3142 lines
No EOL
148 KiB
JSON
3142 lines
No EOL
148 KiB
JSON
{
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"data": {
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"nodes": [
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{
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"data": {
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"description": "Get chat inputs from the Playground.",
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"display_name": "Chat Input",
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"edited": false,
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"id": "ChatInput-8ZRjI",
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"node": {
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"base_classes": [
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"beta": false,
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"conditional_paths": [],
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"custom_fields": {},
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"description": "Get chat inputs from the Playground.",
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"display_name": "Chat Input",
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"documentation": "",
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"edited": true,
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"field_order": [
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"input_value",
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"sender",
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"sender_name",
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"session_id",
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"files"
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],
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"frozen": false,
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"icon": "ChatInput",
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"output_types": [],
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"outputs": [
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{
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"cache": true,
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"display_name": "Message",
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"hidden": false,
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"method": "message_response",
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"name": "message",
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"selected": "Message",
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"types": [
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"Message"
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],
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"value": "__UNDEFINED__"
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}
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],
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"pinned": false,
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"template": {
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"_type": "Component",
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"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=\"User\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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 ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\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 )\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n self.store_message(message)\n self.message.value = message\n\n self.status = message\n return message\n"
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},
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"files": {
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"advanced": true,
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"display_name": "Files",
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"dynamic": false,
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"fileTypes": [
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"txt",
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"md",
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"mdx",
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"csv",
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"json",
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"yaml",
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"yml",
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"xml",
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"html",
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"htm",
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"pdf",
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"docx",
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"py",
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"sh",
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"sql",
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"js",
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"ts",
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"tsx",
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"jpg",
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"jpeg",
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"png",
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"bmp",
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"image"
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],
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"file_path": "",
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"info": "Files to be sent with the message.",
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"list": true,
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"name": "files",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_metadata": true,
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"type": "file",
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"value": ""
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},
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"input_value": {
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"advanced": false,
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"display_name": "Text",
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"dynamic": false,
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"info": "Message to be passed as input.",
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"input_types": [
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"Message"
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],
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "input_value",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": ""
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},
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"sender": {
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|
"advanced": true,
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|
"display_name": "Sender Type",
|
|
"dynamic": false,
|
|
"info": "Type of sender.",
|
|
"name": "sender",
|
|
"options": [
|
|
"Machine",
|
|
"User"
|
|
],
|
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"placeholder": "",
|
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_metadata": true,
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"type": "str",
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"value": "User"
|
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},
|
|
"sender_name": {
|
|
"advanced": true,
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|
"display_name": "Sender Name",
|
|
"dynamic": false,
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"info": "Name of the sender.",
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"input_types": [
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"Message"
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],
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"list": false,
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"load_from_db": false,
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"name": "sender_name",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": "User"
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},
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"session_id": {
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"advanced": true,
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"display_name": "Session ID",
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"dynamic": false,
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"info": "Session ID for the message.",
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"input_types": [
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"Message"
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],
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"list": false,
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"load_from_db": false,
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"name": "session_id",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": ""
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}
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}
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},
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"type": "ChatInput"
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},
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"dragging": false,
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"height": 309,
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"id": "ChatInput-8ZRjI",
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"position": {
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"x": 682.002772470747,
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"y": 253.67030039648512
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},
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"positionAbsolute": {
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"x": 682.002772470747,
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"y": 253.67030039648512
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},
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"selected": false,
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"type": "genericNode",
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"width": 384
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},
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{
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"data": {
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"id": "OpenAIEmbeddings-hNOwh",
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"node": {
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"base_classes": [
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"Embeddings"
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],
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"beta": false,
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"custom_fields": {
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"allowed_special": null,
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"chunk_size": null,
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"client": null,
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"default_headers": null,
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"default_query": null,
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"deployment": null,
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"disallowed_special": null,
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"embedding_ctx_length": null,
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"max_retries": null,
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"model": null,
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"model_kwargs": null,
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"openai_api_base": null,
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"openai_api_key": null,
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"openai_api_type": null,
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"openai_api_version": null,
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"openai_organization": null,
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"openai_proxy": null,
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"request_timeout": null,
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"show_progress_bar": null,
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"skip_empty": null,
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"tiktoken_enable": null,
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"tiktoken_model_name": null
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},
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"description": "Generate embeddings using OpenAI models.",
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"display_name": "OpenAI Embeddings",
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"documentation": "",
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"field_formatters": {},
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"field_order": [],
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"frozen": false,
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"icon": "OpenAI",
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"output_types": [],
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"outputs": [
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{
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"cache": true,
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"display_name": "Embeddings",
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"hidden": false,
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"method": "build_embeddings",
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"name": "embeddings",
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"selected": "Embeddings",
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"types": [
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"Embeddings"
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],
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"value": "__UNDEFINED__"
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}
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],
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"template": {
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"_type": "Component",
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"chunk_size": {
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"advanced": true,
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"display_name": "Chunk Size",
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"dynamic": false,
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"info": "",
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"list": false,
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"name": "chunk_size",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "int",
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"value": 1000
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},
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"client": {
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"advanced": true,
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"display_name": "Client",
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"dynamic": false,
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"info": "",
|
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"input_types": [
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"Message",
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"str"
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],
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"list": false,
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"load_from_db": false,
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"name": "client",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, MessageTextInput\n\n\nclass OpenAIEmbeddingsComponent(LCEmbeddingsModel):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n icon = \"OpenAI\"\n inputs = [\n DictInput(\n name=\"default_headers\",\n display_name=\"Default Headers\",\n advanced=True,\n info=\"Default headers to use for the API request.\",\n ),\n DictInput(\n name=\"default_query\",\n display_name=\"Default Query\",\n advanced=True,\n info=\"Default query parameters to use for the API request.\",\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n MessageTextInput(name=\"client\", display_name=\"Client\", advanced=True),\n MessageTextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n DropdownInput(\n name=\"model\",\n display_name=\"Model\",\n advanced=False,\n options=[\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n value=\"text-embedding-3-small\",\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n MessageTextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n MessageTextInput(\n name=\"openai_organization\",\n display_name=\"OpenAI Organization\",\n advanced=True,\n ),\n MessageTextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n MessageTextInput(\n name=\"tiktoken_model_name\",\n display_name=\"TikToken Model Name\",\n advanced=True,\n ),\n BoolInput(\n name=\"tiktoken_enable\",\n display_name=\"TikToken Enable\",\n advanced=True,\n value=True,\n info=\"If False, you must have transformers installed.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n return OpenAIEmbeddings(\n tiktoken_enabled=self.tiktoken_enable,\n default_headers=self.default_headers,\n default_query=self.default_query,\n allowed_special=\"all\",\n disallowed_special=\"all\",\n chunk_size=self.chunk_size,\n deployment=self.deployment,\n embedding_ctx_length=self.embedding_ctx_length,\n max_retries=self.max_retries,\n model=self.model,\n model_kwargs=self.model_kwargs,\n base_url=self.openai_api_base,\n api_key=self.openai_api_key,\n openai_api_type=self.openai_api_type,\n api_version=self.openai_api_version,\n organization=self.openai_organization,\n openai_proxy=self.openai_proxy,\n timeout=self.request_timeout or None,\n show_progress_bar=self.show_progress_bar,\n skip_empty=self.skip_empty,\n tiktoken_model_name=self.tiktoken_model_name,\n )\n"
|
|
},
|
|
"default_headers": {
|
|
"advanced": true,
|
|
"display_name": "Default Headers",
|
|
"dynamic": false,
|
|
"info": "Default headers to use for the API request.",
|
|
"list": false,
|
|
"name": "default_headers",
|
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"placeholder": "",
|
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"required": false,
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"show": true,
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"title_case": false,
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"type": "dict",
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"value": {}
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},
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"default_query": {
|
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"advanced": true,
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"display_name": "Default Query",
|
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"dynamic": false,
|
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"info": "Default query parameters to use for the API request.",
|
|
"list": false,
|
|
"name": "default_query",
|
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"placeholder": "",
|
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"required": false,
|
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"show": true,
|
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"title_case": false,
|
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"type": "dict",
|
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"value": {}
|
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},
|
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"deployment": {
|
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"advanced": true,
|
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"display_name": "Deployment",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
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"Message",
|
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"str"
|
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],
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"list": false,
|
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"load_from_db": false,
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"name": "deployment",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"embedding_ctx_length": {
|
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"advanced": true,
|
|
"display_name": "Embedding Context Length",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"name": "embedding_ctx_length",
|
|
"placeholder": "",
|
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"required": false,
|
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"show": true,
|
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"title_case": false,
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"type": "int",
|
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"value": 1536
|
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},
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"max_retries": {
|
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"advanced": true,
|
|
"display_name": "Max Retries",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"name": "max_retries",
|
|
"placeholder": "",
|
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"required": false,
|
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"show": true,
|
|
"title_case": false,
|
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"type": "int",
|
|
"value": 3
|
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},
|
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"model": {
|
|
"advanced": false,
|
|
"display_name": "Model",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"name": "model",
|
|
"options": [
|
|
"text-embedding-3-small",
|
|
"text-embedding-3-large",
|
|
"text-embedding-ada-002"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "text-embedding-3-small"
|
|
},
|
|
"model_kwargs": {
|
|
"advanced": true,
|
|
"display_name": "Model Kwargs",
|
|
"dynamic": false,
|
|
"info": "",
|
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"list": false,
|
|
"name": "model_kwargs",
|
|
"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "dict",
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"value": {}
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},
|
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"openai_api_base": {
|
|
"advanced": true,
|
|
"display_name": "OpenAI API Base",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [],
|
|
"load_from_db": true,
|
|
"name": "openai_api_base",
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|
"password": true,
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"openai_api_key": {
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"advanced": false,
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"display_name": "OpenAI API Key",
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"dynamic": false,
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"info": "",
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"input_types": [],
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|
"load_from_db": false,
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"name": "openai_api_key",
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"password": true,
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"placeholder": "",
|
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"required": false,
|
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"openai_api_type": {
|
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"advanced": true,
|
|
"display_name": "OpenAI API Type",
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"dynamic": false,
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|
"info": "",
|
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"input_types": [],
|
|
"load_from_db": true,
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"name": "openai_api_type",
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"password": true,
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"openai_api_version": {
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"advanced": true,
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"display_name": "OpenAI API Version",
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"dynamic": false,
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"info": "",
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"input_types": [
|
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"Message",
|
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"str"
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],
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"list": false,
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"load_from_db": false,
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"name": "openai_api_version",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"display_name": "Skip Empty",
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"dynamic": false,
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"info": "If False, you must have transformers installed.",
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"list": false,
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"name": "tiktoken_enable",
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"Message",
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"str"
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"display_name": "OpenAI",
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"edited": false,
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"id": "OpenAIModel-euVNy",
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"node": {
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"base_classes": [
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"LanguageModel",
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"Message"
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],
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"conditional_paths": [],
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"custom_fields": {},
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"description": "Generates text using OpenAI LLMs.",
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"display_name": "OpenAI",
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"value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n MessageInput(name=\"input_value\", display_name=\"Input\"),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n 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.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n # self.output_schea is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict)\n seed = self.seed\n model_kwargs[\"seed\"] = seed\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n )\n if json_mode:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return\n"
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},
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"input_value": {
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"advanced": false,
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"display_name": "Input",
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"dynamic": false,
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"info": "",
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"input_types": [
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"Message"
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],
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"list": false,
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"load_from_db": false,
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"name": "input_value",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"max_tokens": {
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"advanced": true,
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"display_name": "Max Tokens",
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"dynamic": false,
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"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
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"list": false,
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"name": "max_tokens",
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"placeholder": "",
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"required": false,
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"type": "int",
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"list": false,
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"name": "model_kwargs",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "dict",
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"value": {}
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},
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"model_name": {
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"display_name": "Model Name",
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"dynamic": false,
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"info": "",
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"name": "model_name",
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"options": [
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"gpt-4o",
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"gpt-4-turbo",
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"gpt-4-turbo-preview",
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-0125"
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],
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": "gpt-4-turbo"
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},
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"openai_api_base": {
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"advanced": true,
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"display_name": "OpenAI API Base",
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"dynamic": false,
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"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.",
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"list": false,
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"load_from_db": false,
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"name": "openai_api_base",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"openai_api_key": {
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"advanced": false,
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"display_name": "OpenAI API Key",
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"dynamic": false,
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"info": "The OpenAI API Key to use for the OpenAI model.",
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"input_types": [],
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"load_from_db": false,
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"name": "openai_api_key",
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"password": true,
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"output_schema": {
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"advanced": true,
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"display_name": "Schema",
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"dynamic": false,
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"info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
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"list": true,
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"name": "output_schema",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "dict",
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"value": {}
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},
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"seed": {
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"advanced": true,
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"display_name": "Seed",
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"dynamic": false,
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"info": "The seed controls the reproducibility of the job.",
|
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"list": false,
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"name": "seed",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "int",
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"value": 1
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},
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"stream": {
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"advanced": true,
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"display_name": "Stream",
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"dynamic": false,
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|
"info": "Stream the response from the model. Streaming works only in Chat.",
|
|
"list": false,
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"name": "stream",
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"placeholder": "",
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"required": false,
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"type": "bool",
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"dynamic": false,
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"info": "System message to pass to the model.",
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"list": false,
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"load_from_db": false,
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"name": "system_message",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"temperature": {
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"advanced": false,
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"display_name": "Temperature",
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"dynamic": false,
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"info": "",
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"list": false,
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"name": "temperature",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "float",
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"value": 0.1
|
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}
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}
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},
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"type": "OpenAIModel"
|
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},
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"dragging": false,
|
|
"height": 623,
|
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"id": "OpenAIModel-euVNy",
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"position": {
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"x": 3243.967394111999,
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"y": 392.861541437184
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},
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"positionAbsolute": {
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"x": 3243.967394111999,
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"type": "genericNode",
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"width": 384
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},
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{
|
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"data": {
|
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"description": "Display a chat message in the Playground.",
|
|
"display_name": "Chat Output",
|
|
"edited": false,
|
|
"id": "ChatOutput-1eddV",
|
|
"node": {
|
|
"base_classes": [
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"Message"
|
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],
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"beta": false,
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"conditional_paths": [],
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"custom_fields": {},
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"description": "Display a chat message in the Playground.",
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"display_name": "Chat Output",
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"documentation": "",
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"edited": true,
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"field_order": [
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"input_value",
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"sender",
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"sender_name",
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"session_id",
|
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"data_template"
|
|
],
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|
"frozen": false,
|
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"icon": "ChatOutput",
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"output_types": [],
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"outputs": [
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{
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|
"cache": true,
|
|
"display_name": "Message",
|
|
"method": "message_response",
|
|
"name": "message",
|
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"selected": "Message",
|
|
"types": [
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"Message"
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],
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"value": "__UNDEFINED__"
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}
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],
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"pinned": false,
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"template": {
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"_type": "Component",
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n MessageTextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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 ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n )\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n self.store_message(message)\n self.message.value = message\n\n self.status = message\n return message\n"
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},
|
|
"data_template": {
|
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"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"
|
|
],
|
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"list": false,
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"load_from_db": false,
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"name": "data_template",
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"placeholder": "",
|
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"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "{text}"
|
|
},
|
|
"input_value": {
|
|
"advanced": false,
|
|
"display_name": "Text",
|
|
"dynamic": false,
|
|
"info": "Message to be passed as output.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "input_value",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_input": true,
|
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"trace_as_metadata": true,
|
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"type": "str",
|
|
"value": ""
|
|
},
|
|
"sender": {
|
|
"advanced": true,
|
|
"display_name": "Sender Type",
|
|
"dynamic": false,
|
|
"info": "Type of sender.",
|
|
"name": "sender",
|
|
"options": [
|
|
"Machine",
|
|
"User"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "Machine"
|
|
},
|
|
"sender_name": {
|
|
"advanced": true,
|
|
"display_name": "Sender Name",
|
|
"dynamic": false,
|
|
"info": "Name of the sender.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "sender_name",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": "AI"
|
|
},
|
|
"session_id": {
|
|
"advanced": true,
|
|
"display_name": "Session ID",
|
|
"dynamic": false,
|
|
"info": "Session ID for the message.",
|
|
"input_types": [
|
|
"Message"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "session_id",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"trace_as_input": true,
|
|
"trace_as_metadata": true,
|
|
"type": "str",
|
|
"value": ""
|
|
}
|
|
}
|
|
},
|
|
"type": "ChatOutput"
|
|
},
|
|
"dragging": false,
|
|
"height": 309,
|
|
"id": "ChatOutput-1eddV",
|
|
"position": {
|
|
"x": 3788.786948642587,
|
|
"y": 608.4077159222614
|
|
},
|
|
"positionAbsolute": {
|
|
"x": 3788.786948642587,
|
|
"y": 608.4077159222614
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|
},
|
|
"selected": false,
|
|
"type": "genericNode",
|
|
"width": 384
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "File-p2YBf",
|
|
"node": {
|
|
"base_classes": [
|
|
"Record"
|
|
],
|
|
"beta": false,
|
|
"custom_fields": {
|
|
"path": null,
|
|
"silent_errors": null
|
|
},
|
|
"description": "A generic file loader.",
|
|
"display_name": "File",
|
|
"documentation": "",
|
|
"field_formatters": {},
|
|
"field_order": [],
|
|
"frozen": false,
|
|
"icon": "file-text",
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"cache": true,
|
|
"display_name": "Data",
|
|
"hidden": false,
|
|
"method": "load_file",
|
|
"name": "data",
|
|
"selected": "Data",
|
|
"types": [
|
|
"Data"
|
|
],
|
|
"value": "__UNDEFINED__"
|
|
}
|
|
],
|
|
"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 pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n inputs = [\n FileInput(\n name=\"path\",\n display_name=\"Path\",\n file_types=TEXT_FILE_TYPES,\n info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n ),\n BoolInput(\n name=\"silent_errors\",\n display_name=\"Silent Errors\",\n advanced=True,\n info=\"If true, errors will not raise an exception.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n ]\n\n def load_file(self) -> Data:\n if not self.path:\n raise ValueError(\"Please, upload a file to use this component.\")\n resolved_path = self.resolve_path(self.path)\n silent_errors = self.silent_errors\n\n extension = Path(resolved_path).suffix[1:].lower()\n\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n\n data = parse_text_file_to_data(resolved_path, silent_errors)\n self.status = data if data else \"No data\"\n return data or Data()\n"
|
|
},
|
|
"path": {
|
|
"advanced": false,
|
|
"display_name": "Path",
|
|
"dynamic": false,
|
|
"fileTypes": [
|
|
"txt",
|
|
"md",
|
|
"mdx",
|
|
"csv",
|
|
"json",
|
|
"yaml",
|
|
"yml",
|
|
"xml",
|
|
"html",
|
|
"htm",
|
|
"pdf",
|
|
"docx",
|
|
"py",
|
|
"sh",
|
|
"sql",
|
|
"js",
|
|
"ts",
|
|
"tsx"
|
|
],
|
|
"file_path": "bba1609b-3af2-431d-a884-322cc253c69d/flatland.pdf",
|
|
"info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
|
|
"list": false,
|
|
"name": "path",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "file",
|
|
"value": ""
|
|
},
|
|
"silent_errors": {
|
|
"advanced": true,
|
|
"display_name": "Silent Errors",
|
|
"dynamic": false,
|
|
"info": "If true, errors will not raise an exception.",
|
|
"list": false,
|
|
"name": "silent_errors",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "bool",
|
|
"value": false
|
|
}
|
|
}
|
|
},
|
|
"type": "File"
|
|
},
|
|
"dragging": false,
|
|
"height": 301,
|
|
"id": "File-p2YBf",
|
|
"position": {
|
|
"x": 1435.8917804347734,
|
|
"y": 1603.546667861399
|
|
},
|
|
"positionAbsolute": {
|
|
"x": 1435.8917804347734,
|
|
"y": 1603.546667861399
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode",
|
|
"width": 384
|
|
},
|
|
{
|
|
"data": {
|
|
"description": "Implementation of Vector Store using Astra DB with search capabilities",
|
|
"display_name": "Astra DB Vector Store",
|
|
"id": "AstraDB-p6135",
|
|
"node": {
|
|
"base_classes": [
|
|
"Data"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Implementation of Vector Store using Astra DB with search capabilities",
|
|
"display_name": "Astra DB Vector Store",
|
|
"documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb",
|
|
"edited": false,
|
|
"field_order": [
|
|
"collection_name",
|
|
"token",
|
|
"api_endpoint",
|
|
"vector_store_inputs",
|
|
"embedding",
|
|
"namespace",
|
|
"metric",
|
|
"batch_size",
|
|
"bulk_insert_batch_concurrency",
|
|
"bulk_insert_overwrite_concurrency",
|
|
"bulk_delete_concurrency",
|
|
"setup_mode",
|
|
"pre_delete_collection",
|
|
"metadata_indexing_include",
|
|
"metadata_indexing_exclude",
|
|
"collection_indexing_policy",
|
|
"add_to_vector_store",
|
|
"search_input",
|
|
"search_type",
|
|
"number_of_results"
|
|
],
|
|
"frozen": false,
|
|
"icon": "AstraDB",
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"cache": true,
|
|
"display_name": "Retriever",
|
|
"method": "build_base_retriever",
|
|
"name": "base_retriever",
|
|
"selected": "Data",
|
|
"types": [
|
|
"Data"
|
|
],
|
|
"value": "__UNDEFINED__"
|
|
},
|
|
{
|
|
"cache": true,
|
|
"display_name": "Search Results",
|
|
"hidden": false,
|
|
"method": "search_documents",
|
|
"name": "search_results",
|
|
"selected": "Data",
|
|
"types": [
|
|
"Data"
|
|
],
|
|
"value": "__UNDEFINED__"
|
|
}
|
|
],
|
|
"pinned": false,
|
|
"template": {
|
|
"_type": "Component",
|
|
"add_to_vector_store": {
|
|
"advanced": false,
|
|
"display_name": "Add to Vector Store",
|
|
"dynamic": false,
|
|
"info": "If true, the Vector Store Inputs will be added to the Vector Store.",
|
|
"list": false,
|
|
"name": "add_to_vector_store",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "bool",
|
|
"value": false
|
|
},
|
|
"api_endpoint": {
|
|
"advanced": false,
|
|
"display_name": "API Endpoint",
|
|
"dynamic": false,
|
|
"info": "API endpoint URL for the Astra DB service.",
|
|
"input_types": [],
|
|
"load_from_db": false,
|
|
"name": "api_endpoint",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"batch_size": {
|
|
"advanced": true,
|
|
"display_name": "Batch Size",
|
|
"dynamic": false,
|
|
"info": "Optional number of data to process in a single batch.",
|
|
"list": false,
|
|
"name": "batch_size",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": ""
|
|
},
|
|
"bulk_delete_concurrency": {
|
|
"advanced": true,
|
|
"display_name": "Bulk Delete Concurrency",
|
|
"dynamic": false,
|
|
"info": "Optional concurrency level for bulk delete operations.",
|
|
"list": false,
|
|
"name": "bulk_delete_concurrency",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": ""
|
|
},
|
|
"bulk_insert_batch_concurrency": {
|
|
"advanced": true,
|
|
"display_name": "Bulk Insert Batch Concurrency",
|
|
"dynamic": false,
|
|
"info": "Optional concurrency level for bulk insert operations.",
|
|
"list": false,
|
|
"name": "bulk_insert_batch_concurrency",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": ""
|
|
},
|
|
"bulk_insert_overwrite_concurrency": {
|
|
"advanced": true,
|
|
"display_name": "Bulk Insert Overwrite Concurrency",
|
|
"dynamic": false,
|
|
"info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
|
|
"list": false,
|
|
"name": "bulk_insert_overwrite_concurrency",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"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 loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB Vector Store\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n icon: str = \"AstraDB\"\n\n inputs = [\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n ),\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n ),\n HandleInput(\n name=\"vector_store_inputs\",\n display_name=\"Vector Store Inputs\",\n input_types=[\"Document\", \"Data\"],\n is_list=True,\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"],\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n BoolInput(\n name=\"add_to_vector_store\",\n display_name=\"Add to Vector Store\",\n info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n ]\n\n def build_vector_store(self):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError:\n raise ImportError(\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n vector_store_kwargs = {\n \"embedding\": self.embedding,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n if self.add_to_vector_store:\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def build_base_retriever(self):\n vector_store = self.build_vector_store()\n self.status = self._astradb_collection_to_data(vector_store.collection)\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.vector_store_inputs or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n if documents and self.embedding is not None:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def search_documents(self):\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n if self.search_type == \"Similarity\":\n docs = vector_store.similarity_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n elif self.search_type == \"MMR\":\n docs = vector_store.max_marginal_relevance_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n else:\n raise ValueError(f\"Invalid search type: {self.search_type}\")\n except Exception as e:\n raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = [Data.from_document(doc) for doc in docs]\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n else:\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def _astradb_collection_to_data(self, collection):\n data = []\n data_dict = collection.find()\n if data_dict and \"data\" in data_dict:\n data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n for item in data_dict:\n data.append(Data(content=item[\"content\"]))\n return data\n"
|
|
},
|
|
"collection_indexing_policy": {
|
|
"advanced": true,
|
|
"display_name": "Collection Indexing Policy",
|
|
"dynamic": false,
|
|
"info": "Optional dictionary defining the indexing policy for the collection.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "collection_indexing_policy",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"collection_name": {
|
|
"advanced": false,
|
|
"display_name": "Collection Name",
|
|
"dynamic": false,
|
|
"info": "The name of the collection within Astra DB where the vectors will be stored.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "collection_name",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "langflow"
|
|
},
|
|
"embedding": {
|
|
"advanced": false,
|
|
"display_name": "Embedding",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Embeddings"
|
|
],
|
|
"list": false,
|
|
"name": "embedding",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"metadata_indexing_exclude": {
|
|
"advanced": true,
|
|
"display_name": "Metadata Indexing Exclude",
|
|
"dynamic": false,
|
|
"info": "Optional list of metadata fields to exclude from the indexing.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "metadata_indexing_exclude",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"metadata_indexing_include": {
|
|
"advanced": true,
|
|
"display_name": "Metadata Indexing Include",
|
|
"dynamic": false,
|
|
"info": "Optional list of metadata fields to include in the indexing.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "metadata_indexing_include",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"metric": {
|
|
"advanced": true,
|
|
"display_name": "Metric",
|
|
"dynamic": false,
|
|
"info": "Optional distance metric for vector comparisons in the vector store.",
|
|
"name": "metric",
|
|
"options": [
|
|
"cosine",
|
|
"dot_product",
|
|
"euclidean"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"namespace": {
|
|
"advanced": true,
|
|
"display_name": "Namespace",
|
|
"dynamic": false,
|
|
"info": "Optional namespace within Astra DB to use for the collection.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "namespace",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"number_of_results": {
|
|
"advanced": true,
|
|
"display_name": "Number of Results",
|
|
"dynamic": false,
|
|
"info": "Number of results to return.",
|
|
"list": false,
|
|
"name": "number_of_results",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": 4
|
|
},
|
|
"pre_delete_collection": {
|
|
"advanced": true,
|
|
"display_name": "Pre Delete Collection",
|
|
"dynamic": false,
|
|
"info": "Boolean flag to determine whether to delete the collection before creating a new one.",
|
|
"list": false,
|
|
"name": "pre_delete_collection",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "bool",
|
|
"value": false
|
|
},
|
|
"search_input": {
|
|
"advanced": false,
|
|
"display_name": "Search Input",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Message",
|
|
"str"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "search_input",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"search_type": {
|
|
"advanced": false,
|
|
"display_name": "Search Type",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"name": "search_type",
|
|
"options": [
|
|
"Similarity",
|
|
"MMR"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "Similarity"
|
|
},
|
|
"setup_mode": {
|
|
"advanced": true,
|
|
"display_name": "Setup Mode",
|
|
"dynamic": false,
|
|
"info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
|
|
"name": "setup_mode",
|
|
"options": [
|
|
"Sync",
|
|
"Async",
|
|
"Off"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "Sync"
|
|
},
|
|
"token": {
|
|
"advanced": false,
|
|
"display_name": "Astra DB Application Token",
|
|
"dynamic": false,
|
|
"info": "Authentication token for accessing Astra DB.",
|
|
"input_types": [],
|
|
"load_from_db": false,
|
|
"name": "token",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"vector_store_inputs": {
|
|
"advanced": false,
|
|
"display_name": "Vector Store Inputs",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Document",
|
|
"Data"
|
|
],
|
|
"list": true,
|
|
"name": "vector_store_inputs",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "other",
|
|
"value": ""
|
|
}
|
|
}
|
|
},
|
|
"type": "AstraDB"
|
|
},
|
|
"dragging": false,
|
|
"height": 917,
|
|
"id": "AstraDB-p6135",
|
|
"position": {
|
|
"x": 1298.4611042465333,
|
|
"y": 160.7181472642742
|
|
},
|
|
"positionAbsolute": {
|
|
"x": 1298.4611042465333,
|
|
"y": 160.7181472642742
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode",
|
|
"width": 384
|
|
},
|
|
{
|
|
"data": {
|
|
"id": "ParseData-9DrmC",
|
|
"node": {
|
|
"base_classes": [
|
|
"Message"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {},
|
|
"description": "Convert Data into plain text following a specified template.",
|
|
"display_name": "Parse Data",
|
|
"documentation": "",
|
|
"field_order": [
|
|
"data",
|
|
"template",
|
|
"sep"
|
|
],
|
|
"frozen": false,
|
|
"icon": "braces",
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"cache": true,
|
|
"display_name": "Text",
|
|
"hidden": false,
|
|
"method": "parse_data",
|
|
"name": "text",
|
|
"selected": "Message",
|
|
"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.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n display_name = \"Parse Data\"\n description = \"Convert Data into plain text following a specified template.\"\n icon = \"braces\"\n\n inputs = [\n DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n value=\"{text}\",\n ),\n StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n ]\n\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n ]\n\n def parse_data(self) -> Message:\n data = self.data if isinstance(self.data, list) else [self.data]\n template = self.template\n\n result_string = data_to_text(template, data, sep=self.sep)\n self.status = result_string\n return Message(text=result_string)\n"
|
|
},
|
|
"data": {
|
|
"advanced": false,
|
|
"display_name": "Data",
|
|
"dynamic": false,
|
|
"info": "The data to convert to text.",
|
|
"input_types": [
|
|
"Data"
|
|
],
|
|
"list": false,
|
|
"name": "data",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"sep": {
|
|
"advanced": true,
|
|
"display_name": "Separator",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "sep",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "---"
|
|
},
|
|
"template": {
|
|
"advanced": false,
|
|
"display_name": "Template",
|
|
"dynamic": false,
|
|
"info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
|
|
"input_types": [
|
|
"Message",
|
|
"str"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "template",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "{text}"
|
|
}
|
|
}
|
|
},
|
|
"type": "ParseData"
|
|
},
|
|
"dragging": false,
|
|
"height": 385,
|
|
"id": "ParseData-9DrmC",
|
|
"position": {
|
|
"x": 1911.4866480237615,
|
|
"y": 566.903831987901
|
|
},
|
|
"positionAbsolute": {
|
|
"x": 1911.4866480237615,
|
|
"y": 566.903831987901
|
|
},
|
|
"selected": false,
|
|
"type": "genericNode",
|
|
"width": 384
|
|
},
|
|
{
|
|
"data": {
|
|
"description": "Create a prompt template with dynamic variables.",
|
|
"display_name": "Prompt",
|
|
"id": "Prompt-EEXgw",
|
|
"node": {
|
|
"base_classes": [
|
|
"Message"
|
|
],
|
|
"beta": false,
|
|
"conditional_paths": [],
|
|
"custom_fields": {
|
|
"template": [
|
|
"context",
|
|
"question"
|
|
]
|
|
},
|
|
"description": "Create a prompt template with dynamic variables.",
|
|
"display_name": "Prompt",
|
|
"documentation": "",
|
|
"edited": false,
|
|
"error": null,
|
|
"field_order": [
|
|
"template"
|
|
],
|
|
"frozen": false,
|
|
"full_path": null,
|
|
"icon": "prompts",
|
|
"is_composition": null,
|
|
"is_input": null,
|
|
"is_output": null,
|
|
"name": "",
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"cache": true,
|
|
"display_name": "Prompt Message",
|
|
"hidden": false,
|
|
"method": "build_prompt",
|
|
"name": "prompt",
|
|
"selected": "Message",
|
|
"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.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n async def build_prompt(\n self,\n ) -> Message:\n prompt = await Message.from_template_and_variables(**self._attributes)\n self.status = prompt.text\n return prompt\n\n def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n \"\"\"\n This function is called after the code validation is done.\n \"\"\"\n frontend_node = super().post_code_processing(new_build_config, current_build_config)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n _ = process_prompt_template(\n template=template,\n name=\"template\",\n custom_fields=frontend_node[\"custom_fields\"],\n frontend_node_template=frontend_node[\"template\"],\n )\n # Now that template is updated, we need to grab any values that were set in the current_build_config\n # and update the frontend_node with those values\n update_template_values(frontend_template=frontend_node, raw_template=current_build_config[\"template\"])\n return frontend_node\n"
|
|
},
|
|
"context": {
|
|
"advanced": false,
|
|
"display_name": "context",
|
|
"dynamic": false,
|
|
"field_type": "str",
|
|
"fileTypes": [],
|
|
"file_path": "",
|
|
"info": "",
|
|
"input_types": [
|
|
"Message",
|
|
"Text"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "context",
|
|
"password": false,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"question": {
|
|
"advanced": false,
|
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"display_name": "question",
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"field_type": "str",
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"input_types": [
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"Message",
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"Text"
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],
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "question",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"template": {
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"advanced": false,
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"display_name": "Template",
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"show": true,
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"title_case": false,
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"trace_as_input": true,
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"type": "prompt",
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"value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: "
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}
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}
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},
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"type": "Prompt"
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"node": {
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"base_classes": [
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"Data"
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],
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"beta": false,
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"conditional_paths": [],
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"custom_fields": {},
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"description": "Split text into chunks based on specified criteria.",
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"display_name": "Split Text",
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"documentation": "",
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"edited": false,
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"field_order": [
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"data_inputs",
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"chunk_overlap",
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"chunk_size",
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"separator"
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"frozen": false,
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"icon": "scissors-line-dashed",
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"output_types": [],
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"outputs": [
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{
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"cache": true,
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"display_name": "Chunks",
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"hidden": false,
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"method": "split_text",
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"name": "chunks",
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"selected": "Data",
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"types": [
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"Data"
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],
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"value": "__UNDEFINED__"
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}
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],
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"pinned": false,
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"template": {
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"_type": "Component",
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"chunk_overlap": {
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"advanced": false,
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"display_name": "Chunk Overlap",
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"dynamic": false,
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"info": "Number of characters to overlap between chunks.",
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"list": false,
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"name": "chunk_overlap",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_metadata": true,
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"type": "int",
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"value": 200
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},
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"chunk_size": {
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"advanced": false,
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"display_name": "Chunk Size",
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"dynamic": false,
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"info": "The maximum number of characters in each chunk.",
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"list": false,
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"name": "chunk_size",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_metadata": true,
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"type": "int",
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"value": 1000
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},
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"code": {
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"advanced": true,
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"dynamic": true,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"type": "code",
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"value": "from typing import List\n\nfrom langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.io import HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n display_name: str = \"Split Text\"\n description: str = \"Split text into chunks based on specified criteria.\"\n icon = \"scissors-line-dashed\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"Data Inputs\",\n info=\"The data to split.\",\n input_types=[\"Data\"],\n is_list=True,\n ),\n IntInput(\n name=\"chunk_overlap\",\n display_name=\"Chunk Overlap\",\n info=\"Number of characters to overlap between chunks.\",\n value=200,\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"The maximum number of characters in each chunk.\",\n value=1000,\n ),\n MessageTextInput(\n name=\"separator\",\n display_name=\"Separator\",\n info=\"The character to split on. Defaults to newline.\",\n value=\"\\n\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Chunks\", name=\"chunks\", method=\"split_text\"),\n ]\n\n def _docs_to_data(self, docs):\n data = []\n for doc in docs:\n data.append(Data(text=doc.page_content, data=doc.metadata))\n return data\n\n def split_text(self) -> List[Data]:\n separator = unescape_string(self.separator)\n\n documents = []\n for _input in self.data_inputs:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n\n splitter = CharacterTextSplitter(\n chunk_overlap=self.chunk_overlap,\n chunk_size=self.chunk_size,\n separator=separator,\n )\n docs = splitter.split_documents(documents)\n data = self._docs_to_data(docs)\n self.status = data\n return data\n"
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},
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"data_inputs": {
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"advanced": false,
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"display_name": "Data Inputs",
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"dynamic": false,
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"info": "The data to split.",
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"input_types": [
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"Data"
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],
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"list": true,
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"name": "data_inputs",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_metadata": true,
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"type": "other",
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"value": ""
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},
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"separator": {
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"advanced": false,
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"display_name": "Separator",
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"dynamic": false,
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"info": "The character to split on. Defaults to newline.",
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"input_types": [
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"Message"
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],
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"list": false,
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"load_from_db": false,
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"name": "separator",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": "\n"
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}
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}
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},
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"type": "SplitText"
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},
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"dragging": false,
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"height": 557,
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"id": "SplitText-qI0jS",
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"position": {
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"x": 1992.5159478772907,
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"y": 1190.8288727494814
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"type": "genericNode",
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},
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{
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"id": "AstraDB-AX2Xz",
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"type": "genericNode",
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"position": {
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"x": 2773.0562333179937,
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"y": 1160.0660495763536
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},
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"data": {
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"description": "Implementation of Vector Store using Astra DB with search capabilities",
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"display_name": "Astra DB Vector Store",
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"id": "AstraDB-AX2Xz",
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"node": {
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"base_classes": [
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"Data"
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],
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"beta": false,
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"conditional_paths": [],
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"custom_fields": {},
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"description": "Implementation of Vector Store using Astra DB with search capabilities",
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"display_name": "Astra DB Vector Store",
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"documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb",
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"edited": false,
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"field_order": [
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"collection_name",
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"token",
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"api_endpoint",
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"vector_store_inputs",
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"embedding",
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"namespace",
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"metric",
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"batch_size",
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"bulk_insert_batch_concurrency",
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"bulk_insert_overwrite_concurrency",
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"bulk_delete_concurrency",
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"setup_mode",
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"pre_delete_collection",
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"metadata_indexing_include",
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"metadata_indexing_exclude",
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"collection_indexing_policy",
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"add_to_vector_store",
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"search_input",
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"search_type",
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"number_of_results"
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],
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"frozen": false,
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"icon": "AstraDB",
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"output_types": [],
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"outputs": [
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{
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"cache": true,
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"display_name": "Retriever",
|
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"method": "build_base_retriever",
|
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"name": "base_retriever",
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"selected": "Data",
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"types": [
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"Data"
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],
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"value": "__UNDEFINED__"
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},
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{
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"cache": true,
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"display_name": "Search Results",
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"hidden": false,
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"method": "search_documents",
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"name": "search_results",
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"selected": "Data",
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"types": [
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"Data"
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],
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}
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],
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"pinned": false,
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"template": {
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"_type": "Component",
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"add_to_vector_store": {
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"advanced": false,
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"display_name": "Add to Vector Store",
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"dynamic": false,
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"info": "If true, the Vector Store Inputs will be added to the Vector Store.",
|
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"list": false,
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"name": "add_to_vector_store",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "bool",
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"value": true
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},
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"api_endpoint": {
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"advanced": false,
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"display_name": "API Endpoint",
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"dynamic": false,
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"info": "API endpoint URL for the Astra DB service.",
|
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"input_types": [],
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"load_from_db": false,
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"name": "api_endpoint",
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"password": true,
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "str",
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"value": ""
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},
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"batch_size": {
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"advanced": true,
|
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"display_name": "Batch Size",
|
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"dynamic": false,
|
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"info": "Optional number of data to process in a single batch.",
|
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"list": false,
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"name": "batch_size",
|
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"placeholder": "",
|
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"required": false,
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"show": true,
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"title_case": false,
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"type": "int",
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"value": ""
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},
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"bulk_delete_concurrency": {
|
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"advanced": true,
|
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"display_name": "Bulk Delete Concurrency",
|
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"dynamic": false,
|
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"info": "Optional concurrency level for bulk delete operations.",
|
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"list": false,
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"name": "bulk_delete_concurrency",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "int",
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"value": ""
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},
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"bulk_insert_batch_concurrency": {
|
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"advanced": true,
|
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"display_name": "Bulk Insert Batch Concurrency",
|
|
"dynamic": false,
|
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"info": "Optional concurrency level for bulk insert operations.",
|
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"list": false,
|
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"name": "bulk_insert_batch_concurrency",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "int",
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"value": ""
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},
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"bulk_insert_overwrite_concurrency": {
|
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"advanced": true,
|
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"display_name": "Bulk Insert Overwrite Concurrency",
|
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"dynamic": false,
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"info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
|
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"list": false,
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"name": "bulk_insert_overwrite_concurrency",
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"placeholder": "",
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"required": false,
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"show": true,
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"title_case": false,
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"type": "int",
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"value": ""
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},
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"code": {
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"advanced": true,
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"dynamic": true,
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"fileTypes": [],
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"file_path": "",
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"info": "",
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"list": false,
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"load_from_db": false,
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"multiline": true,
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"name": "code",
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"password": false,
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"placeholder": "",
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"required": true,
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB Vector Store\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n icon: str = \"AstraDB\"\n\n inputs = [\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n ),\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n ),\n HandleInput(\n name=\"vector_store_inputs\",\n display_name=\"Vector Store Inputs\",\n input_types=[\"Document\", \"Data\"],\n is_list=True,\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"],\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n BoolInput(\n name=\"add_to_vector_store\",\n display_name=\"Add to Vector Store\",\n info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n ]\n\n def build_vector_store(self):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError:\n raise ImportError(\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n vector_store_kwargs = {\n \"embedding\": self.embedding,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n if self.add_to_vector_store:\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def build_base_retriever(self):\n vector_store = self.build_vector_store()\n self.status = self._astradb_collection_to_data(vector_store.collection)\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.vector_store_inputs or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n if documents and self.embedding is not None:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def search_documents(self):\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n if self.search_type == \"Similarity\":\n docs = vector_store.similarity_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n elif self.search_type == \"MMR\":\n docs = vector_store.max_marginal_relevance_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n else:\n raise ValueError(f\"Invalid search type: {self.search_type}\")\n except Exception as e:\n raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = [Data.from_document(doc) for doc in docs]\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n else:\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def _astradb_collection_to_data(self, collection):\n data = []\n data_dict = collection.find()\n if data_dict and \"data\" in data_dict:\n data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n for item in data_dict:\n data.append(Data(content=item[\"content\"]))\n return data\n"
|
|
},
|
|
"collection_indexing_policy": {
|
|
"advanced": true,
|
|
"display_name": "Collection Indexing Policy",
|
|
"dynamic": false,
|
|
"info": "Optional dictionary defining the indexing policy for the collection.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "collection_indexing_policy",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"collection_name": {
|
|
"advanced": false,
|
|
"display_name": "Collection Name",
|
|
"dynamic": false,
|
|
"info": "The name of the collection within Astra DB where the vectors will be stored.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "collection_name",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "langflow"
|
|
},
|
|
"embedding": {
|
|
"advanced": false,
|
|
"display_name": "Embedding",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Embeddings"
|
|
],
|
|
"list": false,
|
|
"name": "embedding",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "other",
|
|
"value": ""
|
|
},
|
|
"metadata_indexing_exclude": {
|
|
"advanced": true,
|
|
"display_name": "Metadata Indexing Exclude",
|
|
"dynamic": false,
|
|
"info": "Optional list of metadata fields to exclude from the indexing.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "metadata_indexing_exclude",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"metadata_indexing_include": {
|
|
"advanced": true,
|
|
"display_name": "Metadata Indexing Include",
|
|
"dynamic": false,
|
|
"info": "Optional list of metadata fields to include in the indexing.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "metadata_indexing_include",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"metric": {
|
|
"advanced": true,
|
|
"display_name": "Metric",
|
|
"dynamic": false,
|
|
"info": "Optional distance metric for vector comparisons in the vector store.",
|
|
"name": "metric",
|
|
"options": [
|
|
"cosine",
|
|
"dot_product",
|
|
"euclidean"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"namespace": {
|
|
"advanced": true,
|
|
"display_name": "Namespace",
|
|
"dynamic": false,
|
|
"info": "Optional namespace within Astra DB to use for the collection.",
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "namespace",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"number_of_results": {
|
|
"advanced": true,
|
|
"display_name": "Number of Results",
|
|
"dynamic": false,
|
|
"info": "Number of results to return.",
|
|
"list": false,
|
|
"name": "number_of_results",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": 4
|
|
},
|
|
"pre_delete_collection": {
|
|
"advanced": true,
|
|
"display_name": "Pre Delete Collection",
|
|
"dynamic": false,
|
|
"info": "Boolean flag to determine whether to delete the collection before creating a new one.",
|
|
"list": false,
|
|
"name": "pre_delete_collection",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "bool",
|
|
"value": false
|
|
},
|
|
"search_input": {
|
|
"advanced": false,
|
|
"display_name": "Search Input",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Message",
|
|
"str"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"multiline": true,
|
|
"name": "search_input",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"search_type": {
|
|
"advanced": false,
|
|
"display_name": "Search Type",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"name": "search_type",
|
|
"options": [
|
|
"Similarity",
|
|
"MMR"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "Similarity"
|
|
},
|
|
"setup_mode": {
|
|
"advanced": true,
|
|
"display_name": "Setup Mode",
|
|
"dynamic": false,
|
|
"info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
|
|
"name": "setup_mode",
|
|
"options": [
|
|
"Sync",
|
|
"Async",
|
|
"Off"
|
|
],
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": "Sync"
|
|
},
|
|
"token": {
|
|
"advanced": false,
|
|
"display_name": "Astra DB Application Token",
|
|
"dynamic": false,
|
|
"info": "Authentication token for accessing Astra DB.",
|
|
"input_types": [],
|
|
"load_from_db": false,
|
|
"name": "token",
|
|
"password": true,
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"vector_store_inputs": {
|
|
"advanced": false,
|
|
"display_name": "Vector Store Inputs",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Document",
|
|
"Data"
|
|
],
|
|
"list": true,
|
|
"name": "vector_store_inputs",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "other",
|
|
"value": ""
|
|
}
|
|
}
|
|
},
|
|
"type": "AstraDB"
|
|
},
|
|
"selected": false,
|
|
"width": 384,
|
|
"height": 917,
|
|
"positionAbsolute": {
|
|
"x": 2773.0562333179937,
|
|
"y": 1160.0660495763536
|
|
},
|
|
"dragging": false
|
|
},
|
|
{
|
|
"id": "OpenAIEmbeddings-PQc6x",
|
|
"type": "genericNode",
|
|
"position": {
|
|
"x": 1992.590633037642,
|
|
"y": 1850.2707576021312
|
|
},
|
|
"data": {
|
|
"id": "OpenAIEmbeddings-PQc6x",
|
|
"node": {
|
|
"base_classes": [
|
|
"Embeddings"
|
|
],
|
|
"beta": false,
|
|
"custom_fields": {
|
|
"allowed_special": null,
|
|
"chunk_size": null,
|
|
"client": null,
|
|
"default_headers": null,
|
|
"default_query": null,
|
|
"deployment": null,
|
|
"disallowed_special": null,
|
|
"embedding_ctx_length": null,
|
|
"max_retries": null,
|
|
"model": null,
|
|
"model_kwargs": null,
|
|
"openai_api_base": null,
|
|
"openai_api_key": null,
|
|
"openai_api_type": null,
|
|
"openai_api_version": null,
|
|
"openai_organization": null,
|
|
"openai_proxy": null,
|
|
"request_timeout": null,
|
|
"show_progress_bar": null,
|
|
"skip_empty": null,
|
|
"tiktoken_enable": null,
|
|
"tiktoken_model_name": null
|
|
},
|
|
"description": "Generate embeddings using OpenAI models.",
|
|
"display_name": "OpenAI Embeddings",
|
|
"documentation": "",
|
|
"field_formatters": {},
|
|
"field_order": [],
|
|
"frozen": false,
|
|
"icon": "OpenAI",
|
|
"output_types": [],
|
|
"outputs": [
|
|
{
|
|
"cache": true,
|
|
"display_name": "Embeddings",
|
|
"hidden": false,
|
|
"method": "build_embeddings",
|
|
"name": "embeddings",
|
|
"selected": "Embeddings",
|
|
"types": [
|
|
"Embeddings"
|
|
],
|
|
"value": "__UNDEFINED__"
|
|
}
|
|
],
|
|
"template": {
|
|
"_type": "Component",
|
|
"chunk_size": {
|
|
"advanced": true,
|
|
"display_name": "Chunk Size",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"name": "chunk_size",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": 1000
|
|
},
|
|
"client": {
|
|
"advanced": true,
|
|
"display_name": "Client",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Message",
|
|
"str"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "client",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"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 langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, MessageTextInput\n\n\nclass OpenAIEmbeddingsComponent(LCEmbeddingsModel):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n icon = \"OpenAI\"\n inputs = [\n DictInput(\n name=\"default_headers\",\n display_name=\"Default Headers\",\n advanced=True,\n info=\"Default headers to use for the API request.\",\n ),\n DictInput(\n name=\"default_query\",\n display_name=\"Default Query\",\n advanced=True,\n info=\"Default query parameters to use for the API request.\",\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n MessageTextInput(name=\"client\", display_name=\"Client\", advanced=True),\n MessageTextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n DropdownInput(\n name=\"model\",\n display_name=\"Model\",\n advanced=False,\n options=[\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n value=\"text-embedding-3-small\",\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n MessageTextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n MessageTextInput(\n name=\"openai_organization\",\n display_name=\"OpenAI Organization\",\n advanced=True,\n ),\n MessageTextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n MessageTextInput(\n name=\"tiktoken_model_name\",\n display_name=\"TikToken Model Name\",\n advanced=True,\n ),\n BoolInput(\n name=\"tiktoken_enable\",\n display_name=\"TikToken Enable\",\n advanced=True,\n value=True,\n info=\"If False, you must have transformers installed.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n return OpenAIEmbeddings(\n tiktoken_enabled=self.tiktoken_enable,\n default_headers=self.default_headers,\n default_query=self.default_query,\n allowed_special=\"all\",\n disallowed_special=\"all\",\n chunk_size=self.chunk_size,\n deployment=self.deployment,\n embedding_ctx_length=self.embedding_ctx_length,\n max_retries=self.max_retries,\n model=self.model,\n model_kwargs=self.model_kwargs,\n base_url=self.openai_api_base,\n api_key=self.openai_api_key,\n openai_api_type=self.openai_api_type,\n api_version=self.openai_api_version,\n organization=self.openai_organization,\n openai_proxy=self.openai_proxy,\n timeout=self.request_timeout or None,\n show_progress_bar=self.show_progress_bar,\n skip_empty=self.skip_empty,\n tiktoken_model_name=self.tiktoken_model_name,\n )\n"
|
|
},
|
|
"default_headers": {
|
|
"advanced": true,
|
|
"display_name": "Default Headers",
|
|
"dynamic": false,
|
|
"info": "Default headers to use for the API request.",
|
|
"list": false,
|
|
"name": "default_headers",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "dict",
|
|
"value": {}
|
|
},
|
|
"default_query": {
|
|
"advanced": true,
|
|
"display_name": "Default Query",
|
|
"dynamic": false,
|
|
"info": "Default query parameters to use for the API request.",
|
|
"list": false,
|
|
"name": "default_query",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "dict",
|
|
"value": {}
|
|
},
|
|
"deployment": {
|
|
"advanced": true,
|
|
"display_name": "Deployment",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"input_types": [
|
|
"Message",
|
|
"str"
|
|
],
|
|
"list": false,
|
|
"load_from_db": false,
|
|
"name": "deployment",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "str",
|
|
"value": ""
|
|
},
|
|
"embedding_ctx_length": {
|
|
"advanced": true,
|
|
"display_name": "Embedding Context Length",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"name": "embedding_ctx_length",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": 1536
|
|
},
|
|
"max_retries": {
|
|
"advanced": true,
|
|
"display_name": "Max Retries",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"list": false,
|
|
"name": "max_retries",
|
|
"placeholder": "",
|
|
"required": false,
|
|
"show": true,
|
|
"title_case": false,
|
|
"type": "int",
|
|
"value": 3
|
|
},
|
|
"model": {
|
|
"advanced": false,
|
|
"display_name": "Model",
|
|
"dynamic": false,
|
|
"info": "",
|
|
"name": "model",
|
|
"options": [
|
|
"text-embedding-3-small",
|
|
"text-embedding-3-large",
|
|
"text-embedding-ada-002"
|
|
],
|
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|
|
"Embeddings"
|
|
],
|
|
"type": "other"
|
|
},
|
|
"sourceHandle": {
|
|
"dataType": "OpenAIEmbeddings",
|
|
"id": "OpenAIEmbeddings-PQc6x",
|
|
"name": "embeddings",
|
|
"output_types": [
|
|
"Embeddings"
|
|
]
|
|
}
|
|
},
|
|
"id": "reactflow__edge-OpenAIEmbeddings-PQc6x{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-PQc6xœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-AstraDB-AX2Xz{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-AX2Xzœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
|
|
"selected": false
|
|
}
|
|
],
|
|
"viewport": {
|
|
"x": -173.2239134973895,
|
|
"y": 90.45548562382544,
|
|
"zoom": 0.32281188532359306
|
|
}
|
|
},
|
|
"description": "Visit https://docs.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.",
|
|
"name": "Vector Store RAG",
|
|
"last_tested_version": "1.0.0rc1",
|
|
"endpoint_name": null,
|
|
"is_component": false
|
|
} |