恢复GE版本为0.18，生成SNIPPET后自动生成rag_text流程

app/main.py

@@ -24,6 +24,8 @@ from app.models import (
     LLMResponse,
     TableProfilingJobAck,
     TableProfilingJobRequest,
+    TableSnippetRagIngestRequest,
+    TableSnippetRagIngestResponse,
     TableSnippetUpsertRequest,
     TableSnippetUpsertResponse,
 )
@@ -252,6 +254,7 @@ def create_app() -> FastAPI:
             )
             raise HTTPException(status_code=500, detail=str(exc)) from exc
         else:
+            # After snippet_alias is stored, automatically trigger RAG ingest when configured.
             if (
                 payload.action_type == ActionType.SNIPPET_ALIAS
                 and payload.status == ActionStatus.SUCCESS
@@ -267,14 +270,46 @@ def create_app() -> FastAPI:
                     )
                 except Exception:
                     logger.exception(
-                        "Failed to ingest snippet RAG artifacts",
+                        "Failed to ingest snippet RAG artifacts after snippet_alias upsert",
                         extra={
                             "table_id": payload.table_id,
                             "version_ts": payload.version_ts,
+                            "workspace_id": payload.rag_workspace_id,
                         },
                     )
+
             return response
 
+    @application.post(
+        "/v1/table/snippet/rag_ingest",
+        response_model=TableSnippetRagIngestResponse,
+        summary="Merge snippet+alias results from action_results and ingest into RAG.",
+    )
+    async def ingest_snippet_rag(
+        payload: TableSnippetRagIngestRequest,
+        client: httpx.AsyncClient = Depends(get_http_client),
+    ) -> TableSnippetRagIngestResponse:
+        try:
+            rag_item_ids = await ingest_snippet_rag_from_db(
+                table_id=payload.table_id,
+                version_ts=payload.version_ts,
+                workspace_id=payload.workspace_id,
+                rag_item_type=payload.rag_item_type or "SNIPPET",
+                client=client,
+            )
+        except Exception as exc:
+            logger.exception(
+                "Failed to ingest snippet RAG artifacts",
+                extra={
+                    "table_id": payload.table_id,
+                    "version_ts": payload.version_ts,
+                    "workspace_id": payload.workspace_id,
+                },
+            )
+            raise HTTPException(status_code=500, detail=str(exc)) from exc
+
+        return TableSnippetRagIngestResponse(rag_item_ids=rag_item_ids)
+
     @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

@@ -232,6 +232,15 @@ class TableProfilingJobRequest(BaseModel):
         None,
         description="Miscellaneous execution flags applied across pipeline steps.",
     )
+    workspace_id: Optional[int] = Field(
+        None,
+        ge=0,
+        description="Optional workspace identifier forwarded to snippet_alias callback for RAG ingestion.",
+    )
+    rag_item_type: Optional[str] = Field(
+        "SNIPPET",
+        description="Optional RAG item type forwarded to snippet_alias callback.",
+    )
 
 
 class TableProfilingJobAck(BaseModel):
@@ -247,7 +256,7 @@ class TableSnippetUpsertRequest(BaseModel):
         ge=0,
         description="Version timestamp aligned with the pipeline (yyyyMMddHHmmss as integer).",
     )
-    rag_workspace_id: Optional[int] = Field(
+    workspace_id: Optional[int] = Field(
         None,
         ge=0,
         description="Optional workspace identifier for RAG ingestion; when provided and action_type=snippet_alias "
@@ -329,6 +338,24 @@ class TableSnippetUpsertRequest(BaseModel):
         ge=0,
         description="Optional execution duration in milliseconds.",
     )
+
+
+class TableSnippetRagIngestRequest(BaseModel):
+    table_id: int = Field(..., ge=1, description="Unique identifier for the table.")
+    version_ts: int = Field(
+        ...,
+        ge=0,
+        description="Version timestamp aligned with the pipeline (yyyyMMddHHmmss as integer).",
+    )
+    workspace_id: int = Field(..., ge=0, description="Workspace id used when pushing snippets to RAG.")
+    rag_item_type: Optional[str] = Field(
+        "SNIPPET",
+        description="Optional RAG item type used when pushing snippets to RAG. Defaults to 'SNIPPET'.",
+    )
+
+
+class TableSnippetRagIngestResponse(BaseModel):
+    rag_item_ids: List[int] = Field(..., description="List of ingested rag_item_ids.")
     result_checksum: Optional[str] = Field(
         None,
         description="Optional checksum for the result payload (e.g., MD5).",

app/services/table_profiling.py

@@ -24,7 +24,6 @@ from app.services import LLMGateway
 from app.settings import DEFAULT_IMPORT_MODEL
 from app.services.import_analysis import (
     IMPORT_GATEWAY_BASE_URL,
-    build_import_gateway_headers,
     resolve_provider_from_model,
 )
 from app.utils.llm_usage import extract_usage as extract_llm_usage
@@ -533,7 +532,6 @@ async def _call_chat_completions(
     temperature: float = 0.2,
     timeout_seconds: Optional[float] = None,
 ) -> Any:
-    # Normalize model spec to provider+model and issue the unified chat call.
     provider, model_name = resolve_provider_from_model(model_spec)
     payload = {
         "provider": provider.value,
@@ -547,17 +545,16 @@ async def _call_chat_completions(
     payload_size_bytes = len(json.dumps(payload, ensure_ascii=False).encode("utf-8"))
 
     url = f"{IMPORT_GATEWAY_BASE_URL.rstrip('/')}/v1/chat/completions"
-    headers = build_import_gateway_headers()
     try:
+        # log the request whole info
         logger.info(
-            "Calling chat completions API %s with model=%s payload_size=%sB",
+            "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(
+        response = await client.post(url, json=payload, timeout=timeout_seconds)
-            url, json=payload, timeout=timeout_seconds, headers=headers
-        )
 
         response.raise_for_status()
     except httpx.HTTPError as exc:
@@ -706,7 +703,6 @@ async def _run_action_with_callback(
     input_payload: Any = None,
     model_spec: Optional[str] = None,
 ) -> Any:
-    # Execute a pipeline action and always emit a callback capturing success/failure.
     if input_payload is not None:
         logger.info(
             "Pipeline action %s input: %s",
@@ -789,6 +785,8 @@ async def process_table_profiling_job(
         "table_schema_version_id": request.table_schema_version_id,
         "llm_model": request.llm_model,
         "llm_timeout_seconds": timeout_seconds,
+        "workspace_id": request.workspace_id,
+        "rag_item_type": request.rag_item_type,
     }
 
     logging_request_payload = _profiling_request_for_log(request)

app/services/table_snippet.py

@@ -459,6 +459,18 @@ def _stable_rag_item_id(table_id: int, version_ts: int, snippet_id: str) -> int:
     return int(digest[:16], 16) % 9_000_000_000_000_000_000
 
 
+def _to_serializable(value: Any) -> Any:
+    if value is None or isinstance(value, (str, int, float, bool)):
+        return value
+    if isinstance(value, datetime):
+        return value.isoformat()
+    if isinstance(value, dict):
+        return {k: _to_serializable(v) for k, v in value.items()}
+    if isinstance(value, list):
+        return [_to_serializable(v) for v in value]
+    return str(value)
+
+
 def _build_rag_text(snippet: Dict[str, Any]) -> str:
     # Deterministic text concatenation for embedding input.
     parts: List[str] = []
@@ -512,7 +524,8 @@ def _prepare_rag_payloads(
             continue
         rag_item_id = _stable_rag_item_id(table_id, version_ts, snippet_id)
         rag_text = _build_rag_text(snippet)
-        merged_json = json.dumps(snippet, ensure_ascii=False)
+        serializable_snippet = _to_serializable(snippet)
+        merged_json = json.dumps(serializable_snippet, ensure_ascii=False)
         updated_at_raw = snippet.get("updated_at_from_action") or now
         if isinstance(updated_at_raw, str):
             try:

app/settings.py

@@ -23,7 +23,7 @@ PROVIDER_KEY_ENV_MAP: Dict[str, str] = {
 DEFAULT_IMPORT_MODEL = os.getenv("DEFAULT_IMPORT_MODEL", "deepseek:deepseek-chat")
 NEW_API_BASE_URL = os.getenv("NEW_API_BASE_URL")
 NEW_API_AUTH_TOKEN = os.getenv("NEW_API_AUTH_TOKEN")
-RAG_API_BASE_URL = os.getenv("RAG_API_BASE_URL", "http://127.0.0.1:8000")
+RAG_API_BASE_URL = os.getenv("RAG_API_BASE_URL", "https://tchatbi.agentcarrier.cn/chatbi/api")
 RAG_API_AUTH_TOKEN = os.getenv("RAG_API_AUTH_TOKEN")
 
 

pyproject.toml

@@ -4,4 +4,18 @@ version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11"
-dependencies = []
+dependencies = [
+    "fastapi>=0.111.0",
+    "uvicorn[standard]>=0.29.0",
+    "pydantic>=2.6.0",
+    "sqlalchemy>=2.0.28",
+    "pymysql>=1.1.0",
+    "great-expectations[profilers]==0.18.19",
+    "pandas>=2.0",
+    "numpy>=1.24",
+    "openpyxl>=3.1",
+    "httpx==0.27.2",
+    "python-dotenv==1.0.1",
+    "requests>=2.31.0",
+    "PyYAML>=6.0.1",
+]