Merge branch 'main' of github.com:langflow-ai/openrag into ingest-settings

.dockerignore

@@ -1,2 +1,49 @@
+# Environment files
 .env
+.env.local
+.env.development
+.env.production
+
+# Auth files
 .drive.json
+*.json
+
+# Dependencies
+node_modules/
+*/node_modules/
+**/node_modules/
+
+# Python cache
+__pycache__/
+*/__pycache__/
+**/__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+
+# Build outputs
+build/
+dist/
+.next/
+out/
+
+# Development files
+.git/
+.gitignore
+README.md
+*.md
+.vscode/
+.idea/
+
+# Logs
+*.log
+logs/
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Temporary files
+tmp/
+temp/

.env.example

@@ -1,15 +1,24 @@
-# flow id from the the openrag flow json
-FLOW_ID=1098eea1-6649-4e1d-aed1-b77249fb8dd0
+# make one like so https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key
+LANGFLOW_SECRET_KEY=
+
+# flow ids for chat and ingestion flows
+LANGFLOW_CHAT_FLOW_ID=1098eea1-6649-4e1d-aed1-b77249fb8dd0
+LANGFLOW_INGEST_FLOW_ID=5488df7c-b93f-4f87-a446-b67028bc0813
 NUDGES_FLOW_ID=ebc01d31-1976-46ce-a385-b0240327226c
+
 # Set a strong admin password for OpenSearch; a bcrypt hash is generated at
 # container startup from this value. Do not commit real secrets.
+# must match the hashed password in secureconfig, must change for secure deployment!!!
 OPENSEARCH_PASSWORD=
-# make here https://console.cloud.google.com/apis/credentials 
+
+# make here https://console.cloud.google.com/apis/credentials
 GOOGLE_OAUTH_CLIENT_ID=
 GOOGLE_OAUTH_CLIENT_SECRET=
+
 # Azure app registration credentials for SharePoint/OneDrive
 MICROSOFT_GRAPH_OAUTH_CLIENT_ID=
 MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET=
+
 # OPTIONAL: dns routable from google (etc.) to handle continous ingest (something like ngrok works). This enables continous ingestion
 WEBHOOK_BASE_URL=
 

Makefile

@@ -0,0 +1,210 @@
+# OpenRAG Development Makefile
+# Provides easy commands for development workflow
+
+.PHONY: help dev dev-cpu dev-local infra stop clean build logs shell-backend shell-frontend install test backend frontend install-be install-fe build-be build-fe logs-be logs-fe logs-lf logs-os shell-be shell-lf shell-os restart status health db-reset flow-upload quick setup
+
+# Default target
+help:
+	@echo "OpenRAG Development Commands"
+	@echo ""
+	@echo "Development:"
+	@echo "  dev          - Start full stack with GPU support (docker compose)"
+	@echo "  dev-cpu      - Start full stack with CPU only (docker compose)"
+	@echo "  dev-local    - Start infrastructure only, run backend/frontend locally"
+	@echo "  infra        - Start infrastructure services only (alias for dev-local)"
+	@echo "  stop         - Stop all containers"
+	@echo "  restart      - Restart all containers"
+	@echo ""
+	@echo "Local Development:"
+	@echo "  backend      - Run backend locally (requires infrastructure)"
+	@echo "  frontend     - Run frontend locally"
+	@echo "  install      - Install all dependencies"
+	@echo "  install-be   - Install backend dependencies (uv)"
+	@echo "  install-fe   - Install frontend dependencies (npm)"
+	@echo ""
+	@echo "Utilities:"
+	@echo "  build        - Build all Docker images"
+	@echo "  clean        - Stop containers and remove volumes"
+	@echo "  logs         - Show logs from all containers"
+	@echo "  logs-be      - Show backend container logs"
+	@echo "  logs-lf      - Show langflow container logs"
+	@echo "  shell-be     - Shell into backend container"
+	@echo "  shell-lf     - Shell into langflow container"
+	@echo ""
+	@echo "Testing:"
+	@echo "  test         - Run backend tests"
+	@echo "  lint         - Run linting checks"
+	@echo ""
+
+# Development environments
+dev:
+	@echo "🚀 Starting OpenRAG with GPU support..."
+	docker-compose up -d
+	@echo "✅ Services started!"
+	@echo "   Backend: http://localhost:8000"
+	@echo "   Frontend: http://localhost:3000"
+	@echo "   Langflow: http://localhost:7860"
+	@echo "   OpenSearch: http://localhost:9200"
+	@echo "   Dashboards: http://localhost:5601"
+
+dev-cpu:
+	@echo "🚀 Starting OpenRAG with CPU only..."
+	docker-compose -f docker-compose-cpu.yml up -d
+	@echo "✅ Services started!"
+	@echo "   Backend: http://localhost:8000"
+	@echo "   Frontend: http://localhost:3000"
+	@echo "   Langflow: http://localhost:7860"
+	@echo "   OpenSearch: http://localhost:9200"
+	@echo "   Dashboards: http://localhost:5601"
+
+dev-local:
+	@echo "🔧 Starting infrastructure only (for local development)..."
+	docker-compose up -d opensearch dashboards langflow
+	@echo "✅ Infrastructure started!"
+	@echo "   Langflow: http://localhost:7860"
+	@echo "   OpenSearch: http://localhost:9200"
+	@echo "   Dashboards: http://localhost:5601"
+	@echo ""
+	@echo "Now run 'make backend' and 'make frontend' in separate terminals"
+
+infra:
+	@echo "🔧 Starting infrastructure services only..."
+	docker-compose up -d opensearch dashboards langflow
+	@echo "✅ Infrastructure services started!"
+	@echo "   Langflow: http://localhost:7860"
+	@echo "   OpenSearch: http://localhost:9200"
+	@echo "   Dashboards: http://localhost:5601"
+
+# Container management
+stop:
+	@echo "🛑 Stopping all containers..."
+	docker-compose down
+	docker-compose -f docker-compose-cpu.yml down 2>/dev/null || true
+
+restart: stop dev
+
+clean: stop
+	@echo "🧹 Cleaning up containers and volumes..."
+	docker-compose down -v --remove-orphans
+	docker-compose -f docker-compose-cpu.yml down -v --remove-orphans 2>/dev/null || true
+	docker system prune -f
+
+# Local development
+backend:
+	@echo "🐍 Starting backend locally..."
+	@if [ ! -f .env ]; then echo "⚠️  .env file not found. Copy .env.example to .env first"; exit 1; fi
+	uv run python src/main.py
+
+frontend:
+	@echo "⚛️  Starting frontend locally..."
+	@if [ ! -d "frontend/node_modules" ]; then echo "📦 Installing frontend dependencies first..."; cd frontend && npm install; fi
+	cd frontend && npx next dev
+
+# Installation
+install: install-be install-fe
+	@echo "✅ All dependencies installed!"
+
+install-be:
+	@echo "📦 Installing backend dependencies..."
+	uv sync
+
+install-fe:
+	@echo "📦 Installing frontend dependencies..."
+	cd frontend && npm install
+
+# Building
+build:
+	@echo "🔨 Building Docker images..."
+	docker-compose build
+
+build-be:
+	@echo "🔨 Building backend image..."
+	docker build -t openrag-backend -f Dockerfile.backend .
+
+build-fe:
+	@echo "🔨 Building frontend image..."
+	docker build -t openrag-frontend -f Dockerfile.frontend .
+
+# Logging and debugging
+logs:
+	@echo "📋 Showing all container logs..."
+	docker-compose logs -f
+
+logs-be:
+	@echo "📋 Showing backend logs..."
+	docker-compose logs -f openrag-backend
+
+logs-fe:
+	@echo "📋 Showing frontend logs..."
+	docker-compose logs -f openrag-frontend
+
+logs-lf:
+	@echo "📋 Showing langflow logs..."
+	docker-compose logs -f langflow
+
+logs-os:
+	@echo "📋 Showing opensearch logs..."
+	docker-compose logs -f opensearch
+
+# Shell access
+shell-be:
+	@echo "🐚 Opening shell in backend container..."
+	docker-compose exec openrag-backend /bin/bash
+
+shell-lf:
+	@echo "🐚 Opening shell in langflow container..."
+	docker-compose exec langflow /bin/bash
+
+shell-os:
+	@echo "🐚 Opening shell in opensearch container..."
+	docker-compose exec opensearch /bin/bash
+
+# Testing and quality
+test:
+	@echo "🧪 Running backend tests..."
+	uv run pytest
+
+lint:
+	@echo "🔍 Running linting checks..."
+	cd frontend && npm run lint
+	@echo "Frontend linting complete"
+
+# Service status
+status:
+	@echo "📊 Container status:"
+	@docker-compose ps 2>/dev/null || echo "No containers running"
+
+health:
+	@echo "🏥 Health check:"
+	@echo "Backend: $$(curl -s http://localhost:8000/health 2>/dev/null || echo 'Not responding')"
+	@echo "Langflow: $$(curl -s http://localhost:7860/health 2>/dev/null || echo 'Not responding')"
+	@echo "OpenSearch: $$(curl -s -k -u admin:$(shell grep OPENSEARCH_PASSWORD .env | cut -d= -f2) https://localhost:9200 2>/dev/null | jq -r .tagline 2>/dev/null || echo 'Not responding')"
+
+# Database operations
+db-reset:
+	@echo "🗄️ Resetting OpenSearch indices..."
+	curl -X DELETE "http://localhost:9200/documents" -u admin:$$(grep OPENSEARCH_PASSWORD .env | cut -d= -f2) || true
+	curl -X DELETE "http://localhost:9200/knowledge_filters" -u admin:$$(grep OPENSEARCH_PASSWORD .env | cut -d= -f2) || true
+	@echo "Indices reset. Restart backend to recreate."
+
+# Flow management
+flow-upload:
+	@echo "📁 Uploading flow to Langflow..."
+	@if [ -z "$(FLOW_FILE)" ]; then echo "Usage: make flow-upload FLOW_FILE=path/to/flow.json"; exit 1; fi
+	curl -X POST "http://localhost:7860/api/v1/flows" \
+		-H "Content-Type: application/json" \
+		-d @$(FLOW_FILE)
+
+# Quick development shortcuts
+quick: dev-local
+	@echo "🚀 Quick start: infrastructure running!"
+	@echo "Run these in separate terminals:"
+	@echo "  make backend"
+	@echo "  make frontend"
+
+# Environment setup
+setup:
+	@echo "⚙️ Setting up development environment..."
+	@if [ ! -f .env ]; then cp .env.example .env && echo "📝 Created .env from template"; fi
+	@$(MAKE) install
+	@echo "✅ Setup complete! Run 'make dev' to start."

docker-compose-cpu.yml

@@ -15,10 +15,10 @@ services:
       bash -c "
         # Start OpenSearch in background
         /usr/share/opensearch/opensearch-docker-entrypoint.sh opensearch &
-        
+
         # Wait a bit for OpenSearch to start, then apply security config
         sleep 10 && /usr/share/opensearch/setup-security.sh &
-        
+
         # Wait for background processes
         wait
       "
@@ -53,7 +53,8 @@ services:
       - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
       - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER}
       - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD}
-      - FLOW_ID=${FLOW_ID}
+      - LANGFLOW_CHAT_FLOW_ID=${LANGFLOW_CHAT_FLOW_ID}
+      - LANGFLOW_INGEST_FLOW_ID=${LANGFLOW_INGEST_FLOW_ID}
       - NUDGES_FLOW_ID=${NUDGES_FLOW_ID}
       - OPENSEARCH_PORT=9200
       - OPENSEARCH_USERNAME=admin
@@ -98,7 +99,8 @@ services:
       - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
       - JWT="dummy"
       - OPENRAG-QUERY-FILTER="{}"
-      - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER
+      - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD}
+      - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER,OPENSEARCH_PASSWORD
       - LANGFLOW_LOG_LEVEL=DEBUG
       - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN}
       - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER}

docker-compose.yml

@@ -15,10 +15,10 @@ services:
       bash -c "
         # Start OpenSearch in background
         /usr/share/opensearch/opensearch-docker-entrypoint.sh opensearch &
-        
+
         # Wait a bit for OpenSearch to start, then apply security config
         sleep 10 && /usr/share/opensearch/setup-security.sh &
-        
+
         # Wait for background processes
         wait
       "
@@ -52,7 +52,8 @@ services:
       - LANGFLOW_PUBLIC_URL=${LANGFLOW_PUBLIC_URL}
       - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER}
       - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD}
-      - FLOW_ID=${FLOW_ID}
+      - LANGFLOW_CHAT_FLOW_ID=${LANGFLOW_CHAT_FLOW_ID}
+      - LANGFLOW_INGEST_FLOW_ID=${LANGFLOW_INGEST_FLOW_ID}
       - NUDGES_FLOW_ID=${NUDGES_FLOW_ID}
       - OPENSEARCH_PORT=9200
       - OPENSEARCH_USERNAME=admin
@@ -98,7 +99,8 @@ services:
       - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
       - JWT="dummy"
       - OPENRAG-QUERY-FILTER="{}"
-      - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER
+      - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD}
+      - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER,OPENSEARCH_PASSWORD
       - LANGFLOW_LOG_LEVEL=DEBUG
       - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN}
       - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER}

frontend/components/knowledge-dropdown.tsx

@@ -133,24 +133,50 @@ export function KnowledgeDropdown({ active, variant = 'navigation' }: KnowledgeD
         const formData = new FormData()
         formData.append('file', files[0])
         
-        const response = await fetch('/api/upload', {
+        // 1) Upload to Langflow
+        const upRes = await fetch('/api/langflow/files/upload', {
           method: 'POST',
           body: formData,
         })
-
-        const result = await response.json()
-        
-        if (response.ok) {
-          window.dispatchEvent(new CustomEvent('fileUploaded', { 
-            detail: { file: files[0], result } 
-          }))
-          // Trigger search refresh after successful upload
-          window.dispatchEvent(new CustomEvent('knowledgeUpdated'))
-        } else {
-          window.dispatchEvent(new CustomEvent('fileUploadError', { 
-            detail: { filename: files[0].name, error: result.error || 'Upload failed' } 
-          }))
+        const upJson = await upRes.json()
+        if (!upRes.ok) {
+          throw new Error(upJson?.error || 'Upload to Langflow failed')
         }
+
+        const fileId = upJson?.id
+        const filePath = upJson?.path
+        if (!fileId || !filePath) {
+          throw new Error('Langflow did not return file id/path')
+        }
+
+        // 2) Run ingestion flow
+        const runRes = await fetch('/api/langflow/ingest', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ file_paths: [filePath] }),
+        })
+        const runJson = await runRes.json()
+        if (!runRes.ok) {
+          throw new Error(runJson?.error || 'Langflow ingestion failed')
+        }
+
+        // 3) Delete file from Langflow
+        const delRes = await fetch('/api/langflow/files', {
+          method: 'DELETE',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ file_ids: [fileId] }),
+        })
+        const delJson = await delRes.json().catch(() => ({}))
+        if (!delRes.ok) {
+          throw new Error(delJson?.error || 'Langflow file delete failed')
+        }
+
+        // Notify UI
+        window.dispatchEvent(new CustomEvent('fileUploaded', { 
+          detail: { file: files[0], result: { file_id: fileId, file_path: filePath, run: runJson } } 
+        }))
+        // Trigger search refresh after successful ingestion
+        window.dispatchEvent(new CustomEvent('knowledgeUpdated'))
       } catch (error) {
         window.dispatchEvent(new CustomEvent('fileUploadError', { 
           detail: { filename: files[0].name, error: error instanceof Error ? error.message : 'Upload failed' } 

frontend/src/app/admin/page.tsx

@@ -57,7 +57,7 @@ function AdminPage() {
       })
 
       const result = await response.json()
-      
+
       if (response.ok) {
         setUploadStatus(`File uploaded successfully! ID: ${result.id}`)
         setSelectedFile(null)
@@ -132,23 +132,23 @@ function AdminPage() {
       })
 
       const result = await response.json()
-      
+
       if (response.status === 201) {
         // New flow: Got task ID, use centralized tracking
         const taskId = result.task_id || result.id
         const totalFiles = result.total_files || 0
-        
+
         if (!taskId) {
           throw new Error("No task ID received from server")
         }
-        
+
         // Add task to centralized tracking
         addTask(taskId)
-        
+
         setUploadStatus(`🔄 Processing started for ${totalFiles} files. Check the task notification panel for real-time progress. (Task ID: ${taskId})`)
         setFolderPath("")
         setPathUploadLoading(false)
-        
+
       } else if (response.ok) {
         // Original flow: Direct response with results
         const successful = result.results?.filter((r: {status: string}) => r.status === "indexed").length || 0

frontend/src/app/settings/page.tsx

@@ -52,11 +52,11 @@ interface Connector {
 }
 
 interface SyncResult {
-  processed?: number;
-  added?: number;
-  errors?: number;
-  skipped?: number;
-  total?: number;
+	processed?: number;
+	added?: number;
+	errors?: number;
+	skipped?: number;
+	total?: number;
 }
 
 interface Connection {

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "openrag"
-version = "0.1.1"
+version = "0.1.2"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"

src/api/connectors.py

@@ -66,6 +66,7 @@ async def connector_sync(request: Request, connector_service, session_manager):
                     max_files,
                     jwt_token=jwt_token,
                 )
+            task_ids.append(task_id)
         return JSONResponse(
             {
                 "task_ids": task_ids,

src/api/langflow_files.py

@@ -0,0 +1,159 @@
+from starlette.requests import Request
+from starlette.responses import JSONResponse
+
+from services.langflow_file_service import LangflowFileService
+from utils.logging_config import get_logger
+
+logger = get_logger(__name__)
+
+
+async def upload_user_file(
+    request: Request, langflow_file_service: LangflowFileService, session_manager
+):
+    try:
+        logger.debug("upload_user_file endpoint called")
+        form = await request.form()
+        upload_file = form.get("file")
+        if upload_file is None:
+            logger.error("No file provided in upload request")
+            return JSONResponse({"error": "Missing file"}, status_code=400)
+
+        logger.debug(
+            "Processing file", filename=upload_file.filename, size=upload_file.size
+        )
+
+        # starlette UploadFile provides file-like; httpx needs (filename, file, content_type)
+        content = await upload_file.read()
+        file_tuple = (
+            upload_file.filename,
+            content,
+            upload_file.content_type or "application/octet-stream",
+        )
+
+        jwt_token = getattr(request.state, "jwt_token", None)
+        logger.debug("JWT token status", jwt_present=jwt_token is not None)
+
+        logger.debug("Calling langflow_file_service.upload_user_file")
+        result = await langflow_file_service.upload_user_file(file_tuple, jwt_token)
+        logger.debug("Upload successful", result=result)
+        return JSONResponse(result, status_code=201)
+    except Exception as e:
+        logger.error(
+            "upload_user_file endpoint failed",
+            error_type=type(e).__name__,
+            error=str(e),
+        )
+        import traceback
+
+        logger.error("Full traceback", traceback=traceback.format_exc())
+        return JSONResponse({"error": str(e)}, status_code=500)
+
+
+async def run_ingestion(
+    request: Request, langflow_file_service: LangflowFileService, session_manager
+):
+    try:
+        payload = await request.json()
+        file_ids = payload.get("file_ids")
+        file_paths = payload.get("file_paths") or []
+        session_id = payload.get("session_id")
+        tweaks = payload.get("tweaks") or {}
+        settings = payload.get("settings", {})
+
+        # We assume file_paths is provided. If only file_ids are provided, client would need to resolve to paths via Files API (not implemented here).
+        if not file_paths and not file_ids:
+            return JSONResponse(
+                {"error": "Provide file_paths or file_ids"}, status_code=400
+            )
+
+        # Convert UI settings to component tweaks using exact component IDs
+        if settings:
+            logger.debug("Applying ingestion settings", settings=settings)
+
+            # Split Text component tweaks (SplitText-QIKhg)
+            if (
+                settings.get("chunkSize")
+                or settings.get("chunkOverlap")
+                or settings.get("separator")
+            ):
+                if "SplitText-QIKhg" not in tweaks:
+                    tweaks["SplitText-QIKhg"] = {}
+                if settings.get("chunkSize"):
+                    tweaks["SplitText-QIKhg"]["chunk_size"] = settings["chunkSize"]
+                if settings.get("chunkOverlap"):
+                    tweaks["SplitText-QIKhg"]["chunk_overlap"] = settings[
+                        "chunkOverlap"
+                    ]
+                if settings.get("separator"):
+                    tweaks["SplitText-QIKhg"]["separator"] = settings["separator"]
+
+            # OpenAI Embeddings component tweaks (OpenAIEmbeddings-joRJ6)
+            if settings.get("embeddingModel"):
+                if "OpenAIEmbeddings-joRJ6" not in tweaks:
+                    tweaks["OpenAIEmbeddings-joRJ6"] = {}
+                tweaks["OpenAIEmbeddings-joRJ6"]["model"] = settings["embeddingModel"]
+
+            # Note: OpenSearch component tweaks not needed for ingestion
+            # (search parameters are for retrieval, not document processing)
+
+            logger.debug("Final tweaks with settings applied", tweaks=tweaks)
+        # Include user JWT if available
+        jwt_token = getattr(request.state, "jwt_token", None)
+
+        # Extract user info from User object
+        user = getattr(request.state, "user", None)
+        user_id = user.user_id if user else None
+        user_name = user.name if user else None
+        user_email = user.email if user else None
+
+        if jwt_token:
+            # Set auth context for downstream services
+            from auth_context import set_auth_context
+
+            set_auth_context(user_id, jwt_token)
+
+        result = await langflow_file_service.run_ingestion_flow(
+            file_paths=file_paths or [],
+            jwt_token=jwt_token,
+            session_id=session_id,
+            tweaks=tweaks,
+            owner=user_id,
+            owner_name=user_name,
+            owner_email=user_email,
+            connector_type="local",
+        )
+        return JSONResponse(result)
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)
+
+
+async def delete_user_files(
+    request: Request, langflow_file_service: LangflowFileService, session_manager
+):
+    try:
+        payload = await request.json()
+        file_ids = payload.get("file_ids")
+        if not file_ids or not isinstance(file_ids, list):
+            return JSONResponse(
+                {"error": "file_ids must be a non-empty list"}, status_code=400
+            )
+
+        errors = []
+        for fid in file_ids:
+            try:
+                await langflow_file_service.delete_user_file(fid)
+            except Exception as e:
+                errors.append({"file_id": fid, "error": str(e)})
+
+        status = 207 if errors else 200
+        return JSONResponse(
+            {
+                "deleted": [
+                    fid for fid in file_ids if fid not in [e["file_id"] for e in errors]
+                ],
+                "errors": errors,
+            },
+            status_code=status,
+        )
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)

src/api/settings.py

@@ -1,6 +1,10 @@
-import os
 from starlette.responses import JSONResponse
-from config.settings import LANGFLOW_URL, FLOW_ID, LANGFLOW_PUBLIC_URL
+from config.settings import (
+    LANGFLOW_URL,
+    LANGFLOW_CHAT_FLOW_ID,
+    LANGFLOW_INGEST_FLOW_ID,
+    LANGFLOW_PUBLIC_URL,
+)
 
 
 async def get_settings(request, session_manager):
@@ -9,16 +13,92 @@ async def get_settings(request, session_manager):
         # Return public settings that are safe to expose to frontend
         settings = {
             "langflow_url": LANGFLOW_URL,
-            "flow_id": FLOW_ID,
+            "flow_id": LANGFLOW_CHAT_FLOW_ID,
+            "ingest_flow_id": LANGFLOW_INGEST_FLOW_ID,
             "langflow_public_url": LANGFLOW_PUBLIC_URL,
         }
 
-        # Only expose edit URL when a public URL is configured
-        if LANGFLOW_PUBLIC_URL and FLOW_ID:
+        # Only expose edit URLs when a public URL is configured
+        if LANGFLOW_PUBLIC_URL and LANGFLOW_CHAT_FLOW_ID:
             settings["langflow_edit_url"] = (
-                f"{LANGFLOW_PUBLIC_URL.rstrip('/')}/flow/{FLOW_ID}"
+                f"{LANGFLOW_PUBLIC_URL.rstrip('/')}/flow/{LANGFLOW_CHAT_FLOW_ID}"
             )
 
+        if LANGFLOW_PUBLIC_URL and LANGFLOW_INGEST_FLOW_ID:
+            settings["langflow_ingest_edit_url"] = (
+                f"{LANGFLOW_PUBLIC_URL.rstrip('/')}/flow/{LANGFLOW_INGEST_FLOW_ID}"
+            )
+
+        # Fetch ingestion flow configuration to get actual component defaults
+        if LANGFLOW_INGEST_FLOW_ID:
+            try:
+                from config.settings import generate_langflow_api_key
+                import httpx
+
+                api_key = await generate_langflow_api_key()
+                if api_key:
+                    async with httpx.AsyncClient(timeout=10.0) as client:
+                        response = await client.get(
+                            f"{LANGFLOW_URL}/api/v1/flows/{LANGFLOW_INGEST_FLOW_ID}",
+                            headers={"x-api-key": api_key},
+                        )
+                        if response.status_code == 200:
+                            flow_data = response.json()
+
+                            # Extract component defaults (ingestion-specific settings only)
+                            ingestion_defaults = {
+                                "chunkSize": 1000,
+                                "chunkOverlap": 200,
+                                "separator": "\\n",
+                                "embeddingModel": "text-embedding-3-small",
+                            }
+
+                            if flow_data.get("data", {}).get("nodes"):
+                                for node in flow_data["data"]["nodes"]:
+                                    node_template = (
+                                        node.get("data", {})
+                                        .get("node", {})
+                                        .get("template", {})
+                                    )
+
+                                    # Split Text component (SplitText-QIKhg)
+                                    if node.get("id") == "SplitText-QIKhg":
+                                        if node_template.get("chunk_size", {}).get(
+                                            "value"
+                                        ):
+                                            ingestion_defaults["chunkSize"] = (
+                                                node_template["chunk_size"]["value"]
+                                            )
+                                        if node_template.get("chunk_overlap", {}).get(
+                                            "value"
+                                        ):
+                                            ingestion_defaults["chunkOverlap"] = (
+                                                node_template["chunk_overlap"]["value"]
+                                            )
+                                        if node_template.get("separator", {}).get(
+                                            "value"
+                                        ):
+                                            ingestion_defaults["separator"] = (
+                                                node_template["separator"]["value"]
+                                            )
+
+                                    # OpenAI Embeddings component (OpenAIEmbeddings-joRJ6)
+                                    elif node.get("id") == "OpenAIEmbeddings-joRJ6":
+                                        if node_template.get("model", {}).get("value"):
+                                            ingestion_defaults["embeddingModel"] = (
+                                                node_template["model"]["value"]
+                                            )
+
+                                    # Note: OpenSearch component settings are not exposed for ingestion
+                                    # (search-related parameters like number_of_results, score_threshold
+                                    # are for retrieval, not ingestion)
+
+                            settings["ingestion_defaults"] = ingestion_defaults
+
+            except Exception as e:
+                print(f"[WARNING] Failed to fetch ingestion flow defaults: {e}")
+                # Continue without ingestion defaults
+
         return JSONResponse(settings)
 
     except Exception as e:

src/config/settings.py

@@ -1,19 +1,22 @@
 import os
-import requests
 import time
-from dotenv import load_dotenv
-from utils.logging_config import get_logger
 
-logger = get_logger(__name__)
+import httpx
+import requests
+from agentd.patch import patch_openai_with_mcp
+from docling.document_converter import DocumentConverter
+from dotenv import load_dotenv
+from openai import AsyncOpenAI
 from opensearchpy import AsyncOpenSearch
 from opensearchpy._async.http_aiohttp import AIOHttpConnection
-from docling.document_converter import DocumentConverter
-from agentd.patch import patch_openai_with_mcp
-from openai import AsyncOpenAI
+
+from utils.logging_config import get_logger
 
 load_dotenv()
 load_dotenv("../")
 
+logger = get_logger(__name__)
+
 # Environment variables
 OPENSEARCH_HOST = os.getenv("OPENSEARCH_HOST", "localhost")
 OPENSEARCH_PORT = int(os.getenv("OPENSEARCH_PORT", "9200"))
@@ -22,8 +25,18 @@ OPENSEARCH_PASSWORD = os.getenv("OPENSEARCH_PASSWORD")
 LANGFLOW_URL = os.getenv("LANGFLOW_URL", "http://localhost:7860")
 # Optional: public URL for browser links (e.g., http://localhost:7860)
 LANGFLOW_PUBLIC_URL = os.getenv("LANGFLOW_PUBLIC_URL")
-FLOW_ID = os.getenv("FLOW_ID")
+# Backwards compatible flow ID handling with deprecation warnings
+_legacy_flow_id = os.getenv("FLOW_ID")
+
+LANGFLOW_CHAT_FLOW_ID = os.getenv("LANGFLOW_CHAT_FLOW_ID") or _legacy_flow_id
+LANGFLOW_INGEST_FLOW_ID = os.getenv("LANGFLOW_INGEST_FLOW_ID")
 NUDGES_FLOW_ID = os.getenv("NUDGES_FLOW_ID")
+
+if _legacy_flow_id and not os.getenv("LANGFLOW_CHAT_FLOW_ID"):
+    logger.warning("FLOW_ID is deprecated. Please use LANGFLOW_CHAT_FLOW_ID instead")
+    LANGFLOW_CHAT_FLOW_ID = _legacy_flow_id
+
+
 # Langflow superuser credentials for API key generation
 LANGFLOW_SUPERUSER = os.getenv("LANGFLOW_SUPERUSER")
 LANGFLOW_SUPERUSER_PASSWORD = os.getenv("LANGFLOW_SUPERUSER_PASSWORD")
@@ -94,15 +107,47 @@ INDEX_BODY = {
     },
 }
 
+# Convenience base URL for Langflow REST API
+LANGFLOW_BASE_URL = f"{LANGFLOW_URL}/api/v1"
+
 
 async def generate_langflow_api_key():
     """Generate Langflow API key using superuser credentials at startup"""
     global LANGFLOW_KEY
 
+    logger.debug(
+        "generate_langflow_api_key called", current_key_present=bool(LANGFLOW_KEY)
+    )
+
     # If key already provided via env, do not attempt generation
     if LANGFLOW_KEY:
-        logger.info("Using LANGFLOW_KEY from environment, skipping generation")
-        return LANGFLOW_KEY
+        if os.getenv("LANGFLOW_KEY"):
+            logger.info("Using LANGFLOW_KEY from environment; skipping generation")
+            return LANGFLOW_KEY
+        else:
+            # We have a cached key, but let's validate it first
+            logger.debug("Validating cached LANGFLOW_KEY", key_prefix=LANGFLOW_KEY[:8])
+            try:
+                validation_response = requests.get(
+                    f"{LANGFLOW_URL}/api/v1/users/whoami",
+                    headers={"x-api-key": LANGFLOW_KEY},
+                    timeout=5,
+                )
+                if validation_response.status_code == 200:
+                    logger.debug("Cached API key is valid", key_prefix=LANGFLOW_KEY[:8])
+                    return LANGFLOW_KEY
+                else:
+                    logger.warning(
+                        "Cached API key is invalid, generating fresh key",
+                        status_code=validation_response.status_code,
+                    )
+                    LANGFLOW_KEY = None  # Clear invalid key
+            except Exception as e:
+                logger.warning(
+                    "Cached API key validation failed, generating fresh key",
+                    error=str(e),
+                )
+                LANGFLOW_KEY = None  # Clear invalid key
 
     if not LANGFLOW_SUPERUSER or not LANGFLOW_SUPERUSER_PASSWORD:
         logger.warning(
@@ -115,7 +160,6 @@ async def generate_langflow_api_key():
         max_attempts = int(os.getenv("LANGFLOW_KEY_RETRIES", "15"))
         delay_seconds = float(os.getenv("LANGFLOW_KEY_RETRY_DELAY", "2.0"))
 
-        last_error = None
         for attempt in range(1, max_attempts + 1):
             try:
                 # Login to get access token
@@ -148,14 +192,28 @@ async def generate_langflow_api_key():
                 if not api_key:
                     raise KeyError("api_key")
 
-                LANGFLOW_KEY = api_key
-                logger.info(
-                    "Successfully generated Langflow API key",
-                    api_key_preview=api_key[:8],
+                # Validate the API key works
+                validation_response = requests.get(
+                    f"{LANGFLOW_URL}/api/v1/users/whoami",
+                    headers={"x-api-key": api_key},
+                    timeout=10,
                 )
-                return api_key
+                if validation_response.status_code == 200:
+                    LANGFLOW_KEY = api_key
+                    logger.info(
+                        "Successfully generated and validated Langflow API key",
+                        key_prefix=api_key[:8],
+                    )
+                    return api_key
+                else:
+                    logger.error(
+                        "Generated API key validation failed",
+                        status_code=validation_response.status_code,
+                    )
+                    raise ValueError(
+                        f"API key validation failed: {validation_response.status_code}"
+                    )
             except (requests.exceptions.RequestException, KeyError) as e:
-                last_error = e
                 logger.warning(
                     "Attempt to generate Langflow API key failed",
                     attempt=attempt,
@@ -182,6 +240,7 @@ class AppClients:
     def __init__(self):
         self.opensearch = None
         self.langflow_client = None
+        self.langflow_http_client = None
         self.patched_async_client = None
         self.converter = None
 
@@ -204,9 +263,15 @@ class AppClients:
         # Initialize Langflow client with generated/provided API key
         if LANGFLOW_KEY and self.langflow_client is None:
             try:
-                self.langflow_client = AsyncOpenAI(
-                    base_url=f"{LANGFLOW_URL}/api/v1", api_key=LANGFLOW_KEY
-                )
+                if not OPENSEARCH_PASSWORD:
+                    raise ValueError("OPENSEARCH_PASSWORD is not set")
+                else:
+                    await self.ensure_langflow_client()
+                    # Note: OPENSEARCH_PASSWORD global variable should be created automatically
+                    # via LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT in docker-compose
+                    logger.info(
+                        "Langflow client initialized - OPENSEARCH_PASSWORD should be available via environment variables"
+                    )
             except Exception as e:
                 logger.warning("Failed to initialize Langflow client", error=str(e))
                 self.langflow_client = None
@@ -221,6 +286,11 @@ class AppClients:
         # Initialize document converter
         self.converter = DocumentConverter()
 
+        # Initialize Langflow HTTP client
+        self.langflow_http_client = httpx.AsyncClient(
+            base_url=LANGFLOW_URL, timeout=60.0
+        )
+
         return self
 
     async def ensure_langflow_client(self):
@@ -242,6 +312,71 @@ class AppClients:
                 self.langflow_client = None
         return self.langflow_client
 
+    async def langflow_request(self, method: str, endpoint: str, **kwargs):
+        """Central method for all Langflow API requests"""
+        api_key = await generate_langflow_api_key()
+        if not api_key:
+            raise ValueError("No Langflow API key available")
+
+        # Merge headers properly - passed headers take precedence over defaults
+        default_headers = {"x-api-key": api_key, "Content-Type": "application/json"}
+        existing_headers = kwargs.pop("headers", {})
+        headers = {**default_headers, **existing_headers}
+
+        # Remove Content-Type if explicitly set to None (for file uploads)
+        if headers.get("Content-Type") is None:
+            headers.pop("Content-Type", None)
+
+        url = f"{LANGFLOW_URL}{endpoint}"
+
+        return await self.langflow_http_client.request(
+            method=method, url=url, headers=headers, **kwargs
+        )
+
+    async def _create_langflow_global_variable(self, name: str, value: str):
+        """Create a global variable in Langflow via API"""
+        api_key = await generate_langflow_api_key()
+        if not api_key:
+            logger.warning(
+                "Cannot create Langflow global variable: No API key", variable_name=name
+            )
+            return
+
+        url = f"{LANGFLOW_URL}/api/v1/variables/"
+        payload = {
+            "name": name,
+            "value": value,
+            "default_fields": [],
+            "type": "Credential",
+        }
+        headers = {"x-api-key": api_key, "Content-Type": "application/json"}
+
+        try:
+            async with httpx.AsyncClient() as client:
+                response = await client.post(url, headers=headers, json=payload)
+
+                if response.status_code in [200, 201]:
+                    logger.info(
+                        "Successfully created Langflow global variable",
+                        variable_name=name,
+                    )
+                elif response.status_code == 400 and "already exists" in response.text:
+                    logger.info(
+                        "Langflow global variable already exists", variable_name=name
+                    )
+                else:
+                    logger.warning(
+                        "Failed to create Langflow global variable",
+                        variable_name=name,
+                        status_code=response.status_code,
+                    )
+        except Exception as e:
+            logger.error(
+                "Exception creating Langflow global variable",
+                variable_name=name,
+                error=str(e),
+            )
+
     def create_user_opensearch_client(self, jwt_token: str):
         """Create OpenSearch client with user's JWT token for OIDC auth"""
         headers = {"Authorization": f"Bearer {jwt_token}"}

src/connectors/google_drive/connector.py

@@ -400,8 +400,9 @@ class GoogleDriveConnector(BaseConnector):
         export_mime = self._pick_export_mime(mime_type)
         if mime_type.startswith("application/vnd.google-apps."):
             # default fallback if not overridden
-            if not export_mime:
-                export_mime = "application/pdf"
+            #if not export_mime:
+            #    export_mime = "application/pdf"
+            export_mime = "application/pdf"
             # NOTE: export_media does not accept supportsAllDrives/includeItemsFromAllDrives
             request = self.service.files().export_media(fileId=file_id, mimeType=export_mime)
         else:

src/connectors/langflow_connector_service.py

@@ -0,0 +1,302 @@
+import os
+import tempfile
+from typing import Any, Dict, List, Optional
+
+# Create custom processor for connector files using Langflow
+from models.processors import LangflowConnectorFileProcessor
+from services.langflow_file_service import LangflowFileService
+from utils.logging_config import get_logger
+
+from .base import BaseConnector, ConnectorDocument
+from .connection_manager import ConnectionManager
+
+logger = get_logger(__name__)
+
+
+class LangflowConnectorService:
+    """Service to manage connector documents and process them via Langflow"""
+
+    def __init__(
+        self,
+        task_service=None,
+        session_manager=None,
+    ):
+        self.task_service = task_service
+        self.session_manager = session_manager
+        self.connection_manager = ConnectionManager()
+
+        # Initialize LangflowFileService for processing connector documents
+        self.langflow_service = LangflowFileService()
+
+    async def initialize(self):
+        """Initialize the service by loading existing connections"""
+        await self.connection_manager.load_connections()
+
+    async def get_connector(self, connection_id: str) -> Optional[BaseConnector]:
+        """Get a connector by connection ID"""
+        return await self.connection_manager.get_connector(connection_id)
+
+    async def process_connector_document(
+        self,
+        document: ConnectorDocument,
+        owner_user_id: str,
+        connector_type: str,
+        jwt_token: str = None,
+        owner_name: str = None,
+        owner_email: str = None,
+    ) -> Dict[str, Any]:
+        """Process a document from a connector using LangflowFileService pattern"""
+
+        logger.debug(
+            "Processing connector document via Langflow",
+            document_id=document.id,
+            filename=document.filename,
+        )
+
+        suffix = self._get_file_extension(document.mimetype)
+
+        # Create temporary file from document content
+        with tempfile.NamedTemporaryFile(
+            delete=False, suffix=suffix
+        ) as tmp_file:
+            tmp_file.write(document.content)
+            tmp_file.flush()
+
+            try:
+                # Step 1: Upload file to Langflow
+                logger.debug("Uploading file to Langflow", filename=document.filename)
+                content = document.content
+                file_tuple = (
+                    document.filename.replace(" ", "_").replace("/", "_")+suffix,
+                    content,
+                    document.mimetype or "application/octet-stream",
+                )
+
+                upload_result = await self.langflow_service.upload_user_file(
+                    file_tuple, jwt_token
+                )
+                langflow_file_id = upload_result["id"]
+                langflow_file_path = upload_result["path"]
+
+                logger.debug(
+                    "File uploaded to Langflow",
+                    file_id=langflow_file_id,
+                    path=langflow_file_path,
+                )
+
+                # Step 2: Run ingestion flow with the uploaded file
+                logger.debug(
+                    "Running Langflow ingestion flow", file_path=langflow_file_path
+                )
+
+                # Use the same tweaks pattern as LangflowFileService
+                tweaks = {}  # Let Langflow handle the ingestion with default settings
+
+                ingestion_result = await self.langflow_service.run_ingestion_flow(
+                    file_paths=[langflow_file_path],
+                    jwt_token=jwt_token,
+                    tweaks=tweaks,
+                    owner=owner_user_id,
+                    owner_name=owner_name,
+                    owner_email=owner_email,
+                    connector_type=connector_type,
+                )
+
+                logger.debug("Ingestion flow completed", result=ingestion_result)
+
+                # Step 3: Delete the file from Langflow
+                logger.debug("Deleting file from Langflow", file_id=langflow_file_id)
+                await self.langflow_service.delete_user_file(langflow_file_id)
+                logger.debug("File deleted from Langflow", file_id=langflow_file_id)
+
+                return {
+                    "status": "indexed",
+                    "filename": document.filename,
+                    "source_url": document.source_url,
+                    "document_id": document.id,
+                    "connector_type": connector_type,
+                    "langflow_result": ingestion_result,
+                }
+
+            except Exception as e:
+                logger.error(
+                    "Failed to process connector document via Langflow",
+                    document_id=document.id,
+                    error=str(e),
+                )
+                # Try to clean up Langflow file if upload succeeded but processing failed
+                if "langflow_file_id" in locals():
+                    try:
+                        await self.langflow_service.delete_user_file(langflow_file_id)
+                        logger.debug(
+                            "Cleaned up Langflow file after error",
+                            file_id=langflow_file_id,
+                        )
+                    except Exception as cleanup_error:
+                        logger.warning(
+                            "Failed to cleanup Langflow file",
+                            file_id=langflow_file_id,
+                            error=str(cleanup_error),
+                        )
+                raise
+
+            finally:
+                # Clean up temporary file
+                os.unlink(tmp_file.name)
+
+    def _get_file_extension(self, mimetype: str) -> str:
+        """Get file extension based on MIME type"""
+        mime_to_ext = {
+            "application/pdf": ".pdf",
+            "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx",
+            "application/msword": ".doc",
+            "application/vnd.openxmlformats-officedocument.presentationml.presentation": ".pptx",
+            "application/vnd.ms-powerpoint": ".ppt",
+            "text/plain": ".txt",
+            "text/html": ".html",
+            "application/rtf": ".rtf",
+            "application/vnd.google-apps.document": ".pdf",  # Exported as PDF
+            "application/vnd.google-apps.presentation": ".pdf",
+            "application/vnd.google-apps.spreadsheet": ".pdf",
+        }
+        return mime_to_ext.get(mimetype, ".bin")
+
+    async def sync_connector_files(
+        self,
+        connection_id: str,
+        user_id: str,
+        max_files: int = None,
+        jwt_token: str = None,
+    ) -> str:
+        """Sync files from a connector connection using Langflow processing"""
+        if not self.task_service:
+            raise ValueError(
+                "TaskService not available - connector sync requires task service dependency"
+            )
+
+        logger.debug(
+            "Starting Langflow-based sync for connection",
+            connection_id=connection_id,
+            max_files=max_files,
+        )
+
+        connector = await self.get_connector(connection_id)
+        if not connector:
+            raise ValueError(
+                f"Connection '{connection_id}' not found or not authenticated"
+            )
+
+        logger.debug("Got connector", authenticated=connector.is_authenticated)
+
+        if not connector.is_authenticated:
+            raise ValueError(f"Connection '{connection_id}' not authenticated")
+
+        # Collect files to process (limited by max_files)
+        files_to_process = []
+        page_token = None
+
+        # Calculate page size to minimize API calls
+        page_size = min(max_files or 100, 1000) if max_files else 100
+
+        while True:
+            # List files from connector with limit
+            logger.debug(
+                "Calling list_files", page_size=page_size, page_token=page_token
+            )
+            file_list = await connector.list_files(page_token, limit=page_size)
+            logger.debug(
+                "Got files from connector", file_count=len(file_list.get("files", []))
+            )
+            files = file_list["files"]
+
+            if not files:
+                break
+
+            for file_info in files:
+                if max_files and len(files_to_process) >= max_files:
+                    break
+                files_to_process.append(file_info)
+
+            # Stop if we have enough files or no more pages
+            if (max_files and len(files_to_process) >= max_files) or not file_list.get(
+                "nextPageToken"
+            ):
+                break
+
+            page_token = file_list.get("nextPageToken")
+
+        # Get user information
+        user = self.session_manager.get_user(user_id) if self.session_manager else None
+        owner_name = user.name if user else None
+        owner_email = user.email if user else None
+
+        processor = LangflowConnectorFileProcessor(
+            self,
+            connection_id,
+            files_to_process,
+            user_id,
+            jwt_token=jwt_token,
+            owner_name=owner_name,
+            owner_email=owner_email,
+        )
+
+        # Use file IDs as items
+        file_ids = [file_info["id"] for file_info in files_to_process]
+
+        # Create custom task using TaskService
+        task_id = await self.task_service.create_custom_task(
+            user_id, file_ids, processor
+        )
+
+        return task_id
+
+    async def sync_specific_files(
+        self,
+        connection_id: str,
+        user_id: str,
+        file_ids: List[str],
+        jwt_token: str = None,
+    ) -> str:
+        """Sync specific files by their IDs using Langflow processing"""
+        if not self.task_service:
+            raise ValueError(
+                "TaskService not available - connector sync requires task service dependency"
+            )
+
+        connector = await self.get_connector(connection_id)
+        if not connector:
+            raise ValueError(
+                f"Connection '{connection_id}' not found or not authenticated"
+            )
+
+        if not connector.is_authenticated:
+            raise ValueError(f"Connection '{connection_id}' not authenticated")
+
+        if not file_ids:
+            raise ValueError("No file IDs provided")
+
+        # Get user information
+        user = self.session_manager.get_user(user_id) if self.session_manager else None
+        owner_name = user.name if user else None
+        owner_email = user.email if user else None
+
+        processor = LangflowConnectorFileProcessor(
+            self,
+            connection_id,
+            file_ids,
+            user_id,
+            jwt_token=jwt_token,
+            owner_name=owner_name,
+            owner_email=owner_email,
+        )
+
+        # Create custom task using TaskService
+        task_id = await self.task_service.create_custom_task(
+            user_id, file_ids, processor
+        )
+
+        return task_id
+
+    async def _get_connector(self, connection_id: str) -> Optional[BaseConnector]:
+        """Get a connector by connection ID (alias for get_connector)"""
+        return await self.get_connector(connection_id)

src/main.py

@@ -1,6 +1,7 @@
 
 # Configure structured logging early
 from services.flows_service import FlowsService
+from connectors.langflow_connector_service import LangflowConnectorService
 from utils.logging_config import configure_from_env, get_logger
 
 configure_from_env()
@@ -12,34 +13,58 @@ import multiprocessing
 import os
 import subprocess
 from functools import partial
+
 from starlette.applications import Starlette
 from starlette.routing import Route
 
 # Set multiprocessing start method to 'spawn' for CUDA compatibility
 multiprocessing.set_start_method("spawn", force=True)
 
+# Create process pool FIRST, before any torch/CUDA imports
 from utils.process_pool import process_pool
 
 import torch
 
+# API endpoints
+from api import (
+    auth,
+    chat,
+    connectors,
+    knowledge_filter,
+    langflow_files,
+    oidc,
+    search,
+    settings,
+    tasks,
+    upload,
+)
+from auth_middleware import optional_auth, require_auth
+
 # Configuration and setup
-from config.settings import clients, INDEX_NAME, INDEX_BODY, SESSION_SECRET
-from config.settings import is_no_auth_mode
-from utils.gpu_detection import detect_gpu_devices
+from config.settings import (
+    INDEX_BODY,
+    INDEX_NAME,
+    SESSION_SECRET,
+    clients,
+    is_no_auth_mode,
+)
+
+# Existing services
+from services.auth_service import AuthService
+from services.chat_service import ChatService
 
 # Services
 from services.document_service import DocumentService
+from services.knowledge_filter_service import KnowledgeFilterService
+
+# Configuration and setup
+# Services
+from services.langflow_file_service import LangflowFileService
+from services.monitor_service import MonitorService
 from services.search_service import SearchService
 from services.task_service import TaskService
-from services.auth_service import AuthService
-from services.chat_service import ChatService
-from services.knowledge_filter_service import KnowledgeFilterService
-from services.monitor_service import MonitorService
-
-# Existing services
-from connectors.service import ConnectorService
 from session_manager import SessionManager
-from auth_middleware import require_auth, optional_auth
+from utils.process_pool import process_pool
 
 # API endpoints
 from api import (
@@ -217,7 +242,10 @@ async def ingest_default_documents_when_ready(services):
         logger.info("Ingesting default documents when ready")
         base_dir = os.path.abspath(os.path.join(os.getcwd(), "documents"))
         if not os.path.isdir(base_dir):
-            logger.info("Default documents directory not found; skipping ingestion", base_dir=base_dir)
+            logger.info(
+                "Default documents directory not found; skipping ingestion",
+                base_dir=base_dir,
+            )
             return
 
         # Collect files recursively
@@ -228,7 +256,9 @@ async def ingest_default_documents_when_ready(services):
         ]
 
         if not file_paths:
-            logger.info("No default documents found; nothing to ingest", base_dir=base_dir)
+            logger.info(
+                "No default documents found; nothing to ingest", base_dir=base_dir
+            )
             return
 
         # Build a processor that DOES NOT set 'owner' on documents (owner_user_id=None)
@@ -253,12 +283,14 @@ async def ingest_default_documents_when_ready(services):
     except Exception as e:
         logger.error("Default documents ingestion failed", error=str(e))
 
+
 async def startup_tasks(services):
     """Startup tasks"""
     logger.info("Starting startup tasks")
     await init_index()
     await ingest_default_documents_when_ready(services)
 
+
 async def initialize_services():
     """Initialize all services and their dependencies"""
     # Generate JWT keys if they don't exist
@@ -283,11 +315,7 @@ async def initialize_services():
     document_service.process_pool = process_pool
 
     # Initialize connector service
-    connector_service = ConnectorService(
-        patched_async_client=clients.patched_async_client,
-        process_pool=process_pool,
-        embed_model="text-embedding-3-small",
-        index_name=INDEX_NAME,
+    connector_service = LangflowConnectorService(
         task_service=task_service,
         session_manager=session_manager,
     )
@@ -298,7 +326,6 @@ async def initialize_services():
     # Load persisted connector connections at startup so webhooks and syncs
     # can resolve existing subscriptions immediately after server boot
     # Skip in no-auth mode since connectors require OAuth
-    from config.settings import is_no_auth_mode
 
     if not is_no_auth_mode():
         try:
@@ -315,12 +342,15 @@ async def initialize_services():
     else:
         logger.info("[CONNECTORS] Skipping connection loading in no-auth mode")
 
+    langflow_file_service = LangflowFileService()
+
     return {
         "document_service": document_service,
         "search_service": search_service,
         "task_service": task_service,
         "chat_service": chat_service,
         "flows_service": flows_service,
+        "langflow_file_service": langflow_file_service,
         "auth_service": auth_service,
         "connector_service": connector_service,
         "knowledge_filter_service": knowledge_filter_service,
@@ -347,6 +377,40 @@ async def create_app():
             ),
             methods=["POST"],
         ),
+        # Langflow Files endpoints
+        Route(
+            "/langflow/files/upload",
+            optional_auth(services["session_manager"])(
+                partial(
+                    langflow_files.upload_user_file,
+                    langflow_file_service=services["langflow_file_service"],
+                    session_manager=services["session_manager"],
+                )
+            ),
+            methods=["POST"],
+        ),
+        Route(
+            "/langflow/ingest",
+            require_auth(services["session_manager"])(
+                partial(
+                    langflow_files.run_ingestion,
+                    langflow_file_service=services["langflow_file_service"],
+                    session_manager=services["session_manager"],
+                )
+            ),
+            methods=["POST"],
+        ),
+        Route(
+            "/langflow/files",
+            require_auth(services["session_manager"])(
+                partial(
+                    langflow_files.delete_user_files,
+                    langflow_file_service=services["langflow_file_service"],
+                    session_manager=services["session_manager"],
+                )
+            ),
+            methods=["DELETE"],
+        ),
         Route(
             "/upload_context",
             require_auth(services["session_manager"])(

src/models/processors.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict
+from typing import Any
 from .tasks import UploadTask, FileTask
 from utils.logging_config import get_logger
 
@@ -91,10 +91,9 @@ class ConnectorFileProcessor(TaskProcessor):
     ) -> None:
         """Process a connector file using ConnectorService"""
         from models.tasks import TaskStatus
-        import time
 
         file_id = item  # item is the connector file ID
-        file_info = self.file_info_map.get(file_id)
+        self.file_info_map.get(file_id)
 
         # Get the connector and connection info
         connector = await self.connector_service.get_connector(self.connection_id)
@@ -126,6 +125,79 @@ class ConnectorFileProcessor(TaskProcessor):
         upload_task.successful_files += 1
 
 
+class LangflowConnectorFileProcessor(TaskProcessor):
+    """Processor for connector file uploads using Langflow"""
+
+    def __init__(
+        self,
+        langflow_connector_service,
+        connection_id: str,
+        files_to_process: list,
+        user_id: str = None,
+        jwt_token: str = None,
+        owner_name: str = None,
+        owner_email: str = None,
+    ):
+        self.langflow_connector_service = langflow_connector_service
+        self.connection_id = connection_id
+        self.files_to_process = files_to_process
+        self.user_id = user_id
+        self.jwt_token = jwt_token
+        self.owner_name = owner_name
+        self.owner_email = owner_email
+        # Create lookup map for file info - handle both file objects and file IDs
+        self.file_info_map = {}
+        for f in files_to_process:
+            if isinstance(f, dict):
+                # Full file info objects
+                self.file_info_map[f["id"]] = f
+            else:
+                # Just file IDs - will need to fetch metadata during processing
+                self.file_info_map[f] = None
+
+    async def process_item(
+        self, upload_task: UploadTask, item: str, file_task: FileTask
+    ) -> None:
+        """Process a connector file using LangflowConnectorService"""
+        from models.tasks import TaskStatus
+
+        file_id = item  # item is the connector file ID
+        self.file_info_map.get(file_id)
+
+        # Get the connector and connection info
+        connector = await self.langflow_connector_service.get_connector(
+            self.connection_id
+        )
+        connection = (
+            await self.langflow_connector_service.connection_manager.get_connection(
+                self.connection_id
+            )
+        )
+        if not connector or not connection:
+            raise ValueError(f"Connection '{self.connection_id}' not found")
+
+        # Get file content from connector (the connector will fetch metadata if needed)
+        document = await connector.get_file_content(file_id)
+
+        # Use the user_id passed during initialization
+        if not self.user_id:
+            raise ValueError("user_id not provided to LangflowConnectorFileProcessor")
+
+        # Process using Langflow pipeline
+        result = await self.langflow_connector_service.process_connector_document(
+            document,
+            self.user_id,
+            connection.connector_type,
+            jwt_token=self.jwt_token,
+            owner_name=self.owner_name,
+            owner_email=self.owner_email,
+        )
+
+        file_task.status = TaskStatus.COMPLETED
+        file_task.result = result
+        upload_task.successful_files += 1
+
+
 class S3FileProcessor(TaskProcessor):
     """Processor for files stored in S3 buckets"""
 

src/services/chat_service.py

@@ -1,4 +1,4 @@
-from config.settings import NUDGES_FLOW_ID, clients, LANGFLOW_URL, FLOW_ID
+from config.settings import NUDGES_FLOW_ID, clients, LANGFLOW_URL
 from agent import (
     async_chat,
     async_langflow,
@@ -6,10 +6,15 @@ from agent import (
 )
 from auth_context import set_auth_context
 import json
+
 from utils.logging_config import get_logger
 
 logger = get_logger(__name__)
 
+from agent import async_chat, async_chat_stream, async_langflow
+from auth_context import set_auth_context
+from config.settings import LANGFLOW_CHAT_FLOW_ID, LANGFLOW_URL, clients
+
 
 class ChatService:
     async def chat(
@@ -59,9 +64,9 @@ class ChatService:
         if not prompt:
             raise ValueError("Prompt is required")
 
-        if not LANGFLOW_URL or not FLOW_ID:
+        if not LANGFLOW_URL or not LANGFLOW_CHAT_FLOW_ID:
             raise ValueError(
-                "LANGFLOW_URL and FLOW_ID environment variables are required"
+                "LANGFLOW_URL and LANGFLOW_CHAT_FLOW_ID environment variables are required"
             )
 
         # Prepare extra headers for JWT authentication
@@ -71,9 +76,9 @@ class ChatService:
 
         # Get context variables for filters, limit, and threshold
         from auth_context import (
+            get_score_threshold,
             get_search_filters,
             get_search_limit,
-            get_score_threshold,
         )
 
         filters = get_search_filters()
@@ -135,7 +140,7 @@ class ChatService:
 
             return async_langflow_chat_stream(
                 langflow_client,
-                FLOW_ID,
+                LANGFLOW_CHAT_FLOW_ID,
                 prompt,
                 user_id,
                 extra_headers=extra_headers,
@@ -146,7 +151,7 @@ class ChatService:
 
             response_text, response_id = await async_langflow_chat(
                 langflow_client,
-                FLOW_ID,
+                LANGFLOW_CHAT_FLOW_ID,
                 prompt,
                 user_id,
                 extra_headers=extra_headers,
@@ -237,9 +242,9 @@ class ChatService:
                     "Langflow client not initialized. Ensure LANGFLOW is reachable or set LANGFLOW_KEY."
                 )
             response_text, response_id = await async_langflow(
-                langflow_client,
-                FLOW_ID,
-                document_prompt,
+                langflow_client=langflow_client,
+                flow_id=LANGFLOW_CHAT_FLOW_ID,
+                prompt=document_prompt,
                 extra_headers=extra_headers,
                 previous_response_id=previous_response_id,
             )
@@ -258,17 +263,17 @@ class ChatService:
 
     async def get_chat_history(self, user_id: str):
         """Get chat conversation history for a user"""
-        from agent import get_user_conversations, active_conversations
+        from agent import active_conversations, get_user_conversations
 
         if not user_id:
             return {"error": "User ID is required", "conversations": []}
 
         # Get metadata from persistent storage
         conversations_dict = get_user_conversations(user_id)
-        
+
         # Get in-memory conversations (with function calls)
         in_memory_conversations = active_conversations.get(user_id, {})
-        
+
         logger.debug(
             "Getting chat history for user",
             user_id=user_id,
@@ -278,7 +283,7 @@ class ChatService:
 
         # Convert conversations dict to list format with metadata
         conversations = []
-        
+
         # First, process in-memory conversations (they have function calls)
         for response_id, conversation_state in in_memory_conversations.items():
             # Filter out system messages
@@ -294,13 +299,13 @@ class ChatService:
                     }
                     if msg.get("response_id"):
                         message_data["response_id"] = msg["response_id"]
-                    
+
                     # Include function call data if present
                     if msg.get("chunks"):
                         message_data["chunks"] = msg["chunks"]
                     if msg.get("response_data"):
                         message_data["response_data"] = msg["response_data"]
-                        
+
                     messages.append(message_data)
 
             if messages:  # Only include conversations with actual messages
@@ -334,25 +339,27 @@ class ChatService:
                             "previous_response_id"
                         ),
                         "total_messages": len(messages),
-                        "source": "in_memory"
+                        "source": "in_memory",
                     }
                 )
-        
+
         # Then, add any persistent metadata that doesn't have in-memory data
         for response_id, metadata in conversations_dict.items():
             if response_id not in in_memory_conversations:
                 # This is metadata-only conversation (no function calls)
-                conversations.append({
-                    "response_id": response_id,
-                    "title": metadata.get("title", "New Chat"),
-                    "endpoint": "chat",
-                    "messages": [],  # No messages in metadata-only
-                    "created_at": metadata.get("created_at"),
-                    "last_activity": metadata.get("last_activity"),
-                    "previous_response_id": metadata.get("previous_response_id"),
-                    "total_messages": metadata.get("total_messages", 0),
-                    "source": "metadata_only"
-                })
+                conversations.append(
+                    {
+                        "response_id": response_id,
+                        "title": metadata.get("title", "New Chat"),
+                        "endpoint": "chat",
+                        "messages": [],  # No messages in metadata-only
+                        "created_at": metadata.get("created_at"),
+                        "last_activity": metadata.get("last_activity"),
+                        "previous_response_id": metadata.get("previous_response_id"),
+                        "total_messages": metadata.get("total_messages", 0),
+                        "source": "metadata_only",
+                    }
+                )
 
         # Sort by last activity (most recent first)
         conversations.sort(key=lambda c: c.get("last_activity", ""), reverse=True)
@@ -368,7 +375,7 @@ class ChatService:
         """Get langflow conversation history for a user - now fetches from both OpenRAG memory and Langflow database"""
         from agent import get_user_conversations
         from services.langflow_history_service import langflow_history_service
-        
+
         if not user_id:
             return {"error": "User ID is required", "conversations": []}
 
@@ -378,27 +385,27 @@ class ChatService:
             # 1. Get local conversation metadata (no actual messages stored here)
             conversations_dict = get_user_conversations(user_id)
             local_metadata = {}
-            
+
             for response_id, conversation_metadata in conversations_dict.items():
                 # Store metadata for later use with Langflow data
                 local_metadata[response_id] = conversation_metadata
-            
+
             # 2. Get actual conversations from Langflow database (source of truth for messages)
             print(f"[DEBUG] Attempting to fetch Langflow history for user: {user_id}")
             langflow_history = (
                 await langflow_history_service.get_user_conversation_history(
-                    user_id, flow_id=FLOW_ID
+                    user_id, flow_id=LANGFLOW_CHAT_FLOW_ID
                 )
             )
 
             if langflow_history.get("conversations"):
                 for conversation in langflow_history["conversations"]:
                     session_id = conversation["session_id"]
-                    
+
                     # Only process sessions that belong to this user (exist in local metadata)
                     if session_id not in local_metadata:
                         continue
-                    
+
                     # Use Langflow messages (with function calls) as source of truth
                     messages = []
                     for msg in conversation.get("messages", []):
@@ -407,62 +414,73 @@ class ChatService:
                             "content": msg["content"],
                             "timestamp": msg.get("timestamp"),
                             "langflow_message_id": msg.get("langflow_message_id"),
-                            "source": "langflow"
+                            "source": "langflow",
                         }
-                        
+
                         # Include function call data if present
                         if msg.get("chunks"):
                             message_data["chunks"] = msg["chunks"]
                         if msg.get("response_data"):
                             message_data["response_data"] = msg["response_data"]
-                            
+
                         messages.append(message_data)
-                    
+
                     if messages:
                         # Use local metadata if available, otherwise generate from Langflow data
                         metadata = local_metadata.get(session_id, {})
-                        
+
                         if not metadata.get("title"):
-                            first_user_msg = next((msg for msg in messages if msg["role"] == "user"), None)
+                            first_user_msg = next(
+                                (msg for msg in messages if msg["role"] == "user"), None
+                            )
                             title = (
                                 first_user_msg["content"][:50] + "..."
-                                if first_user_msg and len(first_user_msg["content"]) > 50
+                                if first_user_msg
+                                and len(first_user_msg["content"]) > 50
                                 else first_user_msg["content"]
                                 if first_user_msg
                                 else "Langflow chat"
                             )
                         else:
                             title = metadata["title"]
-                        
-                        all_conversations.append({
-                            "response_id": session_id,
-                            "title": title,
-                            "endpoint": "langflow",
-                            "messages": messages,  # Function calls preserved from Langflow
-                            "created_at": metadata.get("created_at") or conversation.get("created_at"),
-                            "last_activity": metadata.get("last_activity") or conversation.get("last_activity"),
-                            "total_messages": len(messages),
-                            "source": "langflow_enhanced",
-                            "langflow_session_id": session_id,
-                            "langflow_flow_id": conversation.get("flow_id")
-                        })
-            
+
+                        all_conversations.append(
+                            {
+                                "response_id": session_id,
+                                "title": title,
+                                "endpoint": "langflow",
+                                "messages": messages,  # Function calls preserved from Langflow
+                                "created_at": metadata.get("created_at")
+                                or conversation.get("created_at"),
+                                "last_activity": metadata.get("last_activity")
+                                or conversation.get("last_activity"),
+                                "total_messages": len(messages),
+                                "source": "langflow_enhanced",
+                                "langflow_session_id": session_id,
+                                "langflow_flow_id": conversation.get("flow_id"),
+                            }
+                        )
+
             # 3. Add any local metadata that doesn't have Langflow data yet (recent conversations)
             for response_id, metadata in local_metadata.items():
                 if not any(c["response_id"] == response_id for c in all_conversations):
-                    all_conversations.append({
-                        "response_id": response_id,
-                        "title": metadata.get("title", "New Chat"),
-                        "endpoint": "langflow", 
-                        "messages": [],  # Will be filled when Langflow sync catches up
-                        "created_at": metadata.get("created_at"),
-                        "last_activity": metadata.get("last_activity"),
-                        "total_messages": metadata.get("total_messages", 0),
-                        "source": "metadata_only"
-                    })
-                
+                    all_conversations.append(
+                        {
+                            "response_id": response_id,
+                            "title": metadata.get("title", "New Chat"),
+                            "endpoint": "langflow",
+                            "messages": [],  # Will be filled when Langflow sync catches up
+                            "created_at": metadata.get("created_at"),
+                            "last_activity": metadata.get("last_activity"),
+                            "total_messages": metadata.get("total_messages", 0),
+                            "source": "metadata_only",
+                        }
+                    )
+
             if langflow_history.get("conversations"):
-                print(f"[DEBUG] Added {len(langflow_history['conversations'])} historical conversations from Langflow")
+                print(
+                    f"[DEBUG] Added {len(langflow_history['conversations'])} historical conversations from Langflow"
+                )
             elif langflow_history.get("error"):
                 print(
                     f"[DEBUG] Could not fetch Langflow history for user {user_id}: {langflow_history['error']}"
@@ -473,12 +491,14 @@ class ChatService:
         except Exception as e:
             print(f"[ERROR] Failed to fetch Langflow history: {e}")
             # Continue with just in-memory conversations
-        
+
         # Sort by last activity (most recent first)
         all_conversations.sort(key=lambda c: c.get("last_activity", ""), reverse=True)
-        
-        print(f"[DEBUG] Returning {len(all_conversations)} conversations ({len(local_metadata)} from local metadata)")
-        
+
+        print(
+            f"[DEBUG] Returning {len(all_conversations)} conversations ({len(local_metadata)} from local metadata)"
+        )
+
         return {
             "user_id": user_id,
             "endpoint": "langflow",

src/services/langflow_file_service.py

@@ -0,0 +1,157 @@
+from typing import Any, Dict, List, Optional
+
+from config.settings import LANGFLOW_INGEST_FLOW_ID, clients
+from utils.logging_config import get_logger
+
+logger = get_logger(__name__)
+
+
+class LangflowFileService:
+    def __init__(self):
+        self.flow_id_ingest = LANGFLOW_INGEST_FLOW_ID
+
+    async def upload_user_file(
+        self, file_tuple, jwt_token: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """Upload a file using Langflow Files API v2: POST /api/v2/files.
+        Returns JSON with keys: id, name, path, size, provider.
+        """
+        logger.debug("[LF] Upload (v2) -> /api/v2/files")
+        resp = await clients.langflow_request(
+            "POST",
+            "/api/v2/files",
+            files={"file": file_tuple},
+            headers={"Content-Type": None},
+        )
+        logger.debug(
+            "[LF] Upload response",
+            status_code=resp.status_code,
+            reason=resp.reason_phrase,
+        )
+        if resp.status_code >= 400:
+            logger.error(
+                "[LF] Upload failed",
+                status_code=resp.status_code,
+                reason=resp.reason_phrase,
+                body=resp.text,
+            )
+        resp.raise_for_status()
+        return resp.json()
+
+    async def delete_user_file(self, file_id: str) -> None:
+        """Delete a file by id using v2: DELETE /api/v2/files/{id}."""
+        # NOTE: use v2 root, not /api/v1
+        logger.debug("[LF] Delete (v2) -> /api/v2/files/{id}", file_id=file_id)
+        resp = await clients.langflow_request("DELETE", f"/api/v2/files/{file_id}")
+        logger.debug(
+            "[LF] Delete response",
+            status_code=resp.status_code,
+            reason=resp.reason_phrase,
+        )
+        if resp.status_code >= 400:
+            logger.error(
+                "[LF] Delete failed",
+                status_code=resp.status_code,
+                reason=resp.reason_phrase,
+                body=resp.text[:500],
+            )
+        resp.raise_for_status()
+
+    async def run_ingestion_flow(
+        self,
+        file_paths: List[str],
+        jwt_token: str,
+        session_id: Optional[str] = None,
+        tweaks: Optional[Dict[str, Any]] = None,
+        owner: Optional[str] = None,
+        owner_name: Optional[str] = None,
+        owner_email: Optional[str] = None,
+        connector_type: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        Trigger the ingestion flow with provided file paths.
+        The flow must expose a File component path in input schema or accept files parameter.
+        """
+        if not self.flow_id_ingest:
+            logger.error("[LF] LANGFLOW_INGEST_FLOW_ID is not configured")
+            raise ValueError("LANGFLOW_INGEST_FLOW_ID is not configured")
+
+        payload: Dict[str, Any] = {
+            "input_value": "Ingest files",
+            "input_type": "chat",
+            "output_type": "text",  # Changed from "json" to "text"
+        }
+        if not tweaks:
+            tweaks = {}
+
+        # Pass files via tweaks to File component (File-PSU37 from the flow)
+        if file_paths:
+            tweaks["File-PSU37"] = {"path": file_paths}
+
+        # Pass JWT token via tweaks using the x-langflow-global-var- pattern
+        if jwt_token:
+            # Using the global variable pattern that Langflow expects for OpenSearch components
+            tweaks["OpenSearchHybrid-Ve6bS"] = {"jwt_token": jwt_token}
+            logger.debug("[LF] Added JWT token to tweaks for OpenSearch components")
+        else:
+            logger.warning("[LF] No JWT token provided")
+
+        # Pass metadata via tweaks to OpenSearch component
+        metadata_tweaks = []
+        if owner:
+            metadata_tweaks.append({"key": "owner", "value": owner})
+        if owner_name:
+            metadata_tweaks.append({"key": "owner_name", "value": owner_name})
+        if owner_email:
+            metadata_tweaks.append({"key": "owner_email", "value": owner_email})
+        if connector_type:
+            metadata_tweaks.append({"key": "connector_type", "value": connector_type})
+
+        if metadata_tweaks:
+            # Initialize the OpenSearch component tweaks if not already present
+            if "OpenSearchHybrid-Ve6bS" not in tweaks:
+                tweaks["OpenSearchHybrid-Ve6bS"] = {}
+            tweaks["OpenSearchHybrid-Ve6bS"]["docs_metadata"] = metadata_tweaks
+            logger.debug(
+                "[LF] Added metadata to tweaks", metadata_count=len(metadata_tweaks)
+            )
+        if tweaks:
+            payload["tweaks"] = tweaks
+        if session_id:
+            payload["session_id"] = session_id
+
+        logger.debug(
+            "[LF] Run ingestion -> /run/%s | files=%s session_id=%s tweaks_keys=%s jwt_present=%s",
+            self.flow_id_ingest,
+            len(file_paths) if file_paths else 0,
+            session_id,
+            list(tweaks.keys()) if isinstance(tweaks, dict) else None,
+            bool(jwt_token),
+        )
+
+        # Avoid logging full payload to prevent leaking sensitive data (e.g., JWT)
+
+        resp = await clients.langflow_request(
+            "POST", f"/api/v1/run/{self.flow_id_ingest}", json=payload
+        )
+        logger.debug(
+            "[LF] Run response", status_code=resp.status_code, reason=resp.reason_phrase
+        )
+        if resp.status_code >= 400:
+            logger.error(
+                "[LF] Run failed",
+                status_code=resp.status_code,
+                reason=resp.reason_phrase,
+                body=resp.text[:1000],
+            )
+        resp.raise_for_status()
+        try:
+            resp_json = resp.json()
+        except Exception as e:
+            logger.error(
+                "[LF] Failed to parse run response as JSON",
+                body=resp.text[:1000],
+                error=str(e),
+            )
+            raise
+        return resp_json

src/services/search_service.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 from agentd.tool_decorator import tool
 from config.settings import clients, INDEX_NAME, EMBED_MODEL
 from auth_context import get_auth_context
@@ -166,11 +166,11 @@ class SearchService:
         for hit in results["hits"]["hits"]:
             chunks.append(
                 {
-                    "filename": hit["_source"]["filename"],
-                    "mimetype": hit["_source"]["mimetype"],
-                    "page": hit["_source"]["page"],
-                    "text": hit["_source"]["text"],
-                    "score": hit["_score"],
+                    "filename": hit["_source"].get("filename"),
+                    "mimetype": hit["_source"].get("mimetype"),
+                    "page": hit["_source"].get("page"),
+                    "text": hit["_source"].get("text"),
+                    "score": hit.get("_score"),
                     "source_url": hit["_source"].get("source_url"),
                     "owner": hit["_source"].get("owner"),
                     "owner_name": hit["_source"].get("owner_name"),

src/services/task_service.py

@@ -1,12 +1,11 @@
 import asyncio
-import uuid
-import time
 import random
-from typing import Dict, Optional
+import time
+import uuid
 
-from models.tasks import TaskStatus, UploadTask, FileTask
-from utils.gpu_detection import get_worker_count
+from models.tasks import FileTask, TaskStatus, UploadTask
 from session_manager import AnonymousUser
+from utils.gpu_detection import get_worker_count
 from utils.logging_config import get_logger
 
 logger = get_logger(__name__)
@@ -16,9 +15,7 @@ class TaskService:
     def __init__(self, document_service=None, process_pool=None):
         self.document_service = document_service
         self.process_pool = process_pool
-        self.task_store: Dict[
-            str, Dict[str, UploadTask]
-        ] = {}  # user_id -> {task_id -> UploadTask}
+        self.task_store: dict[str, dict[str, UploadTask]] = {}  # user_id -> {task_id -> UploadTask}
         self.background_tasks = set()
 
         if self.process_pool is None:
@@ -69,9 +66,7 @@ class TaskService:
         self.task_store[user_id][task_id] = upload_task
 
         # Start background processing
-        background_task = asyncio.create_task(
-            self.background_custom_processor(user_id, task_id, items)
-        )
+        background_task = asyncio.create_task(self.background_custom_processor(user_id, task_id, items))
         self.background_tasks.add(background_task)
         background_task.add_done_callback(self.background_tasks.discard)
 
@@ -89,27 +84,18 @@ class TaskService:
 
             # Process files with limited concurrency to avoid overwhelming the system
             max_workers = get_worker_count()
-            semaphore = asyncio.Semaphore(
-                max_workers * 2
-            )  # Allow 2x process pool size for async I/O
+            semaphore = asyncio.Semaphore(max_workers * 2)  # Allow 2x process pool size for async I/O
 
             async def process_with_semaphore(file_path: str):
                 async with semaphore:
-                    await self.document_service.process_single_file_task(
-                        upload_task, file_path
-                    )
+                    await self.document_service.process_single_file_task(upload_task, file_path)
 
-            tasks = [
-                process_with_semaphore(file_path)
-                for file_path in upload_task.file_tasks.keys()
-            ]
+            tasks = [process_with_semaphore(file_path) for file_path in upload_task.file_tasks.keys()]
 
             await asyncio.gather(*tasks, return_exceptions=True)
 
         except Exception as e:
-            logger.error(
-                "Background upload processor failed", task_id=task_id, error=str(e)
-            )
+            logger.error("Background upload processor failed", task_id=task_id, error=str(e))
             import traceback
 
             traceback.print_exc()
@@ -117,9 +103,7 @@ class TaskService:
                 self.task_store[user_id][task_id].status = TaskStatus.FAILED
                 self.task_store[user_id][task_id].updated_at = time.time()
 
-    async def background_custom_processor(
-        self, user_id: str, task_id: str, items: list
-    ) -> None:
+    async def background_custom_processor(self, user_id: str, task_id: str, items: list) -> None:
         """Background task to process items using custom processor"""
         try:
             upload_task = self.task_store[user_id][task_id]
@@ -141,9 +125,7 @@ class TaskService:
                     try:
                         await processor.process_item(upload_task, item, file_task)
                     except Exception as e:
-                        logger.error(
-                            "Failed to process item", item=str(item), error=str(e)
-                        )
+                        logger.error("Failed to process item", item=str(item), error=str(e))
                         import traceback
 
                         traceback.print_exc()
@@ -170,9 +152,7 @@ class TaskService:
                 pass
             raise  # Re-raise to properly handle cancellation
         except Exception as e:
-            logger.error(
-                "Background custom processor failed", task_id=task_id, error=str(e)
-            )
+            logger.error("Background custom processor failed", task_id=task_id, error=str(e))
             import traceback
 
             traceback.print_exc()
@@ -180,7 +160,7 @@ class TaskService:
                 self.task_store[user_id][task_id].status = TaskStatus.FAILED
                 self.task_store[user_id][task_id].updated_at = time.time()
 
-    def get_task_status(self, user_id: str, task_id: str) -> Optional[dict]:
+    def get_task_status(self, user_id: str, task_id: str) -> dict | None:
         """Get the status of a specific upload task
 
         Includes fallback to shared tasks stored under the "anonymous" user key
@@ -194,10 +174,7 @@ class TaskService:
 
         upload_task = None
         for candidate_user_id in candidate_user_ids:
-            if (
-                candidate_user_id in self.task_store
-                and task_id in self.task_store[candidate_user_id]
-            ):
+            if candidate_user_id in self.task_store and task_id in self.task_store[candidate_user_id]:
                 upload_task = self.task_store[candidate_user_id][task_id]
                 break
 
@@ -271,10 +248,7 @@ class TaskService:
 
         store_user_id = None
         for candidate_user_id in candidate_user_ids:
-            if (
-                candidate_user_id in self.task_store
-                and task_id in self.task_store[candidate_user_id]
-            ):
+            if candidate_user_id in self.task_store and task_id in self.task_store[candidate_user_id]:
                 store_user_id = candidate_user_id
                 break
 
@@ -288,10 +262,7 @@ class TaskService:
             return False
 
         # Cancel the background task to stop scheduling new work
-        if (
-            hasattr(upload_task, "background_task")
-            and not upload_task.background_task.done()
-        ):
+        if hasattr(upload_task, "background_task") and not upload_task.background_task.done():
             upload_task.background_task.cancel()
 
         # Mark task as failed (cancelled)

src/tui/managers/env_manager.py

@@ -1,23 +1,23 @@
 """Environment configuration manager for OpenRAG TUI."""
 
-import os
 import secrets
 import string
+from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
-from typing import Dict, Optional, List
-from dataclasses import dataclass, field
+from typing import Dict, List, Optional
+
 from utils.logging_config import get_logger
 
 logger = get_logger(__name__)
 
 from ..utils.validation import (
-    validate_openai_api_key,
+    sanitize_env_value,
+    validate_documents_paths,
     validate_google_oauth_client_id,
     validate_non_empty,
+    validate_openai_api_key,
     validate_url,
-    validate_documents_paths,
-    sanitize_env_value,
 )
 
 
@@ -31,7 +31,8 @@ class EnvConfig:
     langflow_secret_key: str = ""
     langflow_superuser: str = "admin"
     langflow_superuser_password: str = ""
-    flow_id: str = "1098eea1-6649-4e1d-aed1-b77249fb8dd0"
+    langflow_chat_flow_id: str = "1098eea1-6649-4e1d-aed1-b77249fb8dd0"
+    langflow_ingest_flow_id: str = "5488df7c-b93f-4f87-a446-b67028bc0813"
 
     # OAuth settings
     google_oauth_client_id: str = ""
@@ -98,7 +99,8 @@ class EnvManager:
                             "LANGFLOW_SECRET_KEY": "langflow_secret_key",
                             "LANGFLOW_SUPERUSER": "langflow_superuser",
                             "LANGFLOW_SUPERUSER_PASSWORD": "langflow_superuser_password",
-                            "FLOW_ID": "flow_id",
+                            "LANGFLOW_CHAT_FLOW_ID": "langflow_chat_flow_id",
+                            "LANGFLOW_INGEST_FLOW_ID": "langflow_ingest_flow_id",
                             "NUDGES_FLOW_ID": "nudges_flow_id",
                             "GOOGLE_OAUTH_CLIENT_ID": "google_oauth_client_id",
                             "GOOGLE_OAUTH_CLIENT_SECRET": "google_oauth_client_secret",
@@ -235,7 +237,10 @@ class EnvManager:
                 f.write(
                     f"LANGFLOW_SUPERUSER_PASSWORD={self.config.langflow_superuser_password}\n"
                 )
-                f.write(f"FLOW_ID={self.config.flow_id}\n")
+                f.write(f"LANGFLOW_CHAT_FLOW_ID={self.config.langflow_chat_flow_id}\n")
+                f.write(
+                    f"LANGFLOW_INGEST_FLOW_ID={self.config.langflow_ingest_flow_id}\n"
+                )
                 f.write(f"NUDGES_FLOW_ID={self.config.nudges_flow_id}\n")
                 f.write(f"OPENSEARCH_PASSWORD={self.config.opensearch_password}\n")
                 f.write(f"OPENAI_API_KEY={self.config.openai_api_key}\n")

src/tui/screens/diagnostics.py

@@ -63,6 +63,7 @@ class DiagnosticsScreen(Screen):
                 yield Button("Refresh", variant="primary", id="refresh-btn")
                 yield Button("Check Podman", variant="default", id="check-podman-btn")
                 yield Button("Check Docker", variant="default", id="check-docker-btn")
+                yield Button("Check OpenSearch Security", variant="default", id="check-opensearch-security-btn")
                 yield Button("Copy to Clipboard", variant="default", id="copy-btn")
                 yield Button("Save to File", variant="default", id="save-btn")
                 yield Button("Back", variant="default", id="back-btn")
@@ -92,6 +93,8 @@ class DiagnosticsScreen(Screen):
             asyncio.create_task(self.check_podman())
         elif event.button.id == "check-docker-btn":
             asyncio.create_task(self.check_docker())
+        elif event.button.id == "check-opensearch-security-btn":
+            asyncio.create_task(self.check_opensearch_security())
         elif event.button.id == "copy-btn":
             self.copy_to_clipboard()
         elif event.button.id == "save-btn":
@@ -415,5 +418,208 @@ class DiagnosticsScreen(Screen):
 
         log.write("")
 
+    async def check_opensearch_security(self) -> None:
+        """Run OpenSearch security configuration diagnostics."""
+        log = self.query_one("#diagnostics-log", Log)
+        log.write("[bold green]OpenSearch Security Diagnostics[/bold green]")
+
+        # Get OpenSearch password from environment or prompt user that it's needed
+        opensearch_password = os.getenv("OPENSEARCH_PASSWORD")
+        if not opensearch_password:
+            log.write("[red]OPENSEARCH_PASSWORD environment variable not set[/red]")
+            log.write("[yellow]Set OPENSEARCH_PASSWORD to test security configuration[/yellow]")
+            log.write("")
+            return
+
+        # Test basic authentication
+        log.write("Testing basic authentication...")
+        cmd = [
+            "curl", "-s", "-k", "-w", "%{http_code}",
+            "-u", f"admin:{opensearch_password}",
+            "https://localhost:9200"
+        ]
+        process = await asyncio.create_subprocess_exec(
+            *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
+        )
+        stdout, stderr = await process.communicate()
+        
+        if process.returncode == 0:
+            response = stdout.decode().strip()
+            # Extract HTTP status code (last 3 characters)
+            if len(response) >= 3:
+                status_code = response[-3:]
+                response_body = response[:-3]
+                if status_code == "200":
+                    log.write("[green]✓ Basic authentication successful[/green]")
+                    try:
+                        import json
+                        info = json.loads(response_body)
+                        if "version" in info and "distribution" in info["version"]:
+                            log.write(f"  OpenSearch version: {info['version']['number']}")
+                    except:
+                        pass
+                else:
+                    log.write(f"[red]✗ Basic authentication failed with status {status_code}[/red]")
+            else:
+                log.write("[red]✗ Unexpected response from OpenSearch[/red]")
+        else:
+            log.write(f"[red]✗ Failed to connect to OpenSearch: {stderr.decode().strip()}[/red]")
+
+        # Test security plugin account info
+        log.write("Testing security plugin account info...")
+        cmd = [
+            "curl", "-s", "-k", "-w", "%{http_code}",
+            "-u", f"admin:{opensearch_password}",
+            "https://localhost:9200/_plugins/_security/api/account"
+        ]
+        process = await asyncio.create_subprocess_exec(
+            *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
+        )
+        stdout, stderr = await process.communicate()
+        
+        if process.returncode == 0:
+            response = stdout.decode().strip()
+            if len(response) >= 3:
+                status_code = response[-3:]
+                response_body = response[:-3]
+                if status_code == "200":
+                    log.write("[green]✓ Security plugin accessible[/green]")
+                    try:
+                        import json
+                        user_info = json.loads(response_body)
+                        if "user_name" in user_info:
+                            log.write(f"  Current user: {user_info['user_name']}")
+                        if "roles" in user_info:
+                            log.write(f"  Roles: {', '.join(user_info['roles'])}")
+                        if "tenants" in user_info:
+                            tenants = list(user_info['tenants'].keys())
+                            log.write(f"  Tenants: {', '.join(tenants)}")
+                    except:
+                        log.write("  Account info retrieved but couldn't parse JSON")
+                else:
+                    log.write(f"[red]✗ Security plugin returned status {status_code}[/red]")
+        else:
+            log.write(f"[red]✗ Failed to access security plugin: {stderr.decode().strip()}[/red]")
+
+        # Test internal users
+        log.write("Testing internal users configuration...")
+        cmd = [
+            "curl", "-s", "-k", "-w", "%{http_code}",
+            "-u", f"admin:{opensearch_password}",
+            "https://localhost:9200/_plugins/_security/api/internalusers"
+        ]
+        process = await asyncio.create_subprocess_exec(
+            *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
+        )
+        stdout, stderr = await process.communicate()
+        
+        if process.returncode == 0:
+            response = stdout.decode().strip()
+            if len(response) >= 3:
+                status_code = response[-3:]
+                response_body = response[:-3]
+                if status_code == "200":
+                    try:
+                        import json
+                        users = json.loads(response_body)
+                        if "admin" in users:
+                            log.write("[green]✓ Admin user configured[/green]")
+                            admin_user = users["admin"]
+                            if admin_user.get("reserved"):
+                                log.write("  Admin user is reserved (protected)")
+                        log.write(f"  Total internal users: {len(users)}")
+                    except:
+                        log.write("[green]✓ Internal users endpoint accessible[/green]")
+                else:
+                    log.write(f"[red]✗ Internal users returned status {status_code}[/red]")
+        else:
+            log.write(f"[red]✗ Failed to access internal users: {stderr.decode().strip()}[/red]")
+
+        # Test authentication domains configuration
+        log.write("Testing authentication configuration...")
+        cmd = [
+            "curl", "-s", "-k", "-w", "%{http_code}",
+            "-u", f"admin:{opensearch_password}",
+            "https://localhost:9200/_plugins/_security/api/securityconfig"
+        ]
+        process = await asyncio.create_subprocess_exec(
+            *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
+        )
+        stdout, stderr = await process.communicate()
+        
+        if process.returncode == 0:
+            response = stdout.decode().strip()
+            if len(response) >= 3:
+                status_code = response[-3:]
+                response_body = response[:-3]
+                if status_code == "200":
+                    try:
+                        import json
+                        config = json.loads(response_body)
+                        if "config" in config and "dynamic" in config["config"] and "authc" in config["config"]["dynamic"]:
+                            authc = config["config"]["dynamic"]["authc"]
+                            if "openid_auth_domain" in authc:
+                                log.write("[green]✓ OpenID Connect authentication domain configured[/green]")
+                                oidc_config = authc["openid_auth_domain"].get("http_authenticator", {}).get("config", {})
+                                if "openid_connect_url" in oidc_config:
+                                    log.write(f"  OIDC URL: {oidc_config['openid_connect_url']}")
+                                if "subject_key" in oidc_config:
+                                    log.write(f"  Subject key: {oidc_config['subject_key']}")
+                            if "basic_internal_auth_domain" in authc:
+                                log.write("[green]✓ Basic internal authentication domain configured[/green]")
+                            
+                            # Check for multi-tenancy
+                            if "kibana" in config["config"]["dynamic"]:
+                                kibana_config = config["config"]["dynamic"]["kibana"]
+                                if kibana_config.get("multitenancy_enabled"):
+                                    log.write("[green]✓ Multi-tenancy enabled[/green]")
+                        else:
+                            log.write("[yellow]⚠ Authentication configuration not found in expected format[/yellow]")
+                    except Exception as e:
+                        log.write("[green]✓ Security config endpoint accessible[/green]")
+                        log.write(f"  (Could not parse JSON: {str(e)[:50]}...)")
+                else:
+                    log.write(f"[red]✗ Security config returned status {status_code}[/red]")
+        else:
+            log.write(f"[red]✗ Failed to access security config: {stderr.decode().strip()}[/red]")
+
+        # Test indices with potential security filtering
+        log.write("Testing index access...")
+        cmd = [
+            "curl", "-s", "-k", "-w", "%{http_code}",
+            "-u", f"admin:{opensearch_password}",
+            "https://localhost:9200/_cat/indices?v"
+        ]
+        process = await asyncio.create_subprocess_exec(
+            *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
+        )
+        stdout, stderr = await process.communicate()
+        
+        if process.returncode == 0:
+            response = stdout.decode().strip()
+            if len(response) >= 3:
+                status_code = response[-3:]
+                response_body = response[:-3]
+                if status_code == "200":
+                    log.write("[green]✓ Index listing accessible[/green]")
+                    lines = response_body.strip().split('\n')
+                    if len(lines) > 1:  # Skip header
+                        indices_found = []
+                        for line in lines[1:]:
+                            if 'documents' in line:
+                                indices_found.append('documents')
+                            elif 'knowledge_filters' in line:
+                                indices_found.append('knowledge_filters')
+                            elif '.opendistro_security' in line:
+                                indices_found.append('.opendistro_security')
+                        if indices_found:
+                            log.write(f"  Key indices found: {', '.join(indices_found)}")
+                else:
+                    log.write(f"[red]✗ Index listing returned status {status_code}[/red]")
+        else:
+            log.write(f"[red]✗ Failed to list indices: {stderr.decode().strip()}[/red]")
+
+        log.write("")
+
 
 # Made with Bob

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.13"
 resolution-markers = [
     "sys_platform == 'darwin'",
@@ -1405,7 +1405,7 @@ wheels = [
 
 [[package]]
 name = "openrag"
-version = "0.1.0"
+version = "0.1.1"
 source = { editable = "." }
 dependencies = [
     { name = "agentd" },

warm_up_docling.py

@@ -1,6 +1,7 @@
-from docling.document_converter import DocumentConverter
 import logging
 
+from docling.document_converter import DocumentConverter
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)