392322077f
Switched OpenRAG backend and frontend in docker-compose.yml to use local Dockerfile builds instead of remote images. Updated environment variables for better clarity and system integration. In flows/openrag_agent.json and langflow_file_service, improved handling of docs_metadata to support Data objects and added logging for metadata ingestion. Added agent_llm edge to agent node in flow definition.
325 lines
12 KiB
Python
325 lines
12 KiB
Python
from typing import Any, Dict, List, Optional
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from config.settings import LANGFLOW_INGEST_FLOW_ID, clients
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from utils.logging_config import get_logger
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logger = get_logger(__name__)
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class LangflowFileService:
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def __init__(self):
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self.flow_id_ingest = LANGFLOW_INGEST_FLOW_ID
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async def upload_user_file(
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self, file_tuple, jwt_token: Optional[str] = None
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) -> Dict[str, Any]:
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"""Upload a file using Langflow Files API v2: POST /api/v2/files.
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Returns JSON with keys: id, name, path, size, provider.
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"""
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logger.debug("[LF] Upload (v2) -> /api/v2/files")
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resp = await clients.langflow_request(
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"POST",
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"/api/v2/files",
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files={"file": file_tuple},
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headers={"Content-Type": None},
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)
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logger.debug(
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"[LF] Upload response",
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status_code=resp.status_code,
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reason=resp.reason_phrase,
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)
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if resp.status_code >= 400:
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logger.error(
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"[LF] Upload failed",
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status_code=resp.status_code,
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reason=resp.reason_phrase,
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body=resp.text,
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)
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resp.raise_for_status()
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return resp.json()
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async def delete_user_file(self, file_id: str) -> None:
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"""Delete a file by id using v2: DELETE /api/v2/files/{id}."""
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# NOTE: use v2 root, not /api/v1
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logger.debug("[LF] Delete (v2) -> /api/v2/files/{id}", file_id=file_id)
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resp = await clients.langflow_request("DELETE", f"/api/v2/files/{file_id}")
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logger.debug(
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"[LF] Delete response",
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status_code=resp.status_code,
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reason=resp.reason_phrase,
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)
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if resp.status_code >= 400:
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logger.error(
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"[LF] Delete failed",
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status_code=resp.status_code,
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reason=resp.reason_phrase,
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body=resp.text[:500],
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)
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resp.raise_for_status()
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async def run_ingestion_flow(
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self,
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file_paths: List[str],
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jwt_token: str,
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session_id: Optional[str] = None,
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tweaks: Optional[Dict[str, Any]] = None,
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owner: Optional[str] = None,
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owner_name: Optional[str] = None,
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owner_email: Optional[str] = None,
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connector_type: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Trigger the ingestion flow with provided file paths.
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The flow must expose a File component path in input schema or accept files parameter.
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"""
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if not self.flow_id_ingest:
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logger.error("[LF] LANGFLOW_INGEST_FLOW_ID is not configured")
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raise ValueError("LANGFLOW_INGEST_FLOW_ID is not configured")
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payload: Dict[str, Any] = {
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"input_value": "Ingest files",
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"input_type": "chat",
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"output_type": "text", # Changed from "json" to "text"
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}
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if not tweaks:
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tweaks = {}
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# Pass files via tweaks to File component (File-PSU37 from the flow)
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if file_paths:
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tweaks["File-PSU37"] = {"path": file_paths}
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# Pass JWT token via tweaks using the x-langflow-global-var- pattern
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if jwt_token:
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# Using the global variable pattern that Langflow expects for OpenSearch components
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tweaks["OpenSearchHybrid-Ve6bS"] = {"jwt_token": jwt_token}
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logger.debug("[LF] Added JWT token to tweaks for OpenSearch components")
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else:
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logger.warning("[LF] No JWT token provided")
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# Pass metadata via tweaks to OpenSearch component
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metadata_tweaks = []
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if owner or owner is None:
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metadata_tweaks.append({"key": "owner", "value": owner})
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if owner_name:
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metadata_tweaks.append({"key": "owner_name", "value": owner_name})
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if owner_email:
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metadata_tweaks.append({"key": "owner_email", "value": owner_email})
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if connector_type:
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metadata_tweaks.append({"key": "connector_type", "value": connector_type})
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logger.info(f"[LF] Metadata tweaks {metadata_tweaks}")
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# if metadata_tweaks:
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# # Initialize the OpenSearch component tweaks if not already present
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# if "OpenSearchHybrid-Ve6bS" not in tweaks:
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# tweaks["OpenSearchHybrid-Ve6bS"] = {}
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# tweaks["OpenSearchHybrid-Ve6bS"]["docs_metadata"] = metadata_tweaks
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# logger.debug(
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# "[LF] Added metadata to tweaks", metadata_count=len(metadata_tweaks)
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# )
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if tweaks:
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payload["tweaks"] = tweaks
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logger.debug(f"[LF] Tweaks {tweaks}")
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if session_id:
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payload["session_id"] = session_id
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logger.debug(
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"[LF] Run ingestion -> /run/%s | files=%s session_id=%s tweaks_keys=%s jwt_present=%s",
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self.flow_id_ingest,
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len(file_paths) if file_paths else 0,
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session_id,
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list(tweaks.keys()) if isinstance(tweaks, dict) else None,
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bool(jwt_token),
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)
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# Avoid logging full payload to prevent leaking sensitive data (e.g., JWT)
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headers={
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"X-Langflow-Global-Var-JWT": str(jwt_token),
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"X-Langflow-Global-Var-OWNER": str(owner),
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"X-Langflow-Global-Var-OWNER_NAME": str(owner_name),
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"X-Langflow-Global-Var-OWNER_EMAIL": str(owner_email),
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"X-Langflow-Global-Var-CONNECTOR_TYPE": str(connector_type),
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}
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logger.info(f"[LF] Headers {headers}")
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logger.info(f"[LF] Payload {payload}")
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resp = await clients.langflow_request(
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"POST",
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f"/api/v1/run/{self.flow_id_ingest}",
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json=payload,
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headers=headers,
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)
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logger.debug(
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"[LF] Run response", status_code=resp.status_code, reason=resp.reason_phrase
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)
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if resp.status_code >= 400:
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logger.error(
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"[LF] Run failed",
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status_code=resp.status_code,
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reason=resp.reason_phrase,
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body=resp.text[:1000],
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)
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resp.raise_for_status()
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try:
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resp_json = resp.json()
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except Exception as e:
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logger.error(
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"[LF] Failed to parse run response as JSON",
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body=resp.text[:1000],
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error=str(e),
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)
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raise
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return resp_json
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async def upload_and_ingest_file(
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self,
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file_tuple,
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session_id: Optional[str] = None,
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tweaks: Optional[Dict[str, Any]] = None,
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settings: Optional[Dict[str, Any]] = None,
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jwt_token: Optional[str] = None,
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delete_after_ingest: bool = True,
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owner: Optional[str] = None,
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owner_name: Optional[str] = None,
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owner_email: Optional[str] = None,
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connector_type: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Combined upload, ingest, and delete operation.
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First uploads the file, then runs ingestion on it, then optionally deletes the file.
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Args:
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file_tuple: File tuple (filename, content, content_type)
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session_id: Optional session ID for the ingestion flow
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tweaks: Optional tweaks for the ingestion flow
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settings: Optional UI settings to convert to component tweaks
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jwt_token: Optional JWT token for authentication
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delete_after_ingest: Whether to delete the file from Langflow after ingestion (default: True)
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Returns:
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Combined result with upload info, ingestion result, and deletion status
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"""
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logger.debug("[LF] Starting combined upload and ingest operation")
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# Step 1: Upload the file
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try:
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upload_result = await self.upload_user_file(file_tuple, jwt_token=jwt_token)
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logger.debug(
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"[LF] Upload completed successfully",
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extra={
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"file_id": upload_result.get("id"),
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"file_path": upload_result.get("path"),
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},
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)
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except Exception as e:
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logger.error(
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"[LF] Upload failed during combined operation", extra={"error": str(e)}
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)
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raise Exception(f"Upload failed: {str(e)}")
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# Step 2: Prepare for ingestion
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file_path = upload_result.get("path")
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if not file_path:
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raise ValueError("Upload successful but no file path returned")
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# Convert UI settings to component tweaks if provided
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final_tweaks = tweaks.copy() if tweaks else {}
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if settings:
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logger.debug(
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"[LF] Applying ingestion settings", extra={"settings": settings}
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)
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# Split Text component tweaks (SplitText-QIKhg)
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if (
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settings.get("chunkSize")
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or settings.get("chunkOverlap")
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or settings.get("separator")
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):
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if "SplitText-QIKhg" not in final_tweaks:
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final_tweaks["SplitText-QIKhg"] = {}
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if settings.get("chunkSize"):
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final_tweaks["SplitText-QIKhg"]["chunk_size"] = settings[
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"chunkSize"
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]
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if settings.get("chunkOverlap"):
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final_tweaks["SplitText-QIKhg"]["chunk_overlap"] = settings[
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"chunkOverlap"
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]
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if settings.get("separator"):
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final_tweaks["SplitText-QIKhg"]["separator"] = settings["separator"]
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# OpenAI Embeddings component tweaks (OpenAIEmbeddings-joRJ6)
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if settings.get("embeddingModel"):
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if "OpenAIEmbeddings-joRJ6" not in final_tweaks:
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final_tweaks["OpenAIEmbeddings-joRJ6"] = {}
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final_tweaks["OpenAIEmbeddings-joRJ6"]["model"] = settings[
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"embeddingModel"
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]
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logger.debug(
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"[LF] Final tweaks with settings applied",
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extra={"tweaks": final_tweaks},
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)
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# Step 3: Run ingestion
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try:
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ingest_result = await self.run_ingestion_flow(
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file_paths=[file_path],
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session_id=session_id,
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tweaks=final_tweaks,
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jwt_token=jwt_token,
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owner=owner,
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owner_name=owner_name,
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owner_email=owner_email,
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connector_type=connector_type,
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)
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logger.debug("[LF] Ingestion completed successfully")
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except Exception as e:
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logger.error(
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"[LF] Ingestion failed during combined operation",
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extra={"error": str(e), "file_path": file_path},
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)
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# Note: We could optionally delete the uploaded file here if ingestion fails
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raise Exception(f"Ingestion failed: {str(e)}")
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# Step 4: Delete file from Langflow (optional)
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file_id = upload_result.get("id")
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delete_result = None
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delete_error = None
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if delete_after_ingest and file_id:
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try:
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logger.debug(
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"[LF] Deleting file after successful ingestion",
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extra={"file_id": file_id},
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)
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await self.delete_user_file(file_id)
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delete_result = {"status": "deleted", "file_id": file_id}
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logger.debug("[LF] File deleted successfully")
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except Exception as e:
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delete_error = str(e)
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logger.warning(
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"[LF] Failed to delete file after ingestion",
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extra={"error": delete_error, "file_id": file_id},
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)
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delete_result = {
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"status": "delete_failed",
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"file_id": file_id,
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"error": delete_error,
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}
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# Return combined result
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result = {
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"status": "success",
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"upload": upload_result,
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"ingestion": ingest_result,
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"message": f"File '{upload_result.get('name')}' uploaded and ingested successfully",
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}
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if delete_after_ingest:
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result["deletion"] = delete_result
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if delete_result and delete_result.get("status") == "deleted":
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result["message"] += " and cleaned up"
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elif delete_error:
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result["message"] += f" (cleanup warning: {delete_error})"
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return result
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