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
import tiktoken
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")

_enc = tiktoken.get_encoding("cl100k_base")

_SUPPORTED_DB_TYPES = {"postgres", "supabase", "mysql"}
_MAX_RETRIES = 3
_MAX_LIMIT = 500
_FK_EXPANSION_MAX_TABLES = 5

_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.

Database dialect: {dialect}

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 dialect-appropriate functions ({dialect} syntax).

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,
        question: str,
        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, question, 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,
        question: str,
        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

        # Hit tables = tables retrieval pointed at directly. Get full per-column
        # schema for these. Related tables (one FK hop away, both directions) are
        # fetched separately in abbreviated form to give the LLM enough context
        # to JOIN without paying the per-column profile token cost.
        hit_tables = list({
            r.metadata.get("data", {}).get("table_name")
            for r in results
            if r.metadata.get("data", {}).get("table_name")
        })
        if not hit_tables:
            logger.warning("no table_name on any retrieval result", client_id=client_id)
            return None

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

        related_tables = await self._find_related_tables(client_id, user_id, hit_tables)
        related_schema = (
            await self._fetch_abbreviated_schema(client_id, user_id, related_tables)
            if related_tables else {}
        )

        schema_ctx = self._build_schema_context(full_schema, related_schema)
        capped_limit = min(limit, _MAX_LIMIT)
        dialect = client.db_type

        # SQL generation with retry
        validated_sql: str | None = None
        prev_error: str = ""
        prev_reasoning: str = ""
        for attempt in range(_MAX_RETRIES):
            if prev_error:
                error_section = (
                    f"Previous attempt reasoning: {prev_reasoning}\n"
                    f"Previous attempt failed: {prev_error}\n"
                    "Fix the issue above."
                )
            else:
                error_section = ""
            try:
                prompt_text = schema_ctx + error_section + question
                input_tokens = len(_enc.encode(prompt_text))
                logger.info("sql generation input tokens", attempt=attempt + 1, tokens=input_tokens)

                result: SQLQuery = await self._chain.ainvoke({
                    "schema": schema_ctx,
                    "dialect": dialect,
                    "limit": capped_limit,
                    "error_section": error_section,
                    "question": question,
                })
                sql = result.sql.strip()
                allowed_tables = set(full_schema) | set(related_schema)
                validation_error = self._validate(sql, allowed_tables, capped_limit)
                if validation_error:
                    prev_error = validation_error
                    prev_reasoning = result.reasoning
                    logger.warning("sql validation failed", attempt=attempt + 1, error=validation_error)
                    continue
                validated_sql = self._enforce_limit(sql, capped_limit)
                output_tokens = len(_enc.encode(result.sql)) + len(_enc.encode(result.reasoning))
                logger.info(
                    "sql generated",
                    attempt=attempt + 1,
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    total_tokens=input_tokens + output_tokens,
                    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(hit_tables),
            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 _find_related_tables(
        self,
        client_id: str,
        user_id: str,
        hit_tables: list[str],
    ) -> list[str]:
        """One-hop FK neighbours of `hit_tables`, both directions, excluding hits.

        Prefers chunk_level='table' rows; if none exist for the client (legacy
        ingest predating Phase 1), falls back to aggregating from column-chunk
        metadata. Returns [] when no FK metadata is available.

        Capped at _FK_EXPANSION_MAX_TABLES, ranked by edge count desc then
        table name asc. A warning is logged when the cap kicks in.
        """
        if not hit_tables:
            return []

        hit_set = set(hit_tables)
        # edge_counts[related_table] = number of FK edges connecting it to the hit set
        edge_counts: dict[str, int] = defaultdict(int)

        # ---- Primary path: table-level chunks ----
        sql = text("""
            SELECT lpe.cmetadata
            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->>'chunk_level' = 'table'
        """)
        async with _pgvector_engine.connect() as conn:
            result = await conn.execute(sql, {"user_id": user_id, "client_id": client_id})
            table_rows = result.fetchall()

        if table_rows:
            for row in table_rows:
                data = row.cmetadata.get("data", {})
                table = data.get("table_name")
                fks = data.get("foreign_keys") or []
                if not table:
                    continue
                if table in hit_set:
                    # Outgoing: this hit's FKs point at related tables
                    for fk in fks:
                        target = fk.get("target_table")
                        if target and target not in hit_set:
                            edge_counts[target] += 1
                else:
                    # Incoming: this non-hit table's FKs point into the hit set
                    for fk in fks:
                        target = fk.get("target_table")
                        if target in hit_set:
                            edge_counts[table] += 1
        else:
            # ---- Fallback: aggregate from column chunks ----
            sql = text("""
                SELECT lpe.cmetadata->'data'->>'table_name' AS src_table,
                       lpe.cmetadata->'data'->>'foreign_key' AS fk
                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->>'chunk_level' = 'column'
                  AND lpe.cmetadata->'data'->>'foreign_key' IS NOT NULL
            """)
            async with _pgvector_engine.connect() as conn:
                result = await conn.execute(sql, {"user_id": user_id, "client_id": client_id})
                col_rows = result.fetchall()

            for row in col_rows:
                src = row.src_table
                fk = row.fk
                if not src or not fk:
                    continue
                target = fk.split(".", 1)[0]
                if src in hit_set and target and target not in hit_set:
                    edge_counts[target] += 1
                elif src not in hit_set and target in hit_set:
                    edge_counts[src] += 1

        if not edge_counts:
            return []

        ranked = sorted(edge_counts.items(), key=lambda kv: (-kv[1], kv[0]))
        if len(ranked) > _FK_EXPANSION_MAX_TABLES:
            logger.warning(
                "fk expansion cap hit",
                client_id=client_id,
                total=len(ranked),
                cap=_FK_EXPANSION_MAX_TABLES,
                dropped=[t for t, _ in ranked[_FK_EXPANSION_MAX_TABLES:]],
            )
            ranked = ranked[:_FK_EXPANSION_MAX_TABLES]

        related = [t for t, _ in ranked]
        logger.info("fk-related tables", hit=sorted(hit_set), related=related)
        return related

    async def _fetch_abbreviated_schema(
        self,
        client_id: str,
        user_id: str,
        table_names: list[str],
    ) -> dict[str, dict[str, Any]]:
        """Abbreviated schema: name, row_count, PK, FKs, column names — no profiles.

        Prefers chunk_level='table' rows. Falls back to aggregating column-chunk
        metadata when table chunks are missing for a given table_name.

        Returns {table_name: {"row_count": int|None, "primary_key": [str],
        "foreign_keys": [{column, target_table, target_column}],
        "column_names": [str]}}.
        """
        if not table_names:
            return {}

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

        # Primary path: one row per table from chunk_level='table'
        sql_table = text(f"""
            SELECT lpe.cmetadata
            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->>'chunk_level' = 'table'
              AND lpe.cmetadata->'data'->>'table_name' IN ({placeholders})
        """)
        async with _pgvector_engine.connect() as conn:
            result = await conn.execute(sql_table, params)
            t_rows = result.fetchall()

        out: dict[str, dict[str, Any]] = {}
        for row in t_rows:
            data = row.cmetadata.get("data", {})
            tname = data.get("table_name")
            if not tname:
                continue
            out[tname] = {
                "row_count": data.get("row_count"),
                "primary_key": list(data.get("primary_key") or []),
                "foreign_keys": list(data.get("foreign_keys") or []),
                "column_names": list(data.get("column_names") or []),
            }

        # Fallback for tables with no table-chunk: aggregate column chunks
        missing = [t for t in table_names if t not in out]
        if missing:
            placeholders_m = ", ".join(f":m{i}" for i in range(len(missing)))
            params_m: dict[str, Any] = {"user_id": user_id, "client_id": client_id}
            for i, name in enumerate(missing):
                params_m[f"m{i}"] = name
            sql_col = text(f"""
                SELECT lpe.cmetadata
                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->>'chunk_level' = 'column'
                  AND lpe.cmetadata->'data'->>'table_name' IN ({placeholders_m})
                ORDER BY lpe.cmetadata->'data'->>'table_name', lpe.cmetadata->'data'->>'column_name'
            """)
            async with _pgvector_engine.connect() as conn:
                result = await conn.execute(sql_col, params_m)
                c_rows = result.fetchall()

            agg: dict[str, dict[str, Any]] = {
                t: {"row_count": None, "primary_key": [], "foreign_keys": [], "column_names": []}
                for t in missing
            }
            for row in c_rows:
                data = row.cmetadata.get("data", {})
                tname = data.get("table_name")
                cname = data.get("column_name")
                if not tname or tname not in agg or not cname:
                    continue
                bucket = agg[tname]
                bucket["column_names"].append(cname)
                if data.get("is_primary_key"):
                    bucket["primary_key"].append(cname)
                fk = data.get("foreign_key")
                if fk:
                    target_table, _, target_col = fk.partition(".")
                    bucket["foreign_keys"].append({
                        "column": cname,
                        "target_table": target_table,
                        "target_column": target_col,
                    })
            for t, v in agg.items():
                if v["column_names"]:
                    out[t] = v

        return out

    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->>'chunk_level' = 'column'
              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]]],
        related_schema: dict[str, dict[str, Any]] | None = None,
    ) -> 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("")

        related_block = self._build_related_schema_block(related_schema or {})
        if related_block:
            lines.append(related_block)

        return "\n".join(lines).strip()

    def _build_related_schema_block(self, related_schema: dict[str, dict[str, Any]]) -> str:
        """Format the abbreviated FK-related-tables section. Empty string when no related."""
        if not related_schema:
            return ""
        lines: list[str] = ["Related tables (one hop via FK, abbreviated — use for JOINs only):"]
        for table, info in related_schema.items():
            row_count = info.get("row_count")
            header = f"- {table} ({row_count} rows)" if row_count is not None else f"- {table}"
            lines.append(header)
            pk = info.get("primary_key") or []
            lines.append(f"    Primary key: {', '.join(pk) if pk else '(none)'}")
            fks = info.get("foreign_keys") or []
            if fks:
                fk_strs = [
                    f"{fk.get('column')} -> {fk.get('target_table')}.{fk.get('target_column')}"
                    for fk in fks
                ]
                lines.append(f"    Foreign keys: {', '.join(fk_strs)}")
            else:
                lines.append("    Foreign keys: (none)")
            cols = info.get("column_names") or []
            lines.append(f"    Columns: {', '.join(cols)}")
        return "\n".join(lines)

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

    def _validate(self, sql: str, allowed_tables: set[str], limit: int) -> str:
        """Return an error string if validation fails, empty string if OK.

        `allowed_tables` is the union of hit-table names and FK-related table
        names — both are legal targets for SELECT/JOIN.
        """
        # 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 anywhere in the AST (including writeable CTEs)
        for node in parsed.find_all((exp.Insert, exp.Update, exp.Delete)):
            return f"DML ({type(node).__name__}) is not allowed."

        # Layer 2: schema grounding — table names
        known_tables = {t.lower() for t in allowed_tables}
        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", limit)
        else:
            parsed = parsed.limit(limit)
        return parsed.sql()

    def _run_sql(self, engine: Any, sql: str) -> list[dict]:
        # Ensure the user DB connection is a read-only credential — sqlglot validation alone is not sufficient.
        with engine.connect() as conn:
            result = conn.execute(text(sql))
            return [dict(row) for row in result.mappings()]


db_executor = DbExecutor()