src/query/executors/db_executor.py

"""Executor for registered database sources (source_type="database").

Flow per (client_id, question):
  1. Collect all relevant (table_name, column_name) pairs from retrieval results.
  2. Fetch the FULL schema for those tables from PGVector (not just top-k columns).
  3. Build a schema context string and send to LLM → structured SQLQuery output.
  4. Validate via sqlglot: SELECT-only, schema-grounded, LIMIT enforced.
  5. Execute on the user's DB via engine_scope + asyncio.to_thread.
  6. Return QueryResult per client_id (may span multiple tables via JOINs).

Supported db_types: postgres, supabase, mysql.
Other types are skipped with a warning — they do not raise.
"""

import asyncio
from collections import defaultdict
from typing import Any

import sqlglot
import sqlglot.expressions as exp
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import AzureChatOpenAI
from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession

from src.config.settings import settings
from src.database_client.database_client_service import database_client_service
from src.db.postgres.connection import _pgvector_engine
from src.middlewares.logging import get_logger
from src.models.sql_query import SQLQuery
from src.pipeline.db_pipeline import db_pipeline_service
from src.query.base import BaseExecutor, QueryResult
from src.rag.base import RetrievalResult
from src.utils.db_credential_encryption import decrypt_credentials_dict

logger = get_logger("db_executor")

_SUPPORTED_DB_TYPES = {"postgres", "supabase", "mysql"}
_MAX_RETRIES = 3
_MAX_LIMIT = 500

_SQL_SYSTEM_PROMPT = """\
You are a SQL data analyst working with a user's database.
Generate a single SQL SELECT statement that answers the user's question.

Rules:
- ONLY reference tables and columns listed in the schema below. Do not invent names.
- Always include a LIMIT clause (max {limit}).
- Do not use DELETE, UPDATE, INSERT, DROP, TRUNCATE, ALTER, CREATE, or any DDL.
- Prefer explicit JOINs over subqueries when combining tables.
- For aggregations, always alias the result column (e.g. COUNT(*) AS order_count).
- For date filtering, use standard SQL date functions appropriate for the dialect.

Schema:
{schema}

{error_section}"""


class DbExecutor(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", _SQL_SYSTEM_PROMPT),
            ("human", "{question}"),
        ])
        self._chain = self._prompt | self._llm.with_structured_output(SQLQuery)

    # ------------------------------------------------------------------
    # Public interface
    # ------------------------------------------------------------------

    async def execute(
        self,
        results: list[RetrievalResult],
        user_id: str,
        db: AsyncSession,
        limit: int = 100,
    ) -> list[QueryResult]:
        db_results = [r for r in results if r.source_type == "database"]
        if not db_results:
            return []

        # Group by client_id — one SQL generation + execution pass per client
        by_client: dict[str, list[RetrievalResult]] = defaultdict(list)
        for r in db_results:
            client_id = r.metadata.get("database_client_id", "")
            if client_id:
                by_client[client_id].append(r)
            else:
                logger.warning("db result missing database_client_id, skipping")

        query_results: list[QueryResult] = []
        for client_id, client_results in by_client.items():
            try:
                qr = await self._execute_for_client(client_id, client_results, user_id, db, limit)
                if qr:
                    query_results.append(qr)
            except Exception as e:
                logger.error("db executor failed for client", client_id=client_id, error=str(e))

        return query_results

    # ------------------------------------------------------------------
    # Per-client execution
    # ------------------------------------------------------------------

    async def _execute_for_client(
        self,
        client_id: str,
        results: list[RetrievalResult],
        user_id: str,
        db: AsyncSession,
        limit: int,
    ) -> QueryResult | None:
        client = await database_client_service.get(db, client_id)
        if not client:
            logger.warning("database client not found", client_id=client_id)
            return None
        if client.user_id != user_id:
            logger.warning("client ownership mismatch", client_id=client_id)
            return None
        if client.db_type not in _SUPPORTED_DB_TYPES:
            logger.warning("unsupported db_type for query execution", db_type=client.db_type)
            return None

        # Distinct table names from retrieval results
        table_names = list({
            r.metadata.get("data", {}).get("table_name")
            for r in results
            if r.metadata.get("data", {}).get("table_name")
        })

        full_schema = await self._fetch_full_schema(client_id, table_names, user_id)
        if not full_schema:
            logger.warning("no schema found in vector store", client_id=client_id, tables=table_names)
            return None

        schema_ctx = self._build_schema_context(full_schema)
        question = self._extract_question(results)
        capped_limit = min(limit, _MAX_LIMIT)

        # SQL generation with retry
        validated_sql: str | None = None
        prev_error: str = ""
        for attempt in range(_MAX_RETRIES):
            error_section = f"Previous attempt failed: {prev_error}\nFix the issue above." if prev_error else ""
            try:
                result: SQLQuery = await self._chain.ainvoke({
                    "schema": schema_ctx,
                    "limit": capped_limit,
                    "error_section": error_section,
                    "question": question,
                })
                sql = result.sql.strip()
                validation_error = self._validate(sql, full_schema, capped_limit)
                if validation_error:
                    prev_error = validation_error
                    logger.warning("sql validation failed", attempt=attempt + 1, error=validation_error)
                    continue
                validated_sql = sql
                logger.info("sql generated", attempt=attempt + 1, reasoning=result.reasoning)
                break
            except Exception as e:
                prev_error = str(e)
                logger.warning("sql generation error", attempt=attempt + 1, error=prev_error)

        if not validated_sql:
            logger.error("sql generation failed after retries", client_id=client_id)
            return None

        # Execute on user's DB
        creds = decrypt_credentials_dict(client.credentials)
        with db_pipeline_service.engine_scope(client.db_type, creds) as engine:
            rows = await asyncio.to_thread(self._run_sql, engine, validated_sql)

        column_types = {
            col["name"]: col["type"]
            for cols in full_schema.values()
            for col in cols
        }
        columns = list(rows[0].keys()) if rows else []

        return QueryResult(
            source_type="database",
            source_id=client_id,
            table_or_file=", ".join(table_names),
            columns=columns,
            rows=rows,
            row_count=len(rows),
            metadata={
                "db_type": client.db_type,
                "client_name": client.name,
                "sql": validated_sql,
                "column_types": {c: column_types.get(c, "unknown") for c in columns},
            },
        )

    # ------------------------------------------------------------------
    # Schema helpers
    # ------------------------------------------------------------------

    async def _fetch_full_schema(
        self,
        client_id: str,
        table_names: list[str],
        user_id: str,
    ) -> dict[str, list[dict[str, Any]]]:
        """Fetch ALL column chunks for the given tables from PGVector.

        Returns {table_name: [{"name": ..., "type": ..., "is_primary_key": ...,
                                "foreign_key": ..., "content": ...}]}
        """
        placeholders = ", ".join(f":t{i}" for i in range(len(table_names)))
        sql = text(f"""
            SELECT lpe.cmetadata, lpe.document
            FROM langchain_pg_embedding lpe
            JOIN langchain_pg_collection lpc ON lpe.collection_id = lpc.uuid
            WHERE lpc.name = 'document_embeddings'
              AND lpe.cmetadata->>'user_id' = :user_id
              AND lpe.cmetadata->>'source_type' = 'database'
              AND lpe.cmetadata->>'database_client_id' = :client_id
              AND lpe.cmetadata->'data'->>'table_name' IN ({placeholders})
            ORDER BY lpe.cmetadata->'data'->>'table_name', lpe.cmetadata->'data'->>'column_name'
        """)

        params: dict[str, Any] = {"user_id": user_id, "client_id": client_id}
        for i, name in enumerate(table_names):
            params[f"t{i}"] = name

        async with _pgvector_engine.connect() as conn:
            result = await conn.execute(sql, params)
            rows = result.fetchall()

        schema: dict[str, list[dict[str, Any]]] = defaultdict(list)
        for row in rows:
            data = row.cmetadata.get("data", {})
            table = data.get("table_name")
            if table:
                schema[table].append({
                    "name": data.get("column_name", ""),
                    "type": data.get("column_type", ""),
                    "is_primary_key": data.get("is_primary_key", False),
                    "foreign_key": data.get("foreign_key"),
                    "content": row.document,  # chunk text includes top values / samples
                })
        return dict(schema)

    def _build_schema_context(self, schema: dict[str, list[dict[str, Any]]]) -> str:
        lines: list[str] = []
        for table, columns in schema.items():
            lines.append(f"Table: {table}")
            for col in columns:
                flags = []
                if col["is_primary_key"]:
                    flags.append("PRIMARY KEY")
                if col["foreign_key"]:
                    flags.append(f"FK -> {col['foreign_key']}")
                flag_str = f"  [{', '.join(flags)}]" if flags else ""
                lines.append(f"  - {col['name']}  {col['type']}{flag_str}")
                # Include sample/top-values line from chunk content if present
                for line in col["content"].splitlines():
                    if line.startswith(("Top values:", "Sample values:")):
                        lines.append(f"    {line}")
                        break
            lines.append("")
        return "\n".join(lines).strip()

    def _extract_question(self, results: list[RetrievalResult]) -> str:
        # The search_query rewritten by the orchestrator is not in RetrievalResult —
        # the content field carries schema descriptions. Return a generic fallback;
        # callers that have the original question should pass it explicitly.
        # TODO: thread the original user question through to execute() when wiring into the agent.
        return "Answer the user's data question using the schema provided."

    # ------------------------------------------------------------------
    # Guardrails
    # ------------------------------------------------------------------

    def _validate(self, sql: str, schema: dict[str, list[dict]], limit: int) -> str:
        """Return an error string if validation fails, empty string if OK."""
        # Layer 1: sqlglot parse + SELECT-only check
        try:
            parsed = sqlglot.parse_one(sql)
        except sqlglot.errors.ParseError as e:
            return f"SQL parse error: {e}"

        if not isinstance(parsed, exp.Select):
            return f"Only SELECT statements are allowed. Got: {type(parsed).__name__}"

        # Check for DML inside CTEs
        for cte in parsed.find_all(exp.With):
            for node in cte.find_all((exp.Insert, exp.Update, exp.Delete)):
                return f"DML ({type(node).__name__}) inside CTE is not allowed."

        # Layer 2: schema grounding — table names
        known_tables = {t.lower() for t in schema}
        for tbl in parsed.find_all(exp.Table):
            name = tbl.name.lower()
            if name and name not in known_tables:
                return f"Unknown table '{tbl.name}'. Only use tables from the schema."

        # Layer 3: LIMIT enforcement (inject if missing — done before execution)
        return ""

    # ------------------------------------------------------------------
    # SQL execution
    # ------------------------------------------------------------------

    def _enforce_limit(self, sql: str, limit: int) -> str:
        """Inject or cap LIMIT using sqlglot AST manipulation."""
        parsed = sqlglot.parse_one(sql)
        existing = parsed.find(exp.Limit)
        if existing:
            current = int(existing.expression.this)
            if current > limit:
                existing.expression.set("this", str(limit))
        else:
            parsed = parsed.limit(limit)
        return parsed.sql()

    def _run_sql(self, engine: Any, sql: str) -> list[dict]:
        with engine.connect() as conn:
            result = conn.execute(text(sql))
            return [dict(row) for row in result.mappings()]


db_executor = DbExecutor()