部分参数调整

.env

@@ -17,7 +17,7 @@ DEFAULT_IMPORT_MODEL=deepseek:deepseek-chat
 IMPORT_GATEWAY_BASE_URL=http://localhost:8000
 
 # HTTP client configuration
-HTTP_CLIENT_TIMEOUT=30
+HTTP_CLIENT_TIMEOUT=60
 HTTP_CLIENT_TRUST_ENV=false
 # HTTP_CLIENT_PROXY=
 

app/models.py

@@ -77,8 +77,8 @@ class DataImportAnalysisRequest(BaseModel):
         description="Ordered list of table headers associated with the data.",
     )
     llm_model: str = Field(
-        ...,
+        None,
-        description="Model identifier. Accepts 'provider:model' format or plain model name.",
+        description="Model identifier. Accepts 'provider:model_name' format or custom model alias.",
     )
     temperature: Optional[float] = Field(
         None,
@@ -138,6 +138,21 @@ class DataImportAnalysisJobAck(BaseModel):
     status: str = Field("accepted", description="Processing status acknowledgement.")
 
 
+class ActionType(str, Enum):
+    GE_PROFILING = "ge_profiling"
+    GE_RESULT_DESC = "ge_result_desc"
+    SNIPPET = "snippet"
+    SNIPPET_ALIAS = "snippet_alias"
+
+
+class ActionStatus(str, Enum):
+    PENDING = "pending"
+    RUNNING = "running"
+    SUCCESS = "success"
+    FAILED = "failed"
+    PARTIAL = "partial"
+
+
 class TableProfilingJobRequest(BaseModel):
     table_id: str = Field(..., description="Unique identifier for the table to profile.")
     version_ts: str = Field(
@@ -149,6 +164,10 @@ class TableProfilingJobRequest(BaseModel):
         ...,
         description="Callback endpoint invoked after each pipeline action completes.",
     )
+    llm_model: Optional[str] = Field(
+        None,
+        description="Default LLM model spec applied to prompt-based actions when overrides are omitted.",
+    )
     table_schema: Optional[Any] = Field(
         None,
         description="Schema structure snapshot for the current table version.",
@@ -196,10 +215,7 @@ class TableProfilingJobRequest(BaseModel):
         "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).",
@@ -222,3 +238,56 @@ 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.")
+
+
+class TableSnippetUpsertRequest(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).",
+    )
+    action_type: ActionType = Field(..., description="Pipeline action type for this record.")
+    status: ActionStatus = Field(
+        ActionStatus.SUCCESS, description="Execution status for the action."
+    )
+    callback_url: HttpUrl = Field(..., description="Callback URL associated with the action run.")
+    table_schema_version_id: int = Field(..., ge=0, description="Identifier for the schema snapshot.")
+    table_schema: Any = Field(..., description="Schema snapshot payload for the table.")
+    result_json: Optional[Any] = Field(
+        None,
+        description="Primary result payload for the action (e.g., profiling output, snippet array).",
+    )
+    result_summary_json: Optional[Any] = Field(
+        None,
+        description="Optional summary payload (e.g., profiling summary) for the action.",
+    )
+    html_report_url: Optional[str] = Field(
+        None,
+        description="Optional HTML report URL generated by the action.",
+    )
+    error_code: Optional[str] = Field(None, description="Optional error code when status indicates a failure.")
+    error_message: Optional[str] = Field(None, description="Optional error message when status indicates a failure.")
+    started_at: Optional[datetime] = Field(
+        None, description="Timestamp when the action started executing."
+    )
+    finished_at: Optional[datetime] = Field(
+        None, description="Timestamp when the action finished executing."
+    )
+    duration_ms: Optional[int] = Field(
+        None,
+        ge=0,
+        description="Optional execution duration in milliseconds.",
+    )
+    result_checksum: Optional[str] = Field(
+        None,
+        description="Optional checksum for the result payload (e.g., MD5).",
+    )
+
+
+class TableSnippetUpsertResponse(BaseModel):
+    table_id: int
+    version_ts: int
+    action_type: ActionType
+    status: ActionStatus
+    updated: bool

app/services/import_analysis.py

@@ -42,7 +42,7 @@ def _env_float(name: str, default: float) -> float:
         return default
 
 
-IMPORT_CHAT_TIMEOUT_SECONDS = _env_float("IMPORT_CHAT_TIMEOUT_SECONDS", 90.0)
+IMPORT_CHAT_TIMEOUT_SECONDS = _env_float("IMPORT_CHAT_TIMEOUT_SECONDS", 120.0)
 
 SUPPORTED_IMPORT_MODELS = get_supported_import_models()
 
@@ -298,7 +298,7 @@ def parse_llm_analysis_json(llm_response: LLMResponse) -> Dict[str, Any]:
     try:
         return json.loads(json_payload)
     except json.JSONDecodeError as exc:
-        preview = json_payload[:2000]
+        preview = json_payload[:10000]
         logger.error("Failed to parse JSON from LLM response content: %s", preview, exc_info=True)
         raise ProviderAPICallError("LLM response JSON could not be parsed.") from exc
 

ge_v1.py

@@ -121,7 +121,7 @@ def clean_value(value: Any) -> Any:
     if isinstance(value, (np.generic,)):
         return value.item()
     if isinstance(value, pd.Timestamp):
-        return value.isoformat()
+        return str(value)
     if pd.isna(value):
         return None
     return value