table profiling功能开发

app/db.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+import os
+from functools import lru_cache
+
+from sqlalchemy import create_engine
+from sqlalchemy.engine import Engine
+
+
+@lru_cache(maxsize=1)
+def get_engine() -> Engine:
+    """Return a cached SQLAlchemy engine configured from DATABASE_URL."""
+    database_url = os.getenv(
+        "DATABASE_URL",
+        "mysql+pymysql://root:12345678@localhost:3306/data-ge?charset=utf8mb4",
+    )
+    connect_args = {}
+    if database_url.startswith("sqlite"):
+        connect_args["check_same_thread"] = False
+
+    return create_engine(
+        database_url,
+        pool_pre_ping=True,
+        future=True,
+        connect_args=connect_args,
+    )

app/main.py

@@ -2,12 +2,17 @@ from __future__ import annotations
 
 import asyncio
 import logging
+import logging.config
 import os
 from contextlib import asynccontextmanager
 from typing import Any
 
+import yaml
+
 import httpx
 from fastapi import Depends, FastAPI, HTTPException, Request
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import JSONResponse
 
 from app.exceptions import ProviderAPICallError, ProviderConfigurationError
 from app.models import (
@@ -15,30 +20,42 @@ from app.models import (
     DataImportAnalysisJobRequest,
     LLMRequest,
     LLMResponse,
+    TableProfilingJobAck,
+    TableProfilingJobRequest,
+    TableSnippetUpsertRequest,
+    TableSnippetUpsertResponse,
 )
 from app.services import LLMGateway
 from app.services.import_analysis import process_import_analysis_job
+from app.services.table_profiling import process_table_profiling_job
+from app.services.table_snippet import upsert_action_result
+
+
+def _ensure_log_directories(config: dict[str, Any]) -> None:
+    handlers = config.get("handlers", {})
+    for handler_config in handlers.values():
+        filename = handler_config.get("filename")
+        if not filename:
+            continue
+        directory = os.path.dirname(filename)
+        if directory and not os.path.exists(directory):
+            os.makedirs(directory, exist_ok=True)
 
 
 def _configure_logging() -> None:
-    level_name = os.getenv("LOG_LEVEL", "INFO").upper()
-    level = getattr(logging, level_name, logging.INFO)
-    log_format = os.getenv(
-        "LOG_FORMAT",
-        "%(asctime)s %(levelname)s %(name)s:%(lineno)d %(message)s",
+    config_path = os.getenv("LOGGING_CONFIG", "logging.yaml")
+    if os.path.exists(config_path):
+        with open(config_path, "r", encoding="utf-8") as fh:
+            config = yaml.safe_load(fh)
+        if isinstance(config, dict):
+            _ensure_log_directories(config)
+            logging.config.dictConfig(config)
+            return
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s %(name)s:%(lineno)d %(message)s",
     )
 
-    root = logging.getLogger()
-
-    if not root.handlers:
-        logging.basicConfig(level=level, format=log_format)
-    else:
-        root.setLevel(level)
-        formatter = logging.Formatter(log_format)
-        for handler in root.handlers:
-            handler.setLevel(level)
-            handler.setFormatter(formatter)
-
 
 _configure_logging()
 logger = logging.getLogger(__name__)
@@ -119,6 +136,24 @@ def create_app() -> FastAPI:
         lifespan=lifespan,
     )
 
+    @application.exception_handler(RequestValidationError)
+    async def request_validation_exception_handler(
+        request: Request, exc: RequestValidationError
+    ) -> JSONResponse:
+        try:
+            raw_body = await request.body()
+        except Exception:  # pragma: no cover - defensive
+            raw_body = b"<unavailable>"
+        truncated_body = raw_body[:4096]
+        logger.warning(
+            "Validation error on %s %s: %s | body preview=%s",
+            request.method,
+            request.url.path,
+            exc.errors(),
+            truncated_body.decode("utf-8", errors="ignore"),
+        )
+        return JSONResponse(status_code=422, content={"detail": exc.errors()})
+
     @application.post(
         "/v1/chat/completions",
         response_model=LLMResponse,
@@ -164,6 +199,52 @@ def create_app() -> FastAPI:
 
         return DataImportAnalysisJobAck(import_record_id=payload.import_record_id, status="accepted")
 
+    @application.post(
+        "/v1/table/profiling",
+        response_model=TableProfilingJobAck,
+        summary="Run end-to-end GE profiling pipeline and notify via callback per action",
+        status_code=202,
+    )
+    async def run_table_profiling(
+        payload: TableProfilingJobRequest,
+        gateway: LLMGateway = Depends(get_gateway),
+        client: httpx.AsyncClient = Depends(get_http_client),
+    ) -> TableProfilingJobAck:
+        request_copy = payload.model_copy(deep=True)
+
+        async def _runner() -> None:
+            await process_table_profiling_job(request_copy, gateway, client)
+
+        asyncio.create_task(_runner())
+
+        return TableProfilingJobAck(
+            table_id=payload.table_id,
+            version_ts=payload.version_ts,
+            status="accepted",
+        )
+
+    @application.post(
+        "/v1/table/snippet",
+        response_model=TableSnippetUpsertResponse,
+        summary="Persist or update action results, such as table snippets.",
+    )
+    async def upsert_table_snippet(
+        payload: TableSnippetUpsertRequest,
+    ) -> TableSnippetUpsertResponse:
+        request_copy = payload.model_copy(deep=True)
+
+        try:
+            return await asyncio.to_thread(upsert_action_result, request_copy)
+        except Exception as exc:
+            logger.error(
+                "Failed to upsert table snippet: table_id=%s version_ts=%s action_type=%s",
+                payload.table_id,
+                payload.version_ts,
+                payload.action_type,
+                exc_info=True,
+            )
+            raise HTTPException(status_code=500, detail=str(exc)) from exc
+
     @application.post("/__mock__/import-callback")
     async def mock_import_callback(payload: dict[str, Any]) -> dict[str, str]:
         logger.info("Received import analysis callback: %s", payload)

app/models.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from datetime import datetime
 from enum import Enum
 from typing import Any, Dict, List, Optional, Union
 
@@ -135,3 +136,89 @@ class DataImportAnalysisJobRequest(BaseModel):
 class DataImportAnalysisJobAck(BaseModel):
     import_record_id: str = Field(..., description="Echo of the import record identifier")
     status: str = Field("accepted", description="Processing status acknowledgement.")
+
+
+class TableProfilingJobRequest(BaseModel):
+    table_id: str = Field(..., description="Unique identifier for the table to profile.")
+    version_ts: str = Field(
+        ...,
+        pattern=r"^\d{14}$",
+        description="Version timestamp expressed as fourteen digit string (yyyyMMddHHmmss).",
+    )
+    callback_url: HttpUrl = Field(
+        ...,
+        description="Callback endpoint invoked after each pipeline action completes.",
+    )
+    table_schema: Optional[Any] = Field(
+        None,
+        description="Schema structure snapshot for the current table version.",
+    )
+    table_schema_version_id: Optional[str] = Field(
+        None,
+        description="Identifier for the schema snapshot provided in table_schema.",
+    )
+    table_link_info: Optional[Dict[str, Any]] = Field(
+        None,
+        description=(
+            "Information describing how to locate the source table for profiling. "
+            "For example: {'type': 'sql', 'connection_string': 'mysql+pymysql://user:pass@host/db', "
+            "'table': 'schema.table_name'}."
+        ),
+    )
+    table_access_info: Optional[Dict[str, Any]] = Field(
+        None,
+        description=(
+            "Credentials or supplemental parameters required to access the table described in table_link_info. "
+            "These values can be merged into the connection string using Python format placeholders."
+        ),
+    )
+    ge_batch_request: Optional[Dict[str, Any]] = Field(
+        None,
+        description="Optional Great Expectations batch request payload used for profiling.",
+    )
+    ge_expectation_suite_name: Optional[str] = Field(
+        None,
+        description="Expectation suite name used during profiling. Created automatically when absent.",
+    )
+    ge_data_context_root: Optional[str] = Field(
+        None,
+        description="Custom root directory for the Great Expectations data context. Defaults to project ./gx.",
+    )
+    ge_datasource_name: Optional[str] = Field(
+        None,
+        description="Datasource name registered inside the GE context when batch_request is not supplied.",
+    )
+    ge_data_asset_name: Optional[str] = Field(
+        None,
+        description="Data asset reference used when inferring batch request from datasource configuration.",
+    )
+    ge_profiler_type: str = Field(
+        "user_configurable",
+        description="Profiler implementation identifier. Currently supports 'user_configurable' or 'data_assistant'.",
+    )
+    llm_model: Optional[str] = Field(
+        None,
+        description="Default LLM model spec applied to prompt-based actions when overrides are omitted.",
+    )
+    result_desc_model: Optional[str] = Field(
+        None,
+        description="LLM model override used for GE result description (action 2).",
+    )
+    snippet_model: Optional[str] = Field(
+        None,
+        description="LLM model override used for snippet generation (action 3).",
+    )
+    snippet_alias_model: Optional[str] = Field(
+        None,
+        description="LLM model override used for snippet alias enrichment (action 4).",
+    )
+    extra_options: Optional[Dict[str, Any]] = Field(
+        None,
+        description="Miscellaneous execution flags applied across pipeline steps.",
+    )
+
+
+class TableProfilingJobAck(BaseModel):
+    table_id: str = Field(..., description="Echo of the table identifier.")
+    version_ts: str = Field(..., description="Echo of the profiling version timestamp (yyyyMMddHHmmss).")
+    status: str = Field("accepted", description="Processing acknowledgement status.")

app/services/table_profiling.py

@@ -0,0 +1,832 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import os
+import re
+from datetime import date, datetime
+from dataclasses import asdict, dataclass, is_dataclass
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+import httpx
+import great_expectations as gx
+from great_expectations.core.batch import RuntimeBatchRequest
+from great_expectations.core.expectation_suite import ExpectationSuite
+from great_expectations.data_context import AbstractDataContext
+from great_expectations.exceptions import DataContextError, MetricResolutionError
+
+from app.exceptions import ProviderAPICallError
+from app.models import TableProfilingJobRequest
+from app.services import LLMGateway
+from app.settings import DEFAULT_IMPORT_MODEL
+from app.services.import_analysis import (
+    IMPORT_GATEWAY_BASE_URL,
+    resolve_provider_from_model,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+GE_REPORT_RELATIVE_PATH = Path("uncommitted") / "data_docs" / "local_site" / "index.html"
+PROMPT_FILENAMES = {
+    "ge_result_desc": "ge_result_desc_prompt.md",
+    "snippet_generator": "snippet_generator.md",
+    "snippet_alias": "snippet_alias_generator.md",
+}
+DEFAULT_CHAT_TIMEOUT_SECONDS = 90.0
+
+
+@dataclass
+class GEProfilingArtifacts:
+    profiling_result: Dict[str, Any]
+    profiling_summary: Dict[str, Any]
+    docs_path: str
+
+
+class PipelineActionType:
+    GE_PROFILING = "ge_profiling"
+    GE_RESULT_DESC = "ge_result_desc"
+    SNIPPET = "snippet"
+    SNIPPET_ALIAS = "snippet_alias"
+
+
+def _project_root() -> Path:
+    return Path(__file__).resolve().parents[2]
+
+
+def _prompt_dir() -> Path:
+    return _project_root() / "prompt"
+
+
+@lru_cache(maxsize=None)
+def _load_prompt_parts(filename: str) -> Tuple[str, str]:
+    prompt_path = _prompt_dir() / filename
+    if not prompt_path.exists():
+        raise FileNotFoundError(f"Prompt template not found: {prompt_path}")
+
+    raw = prompt_path.read_text(encoding="utf-8")
+    splitter = "用户消息（User）"
+    if splitter not in raw:
+        raise ValueError(f"Prompt template '{filename}' missing separator '{splitter}'.")
+
+    system_raw, user_raw = raw.split(splitter, maxsplit=1)
+    system_text = system_raw.replace("系统角色（System）", "").strip()
+    user_text = user_raw.strip()
+    return system_text, user_text
+
+
+def _render_prompt(template_key: str, replacements: Dict[str, str]) -> Tuple[str, str]:
+    filename = PROMPT_FILENAMES[template_key]
+    system_text, user_template = _load_prompt_parts(filename)
+
+    rendered_user = user_template
+    for key, value in replacements.items():
+        rendered_user = rendered_user.replace(key, value)
+
+    return system_text, rendered_user
+
+
+def _extract_timeout_seconds(options: Optional[Dict[str, Any]]) -> Optional[float]:
+    if not options:
+        return None
+    value = options.get("llm_timeout_seconds")
+    if value is None:
+        return None
+    try:
+        timeout = float(value)
+        if timeout <= 0:
+            raise ValueError
+        return timeout
+    except (TypeError, ValueError):
+        logger.warning(
+            "Invalid llm_timeout_seconds value in extra_options: %r. Falling back to default.",
+            value,
+        )
+        return DEFAULT_CHAT_TIMEOUT_SECONDS
+
+
+def _extract_json_payload(content: str) -> str:
+    fenced = re.search(
+        r"```(?:json)?\s*([\s\S]+?)```",
+        content,
+        flags=re.IGNORECASE,
+    )
+    if fenced:
+        snippet = fenced.group(1).strip()
+        if snippet:
+            return snippet
+
+    stripped = content.strip()
+    if not stripped:
+        raise ValueError("Empty LLM content.")
+
+    for opener, closer in (("{", "}"), ("[", "]")):
+        start = stripped.find(opener)
+        end = stripped.rfind(closer)
+        if start != -1 and end != -1 and end > start:
+            candidate = stripped[start : end + 1].strip()
+            return candidate
+
+    return stripped
+
+
+def _parse_completion_payload(response_payload: Dict[str, Any]) -> Any:
+    choices = response_payload.get("choices") or []
+    if not choices:
+        raise ProviderAPICallError("LLM response did not contain choices to parse.")
+    message = choices[0].get("message") or {}
+    content = message.get("content") or ""
+    if not content.strip():
+        raise ProviderAPICallError("LLM response content is empty.")
+    json_payload = _extract_json_payload(content)
+    try:
+        return json.loads(json_payload)
+    except json.JSONDecodeError as exc:
+        preview = json_payload[:800]
+        logger.error("Failed to parse JSON from LLM response: %s", preview, exc_info=True)
+        raise ProviderAPICallError("LLM response JSON parsing failed.") from exc
+
+
+async def _post_callback(callback_url: str, payload: Dict[str, Any], client: httpx.AsyncClient) -> None:
+    safe_payload = _normalize_for_json(payload)
+    try:
+        logger.info(
+            "Posting pipeline action callback to %s: %s",
+            callback_url,
+            json.dumps(safe_payload, ensure_ascii=False),
+        )
+        response = await client.post(callback_url, json=safe_payload)
+        response.raise_for_status()
+    except httpx.HTTPError as exc:
+        logger.error("Callback delivery to %s failed: %s", callback_url, exc, exc_info=True)
+
+
+def _sanitize_value_set(value: Any, max_values: int) -> Tuple[Any, Optional[Dict[str, int]]]:
+    if not isinstance(value, list):
+        return value, None
+    original_len = len(value)
+    if original_len <= max_values:
+        return value, None
+    trimmed = value[:max_values]
+    return trimmed, {"original_length": original_len, "retained": max_values}
+
+
+def _sanitize_expectation_suite(suite: ExpectationSuite, max_value_set_values: int = 100) -> Dict[str, Any]:
+    suite_dict = suite.to_json_dict()
+    remarks: List[Dict[str, Any]] = []
+
+    for expectation in suite_dict.get("expectations", []):
+        kwargs = expectation.get("kwargs", {})
+        if "value_set" in kwargs:
+            sanitized_value, note = _sanitize_value_set(kwargs["value_set"], max_value_set_values)
+            kwargs["value_set"] = sanitized_value
+            if note:
+                expectation.setdefault("meta", {})
+                expectation["meta"]["value_set_truncated"] = note
+                remarks.append(
+                    {
+                        "column": kwargs.get("column"),
+                        "expectation": expectation.get("expectation_type"),
+                        "note": note,
+                    }
+                )
+
+    if remarks:
+        suite_dict.setdefault("meta", {})
+        suite_dict["meta"]["value_set_truncations"] = remarks
+
+    return suite_dict
+
+
+def _summarize_expectation_suite(suite_dict: Dict[str, Any]) -> Dict[str, Any]:
+    column_map: Dict[str, Dict[str, Any]] = {}
+    table_expectations: List[Dict[str, Any]] = []
+
+    for expectation in suite_dict.get("expectations", []):
+        expectation_type = expectation.get("expectation_type")
+        kwargs = expectation.get("kwargs", {})
+        column = kwargs.get("column")
+        summary_entry: Dict[str, Any] = {"expectation": expectation_type}
+
+        if "value_set" in kwargs and isinstance(kwargs["value_set"], list):
+            summary_entry["value_set_size"] = len(kwargs["value_set"])
+            summary_entry["value_set_preview"] = kwargs["value_set"][:5]
+
+        if column:
+            column_entry = column_map.setdefault(
+                column,
+                {"name": column, "expectations": []},
+            )
+            column_entry["expectations"].append(summary_entry)
+        else:
+            table_expectations.append(summary_entry)
+
+    summary = {
+        "column_profiles": list(column_map.values()),
+        "table_level_expectations": table_expectations,
+        "total_expectations": len(suite_dict.get("expectations", [])),
+    }
+    return summary
+
+
+def _sanitize_identifier(raw: Optional[str], fallback: str) -> str:
+    if not raw:
+        return fallback
+    candidate = re.sub(r"[^0-9A-Za-z_]+", "_", raw).strip("_")
+    return candidate or fallback
+
+
+def _format_connection_string(template: str, access_info: Dict[str, Any]) -> str:
+    if not access_info:
+        return template
+    try:
+        return template.format_map({k: v for k, v in access_info.items()})
+    except KeyError as exc:
+        missing = exc.args[0]
+        raise ValueError(f"table_access_info missing key '{missing}' required by connection_string.") from exc
+
+
+def _ensure_sql_runtime_datasource(
+    context: AbstractDataContext,
+    datasource_name: str,
+    connection_string: str,
+) -> None:
+    try:
+        datasource = context.get_datasource(datasource_name)
+    except (DataContextError, ValueError) as exc:
+        message = str(exc)
+        if "Could not find a datasource" in message or "Unable to load datasource" in message:
+            datasource = None
+        else:  # pragma: no cover - defensive
+            raise RuntimeError(f"Failed to inspect datasource '{datasource_name}'.") from exc
+    except Exception as exc:  # pragma: no cover - defensive
+        raise RuntimeError(f"Failed to inspect datasource '{datasource_name}'.") from exc
+
+    if datasource is not None:
+        execution_engine = getattr(datasource, "execution_engine", None)
+        current_conn = getattr(execution_engine, "connection_string", None)
+        if current_conn and current_conn != connection_string:
+            logger.info(
+                "Existing datasource %s uses different connection string; creating dedicated runtime datasource.",
+                datasource_name,
+            )
+            try:
+                context.delete_datasource(datasource_name)
+            except Exception as exc:  # pragma: no cover - defensive
+                logger.warning(
+                    "Failed to delete datasource %s before recreation: %s",
+                    datasource_name,
+                    exc,
+                )
+            else:
+                datasource = None
+
+    if datasource is not None:
+        return
+
+    runtime_datasource_config = {
+        "name": datasource_name,
+        "class_name": "Datasource",
+        "execution_engine": {
+            "class_name": "SqlAlchemyExecutionEngine",
+            "connection_string": connection_string,
+        },
+        "data_connectors": {
+            "runtime_connector": {
+                "class_name": "RuntimeDataConnector",
+                "batch_identifiers": ["default_identifier_name"],
+            }
+        },
+    }
+    try:
+        context.add_datasource(**runtime_datasource_config)
+    except Exception as exc:  # pragma: no cover - defensive
+        raise RuntimeError(f"Failed to create runtime datasource '{datasource_name}'.") from exc
+
+
+def _build_sql_runtime_batch_request(
+    context: AbstractDataContext,
+    request: TableProfilingJobRequest,
+) -> RuntimeBatchRequest:
+    link_info = request.table_link_info or {}
+    access_info = request.table_access_info or {}
+
+    connection_template = link_info.get("connection_string")
+    if not connection_template:
+        raise ValueError("table_link_info.connection_string is required when using table_link_info.")
+
+    connection_string = _format_connection_string(connection_template, access_info)
+
+    source_type = (link_info.get("type") or "sql").lower()
+    if source_type != "sql":
+        raise ValueError(f"Unsupported table_link_info.type='{source_type}'. Only 'sql' is supported.")
+
+    query = link_info.get("query")
+    table_name = link_info.get("table") or link_info.get("table_name")
+    schema_name = link_info.get("schema")
+
+    if not query and not table_name:
+        raise ValueError("Either table_link_info.query or table_link_info.table must be provided.")
+
+    if not query:
+        if not table_name:
+            raise ValueError("table_link_info.table must be provided when query is omitted.")
+
+        identifier = re.compile(r"^[A-Za-z_][A-Za-z0-9_$]*$")
+
+        def _quote(name: str) -> str:
+            if identifier.match(name):
+                return name
+            return f"`{name.replace('`', '``')}`"
+
+        if schema_name:
+            schema_part = schema_name if "." not in schema_name else schema_name.split(".")[-1]
+            table_part = table_name if "." not in table_name else table_name.split(".")[-1]
+            qualified_table = f"{_quote(schema_part)}.{_quote(table_part)}"
+        else:
+            qualified_table = _quote(table_name)
+
+        query = f"SELECT * FROM {qualified_table}"
+        limit = link_info.get("limit")
+        if isinstance(limit, int) and limit > 0:
+            query = f"{query} LIMIT {limit}"
+
+    datasource_name = request.ge_datasource_name or _sanitize_identifier(
+        f"{request.table_id}_runtime_ds", "runtime_ds"
+    )
+    data_asset_name = request.ge_data_asset_name or _sanitize_identifier(
+        table_name or "runtime_query", "runtime_query"
+    )
+
+    _ensure_sql_runtime_datasource(context, datasource_name, connection_string)
+
+    batch_identifiers = {
+        "default_identifier_name": f"{request.table_id}:{request.version_ts}",
+    }
+
+    return RuntimeBatchRequest(
+        datasource_name=datasource_name,
+        data_connector_name="runtime_connector",
+        data_asset_name=data_asset_name,
+        runtime_parameters={"query": query},
+        batch_identifiers=batch_identifiers,
+    )
+
+
+def _run_onboarding_assistant(
+    context: AbstractDataContext,
+    batch_request: Any,
+    suite_name: str,
+) -> Tuple[ExpectationSuite, Any]:
+    assistant = context.assistants.onboarding
+    assistant_result = assistant.run(batch_request=batch_request)
+    suite = assistant_result.get_expectation_suite(expectation_suite_name=suite_name)
+    context.save_expectation_suite(suite, expectation_suite_name=suite_name)
+    validation_getter = getattr(assistant_result, "get_validation_result", None)
+    if callable(validation_getter):
+        validation_result = validation_getter()
+    else:
+        validation_result = getattr(assistant_result, "validation_result", None)
+    if validation_result is None:
+        # Fallback: rerun validation using the freshly generated expectation suite.
+        validator = context.get_validator(
+            batch_request=batch_request,
+            expectation_suite_name=suite_name,
+        )
+        validation_result = validator.validate()
+    return suite, validation_result
+
+
+def _resolve_context(request: TableProfilingJobRequest) -> AbstractDataContext:
+    context_kwargs: Dict[str, Any] = {}
+    if request.ge_data_context_root:
+        context_kwargs["project_root_dir"] = request.ge_data_context_root
+    elif os.environ.get("GE_DATA_CONTEXT_ROOT"):
+        context_kwargs["project_root_dir"] = os.environ["GE_DATA_CONTEXT_ROOT"]
+    else:
+        context_kwargs["project_root_dir"] = str(_project_root())
+
+    return gx.get_context(**context_kwargs)
+
+
+def _build_batch_request(
+    context: AbstractDataContext,
+    request: TableProfilingJobRequest,
+) -> Any:
+    if request.ge_batch_request:
+        from great_expectations.core.batch import BatchRequest
+
+        return BatchRequest(**request.ge_batch_request)
+
+    if request.table_link_info:
+        return _build_sql_runtime_batch_request(context, request)
+
+    if not request.ge_datasource_name or not request.ge_data_asset_name:
+        raise ValueError(
+            "ge_batch_request or (ge_datasource_name and ge_data_asset_name) must be provided."
+        )
+
+    datasource = context.get_datasource(request.ge_datasource_name)
+    data_asset = datasource.get_asset(request.ge_data_asset_name)
+    return data_asset.build_batch_request()
+
+
+async def _run_ge_profiling(request: TableProfilingJobRequest) -> GEProfilingArtifacts:
+    def _execute() -> GEProfilingArtifacts:
+        context = _resolve_context(request)
+        suite_name = (
+            request.ge_expectation_suite_name
+            or f"{request.table_id}_profiling"
+        )
+
+        batch_request = _build_batch_request(context, request)
+        try:
+            context.get_expectation_suite(suite_name)
+        except DataContextError:
+            context.add_expectation_suite(suite_name)
+
+        validator = context.get_validator(
+            batch_request=batch_request,
+            expectation_suite_name=suite_name,
+        )
+
+        profiler_type = (request.ge_profiler_type or "user_configurable").lower()
+
+        if profiler_type == "data_assistant":
+            suite, validation_result = _run_onboarding_assistant(
+                context,
+                batch_request,
+                suite_name,
+            )
+        else:
+            try:
+                from great_expectations.profile.user_configurable_profiler import (
+                    UserConfigurableProfiler,
+                )
+            except ImportError as err:  # pragma: no cover - dependency guard
+                raise RuntimeError(
+                    "UserConfigurableProfiler is unavailable; install great_expectations profiling extra or switch profiler."
+                ) from err
+
+            profiler = UserConfigurableProfiler(profile_dataset=validator)
+            try:
+                suite = profiler.build_suite()
+                context.save_expectation_suite(suite, expectation_suite_name=suite_name)
+                validator.expectation_suite = suite
+                validation_result = validator.validate()
+            except MetricResolutionError as exc:
+                logger.warning(
+                    "UserConfigurableProfiler failed (%s); falling back to data assistant profiling.",
+                    exc,
+                )
+                suite, validation_result = _run_onboarding_assistant(
+                    context,
+                    batch_request,
+                    suite_name,
+                )
+
+        sanitized_suite = _sanitize_expectation_suite(suite)
+        summary = _summarize_expectation_suite(sanitized_suite)
+        validation_dict = validation_result.to_json_dict()
+
+        context.build_data_docs()
+        docs_path = Path(context.root_directory) / GE_REPORT_RELATIVE_PATH
+
+        profiling_result = {
+            "expectation_suite": sanitized_suite,
+            "validation_result": validation_dict,
+            "batch_request": getattr(batch_request, "to_json_dict", lambda: None)() or getattr(batch_request, "dict", lambda: None)(),
+        }
+
+        return GEProfilingArtifacts(
+            profiling_result=profiling_result,
+            profiling_summary=summary,
+            docs_path=str(docs_path),
+        )
+
+    return await asyncio.to_thread(_execute)
+
+
+async def _call_chat_completions(
+    *,
+    model_spec: str,
+    system_prompt: str,
+    user_prompt: str,
+    client: httpx.AsyncClient,
+    temperature: float = 0.2,
+    timeout_seconds: Optional[float] = None,
+) -> Any:
+    provider, model_name = resolve_provider_from_model(model_spec)
+    payload = {
+        "provider": provider.value,
+        "model": model_name,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        "temperature": temperature,
+    }
+    payload_size_bytes = len(json.dumps(payload, ensure_ascii=False).encode("utf-8"))
+
+    url = f"{IMPORT_GATEWAY_BASE_URL.rstrip('/')}/v1/chat/completions"
+    try:
+        # log the request whole info
+        logger.info(
+            "Calling chat completions API %s with model %s and size %s and payload %s",
+            url,
+            model_name,
+            payload_size_bytes,
+            payload,
+        )
+        response = await client.post(url, json=payload, timeout=timeout_seconds)
+
+        response.raise_for_status()
+    except httpx.HTTPError as exc:
+        error_name = exc.__class__.__name__
+        detail = str(exc).strip()
+        if detail:
+            message = f"Chat completions request failed ({error_name}): {detail}"
+        else:
+            message = f"Chat completions request failed ({error_name})."
+        raise ProviderAPICallError(message) from exc
+
+    try:
+        response_payload = response.json()
+    except ValueError as exc:
+        raise ProviderAPICallError("Chat completions response was not valid JSON.") from exc
+
+    return _parse_completion_payload(response_payload)
+
+
+def _normalize_for_json(value: Any) -> Any:
+    if value is None or isinstance(value, (str, int, float, bool)):
+        return value
+    if isinstance(value, (datetime, date)):
+        return str(value)
+    if hasattr(value, "model_dump"):
+        try:
+            return value.model_dump()
+        except Exception:  # pragma: no cover - defensive
+            pass
+    if is_dataclass(value):
+        return asdict(value)
+    if isinstance(value, dict):
+        return {k: _normalize_for_json(v) for k, v in value.items()}
+    if isinstance(value, (list, tuple, set)):
+        return [_normalize_for_json(v) for v in value]
+    if hasattr(value, "to_json_dict"):
+        try:
+            return value.to_json_dict()
+        except Exception:  # pragma: no cover - defensive
+            pass
+    if hasattr(value, "__dict__"):
+        return _normalize_for_json(value.__dict__)
+    return repr(value)
+
+
+def _json_dumps(data: Any) -> str:
+    normalised = _normalize_for_json(data)
+    return json.dumps(normalised, ensure_ascii=False, indent=2)
+
+
+def _preview_for_log(data: Any) -> str:
+    try:
+        serialised = _json_dumps(data)
+    except Exception:
+        serialised = repr(data)
+
+    return serialised
+
+
+def _profiling_request_for_log(request: TableProfilingJobRequest) -> Dict[str, Any]:
+    payload = request.model_dump()
+    access_info = payload.get("table_access_info")
+    if isinstance(access_info, dict):
+        payload["table_access_info"] = {key: "***" for key in access_info.keys()}
+    return payload
+
+
+async def _execute_result_desc(
+    profiling_json: Dict[str, Any],
+    _request: TableProfilingJobRequest,
+    llm_model: str,
+    client: httpx.AsyncClient,
+    timeout_seconds: Optional[float],
+) -> Dict[str, Any]:
+    system_prompt, user_prompt = _render_prompt(
+        "ge_result_desc",
+        {"{{GE_RESULT_JSON}}": _json_dumps(profiling_json)},
+    )
+    llm_output = await _call_chat_completions(
+        model_spec=llm_model,
+        system_prompt=system_prompt,
+        user_prompt=user_prompt,
+        client=client,
+        timeout_seconds=timeout_seconds,
+    )
+    if not isinstance(llm_output, dict):
+        raise ProviderAPICallError("GE result description payload must be a JSON object.")
+    return llm_output
+
+
+async def _execute_snippet_generation(
+    table_desc_json: Dict[str, Any],
+    _request: TableProfilingJobRequest,
+    llm_model: str,
+    client: httpx.AsyncClient,
+    timeout_seconds: Optional[float],
+) -> List[Dict[str, Any]]:
+    system_prompt, user_prompt = _render_prompt(
+        "snippet_generator",
+        {"{{TABLE_PROFILE_JSON}}": _json_dumps(table_desc_json)},
+    )
+    llm_output = await _call_chat_completions(
+        model_spec=llm_model,
+        system_prompt=system_prompt,
+        user_prompt=user_prompt,
+        client=client,
+        timeout_seconds=timeout_seconds,
+    )
+    if not isinstance(llm_output, list):
+        raise ProviderAPICallError("Snippet generator must return a JSON array.")
+    return llm_output
+
+
+async def _execute_snippet_alias(
+    snippets_json: List[Dict[str, Any]],
+    _request: TableProfilingJobRequest,
+    llm_model: str,
+    client: httpx.AsyncClient,
+    timeout_seconds: Optional[float],
+) -> List[Dict[str, Any]]:
+    system_prompt, user_prompt = _render_prompt(
+        "snippet_alias",
+        {"{{SNIPPET_ARRAY}}": _json_dumps(snippets_json)},
+    )
+    llm_output = await _call_chat_completions(
+        model_spec=llm_model,
+        system_prompt=system_prompt,
+        user_prompt=user_prompt,
+        client=client,
+        timeout_seconds=timeout_seconds,
+    )
+    if not isinstance(llm_output, list):
+        raise ProviderAPICallError("Snippet alias generator must return a JSON array.")
+    return llm_output
+
+
+async def _run_action_with_callback(
+    *,
+    action_type: str,
+    runner,
+    callback_base: Dict[str, Any],
+    client: httpx.AsyncClient,
+    callback_url: str,
+    input_payload: Any = None,
+    model_spec: Optional[str] = None,
+) -> Any:
+    if input_payload is not None:
+        logger.info(
+            "Pipeline action %s input: %s",
+            action_type,
+            _preview_for_log(input_payload),
+        )
+    try:
+        result = await runner()
+    except Exception as exc:
+        failure_payload = dict(callback_base)
+        failure_payload.update(
+            {
+                "status": "failed",
+                "action_type": action_type,
+                "error": str(exc),
+            }
+        )
+        if model_spec is not None:
+            failure_payload["model"] = model_spec
+        await _post_callback(callback_url, failure_payload, client)
+        raise
+
+    success_payload = dict(callback_base)
+    success_payload.update(
+        {
+            "status": "success",
+            "action_type": action_type,
+        }
+    )
+    if model_spec is not None:
+        success_payload["model"] = model_spec
+
+    logger.info(
+        "Pipeline action %s output: %s",
+        action_type,
+        _preview_for_log(result),
+    )
+
+    if action_type == PipelineActionType.GE_PROFILING:
+        artifacts: GEProfilingArtifacts = result
+        success_payload["profiling_json"] = artifacts.profiling_result
+        success_payload["profiling_summary"] = artifacts.profiling_summary
+        success_payload["ge_report_path"] = artifacts.docs_path
+    elif action_type == PipelineActionType.GE_RESULT_DESC:
+        success_payload["table_desc_json"] = result
+    elif action_type == PipelineActionType.SNIPPET:
+        success_payload["snippet_json"] = result
+    elif action_type == PipelineActionType.SNIPPET_ALIAS:
+        success_payload["snippet_alias_json"] = result
+
+    await _post_callback(callback_url, success_payload, client)
+    return result
+
+
+async def process_table_profiling_job(
+    request: TableProfilingJobRequest,
+    _gateway: LLMGateway,
+    client: httpx.AsyncClient,
+) -> None:
+    """Sequentially execute the four-step profiling pipeline and emit callbacks per action."""
+
+    timeout_seconds = _extract_timeout_seconds(request.extra_options)
+    if timeout_seconds is None:
+        timeout_seconds = DEFAULT_CHAT_TIMEOUT_SECONDS
+
+    base_payload = {
+        "table_id": request.table_id,
+        "version_ts": request.version_ts,
+        "callback_url": str(request.callback_url),
+        "table_schema": request.table_schema,
+        "table_schema_version_id": request.table_schema_version_id,
+        "llm_model": request.llm_model,
+        "llm_timeout_seconds": timeout_seconds,
+    }
+
+    logging_request_payload = _profiling_request_for_log(request)
+
+    try:
+        artifacts: GEProfilingArtifacts = await _run_action_with_callback(
+            action_type=PipelineActionType.GE_PROFILING,
+            runner=lambda: _run_ge_profiling(request),
+            callback_base=base_payload,
+            client=client,
+            callback_url=str(request.callback_url),
+            input_payload=logging_request_payload,
+            model_spec=request.llm_model,
+        )
+
+        table_desc_json: Dict[str, Any] = await _run_action_with_callback(
+            action_type=PipelineActionType.GE_RESULT_DESC,
+            runner=lambda: _execute_result_desc(
+                artifacts.profiling_result,
+                request,
+                request.llm_model,
+                client,
+                timeout_seconds,
+            ),
+            callback_base=base_payload,
+            client=client,
+            callback_url=str(request.callback_url),
+            input_payload=artifacts.profiling_result,
+            model_spec=request.llm_model,
+        )
+
+        snippet_json: List[Dict[str, Any]] = await _run_action_with_callback(
+            action_type=PipelineActionType.SNIPPET,
+            runner=lambda: _execute_snippet_generation(
+                table_desc_json,
+                request,
+                request.llm_model,
+                client,
+                timeout_seconds,
+            ),
+            callback_base=base_payload,
+            client=client,
+            callback_url=str(request.callback_url),
+            input_payload=table_desc_json,
+            model_spec=request.llm_model,
+        )
+
+        await _run_action_with_callback(
+            action_type=PipelineActionType.SNIPPET_ALIAS,
+            runner=lambda: _execute_snippet_alias(
+                snippet_json,
+                request,
+                request.llm_model,
+                client,
+                timeout_seconds,
+            ),
+            callback_base=base_payload,
+            client=client,
+            callback_url=str(request.callback_url),
+            input_payload=snippet_json,
+            model_spec=request.llm_model,
+        )
+    except Exception:  # pragma: no cover - defensive catch
+        logger.exception(
+            "Table profiling pipeline failed for table_id=%s version_ts=%s",
+            request.table_id,
+            request.version_ts,
+        )

app/services/table_snippet.py

@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, Dict, Tuple
+
+from sqlalchemy import text
+from sqlalchemy.engine import Engine
+from sqlalchemy.exc import SQLAlchemyError
+
+from app.db import get_engine
+from app.models import (
+    ActionType,
+    TableSnippetUpsertRequest,
+    TableSnippetUpsertResponse,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+def _serialize_json(value: Any) -> Tuple[str | None, int | None]:
+    logger.debug("Serializing JSON payload: %s", value)
+    if value is None:
+        return None, None
+    if isinstance(value, str):
+        encoded = value.encode("utf-8")
+        return value, len(encoded)
+    serialized = json.dumps(value, ensure_ascii=False)
+    encoded = serialized.encode("utf-8")
+    return serialized, len(encoded)
+
+
+def _prepare_table_schema(value: Any) -> str:
+    logger.debug("Preparing table_schema payload.")
+    if isinstance(value, str):
+        return value
+    return json.dumps(value, ensure_ascii=False)
+
+
+def _collect_common_columns(request: TableSnippetUpsertRequest) -> Dict[str, Any]:
+    logger.debug(
+        "Collecting common columns for table_id=%s version_ts=%s action_type=%s",
+        request.table_id,
+        request.version_ts,
+        request.action_type,
+    )
+    payload: Dict[str, Any] = {
+        "table_id": request.table_id,
+        "version_ts": request.version_ts,
+        "action_type": request.action_type.value,
+        "status": request.status.value,
+        "callback_url": str(request.callback_url),
+        "table_schema_version_id": request.table_schema_version_id,
+        "table_schema": _prepare_table_schema(request.table_schema),
+    }
+
+    if request.error_code is not None:
+        logger.debug("Adding error_code: %s", request.error_code)
+        payload["error_code"] = request.error_code
+    if request.error_message is not None:
+        logger.debug("Adding error_message: %s", request.error_message)
+        payload["error_message"] = request.error_message
+    if request.started_at is not None:
+        payload["started_at"] = request.started_at
+    if request.finished_at is not None:
+        payload["finished_at"] = request.finished_at
+    if request.duration_ms is not None:
+        payload["duration_ms"] = request.duration_ms
+    if request.result_checksum is not None:
+        payload["result_checksum"] = request.result_checksum
+
+    logger.debug("Collected common payload: %s", payload)
+    return payload
+
+
+def _apply_action_payload(
+    request: TableSnippetUpsertRequest,
+    payload: Dict[str, Any],
+) -> None:
+    logger.debug("Applying action-specific payload for action_type=%s", request.action_type)
+    if request.action_type == ActionType.GE_PROFILING:
+        full_json, full_size = _serialize_json(request.result_json)
+        summary_json, summary_size = _serialize_json(request.result_summary_json)
+        if full_json is not None:
+            payload["ge_profiling_full"] = full_json
+            payload["ge_profiling_full_size_bytes"] = full_size
+        if summary_json is not None:
+            payload["ge_profiling_summary"] = summary_json
+            payload["ge_profiling_summary_size_bytes"] = summary_size
+        if full_size is not None or summary_size is not None:
+            payload["ge_profiling_total_size_bytes"] = (full_size or 0) + (
+                summary_size or 0
+            )
+        if request.html_report_url:
+            payload["ge_profiling_html_report_url"] = request.html_report_url
+    elif request.action_type == ActionType.GE_RESULT_DESC:
+        full_json, full_size = _serialize_json(request.result_json)
+        if full_json is not None:
+            payload["ge_result_desc_full"] = full_json
+            payload["ge_result_desc_full_size_bytes"] = full_size
+    elif request.action_type == ActionType.SNIPPET:
+        full_json, full_size = _serialize_json(request.result_json)
+        if full_json is not None:
+            payload["snippet_full"] = full_json
+            payload["snippet_full_size_bytes"] = full_size
+    elif request.action_type == ActionType.SNIPPET_ALIAS:
+        full_json, full_size = _serialize_json(request.result_json)
+        if full_json is not None:
+            payload["snippet_alias_full"] = full_json
+            payload["snippet_alias_full_size_bytes"] = full_size
+    else:
+        logger.error("Unsupported action type encountered: %s", request.action_type)
+        raise ValueError(f"Unsupported action type '{request.action_type}'.")
+
+    logger.debug("Payload after applying action-specific data: %s", payload)
+
+
+def _build_insert_statement(columns: Dict[str, Any]) -> Tuple[str, Dict[str, Any]]:
+    logger.debug("Building insert statement for columns: %s", list(columns.keys()))
+    column_names = list(columns.keys())
+    placeholders = [f":{name}" for name in column_names]
+    update_assignments = [
+        f"{name}=VALUES({name})"
+        for name in column_names
+        if name not in {"table_id", "version_ts", "action_type"}
+    ]
+    update_assignments.append("updated_at=CURRENT_TIMESTAMP")
+
+    sql = (
+        "INSERT INTO action_results ({cols}) VALUES ({vals}) "
+        "ON DUPLICATE KEY UPDATE {updates}"
+    ).format(
+        cols=", ".join(column_names),
+        vals=", ".join(placeholders),
+        updates=", ".join(update_assignments),
+    )
+    logger.debug("Generated SQL: %s", sql)
+    return sql, columns
+
+
+def _execute_upsert(engine: Engine, sql: str, params: Dict[str, Any]) -> int:
+    logger.info("Executing upsert for table_id=%s version_ts=%s action_type=%s", params.get("table_id"), params.get("version_ts"), params.get("action_type"))
+    with engine.begin() as conn:
+        result = conn.execute(text(sql), params)
+    logger.info("Rows affected: %s", result.rowcount)
+    return result.rowcount
+
+
+def upsert_action_result(request: TableSnippetUpsertRequest) -> TableSnippetUpsertResponse:
+    logger.info(
+        "Received upsert request: table_id=%s version_ts=%s action_type=%s status=%s",
+        request.table_id,
+        request.version_ts,
+        request.action_type,
+        request.status,
+    )
+    logger.debug("Request payload: %s", request.model_dump())
+    columns = _collect_common_columns(request)
+    _apply_action_payload(request, columns)
+
+    sql, params = _build_insert_statement(columns)
+    logger.debug("Final SQL params: %s", params)
+
+    engine = get_engine()
+    try:
+        rowcount = _execute_upsert(engine, sql, params)
+    except SQLAlchemyError as exc:
+        logger.exception(
+            "Failed to upsert action result: table_id=%s version_ts=%s action_type=%s",
+            request.table_id,
+            request.version_ts,
+            request.action_type,
+        )
+        raise RuntimeError(f"Database operation failed: {exc}") from exc
+
+    updated = rowcount > 1
+    return TableSnippetUpsertResponse(
+        table_id=request.table_id,
+        version_ts=request.version_ts,
+        action_type=request.action_type,
+        status=request.status,
+        updated=updated,
+    )

table_snippet.sql

@@ -0,0 +1,54 @@
+CREATE TABLE IF NOT EXISTS action_results (
+  id BIGINT  NOT NULL AUTO_INCREMENT COMMENT '主键',
+  table_id BIGINT NOT NULL COMMENT '表ID',
+  version_ts BIGINT NOT NULL COMMENT '版本时间戳（版本号）',
+  action_type ENUM('ge_profiling','ge_result_desc','snippet','snippet_alias') NOT NULL COMMENT '动作类型',
+
+  status ENUM('pending','running','success','failed','partial') NOT NULL DEFAULT 'pending' COMMENT '执行状态',
+  error_code VARCHAR(128) NULL,
+  error_message TEXT NULL,
+
+  -- 回调 & 观测
+  callback_url VARCHAR(1024) NOT NULL,
+  started_at DATETIME NULL,
+  finished_at DATETIME NULL,
+  duration_ms INT  NULL,
+
+  -- 本次schema信息
+  table_schema_version_id BIGINT NOT NULL,
+  table_schema JSON NOT NULL,
+
+  -- ===== 动作1：GE Profiling =====
+  ge_profiling_full JSON NULL COMMENT 'Profiling完整结果JSON',
+  ge_profiling_full_size_bytes BIGINT  NULL,
+  ge_profiling_summary JSON NULL COMMENT 'Profiling摘要（剔除大value_set等）',
+  ge_profiling_summary_size_bytes BIGINT  NULL,
+  ge_profiling_total_size_bytes BIGINT  NULL COMMENT '上两者合计',
+  ge_profiling_html_report_url VARCHAR(1024) NULL COMMENT 'GE报告HTML路径/URL',
+
+  -- ===== 动作2：GE Result Desc =====
+  ge_result_desc_full JSON NULL COMMENT '表描述结果JSON',
+  ge_result_desc_full_size_bytes BIGINT  NULL,
+
+  -- ===== 动作3：Snippet 生成 =====
+  snippet_full JSON NULL COMMENT 'SQL知识片段结果JSON',
+  snippet_full_size_bytes BIGINT  NULL,
+
+  -- ===== 动作4：Snippet Alias 改写 =====
+  snippet_alias_full JSON NULL COMMENT 'SQL片段改写/丰富结果JSON',
+  snippet_alias_full_size_bytes BIGINT  NULL,
+
+  -- 通用可选指标
+  result_checksum VARBINARY(32) NULL COMMENT '对当前action有效载荷计算的MD5/xxhash',
+  created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+  updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
+
+  PRIMARY KEY (id),
+  UNIQUE KEY uq_table_ver_action (table_id, version_ts, action_type),
+  KEY idx_status (status),
+  KEY idx_table (table_id, updated_at),
+  KEY idx_action_time (action_type, version_ts),
+  KEY idx_schema_version (table_schema_version_id)
+) ENGINE=InnoDB
+  ROW_FORMAT=DYNAMIC
+  COMMENT='数据分析知识片段表';