
            r for r in results
            if r.source_type == "document"
            and r.metadata.get("data", {}).get("file_type") in _TABULAR_FILE_TYPES
        ]

        if not tabular:
            return []

        # Group by (document_id, sheet_name) — one parquet download per group
        groups: dict[tuple[str, str | None], _GroupInfo] = {}
        for r in tabular:
            data = r.metadata.get("data", {})
            doc_id = data.get("document_id")
            if not doc_id:
                continue
            sheet_name = data.get("sheet_name")  # None for CSV
            key = (doc_id, sheet_name)
            if key not in groups:
                groups[key] = {
                    "filename": data.get("filename", ""),
                    "file_type": data.get("file_type", ""),
                }

        async def _process_group(
            doc_id: str, sheet_name: str | None, info: _GroupInfo
        ) -> QueryResult | None:
            try:
                df = await download_parquet(user_id, doc_id, sheet_name)
                df_result = await self._query_with_agent(df, question, limit)

                table_label = info["filename"]
                if sheet_name:
                    table_label += f" / sheet: {sheet_name}"

                logger.info(
                    "tabular query complete",
                    document_id=doc_id,
                    sheet=sheet_name,
                    file_type=info["file_type"],
                    rows=len(df_result),
                    columns=len(df_result.columns),
                )
                return QueryResult(
                    source_type="document",
                    source_id=doc_id,
                    table_or_file=table_label,
                    columns=list(df_result.columns),
                    rows=df_result.to_dict(orient="records"),
                    row_count=len(df_result),
                )
            except Exception as e:
                logger.error(
                    "tabular query failed",
                    document_id=doc_id,
                    sheet=sheet_name,
                    error=str(e),
                )
                return None

        # Each group runs independently — cross-file JOIN is out of scope for v1.
        gathered = await asyncio.gather(*[
            _process_group(doc_id, sheet_name, info)
            for (doc_id, sheet_name), info in groups.items()
        ])
        return [r for r in gathered if r is not None]

    async def _query_with_agent(
        self, df: pd.DataFrame, question: str, limit: int
    ) -> pd.DataFrame:
        schema_ctx = _build_schema_context(df)
        prev_error = ""

        for attempt in range(_MAX_RETRIES):
            error_section = (
                f"Previous attempt failed: {prev_error}\nFix the issue."
                if prev_error else ""
            )
            try:
                op: TabularOperation = await self._chain.ainvoke({
                    "schema": schema_ctx,
                    "error_section": error_section,
                    "question": question,
                })
                logger.info(
                    "tabular operation decided",
                    operation=op.operation,
                    reasoning=op.reasoning,
                )
                return _apply_operation(df, op, limit)
            except Exception as e:
                prev_error = str(e)
                logger.warning("tabular agent error", attempt=attempt + 1, error=prev_error)

        # Fallback: return raw rows (all columns — chat.py caps rows at 20 before LLM)
        logger.warning("tabular agent failed after retries, returning raw rows")
        return df.head(limit)


tabular_executor = TabularExecutor()