From 346b938d98e25af9021ee99f56fb5829a67c869a Mon Sep 17 00:00:00 2001 From: phact Date: Wed, 13 Aug 2025 16:31:35 -0400 Subject: [PATCH] refactor to openrag and knowledge filters --- README.md | 4 +- docker-compose.yml | 22 +- flows/gendb_agent.json | 2247 ----------------- frontend/components/navigation-layout.tsx | 2 +- frontend/components/navigation.tsx | 6 +- frontend/src/app/api/[...path]/route.ts | 2 +- frontend/src/app/auth/callback/page.tsx | 2 +- frontend/src/app/chat/page.tsx | 34 +- .../{contexts => knowledge-filters}/page.tsx | 98 +- frontend/src/app/layout.tsx | 4 +- frontend/src/app/login/page.tsx | 2 +- frontend/src/app/page.tsx | 134 +- frontend/src/components/layout-wrapper.tsx | 2 +- pyproject.toml | 2 +- securityconfig/config.yml | 2 +- securityconfig/roles.yml | 4 +- securityconfig/roles_mapping.yml | 4 +- src/api/contexts.py | 114 - src/api/knowledge_filter.py | 114 + src/api/oidc.py | 2 +- src/main.py | 54 +- src/services/chat_service.py | 4 +- ...service.py => knowledge_filter_service.py} | 76 +- src/session_manager.py | 6 +- uv.lock | 78 +- 25 files changed, 390 insertions(+), 2629 deletions(-) delete mode 100644 flows/gendb_agent.json rename frontend/src/app/{contexts => knowledge-filters}/page.tsx (80%) delete mode 100644 src/api/contexts.py create mode 100644 src/api/knowledge_filter.py rename src/services/{contexts_service.py => knowledge_filter_service.py} (59%) diff --git a/README.md b/README.md index f1ed4de7..b19ce4ae 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -### gendb +### OpenRAG -[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/phact/gendb) +[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/phact/openrag) docker compose build diff --git a/docker-compose.yml b/docker-compose.yml index 7c6d740b..d3d942ad 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -5,7 +5,7 @@ services: dockerfile: Dockerfile container_name: os depends_on: - - gendb-backend + - openrag-backend environment: - discovery.type=single-node - OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_PASSWORD} @@ -37,12 +37,12 @@ services: ports: - "5601:5601" - gendb-backend: - image: phact/gendb-backend:latest + openrag-backend: + image: phact/openrag-backend:latest #build: #context: . #dockerfile: Dockerfile.backend - container_name: gendb-backend + container_name: openrag-backend depends_on: - langflow environment: @@ -67,16 +67,16 @@ services: gpus: all platform: linux/amd64 - gendb-frontend: - image: phact/gendb-frontend:latest + openrag-frontend: + image: phact/openrag-frontend:latest #build: #context: . #dockerfile: Dockerfile.frontend - container_name: gendb-frontend + container_name: openrag-frontend depends_on: - - gendb-backend + - openrag-backend environment: - - GENDB_BACKEND_HOST=gendb-backend + - OPENRAG_BACKEND_HOST=openrag-backend ports: - "3000:3000" volumes: @@ -94,6 +94,6 @@ services: - LANGFLOW_LOAD_FLOWS_PATH=/app/flows - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY} - JWT="dummy" - - GENDB-QUERY-FILTER="{}" - - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT + - OPENRAG-QUERY-FILTER="{}" + - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER - LANGFLOW_LOG_LEVEL=DEBUG diff --git a/flows/gendb_agent.json b/flows/gendb_agent.json deleted file mode 100644 index 302054bc..00000000 --- a/flows/gendb_agent.json +++ /dev/null @@ -1,2247 +0,0 @@ -{ - "data": { - "edges": [ - { - "animated": false, - "className": "", - "data": { - "sourceHandle": { - "dataType": "EmbeddingModel", - "id": "EmbeddingModel-eZ6bT", - "name": "embeddings", - "output_types": [ - "Embeddings" - ] - }, - "targetHandle": { - "fieldName": "embedding", - "id": "OpenSearch-iYfjf", - "inputTypes": [ - "Embeddings" - ], - "type": "other" - } - }, - "id": "xy-edge__EmbeddingModel-eZ6bT{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-eZ6bTœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-OpenSearch-iYfjf{œfieldNameœ:œembeddingœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}", - "selected": false, - "source": "EmbeddingModel-eZ6bT", - "sourceHandle": "{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-eZ6bTœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}", - "target": "OpenSearch-iYfjf", - "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}" - }, - { - "animated": false, - "className": "", - "data": { - "sourceHandle": { - "dataType": "ChatInput", - "id": "ChatInput-bqH7H", - "name": "message", - "output_types": [ - "Message" - ] - }, - "targetHandle": { - "fieldName": "input_value", - "id": "Agent-crjWf", - "inputTypes": [ - "Message" - ], - "type": "str" - } - }, - "id": "xy-edge__ChatInput-bqH7H{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bqH7Hœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Agent-crjWf{œfieldNameœ:œinput_valueœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", - "selected": false, - "source": "ChatInput-bqH7H", - "sourceHandle": "{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bqH7Hœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}", - "target": "Agent-crjWf", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}" - }, - { - "animated": false, - "className": "", - "data": { - "sourceHandle": { - "dataType": "TextInput", - "id": "TextInput-aHsQb", - "name": "text", - "output_types": [ - "Message" - ] - }, - "targetHandle": { - "fieldName": "filter_expression", - "id": "OpenSearch-iYfjf", - "inputTypes": [ - "Message" - ], - "type": "str" - } - }, - "id": "xy-edge__TextInput-aHsQb{œdataTypeœ:œTextInputœ,œidœ:œTextInput-aHsQbœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-OpenSearch-iYfjf{œfieldNameœ:œfilter_expressionœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}", - "selected": false, - "source": "TextInput-aHsQb", - "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-aHsQbœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}", - "target": "OpenSearch-iYfjf", - "targetHandle": "{œfieldNameœ:œfilter_expressionœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}" - }, - { - "animated": false, - "className": "", - "data": { - "sourceHandle": { - "dataType": "Agent", - "id": "Agent-crjWf", - "name": "response", - "output_types": [ - "Message" - ] - }, - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-BMVN5", - "inputTypes": [ - "Data", - "DataFrame", - "Message" - ], - "type": "other" - } - }, - "id": "xy-edge__Agent-crjWf{œdataTypeœ:œAgentœ,œidœ:œAgent-crjWfœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-BMVN5{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-BMVN5œ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}", - "selected": false, - "source": "Agent-crjWf", - "sourceHandle": "{œdataTypeœ:œAgentœ,œidœ:œAgent-crjWfœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}", - "target": "ChatOutput-BMVN5", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-BMVN5œ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}" - }, - { - "data": { - "sourceHandle": { - "dataType": "OpenSearchHybrid", - "id": "OpenSearch-iYfjf", - "name": "component_as_tool", - "output_types": [ - "Tool" - ] - }, - "targetHandle": { - "fieldName": "tools", - "id": "Agent-crjWf", - "inputTypes": [ - "Tool" - ], - "type": "other" - } - }, - "id": "xy-edge__OpenSearch-iYfjf{œdataTypeœ:œOpenSearchHybridœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-crjWf{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}", - "source": "OpenSearch-iYfjf", - "sourceHandle": "{œdataTypeœ:œOpenSearchHybridœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}", - "target": "Agent-crjWf", - "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}" - } - ], - "nodes": [ - { - "data": { - "id": "ChatInput-bqH7H", - "node": { - "base_classes": [ - "Message" - ], - "beta": false, - "category": "inputs", - "conditional_paths": [], - "custom_fields": {}, - "description": "Get chat inputs from the Playground.", - "display_name": "Chat Input", - "documentation": "", - "edited": false, - "field_order": [ - "input_value", - "should_store_message", - "sender", - "sender_name", - "session_id", - "files", - "background_color", - "chat_icon", - "text_color" - ], - "frozen": false, - "icon": "MessagesSquare", - "key": "ChatInput", - "legacy": false, - "lf_version": "1.5.0.post1", - "metadata": {}, - "minimized": true, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Chat Message", - "group_outputs": false, - "method": "message_response", - "name": "message", - "selected": "Message", - "tool_mode": true, - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "score": 0.0020353564437605998, - "template": { - "_type": "Component", - "background_color": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Background Color", - "dynamic": false, - "info": "The background color of the icon.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "background_color", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "chat_icon": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Icon", - "dynamic": false, - "info": "The icon of the message.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "chat_icon", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs.inputs import BoolInput\nfrom langflow.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_USER,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n input_types=[],\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_USER,\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_USER,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n temp_file=True,\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n background_color = self.background_color\n text_color = self.text_color\n icon = self.chat_icon\n\n message = await Message.create(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=self.files,\n properties={\n \"background_color\": background_color,\n \"text_color\": text_color,\n \"icon\": icon,\n },\n )\n if self.session_id and isinstance(message, Message) and self.should_store_message:\n stored_message = await self.send_message(\n message,\n )\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n" - }, - "files": { - "_input_type": "FileInput", - "advanced": true, - "display_name": "Files", - "dynamic": false, - "fileTypes": [ - "txt", - "md", - "mdx", - "csv", - "json", - "yaml", - "yml", - "xml", - "html", - "htm", - "pdf", - "docx", - "py", - "sh", - "sql", - "js", - "ts", - "tsx", - "jpg", - "jpeg", - "png", - "bmp", - "image" - ], - "file_path": "", - "info": "Files to be sent with the message.", - "list": true, - "list_add_label": "Add More", - "name": "files", - "placeholder": "", - "required": false, - "show": true, - "temp_file": true, - "title_case": false, - "trace_as_metadata": true, - "type": "file", - "value": "" - }, - "input_value": { - "_input_type": "MultilineInput", - "advanced": false, - "copy_field": false, - "display_name": "Input Text", - "dynamic": false, - "info": "Message to be passed as input.", - "input_types": [], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "multiline": true, - "name": "input_value", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "sender": { - "_input_type": "DropdownInput", - "advanced": true, - "combobox": false, - "dialog_inputs": {}, - "display_name": "Sender Type", - "dynamic": false, - "info": "Type of sender.", - "name": "sender", - "options": [ - "Machine", - "User" - ], - "options_metadata": [], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "User" - }, - "sender_name": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Sender Name", - "dynamic": false, - "info": "Name of the sender.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "sender_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "User" - }, - "session_id": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Session ID", - "dynamic": false, - "info": "The session ID of the chat. If empty, the current session ID parameter will be used.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "session_id", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "should_store_message": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Store Messages", - "dynamic": false, - "info": "Store the message in the history.", - "list": false, - "list_add_label": "Add More", - "name": "should_store_message", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "text_color": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Text Color", - "dynamic": false, - "info": "The text color of the name", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "text_color", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - } - }, - "tool_mode": false - }, - "selected_output": "message", - "showNode": false, - "type": "ChatInput" - }, - "dragging": false, - "id": "ChatInput-bqH7H", - "measured": { - "height": 48, - "width": 192 - }, - "position": { - "x": 1264.0651279011304, - "y": 1042.1154468545742 - }, - "selected": false, - "type": "genericNode" - }, - { - "data": { - "id": "ChatOutput-BMVN5", - "node": { - "base_classes": [ - "Message" - ], - "beta": false, - "category": "outputs", - "conditional_paths": [], - "custom_fields": {}, - "description": "Display a chat message in the Playground.", - "display_name": "Chat Output", - "documentation": "", - "edited": false, - "field_order": [ - "input_value", - "should_store_message", - "sender", - "sender_name", - "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color", - "clean_data" - ], - "frozen": false, - "icon": "MessagesSquare", - "key": "ChatOutput", - "legacy": false, - "lf_version": "1.5.0.post1", - "metadata": {}, - "minimized": true, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Output Message", - "group_outputs": false, - "method": "message_response", - "name": "message", - "selected": "Message", - "tool_mode": true, - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "score": 0.003169567463043492, - "template": { - "_type": "Component", - "background_color": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Background Color", - "dynamic": false, - "info": "The background color of the icon.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "background_color", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "chat_icon": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Icon", - "dynamic": false, - "info": "The icon of the message.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "chat_icon", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "clean_data": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Basic Clean Data", - "dynamic": false, - "info": "Whether to clean the data", - "list": false, - "list_add_label": "Add More", - "name": "clean_data", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.template.field.base import Output\nfrom langflow.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" - }, - "data_template": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Data Template", - "dynamic": false, - "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "data_template", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "{text}" - }, - "input_value": { - "_input_type": "HandleInput", - "advanced": false, - "display_name": "Inputs", - "dynamic": false, - "info": "Message to be passed as output.", - "input_types": [ - "Data", - "DataFrame", - "Message" - ], - "list": false, - "list_add_label": "Add More", - "name": "input_value", - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "other", - "value": "" - }, - "sender": { - "_input_type": "DropdownInput", - "advanced": true, - "combobox": false, - "dialog_inputs": {}, - "display_name": "Sender Type", - "dynamic": false, - "info": "Type of sender.", - "name": "sender", - "options": [ - "Machine", - "User" - ], - "options_metadata": [], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "Machine" - }, - "sender_name": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Sender Name", - "dynamic": false, - "info": "Name of the sender.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "sender_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "AI" - }, - "session_id": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Session ID", - "dynamic": false, - "info": "The session ID of the chat. If empty, the current session ID parameter will be used.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "session_id", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "should_store_message": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Store Messages", - "dynamic": false, - "info": "Store the message in the history.", - "list": false, - "list_add_label": "Add More", - "name": "should_store_message", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "text_color": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "Text Color", - "dynamic": false, - "info": "The text color of the name", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "text_color", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - } - }, - "tool_mode": false - }, - "showNode": false, - "type": "ChatOutput" - }, - "id": "ChatOutput-BMVN5", - "measured": { - "height": 48, - "width": 192 - }, - "position": { - "x": 2145, - "y": 660 - }, - "selected": false, - "type": "genericNode" - }, - { - "data": { - "id": "OpenSearch-iYfjf", - "node": { - "base_classes": [ - "Data", - "DataFrame", - "VectorStore" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.", - "display_name": "OpenSearch (Hybrid)", - "documentation": "", - "edited": true, - "field_order": [ - "opensearch_url", - "index_name", - "ingest_data", - "search_query", - "should_cache_vector_store", - "embedding", - "vector_field", - "number_of_results", - "filter_expression", - "auth_mode", - "username", - "password", - "jwt_token", - "jwt_header", - "bearer_prefix", - "use_ssl", - "verify_certs" - ], - "frozen": false, - "icon": "OpenSearch", - "last_updated": "2025-08-12T20:26:57.561Z", - "legacy": false, - "metadata": {}, - "minimized": false, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Toolset", - "group_outputs": false, - "hidden": null, - "method": "to_toolkit", - "name": "component_as_tool", - "options": null, - "required_inputs": null, - "selected": "Tool", - "tool_mode": true, - "types": [ - "Tool" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "auth_mode": { - "_input_type": "DropdownInput", - "advanced": false, - "combobox": false, - "dialog_inputs": {}, - "display_name": "Auth Mode", - "dynamic": false, - "info": "Choose Basic (username/password) or JWT (Bearer token).", - "load_from_db": false, - "name": "auth_mode", - "options": [ - "basic", - "jwt" - ], - "options_metadata": [], - "placeholder": "", - "real_time_refresh": true, - "required": false, - "show": true, - "title_case": false, - "toggle": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "jwt" - }, - "bearer_prefix": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Prefix 'Bearer '", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "bearer_prefix", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from __future__ import annotations\n\nimport json\nfrom typing import Any, Dict, List\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom langflow.io import (\n BoolInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n DropdownInput,\n)\nfrom langflow.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n display_name: str = \"OpenSearch (Hybrid)\"\n name: str = \"OpenSearchHybrid\"\n icon: str = \"OpenSearch\"\n description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n # Keys we consider baseline\n default_keys: list[str] = [\n \"opensearch_url\",\n \"index_name\",\n *[i.name for i in LCVectorStoreComponent.inputs], # search_query, add_documents, etc.\n \"embedding\",\n \"vector_field\",\n \"number_of_results\",\n \"auth_mode\",\n \"username\",\n \"password\",\n \"jwt_token\",\n \"jwt_header\",\n \"bearer_prefix\",\n \"use_ssl\",\n \"verify_certs\",\n \"filter_expression\",\n ]\n\n inputs = [\n StrInput(\n name=\"opensearch_url\",\n display_name=\"OpenSearch URL\",\n value=\"http://localhost:9200\",\n info=\"URL for your OpenSearch cluster.\"\n ),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n value=\"langflow\",\n info=\"The index to search.\"\n ),\n *LCVectorStoreComponent.inputs, # includes search_query, add_documents, etc.\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"]\n ),\n StrInput(\n name=\"vector_field\",\n display_name=\"Vector Field\",\n value=\"chunk_embedding\",\n advanced=True,\n info=\"Vector field used for KNN.\"\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Default Size (limit)\",\n value=10,\n advanced=True,\n info=\"Default number of hits when no limit provided in filter_expression.\"\n ),\n MultilineInput(\n name=\"filter_expression\",\n display_name=\"Filter Expression (JSON)\",\n value=\"\",\n info=(\n \"Optional JSON to control filters/limit/score threshold.\\n\"\n \"Accepted shapes:\\n\"\n '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n )\n ),\n\n # ----- Auth controls (dynamic) -----\n DropdownInput(\n name=\"auth_mode\",\n display_name=\"Auth Mode\",\n value=\"basic\",\n options=[\"basic\", \"jwt\"],\n info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n real_time_refresh=True,\n advanced=False,\n ),\n StrInput(\n name=\"username\",\n display_name=\"Username\",\n value=\"admin\",\n show=True,\n ),\n SecretStrInput(\n name=\"password\",\n display_name=\"Password\",\n value=\"admin\",\n show=True,\n ),\n SecretStrInput(\n name=\"jwt_token\",\n display_name=\"JWT Token\",\n value=\"\",\n show=False,\n info=\"Paste a valid JWT (sent as a header).\",\n ),\n StrInput(\n name=\"jwt_header\",\n display_name=\"JWT Header Name\",\n value=\"Authorization\",\n show=False,\n advanced=True,\n ),\n BoolInput(\n name=\"bearer_prefix\",\n display_name=\"Prefix 'Bearer '\",\n value=True,\n show=False,\n advanced=True,\n ),\n\n # ----- TLS -----\n BoolInput(\n name=\"use_ssl\",\n display_name=\"Use SSL\",\n value=True,\n advanced=True\n ),\n BoolInput(\n name=\"verify_certs\",\n display_name=\"Verify Certificates\",\n value=False,\n advanced=True\n ),\n ]\n\n # ---------- auth / client ----------\n def _build_auth_kwargs(self) -> Dict[str, Any]:\n mode = (self.auth_mode or \"basic\").strip().lower()\n if mode == \"jwt\":\n token = (self.jwt_token or \"\").strip()\n if not token:\n raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n header_name = (self.jwt_header or \"Authorization\").strip()\n header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n return {\"headers\": {header_name: header_value}}\n user = (self.username or \"\").strip()\n pwd = (self.password or \"\").strip()\n if not user or not pwd:\n raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n return {\"http_auth\": (user, pwd)}\n\n def build_client(self) -> OpenSearch:\n auth_kwargs = self._build_auth_kwargs()\n return OpenSearch(\n hosts=[self.opensearch_url],\n use_ssl=self.use_ssl,\n verify_certs=self.verify_certs,\n ssl_assert_hostname=False,\n ssl_show_warn=False,\n **auth_kwargs,\n )\n\n @check_cached_vector_store\n def build_vector_store(self) -> OpenSearch:\n # Return raw OpenSearch client as our “vector store.”\n return self.build_client()\n\n # ---------- ingest ----------\n def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n docs = self._prepare_ingest_data() or []\n if not docs:\n self.log(\"No documents to ingest.\")\n return\n\n texts = [d.to_lc_document().page_content for d in docs]\n if not self.embedding:\n raise ValueError(\"Embedding handle is required to embed documents.\")\n vectors = self.embedding.embed_documents(texts)\n\n actions = []\n for doc_obj, vec in zip(docs, vectors):\n lc_doc = doc_obj.to_lc_document()\n body = {\n **lc_doc.metadata,\n \"text\": lc_doc.page_content,\n self.vector_field: vec,\n }\n actions.append({\n \"_op_type\": \"index\",\n \"_index\": self.index_name,\n \"_source\": body,\n })\n\n self.log(f\"Indexing {len(actions)} docs into '{self.index_name}'…\")\n helpers.bulk(client, actions)\n\n # ---------- helpers for filters ----------\n def _is_placeholder_term(self, term_obj: dict) -> bool:\n # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n \"\"\"\n Accepts either:\n A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n \"\"\"\n if not filter_obj:\n return []\n\n # Case A: already an explicit list/dict under \"filter\"\n if \"filter\" in filter_obj:\n raw = filter_obj[\"filter\"]\n if isinstance(raw, dict):\n raw = [raw]\n clauses: List[dict] = []\n for f in (raw or []):\n if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n clauses.append(f)\n elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n field, vals = next(iter(f[\"terms\"].items()))\n if isinstance(vals, list) and len(vals) > 0:\n clauses.append(f)\n return clauses\n\n # Case B: convert context-style maps into clauses\n field_mapping = {\"data_sources\": \"filename\", \"document_types\": \"mimetype\", \"owners\": \"owner\"}\n clauses: List[dict] = []\n for k, values in filter_obj.items():\n if not isinstance(values, list):\n continue\n field = field_mapping.get(k, k)\n if len(values) == 0:\n # Match-nothing placeholder (kept to mirror your tool semantics)\n clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n elif len(values) == 1:\n if values[0] != \"__IMPOSSIBLE_VALUE__\":\n clauses.append({\"term\": {field: values[0]}})\n else:\n clauses.append({\"terms\": {field: values}})\n return clauses\n\n # ---------- search (single hybrid path matching your tool) ----------\n def search(self, query: str | None = None) -> list[dict[str, Any]]:\n client = self.build_client()\n q = (query or \"\").strip()\n\n # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n filter_obj = None\n if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n try:\n self.log(f\"DEBUG FILTER EXPRESSION {self.filter_expression}\")\n filter_obj = json.loads(self.filter_expression)\n except json.JSONDecodeError as e:\n raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n if not self.embedding:\n raise ValueError(\"Embedding is required to run hybrid search (KNN + keyword).\")\n\n # Embed the query\n vec = self.embedding.embed_query(q)\n\n # Build filter clauses (accept both shapes)\n clauses = self._coerce_filter_clauses(filter_obj)\n\n # Respect the tool's limit/threshold defaults\n limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n # Build the same hybrid body as your SearchService\n body = {\n \"query\": {\n \"bool\": {\n \"should\": [\n {\n \"knn\": {\n self.vector_field: {\n \"vector\": vec,\n \"k\": 10, # fixed to match the tool\n \"boost\": 0.7\n }\n }\n },\n {\n \"multi_match\": {\n \"query\": q,\n \"fields\": [\"text^2\", \"filename^1.5\"],\n \"type\": \"best_fields\",\n \"fuzziness\": \"AUTO\",\n \"boost\": 0.3\n }\n }\n ],\n \"minimum_should_match\": 1\n }\n },\n \"aggs\": {\n \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}}\n },\n \"_source\": [\n \"filename\", \"mimetype\", \"page\", \"text\", \"source_url\",\n \"owner\", \"allowed_users\", \"allowed_groups\"\n ],\n \"size\": limit\n }\n\n if clauses:\n body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n # top-level min_score (matches your tool)\n body[\"min_score\"] = score_threshold\n\n self.log(f\"DEBUG HYBRID BODY {body}\")\n resp = client.search(index=self.index_name, body=body)\n hits = resp.get(\"hits\", {}).get(\"hits\", [])\n return [\n {\n \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n \"score\": hit.get(\"_score\"),\n }\n for hit in hits\n ]\n\n def search_documents(self) -> list[Data]:\n try:\n raw = self.search(self.search_query or \"\")\n return [\n Data(file_path=hit[\"metadata\"].get(\"file_path\", \"\"), text=hit[\"page_content\"])\n for hit in raw\n ]\n except Exception as e:\n self.log(f\"search_documents error: {e}\")\n raise\n\n # -------- dynamic UI handling (auth switch) --------\n async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n try:\n if field_name == \"auth_mode\":\n mode = (field_value or \"basic\").strip().lower()\n is_basic = mode == \"basic\"\n is_jwt = mode == \"jwt\"\n\n build_config[\"username\"][\"show\"] = is_basic\n build_config[\"password\"][\"show\"] = is_basic\n\n build_config[\"jwt_token\"][\"show\"] = is_jwt\n build_config[\"jwt_header\"][\"show\"] = is_jwt\n build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n build_config[\"username\"][\"required\"] = is_basic\n build_config[\"password\"][\"required\"] = is_basic\n\n build_config[\"jwt_token\"][\"required\"] = is_jwt\n build_config[\"jwt_header\"][\"required\"] = is_jwt\n build_config[\"bearer_prefix\"][\"required\"] = False\n\n if is_basic:\n build_config[\"jwt_token\"][\"value\"] = \"\"\n\n return build_config\n\n return build_config\n\n except Exception as e:\n self.log(f\"update_build_config error: {e}\")\n return build_config\n" - }, - "embedding": { - "_input_type": "HandleInput", - "advanced": false, - "display_name": "Embedding", - "dynamic": false, - "info": "", - "input_types": [ - "Embeddings" - ], - "list": false, - "list_add_label": "Add More", - "name": "embedding", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "other", - "value": "" - }, - "filter_expression": { - "_input_type": "MultilineInput", - "advanced": false, - "copy_field": false, - "display_name": "Filter Expression (JSON)", - "dynamic": false, - "info": "Optional JSON to control filters/limit/score threshold.\nAccepted shapes:\n1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\n2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\nPlaceholders with __IMPOSSIBLE_VALUE__ are ignored.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "multiline": true, - "name": "filter_expression", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "index_name": { - "_input_type": "StrInput", - "advanced": false, - "display_name": "Index Name", - "dynamic": false, - "info": "The index to search.", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "index_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "documents" - }, - "ingest_data": { - "_input_type": "HandleInput", - "advanced": false, - "display_name": "Ingest Data", - "dynamic": false, - "info": "", - "input_types": [ - "Data", - "DataFrame" - ], - "list": true, - "list_add_label": "Add More", - "name": "ingest_data", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "other", - "value": "" - }, - "jwt_header": { - "_input_type": "StrInput", - "advanced": true, - "display_name": "JWT Header Name", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "jwt_header", - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "Authorization" - }, - "jwt_token": { - "_input_type": "SecretStrInput", - "advanced": false, - "display_name": "JWT Token", - "dynamic": false, - "info": "Paste a valid JWT (sent as a header).", - "input_types": [], - "load_from_db": true, - "name": "jwt_token", - "password": true, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "str", - "value": "JWT" - }, - "number_of_results": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Default Size (limit)", - "dynamic": false, - "info": "Default number of hits when no limit provided in filter_expression.", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "number_of_results", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 4 - }, - "opensearch_url": { - "_input_type": "StrInput", - "advanced": false, - "display_name": "OpenSearch URL", - "dynamic": false, - "info": "URL for your OpenSearch cluster.", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "opensearch_url", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "https://opensearch:9200" - }, - "password": { - "_input_type": "SecretStrInput", - "advanced": false, - "display_name": "Password", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": false, - "name": "password", - "password": true, - "placeholder": "", - "required": false, - "show": false, - "title_case": false, - "type": "str", - "value": "" - }, - "search_query": { - "_input_type": "QueryInput", - "advanced": false, - "display_name": "Search Query", - "dynamic": false, - "info": "Enter a query to run a similarity search.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "search_query", - "placeholder": "Enter a query...", - "required": false, - "show": true, - "title_case": false, - "tool_mode": true, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "query", - "value": "" - }, - "should_cache_vector_store": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Cache Vector Store", - "dynamic": false, - "info": "If True, the vector store will be cached for the current build of the component. This is useful for components that have multiple output methods and want to share the same vector store.", - "list": false, - "list_add_label": "Add More", - "name": "should_cache_vector_store", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "tools_metadata": { - "_input_type": "ToolsInput", - "advanced": false, - "display_name": "Actions", - "dynamic": false, - "info": "Modify tool names and descriptions to help agents understand when to use each tool.", - "is_list": true, - "list_add_label": "Add More", - "name": "tools_metadata", - "placeholder": "", - "real_time_refresh": true, - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "tools", - "value": [ - { - "args": { - "search_query": { - "default": "", - "description": "Enter a query to run a similarity search.", - "title": "Search Query", - "type": "string" - } - }, - "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.", - "display_description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.", - "display_name": "search_documents", - "name": "search_documents", - "readonly": false, - "status": true, - "tags": [ - "search_documents" - ] - }, - { - "args": { - "search_query": { - "default": "", - "description": "Enter a query to run a similarity search.", - "title": "Search Query", - "type": "string" - } - }, - "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.", - "display_description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.", - "display_name": "as_dataframe", - "name": "as_dataframe", - "readonly": false, - "status": true, - "tags": [ - "as_dataframe" - ] - }, - { - "args": { - "search_query": { - "default": "", - "description": "Enter a query to run a similarity search.", - "title": "Search Query", - "type": "string" - } - }, - "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.", - "display_description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.", - "display_name": "as_vector_store", - "name": "as_vector_store", - "readonly": false, - "status": true, - "tags": [ - "as_vector_store" - ] - } - ] - }, - "use_ssl": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Use SSL", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "use_ssl", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "username": { - "_input_type": "StrInput", - "advanced": false, - "display_name": "Username", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "username", - "placeholder": "", - "required": false, - "show": false, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "admin" - }, - "vector_field": { - "_input_type": "StrInput", - "advanced": true, - "display_name": "Vector Field", - "dynamic": false, - "info": "Vector field used for KNN.", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "vector_field", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "chunk_embedding" - }, - "verify_certs": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Verify Certificates", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "verify_certs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": false - } - }, - "tool_mode": true - }, - "selected_output": "search_results", - "showNode": true, - "type": "OpenSearchHybrid" - }, - "dragging": false, - "id": "OpenSearch-iYfjf", - "measured": { - "height": 751, - "width": 320 - }, - "position": { - "x": 1202.1762389080463, - "y": 112.65887163017715 - }, - "selected": true, - "type": "genericNode" - }, - { - "data": { - "id": "EmbeddingModel-eZ6bT", - "node": { - "base_classes": [ - "Embeddings" - ], - "beta": false, - "category": "models", - "conditional_paths": [], - "custom_fields": {}, - "description": "Generate embeddings using a specified provider.", - "display_name": "Embedding Model", - "documentation": "https://docs.langflow.org/components-embedding-models", - "edited": false, - "field_order": [ - "provider", - "model", - "api_key", - "api_base", - "dimensions", - "chunk_size", - "request_timeout", - "max_retries", - "show_progress_bar", - "model_kwargs" - ], - "frozen": false, - "icon": "binary", - "key": "EmbeddingModel", - "last_updated": "2025-08-12T19:55:29.859Z", - "legacy": false, - "lf_version": "1.5.0.post1", - "metadata": {}, - "minimized": false, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Embedding Model", - "group_outputs": false, - "method": "build_embeddings", - "name": "embeddings", - "options": null, - "required_inputs": null, - "selected": "Embeddings", - "tool_mode": true, - "types": [ - "Embeddings" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "score": 0.002833550413469189, - "template": { - "_type": "Component", - "api_base": { - "_input_type": "MessageTextInput", - "advanced": true, - "display_name": "API Base URL", - "dynamic": false, - "info": "Base URL for the API. Leave empty for default.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "api_base", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "api_key": { - "_input_type": "SecretStrInput", - "advanced": false, - "display_name": "OpenAI API Key", - "dynamic": false, - "info": "Model Provider API key", - "input_types": [], - "load_from_db": true, - "name": "api_key", - "password": true, - "placeholder": "", - "real_time_refresh": true, - "required": true, - "show": true, - "title_case": false, - "type": "str", - "value": "OPENAI_API_KEY" - }, - "chunk_size": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Chunk Size", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "chunk_size", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 1000 - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom langflow.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}],\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n if field_name == \"provider\" and field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n return build_config\n" - }, - "dimensions": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Dimensions", - "dynamic": false, - "info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.", - "list": false, - "list_add_label": "Add More", - "name": "dimensions", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": "" - }, - "max_retries": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Max Retries", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "max_retries", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 3 - }, - "model": { - "_input_type": "DropdownInput", - "advanced": false, - "combobox": false, - "dialog_inputs": {}, - "display_name": "Model Name", - "dynamic": false, - "info": "Select the embedding model to use", - "name": "model", - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "options_metadata": [], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "toggle": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "text-embedding-3-small" - }, - "model_kwargs": { - "_input_type": "DictInput", - "advanced": true, - "display_name": "Model Kwargs", - "dynamic": false, - "info": "Additional keyword arguments to pass to the model.", - "list": false, - "list_add_label": "Add More", - "name": "model_kwargs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "type": "dict", - "value": {} - }, - "provider": { - "_input_type": "DropdownInput", - "advanced": false, - "combobox": false, - "dialog_inputs": {}, - "display_name": "Model Provider", - "dynamic": false, - "info": "Select the embedding model provider", - "name": "provider", - "options": [ - "OpenAI" - ], - "options_metadata": [ - { - "icon": "OpenAI" - } - ], - "placeholder": "", - "real_time_refresh": true, - "required": false, - "show": true, - "title_case": false, - "toggle": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "OpenAI" - }, - "request_timeout": { - "_input_type": "FloatInput", - "advanced": true, - "display_name": "Request Timeout", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "request_timeout", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "float", - "value": "" - }, - "show_progress_bar": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Show Progress Bar", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "show_progress_bar", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": false - } - }, - "tool_mode": false - }, - "showNode": true, - "type": "EmbeddingModel" - }, - "dragging": false, - "id": "EmbeddingModel-eZ6bT", - "measured": { - "height": 370, - "width": 320 - }, - "position": { - "x": 727.4791597769406, - "y": 518.0820551650631 - }, - "selected": false, - "type": "genericNode" - }, - { - "data": { - "id": "Agent-crjWf", - "node": { - "base_classes": [ - "Data", - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Define the agent's instructions, then enter a task to complete using tools.", - "display_name": "Agent", - "documentation": "https://docs.langflow.org/agents", - "edited": false, - "field_order": [ - "agent_llm", - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "api_key", - "temperature", - "seed", - "max_retries", - "timeout", - "system_prompt", - "n_messages", - "tools", - "input_value", - "handle_parsing_errors", - "verbose", - "max_iterations", - "agent_description", - "add_current_date_tool" - ], - "frozen": false, - "icon": "bot", - "last_updated": "2025-08-12T19:55:29.860Z", - "legacy": false, - "lf_version": "1.5.0.post1", - "metadata": { - "code_hash": "533aac5f6185", - "module": "langflow.components.agents.agent.AgentComponent" - }, - "minimized": false, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Response", - "group_outputs": false, - "method": "message_response", - "name": "response", - "options": null, - "required_inputs": null, - "selected": "Message", - "tool_mode": true, - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - }, - { - "allows_loop": false, - "cache": true, - "display_name": "Structured Response", - "group_outputs": false, - "method": "json_response", - "name": "structured_response", - "options": null, - "required_inputs": null, - "selected": "Data", - "tool_mode": false, - "types": [ - "Data" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "add_current_date_tool": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Current Date", - "dynamic": false, - "info": "If true, will add a tool to the agent that returns the current date.", - "list": false, - "list_add_label": "Add More", - "name": "add_current_date_tool", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "agent_description": { - "_input_type": "MultilineInput", - "advanced": true, - "copy_field": false, - "display_name": "Agent Description [Deprecated]", - "dynamic": false, - "info": "The description of the agent. This is only used when in Tool Mode. Defaults to 'A helpful assistant with access to the following tools:' and tools are added dynamically. This feature is deprecated and will be removed in future versions.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "multiline": true, - "name": "agent_description", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "A helpful assistant with access to the following tools:" - }, - "agent_llm": { - "_input_type": "DropdownInput", - "advanced": false, - "combobox": false, - "dialog_inputs": {}, - "display_name": "Model Provider", - "dynamic": false, - "info": "The provider of the language model that the agent will use to generate responses.", - "input_types": [], - "name": "agent_llm", - "options": [ - "Anthropic", - "Google Generative AI", - "Groq", - "OpenAI", - "Custom" - ], - "options_metadata": [ - { - "icon": "Anthropic" - }, - { - "icon": "GoogleGenerativeAI" - }, - { - "icon": "Groq" - }, - { - "icon": "OpenAI" - }, - { - "icon": "brain" - } - ], - "placeholder": "", - "real_time_refresh": true, - "required": false, - "show": true, - "title_case": false, - "toggle": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "OpenAI" - }, - "api_key": { - "_input_type": "SecretStrInput", - "advanced": false, - "display_name": "OpenAI API Key", - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "input_types": [], - "load_from_db": true, - "name": "api_key", - "password": true, - "placeholder": "", - "real_time_refresh": true, - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "OPENAI_API_KEY" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "import json\nimport re\n\nfrom langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.agents.events import ExceptionWithMessageError\nfrom langflow.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS,\n MODEL_PROVIDERS_DICT,\n MODELS_METADATA,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers.current_date import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.custom.custom_component.component import _get_component_toolkit\nfrom langflow.custom.utils import update_component_build_config\nfrom langflow.field_typing import Tool\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MultilineInput, Output\nfrom langflow.logging import logger\nfrom langflow.schema.data import Data\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nMODEL_PROVIDERS_LIST = [\"Anthropic\", \"Google Generative AI\", \"Groq\", \"OpenAI\"]\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n # Filter out json_mode from OpenAI inputs since we handle structured output differently\n openai_inputs_filtered = [\n input_field\n for input_field in MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"]\n if not (hasattr(input_field, \"name\") and input_field.name == \"json_mode\")\n ]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST, \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST] + [{\"icon\": \"brain\"}],\n ),\n *openai_inputs_filtered,\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n *LCToolsAgentComponent._base_inputs,\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(name=\"structured_response\", display_name=\"Structured Response\", method=\"json_response\", tool_mode=False),\n ]\n\n async def message_response(self) -> Message:\n try:\n # Get LLM model and validate\n llm_model, display_name = self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n # note the tools are not required to run the agent, hence the validation removed.\n\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n # return result\n\n except (ValueError, TypeError, KeyError) as e:\n logger.error(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n logger.error(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n except Exception as e:\n logger.error(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Convert agent response to structured JSON Data output.\"\"\"\n # Run the regular message response first to get the result\n if not hasattr(self, \"_agent_result\"):\n await self.message_response()\n\n result = self._agent_result\n\n # Extract content from result\n if hasattr(result, \"content\"):\n content = result.content\n elif hasattr(result, \"text\"):\n content = result.text\n else:\n content = str(result)\n\n # Try to parse as JSON\n try:\n json_data = json.loads(content)\n return Data(data=json_data)\n except json.JSONDecodeError:\n # If it's not valid JSON, try to extract JSON from the content\n json_match = re.search(r\"\\{.*\\}\", content, re.DOTALL)\n if json_match:\n try:\n json_data = json.loads(json_match.group())\n return Data(data=json_data)\n except json.JSONDecodeError:\n pass\n\n # If we can't extract JSON, return the raw content as data\n return Data(data={\"content\": content, \"error\": \"Could not parse as JSON\"})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(session_id=self.graph.session_id, order=\"Ascending\", n_messages=self.n_messages)\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except Exception as e:\n logger.error(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n # Filter out json_mode and only use attributes that exist on this component\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())]\n + [{\"icon\": \"brain\"}],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = _get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\", tool_description=description, callbacks=self.get_langchain_callbacks()\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n return tools\n" - }, - "handle_parsing_errors": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Handle Parse Errors", - "dynamic": false, - "info": "Should the Agent fix errors when reading user input for better processing?", - "list": false, - "list_add_label": "Add More", - "name": "handle_parsing_errors", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - }, - "input_value": { - "_input_type": "MessageInput", - "advanced": false, - "display_name": "Input", - "dynamic": false, - "info": "The input provided by the user for the agent to process.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "input_value", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": true, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "max_iterations": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Max Iterations", - "dynamic": false, - "info": "The maximum number of attempts the agent can make to complete its task before it stops.", - "list": false, - "list_add_label": "Add More", - "name": "max_iterations", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 15 - }, - "max_retries": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Max Retries", - "dynamic": false, - "info": "The maximum number of retries to make when generating.", - "list": false, - "list_add_label": "Add More", - "name": "max_retries", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 5 - }, - "max_tokens": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Max Tokens", - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "list": false, - "list_add_label": "Add More", - "name": "max_tokens", - "placeholder": "", - "range_spec": { - "max": 128000, - "min": 0, - "step": 0.1, - "step_type": "float" - }, - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": "" - }, - "model_kwargs": { - "_input_type": "DictInput", - "advanced": true, - "display_name": "Model Kwargs", - "dynamic": false, - "info": "Additional keyword arguments to pass to the model.", - "list": false, - "list_add_label": "Add More", - "name": "model_kwargs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "type": "dict", - "value": {} - }, - "model_name": { - "_input_type": "DropdownInput", - "advanced": false, - "combobox": true, - "dialog_inputs": {}, - "display_name": "Model Name", - "dynamic": false, - "info": "To see the model names, first choose a provider. Then, enter your API key and click the refresh button next to the model name.", - "name": "model_name", - "options": [ - "gpt-4o-mini", - "gpt-4o", - "gpt-4.1", - "gpt-4.1-mini", - "gpt-4.1-nano", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-4", - "gpt-3.5-turbo", - "o1", - "o3-mini", - "o3", - "o3-pro", - "o4-mini", - "o4-mini-high" - ], - "options_metadata": [], - "placeholder": "", - "real_time_refresh": false, - "required": false, - "show": true, - "title_case": false, - "toggle": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "gpt-4o-mini" - }, - "n_messages": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Number of Chat History Messages", - "dynamic": false, - "info": "Number of chat history messages to retrieve.", - "list": false, - "list_add_label": "Add More", - "name": "n_messages", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 100 - }, - "openai_api_base": { - "_input_type": "StrInput", - "advanced": true, - "display_name": "OpenAI API Base", - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "openai_api_base", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "seed": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Seed", - "dynamic": false, - "info": "The seed controls the reproducibility of the job.", - "list": false, - "list_add_label": "Add More", - "name": "seed", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 1 - }, - "system_prompt": { - "_input_type": "MultilineInput", - "advanced": false, - "copy_field": false, - "display_name": "Agent Instructions", - "dynamic": false, - "info": "System Prompt: Initial instructions and context provided to guide the agent's behavior.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "multiline": true, - "name": "system_prompt", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "You are a helpful assistant that can use tools to answer questions and perform tasks." - }, - "temperature": { - "_input_type": "SliderInput", - "advanced": true, - "display_name": "Temperature", - "dynamic": false, - "info": "", - "max_label": "", - "max_label_icon": "", - "min_label": "", - "min_label_icon": "", - "name": "temperature", - "placeholder": "", - "range_spec": { - "max": 1, - "min": 0, - "step": 0.01, - "step_type": "float" - }, - "required": false, - "show": true, - "slider_buttons": false, - "slider_buttons_options": [], - "slider_input": false, - "title_case": false, - "tool_mode": false, - "type": "slider", - "value": 0.1 - }, - "timeout": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Timeout", - "dynamic": false, - "info": "The timeout for requests to OpenAI completion API.", - "list": false, - "list_add_label": "Add More", - "name": "timeout", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "int", - "value": 700 - }, - "tools": { - "_input_type": "HandleInput", - "advanced": false, - "display_name": "Tools", - "dynamic": false, - "info": "These are the tools that the agent can use to help with tasks.", - "input_types": [ - "Tool" - ], - "list": true, - "list_add_label": "Add More", - "name": "tools", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "other", - "value": "" - }, - "verbose": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Verbose", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "verbose", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "type": "bool", - "value": true - } - }, - "tool_mode": false - }, - "selected_output": "response", - "showNode": true, - "type": "Agent" - }, - "dragging": false, - "id": "Agent-crjWf", - "measured": { - "height": 597, - "width": 320 - }, - "position": { - "x": 1686.5732118555798, - "y": 317.94354236557473 - }, - "selected": false, - "type": "genericNode" - }, - { - "data": { - "id": "TextInput-aHsQb", - "node": { - "base_classes": [ - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Get user text inputs.", - "display_name": "Text Input", - "documentation": "https://docs.langflow.org/components-io#text-input", - "edited": true, - "field_order": [ - "input_value" - ], - "frozen": false, - "icon": "type", - "legacy": false, - "lf_version": "1.5.0.post1", - "metadata": {}, - "minimized": false, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Output Text", - "group_outputs": false, - "hidden": null, - "method": "text_response", - "name": "text", - "options": null, - "required_inputs": null, - "selected": "Message", - "tool_mode": true, - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import SecretStrInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Text Input\"\n description = \"Get user text inputs.\"\n documentation: str = \"https://docs.langflow.org/components-io#text-input\"\n icon = \"type\"\n name = \"TextInput\"\n\n inputs = [\n SecretStrInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n return Message(\n text=self.input_value,\n )\n" - }, - "input_value": { - "_input_type": "SecretStrInput", - "advanced": false, - "display_name": "Text", - "dynamic": false, - "info": "Text to be passed as input.", - "input_types": [], - "load_from_db": true, - "name": "input_value", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "GENDB-QUERY-FILTER" - } - }, - "tool_mode": false - }, - "showNode": true, - "type": "TextInput" - }, - "dragging": false, - "id": "TextInput-aHsQb", - "measured": { - "height": 204, - "width": 320 - }, - "position": { - "x": 745.3341059713564, - "y": 95.0152511387621 - }, - "selected": false, - "type": "genericNode" - } - ], - "viewport": { - "x": -459.5606878274141, - "y": -37.34645580037716, - "zoom": 0.835091786312263 - } - }, - "description": "GenDB Open Search Agent", - "endpoint_name": null, - "id": "1098eea1-6649-4e1d-aed1-b77249fb8dd0", - "is_component": false, - "last_tested_version": "1.5.0.post1", - "name": "GenDB Open Search Agent", - "tags": [ - "assistants", - "agents" - ] -} \ No newline at end of file diff --git a/frontend/components/navigation-layout.tsx b/frontend/components/navigation-layout.tsx index e6946688..9e811391 100644 --- a/frontend/components/navigation-layout.tsx +++ b/frontend/components/navigation-layout.tsx @@ -17,7 +17,7 @@ export function NavigationLayout({ children }: NavigationLayoutProps) {

- GenDB + OpenRAG

diff --git a/frontend/components/navigation.tsx b/frontend/components/navigation.tsx index adc03658..56fb8c17 100644 --- a/frontend/components/navigation.tsx +++ b/frontend/components/navigation.tsx @@ -28,10 +28,10 @@ export function Navigation() { active: pathname === "/chat", }, { - label: "Contexts", + label: "Knowledge Filters", icon: BookOpenCheck, - href: "/contexts", - active: pathname.startsWith("/contexts"), + href: "/knowledge-filters", + active: pathname.startsWith("/knowledge-filters"), }, { label: "Connectors", diff --git a/frontend/src/app/api/[...path]/route.ts b/frontend/src/app/api/[...path]/route.ts index ba7c7134..37436fa5 100644 --- a/frontend/src/app/api/[...path]/route.ts +++ b/frontend/src/app/api/[...path]/route.ts @@ -39,7 +39,7 @@ async function proxyRequest( request: NextRequest, params: { path: string[] } ) { - const backendHost = process.env.GENDB_BACKEND_HOST || 'localhost'; + const backendHost = process.env.OPENRAG_BACKEND_HOST || 'localhost'; const path = params.path.join('/'); const searchParams = request.nextUrl.searchParams.toString(); const backendUrl = `http://${backendHost}:8000/${path}${searchParams ? `?${searchParams}` : ''}`; diff --git a/frontend/src/app/auth/callback/page.tsx b/frontend/src/app/auth/callback/page.tsx index d93c99ed..d1c94613 100644 --- a/frontend/src/app/auth/callback/page.tsx +++ b/frontend/src/app/auth/callback/page.tsx @@ -135,7 +135,7 @@ function AuthCallbackContent() { return isAppAuth ? "Signing you in..." : "Connecting..." } if (status === "success") { - return isAppAuth ? "Welcome to GenDB!" : "Connection Successful!" + return isAppAuth ? "Welcome to OpenRAG!" : "Connection Successful!" } if (status === "error") { return isAppAuth ? "Sign In Failed" : "Connection Failed" diff --git a/frontend/src/app/chat/page.tsx b/frontend/src/app/chat/page.tsx index 69be28b9..e1e8d4b2 100644 --- a/frontend/src/app/chat/page.tsx +++ b/frontend/src/app/chat/page.tsx @@ -89,17 +89,17 @@ function ChatPage() { const [scoreThreshold, setScoreThreshold] = useState(0) const [loadedContextName, setLoadedContextName] = useState(null) - // Load context if contextId is provided in URL + // Load knowledge filter if filterId is provided in URL useEffect(() => { - const contextId = searchParams.get('contextId') - if (contextId) { - loadContext(contextId) + const filterId = searchParams.get('filterId') + if (filterId) { + loadKnowledgeFilter(filterId) } }, [searchParams]) - const loadContext = async (contextId: string) => { + const loadKnowledgeFilter = async (filterId: string) => { try { - const response = await fetch(`/api/contexts/${contextId}`, { + const response = await fetch(`/api/knowledge-filter/${filterId}`, { method: "GET", headers: { "Content-Type": "application/json", @@ -108,19 +108,19 @@ function ChatPage() { const result = await response.json() if (response.ok && result.success) { - const context = result.context - const parsedQueryData = JSON.parse(context.query_data) + const filter = result.filter + const parsedQueryData = JSON.parse(filter.query_data) // Load the context data into state setSelectedFilters(parsedQueryData.filters) setResultLimit(parsedQueryData.limit) setScoreThreshold(parsedQueryData.scoreThreshold) - setLoadedContextName(context.name) + setLoadedContextName(filter.name) } else { - console.error("Failed to load context:", result.error) + console.error("Failed to load knowledge filter:", result.error) } } catch (error) { - console.error("Error loading context:", error) + console.error("Error loading knowledge filter:", error) } } @@ -283,10 +283,14 @@ function ChatPage() { const apiEndpoint = endpoint === "chat" ? "/api/chat" : "/api/langflow" try { + const hasFilters = selectedFilters.data_sources.length > 0 || + selectedFilters.document_types.length > 0 || + selectedFilters.owners.length > 0 + const requestBody: RequestBody = { prompt: userMessage.content, stream: true, - filters: selectedFilters, + ...(hasFilters && { filters: selectedFilters }), limit: resultLimit, scoreThreshold: scoreThreshold } @@ -744,9 +748,13 @@ function ChatPage() { try { const apiEndpoint = endpoint === "chat" ? "/api/chat" : "/api/langflow" + const hasFilters = selectedFilters.data_sources.length > 0 || + selectedFilters.document_types.length > 0 || + selectedFilters.owners.length > 0 + const requestBody: RequestBody = { prompt: userMessage.content, - filters: selectedFilters, + ...(hasFilters && { filters: selectedFilters }), limit: resultLimit, scoreThreshold: scoreThreshold } diff --git a/frontend/src/app/contexts/page.tsx b/frontend/src/app/knowledge-filters/page.tsx similarity index 80% rename from frontend/src/app/contexts/page.tsx rename to frontend/src/app/knowledge-filters/page.tsx index 966f0bb1..ef462119 100644 --- a/frontend/src/app/contexts/page.tsx +++ b/frontend/src/app/knowledge-filters/page.tsx @@ -9,7 +9,7 @@ import { Label } from "@/components/ui/label" import { Search, Loader2, BookOpenCheck, Settings, Calendar, MessageCircle } from "lucide-react" import { ProtectedRoute } from "@/components/protected-route" -interface Context { +interface KnowledgeFilter { id: string name: string description: string @@ -30,18 +30,18 @@ interface ParsedQueryData { scoreThreshold: number } -function ContextsPage() { +function KnowledgeFiltersPage() { const router = useRouter() - const [contexts, setContexts] = useState([]) + const [filters, setFilters] = useState([]) const [loading, setLoading] = useState(true) const [searchQuery, setSearchQuery] = useState("") - const [selectedContext, setSelectedContext] = useState(null) + const [selectedFilter, setSelectedFilter] = useState(null) const [parsedQueryData, setParsedQueryData] = useState(null) - const loadContexts = async (query = "") => { + const loadFilters = async (query = "") => { setLoading(true) try { - const response = await fetch("/api/contexts/search", { + const response = await fetch("/api/knowledge-filter/search", { method: "POST", headers: { "Content-Type": "application/json", @@ -54,32 +54,32 @@ function ContextsPage() { const result = await response.json() if (response.ok && result.success) { - setContexts(result.contexts) + setFilters(result.filters) } else { - console.error("Failed to load contexts:", result.error) - setContexts([]) + console.error("Failed to load knowledge filters:", result.error) + setFilters([]) } } catch (error) { - console.error("Error loading contexts:", error) - setContexts([]) + console.error("Error loading knowledge filters:", error) + setFilters([]) } finally { setLoading(false) } } useEffect(() => { - loadContexts() + loadFilters() }, []) const handleSearch = async (e: React.FormEvent) => { e.preventDefault() - await loadContexts(searchQuery) + await loadFilters(searchQuery) } - const handleContextClick = (context: Context) => { - setSelectedContext(context) + const handleFilterClick = (filter: KnowledgeFilter) => { + setSelectedFilter(filter) try { - const parsed = JSON.parse(context.query_data) as ParsedQueryData + const parsed = JSON.parse(filter.query_data) as ParsedQueryData setParsedQueryData(parsed) } catch (error) { console.error("Error parsing query data:", error) @@ -97,16 +97,16 @@ function ContextsPage() { }) } - const handleSearchWithContext = () => { - if (!selectedContext) return + const handleSearchWithFilter = () => { + if (!selectedFilter) return - router.push(`/?contextId=${selectedContext.id}`) + router.push(`/?filterId=${selectedFilter.id}`) } - const handleChatWithContext = () => { - if (!selectedContext) return + const handleChatWithFilter = () => { + if (!selectedFilter) return - router.push(`/chat?contextId=${selectedContext.id}`) + router.push(`/chat?filterId=${selectedFilter.id}`) } return ( @@ -115,14 +115,14 @@ function ContextsPage() {

- Contexts + Knowledge Filters

- Manage your saved search contexts + Manage your saved knowledge filters

- View and manage your saved search queries, filters, and configurations for quick access to your most important searches. + View and manage your saved search configurations that help you focus on specific subsets of your knowledge base.

@@ -131,10 +131,10 @@ function ContextsPage() { - Search Contexts + Search Knowledge Filters - Search through your saved search contexts by name or description + Search through your saved knowledge filters by name or description @@ -142,7 +142,7 @@ function ContextsPage() {
setSearchQuery(e.target.value)} className="h-12 bg-background/50 border-border/50 focus:border-blue-400/50 focus:ring-blue-400/20 flex-1" @@ -170,7 +170,7 @@ function ContextsPage() {
{/* Context List */}
- {contexts.length === 0 ? ( + {filters.length === 0 ? (
@@ -178,40 +178,40 @@ function ContextsPage() {

- No contexts found + No knowledge filters found

- Create your first context by saving a search configuration from the search page. + Create your first knowledge filter by saving a search configuration from the search page.

) : (
- {contexts.map((context) => ( + {filters.map((filter) => ( handleContextClick(context)} + onClick={() => handleFilterClick(filter)} >
-

{context.name}

- {context.description && ( -

{context.description}

+

{filter.name}

+ {filter.description && ( +

{filter.description}

)}
- Created {formatDate(context.created_at)} + Created {formatDate(filter.created_at)}
- {context.updated_at !== context.created_at && ( + {filter.updated_at !== filter.created_at && (
- Updated {formatDate(context.updated_at)} + Updated {formatDate(filter.updated_at)}
)}
@@ -225,12 +225,12 @@ function ContextsPage() {
{/* Context Detail Panel */} - {selectedContext && parsedQueryData && ( + {selectedFilter && parsedQueryData && (

- Context Details + Knowledge Filter Details

@@ -311,21 +311,21 @@ function ContextsPage() { {/* Action Buttons */}
@@ -338,10 +338,10 @@ function ContextsPage() { ) } -export default function ProtectedContextsPage() { +export default function ProtectedKnowledgeFiltersPage() { return ( - + ) } \ No newline at end of file diff --git a/frontend/src/app/layout.tsx b/frontend/src/app/layout.tsx index 151de0e8..cd3d1d97 100644 --- a/frontend/src/app/layout.tsx +++ b/frontend/src/app/layout.tsx @@ -18,8 +18,8 @@ const geistMono = Geist_Mono({ }); export const metadata: Metadata = { - title: "GenDB", - description: "Document search and management system", + title: "OpenRAG", + description: "Open source RAG (Retrieval Augmented Generation) system", }; export default function RootLayout({ diff --git a/frontend/src/app/login/page.tsx b/frontend/src/app/login/page.tsx index ea095f3e..8ba46e44 100644 --- a/frontend/src/app/login/page.tsx +++ b/frontend/src/app/login/page.tsx @@ -43,7 +43,7 @@ function LoginPageContent() {
- Welcome to GenDB + Welcome to OpenRAG Sign in to access your documents and AI chat diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index 14a67aa4..b9b392f9 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -88,48 +88,7 @@ function SearchPage() { const [savingContext, setSavingContext] = useState(false) const [loadedContextName, setLoadedContextName] = useState(null) - const loadContext = useCallback(async (contextId: string) => { - try { - const response = await fetch(`/api/contexts/${contextId}`, { - method: "GET", - headers: { - "Content-Type": "application/json", - }, - }) - - const result = await response.json() - if (response.ok && result.success) { - const context = result.context - const parsedQueryData = JSON.parse(context.query_data) - - // Load the context data into state - setQuery(parsedQueryData.query) - setSelectedFilters(parsedQueryData.filters) - setResultLimit(parsedQueryData.limit) - setScoreThreshold(parsedQueryData.scoreThreshold) - setLoadedContextName(context.name) - - // Automatically perform the search - setTimeout(() => { - handleSearch() - }, 100) - } else { - console.error("Failed to load context:", result.error) - } - } catch (err) { - console.error("Error loading context:", err) - } - }, []) - - // Load context if contextId is provided in URL - useEffect(() => { - const contextId = searchParams.get('contextId') - if (contextId) { - loadContext(contextId) - } - }, [searchParams, loadContext]) - - const handleSearch = async (e?: React.FormEvent) => { + const handleSearch = useCallback(async (e?: React.FormEvent) => { if (e) e.preventDefault() if (!query.trim()) return @@ -202,7 +161,48 @@ function SearchPage() { } finally { setLoading(false) } - } + }, [query, resultLimit, scoreThreshold, searchPerformed, selectedFilters]) + + const loadKnowledgeFilter = useCallback(async (filterId: string) => { + try { + const response = await fetch(`/api/knowledge-filter/${filterId}`, { + method: "GET", + headers: { + "Content-Type": "application/json", + }, + }) + + const result = await response.json() + if (response.ok && result.success) { + const filter = result.filter + const parsedQueryData = JSON.parse(filter.query_data) + + // Load the context data into state + setQuery(parsedQueryData.query) + setSelectedFilters(parsedQueryData.filters) + setResultLimit(parsedQueryData.limit) + setScoreThreshold(parsedQueryData.scoreThreshold) + setLoadedContextName(filter.name) + + // Automatically perform the search + setTimeout(() => { + handleSearch() + }, 100) + } else { + console.error("Failed to load knowledge filter:", result.error) + } + } catch (err) { + console.error("Error loading knowledge filter:", err) + } + }, [handleSearch]) + + // Load knowledge filter if filterId is provided in URL + useEffect(() => { + const filterId = searchParams.get('filterId') + if (filterId) { + loadKnowledgeFilter(filterId) + } + }, [searchParams, loadKnowledgeFilter]) const handleFilterChange = async (facetType: keyof SelectedFilters, value: string, checked: boolean) => { const newFilters = { @@ -277,16 +277,16 @@ function SearchPage() { selectedFilters.owners.length } - const handleSaveContext = async () => { - const contextId = searchParams.get('contextId') + const handleSaveKnowledgeFilter = async () => { + const filterId = searchParams.get('filterId') - // If no contextId present and no title, we need the modal - if (!contextId && !contextTitle.trim()) return + // If no filterId present and no title, we need the modal + if (!filterId && !contextTitle.trim()) return setSavingContext(true) try { - const contextData = { + const filterData = { query, filters: selectedFilters, limit: resultLimit, @@ -295,20 +295,20 @@ function SearchPage() { let response; - if (contextId) { - // Update existing context (upsert) - response = await fetch(`/api/contexts/${contextId}`, { + if (filterId) { + // Update existing knowledge filter (upsert) + response = await fetch(`/api/knowledge-filter/${filterId}`, { method: "PUT", headers: { "Content-Type": "application/json", }, body: JSON.stringify({ - queryData: JSON.stringify(contextData) + queryData: JSON.stringify(filterData) }), }) } else { - // Create new context - response = await fetch("/api/contexts", { + // Create new knowledge filter + response = await fetch("/api/knowledge-filter", { method: "POST", headers: { "Content-Type": "application/json", @@ -316,7 +316,7 @@ function SearchPage() { body: JSON.stringify({ name: contextTitle, description: contextDescription, - queryData: JSON.stringify(contextData) + queryData: JSON.stringify(filterData) }), }) } @@ -324,18 +324,18 @@ function SearchPage() { const result = await response.json() if (response.ok && result.success) { - if (!contextId) { - // Reset modal state only if we were creating a new context + if (!filterId) { + // Reset modal state only if we were creating a new knowledge filter setShowSaveModal(false) setContextTitle("") setContextDescription("") } - toast.success(contextId ? "Context updated successfully" : "Context saved successfully") + toast.success(filterId ? "Knowledge filter updated successfully" : "Knowledge filter saved successfully") } else { - toast.error(contextId ? "Failed to update context" : "Failed to save context") + toast.error(filterId ? "Failed to update knowledge filter" : "Failed to save knowledge filter") } } catch { - toast.error(contextId ? "Error updating context" : "Error saving context") + toast.error(filterId ? "Error updating knowledge filter" : "Error saving knowledge filter") } finally { setSavingContext(false) } @@ -843,13 +843,13 @@ function SearchPage() {
- {/* Save Context Button */} + {/* Save Knowledge Filter Button */}
@@ -938,7 +938,7 @@ function SearchPage() { Cancel