update projects
This commit is contained in:
ogabrielluiz
parent
4b71295caf
commit
aa1d32bc95
6 changed files with 121 additions and 323 deletions
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@ -65,23 +65,23 @@
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"id": "OpenAIModel-SsPHS",
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"name": "text_output",
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"output_types": [
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"Text"
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"Message"
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]
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},
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"targetHandle": {
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"fieldName": "input_value",
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"id": "ChatOutput-xfhJ1",
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"inputTypes": [
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"Text"
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"Message"
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],
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"type": "str"
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}
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},
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"id": "reactflow__edge-OpenAIModel-SsPHS{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-SsPHSœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œTextœ]}-ChatOutput-xfhJ1{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-xfhJ1œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
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"source": "OpenAIModel-SsPHS",
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"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-SsPHSœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œTextœ]}",
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"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-SsPHSœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
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"target": "ChatOutput-xfhJ1",
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"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-xfhJ1œ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
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"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-xfhJ1œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
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}
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],
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"nodes": [
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@ -119,7 +119,7 @@
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"outputs": [
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{
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"cache": true,
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"display_name": "Prompt",
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"display_name": "Prompt Message",
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"method": "build_prompt",
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"name": "prompt",
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"selected": "Message",
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@ -127,17 +127,6 @@
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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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"cache": true,
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"display_name": "Text",
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"method": "format_prompt",
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"name": "text",
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"selected": "Text",
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"types": [
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"Text"
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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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@ -158,7 +147,7 @@
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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.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Message:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n"
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"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\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 kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n prompt_message = Message(text=prompt.format_text(), **kwargs)\n self.status = prompt_message\n return prompt_message\n"
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},
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"template": {
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"advanced": false,
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@ -265,17 +254,6 @@
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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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"cache": true,
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"display_name": "Text",
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"method": "text_response",
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"name": "text",
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"selected": "Text",
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"types": [
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"Text"
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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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@ -296,7 +274,7 @@
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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.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 StrInput(\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 StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n StrInput(\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 Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
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"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\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 TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n TextInput(\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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},
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"input_value": {
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"advanced": false,
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@ -306,7 +284,7 @@
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"file_path": "",
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"info": "Message to be passed as output.",
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"input_types": [
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"Text"
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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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@ -354,7 +332,7 @@
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"file_path": "",
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"info": "Name of the sender.",
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"input_types": [
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"Text"
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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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@ -376,7 +354,7 @@
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"file_path": "",
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"info": "Session ID for the message.",
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"input_types": [
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"Text"
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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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@ -446,17 +424,6 @@
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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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"cache": true,
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"display_name": "Text",
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"method": "text_response",
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"name": "text",
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"selected": "Text",
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"types": [
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"Text"
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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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@ -477,7 +444,7 @@
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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.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\n input_types=[],\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
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"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=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 )\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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"input_value": {
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"advanced": false,
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@ -486,7 +453,9 @@
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"fileTypes": [],
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"file_path": "",
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"info": "Message to be passed as input.",
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"input_types": [],
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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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@ -624,9 +593,9 @@
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"display_name": "Text",
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"method": "text_response",
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"name": "text_output",
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"selected": "Text",
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"selected": "Message",
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"types": [
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"Text"
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"Message"
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],
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"value": "__UNDEFINED__"
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},
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@ -661,7 +630,7 @@
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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 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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 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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = self.json_mode\n seed = self.seed\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n response_format = None\n if json_mode:\n response_format = {\"type\": \"json_object\"}\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n response_format=response_format,\n seed=seed,\n )\n\n return output\n"
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"value": "from langchain_openai import ChatOpenAI\nfrom langflow.schema.message import Message\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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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=\"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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Message:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(self.schema)\n seed = self.seed\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 or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n seed=seed,\n )\n if json_mode:\n output = output.with_structured_output(schema=self.schema, method=\"json_mode\")\n\n return output\n"
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},
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"input_value": {
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"advanced": false,
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@ -40,26 +40,26 @@
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"id": "OpenAIModel-gi29P",
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"name": "text_output",
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"output_types": [
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"Text"
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"Message"
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]
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},
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"targetHandle": {
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"fieldName": "input_value",
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"id": "ChatOutput-JPlxl",
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"inputTypes": [
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"Text"
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"Message"
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],
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"type": "str"
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}
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},
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"id": "reactflow__edge-OpenAIModel-gi29P{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}-ChatOutput-JPlxl{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
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"source": "OpenAIModel-gi29P",
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"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-gi29Pœ, œoutput_typesœ: [œTextœ], œnameœ: œtext_outputœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-gi29Pœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
"target": "ChatOutput-JPlxl",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-JPlxlœ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-JPlxlœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
},
|
||||
{
|
||||
"className": "stroke-gray-900 stroke-connection",
|
||||
|
|
@ -191,7 +191,7 @@
|
|||
"outputs": [
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Prompt",
|
||||
"display_name": "Prompt Message",
|
||||
"method": "build_prompt",
|
||||
"name": "prompt",
|
||||
"selected": "Message",
|
||||
|
|
@ -199,17 +199,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "format_prompt",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -230,7 +219,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Message:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n"
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\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 kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n prompt_message = Message(text=prompt.format_text(), **kwargs)\n self.status = prompt_message\n return prompt_message\n"
|
||||
},
|
||||
"instructions": {
|
||||
"advanced": false,
|
||||
|
|
@ -401,7 +390,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass URLComponent(Component):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n inputs = [\n TextInput(\n name=\"urls\",\n display_name=\"URLs\",\n info=\"Enter one or more URLs, separated by commas.\",\n value=\"\",\n is_list=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n ]\n\n def ensure_url(self, string: str) -> str:\n \"\"\"\n Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n Raises an error if the string is not a valid URL.\n\n Parameters:\n string (str): The string to be checked and possibly modified.\n\n Returns:\n str: The modified string that is ensured to be a URL.\n\n Raises:\n ValueError: If the string is not a valid URL.\n \"\"\"\n if not string.startswith((\"http://\", \"https://\")):\n string = \"http://\" + string\n\n # Basic URL validation regex\n url_regex = re.compile(\n r\"^(http://|https://)?\" # http:// or https://\n r\"(([a-zA-Z0-9\\.-]+)\" # domain\n r\"(\\.[a-zA-Z]{2,}))\" # top-level domain\n r\"(:[0-9]{1,5})?\" # optional port\n r\"(\\/.*)?$\" # optional path\n )\n\n if not re.match(url_regex, string):\n raise ValueError(f\"Invalid URL: {string}\")\n\n return string\n\n def fetch_content(self) -> Data:\n urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n docs = loader.load()\n data = [Data(text_key=\"text\", content=doc.page_content, **doc.metadata) for doc in docs]\n self.status = data\n return data\n"
|
||||
"value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass URLComponent(Component):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n inputs = [\n TextInput(\n name=\"urls\",\n display_name=\"URLs\",\n info=\"Enter one or more URLs, separated by commas.\",\n value=\"\",\n is_list=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n ]\n\n def ensure_url(self, string: str) -> str:\n \"\"\"\n Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n Raises an error if the string is not a valid URL.\n\n Parameters:\n string (str): The string to be checked and possibly modified.\n\n Returns:\n str: The modified string that is ensured to be a URL.\n\n Raises:\n ValueError: If the string is not a valid URL.\n \"\"\"\n if not string.startswith((\"http://\", \"https://\")):\n string = \"http://\" + string\n\n # Basic URL validation regex\n url_regex = re.compile(\n r\"^(http://|https://)?\" # http:// or https://\n r\"(([a-zA-Z0-9\\.-]+)\" # domain\n r\"(\\.[a-zA-Z]{2,}))\" # top-level domain\n r\"(:[0-9]{1,5})?\" # optional port\n r\"(\\/.*)?$\" # optional path\n )\n\n if not re.match(url_regex, string):\n raise ValueError(f\"Invalid URL: {string}\")\n\n return string\n\n def fetch_content(self) -> Data:\n urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n docs = loader.load()\n data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n self.status = data\n return data\n"
|
||||
},
|
||||
"urls": {
|
||||
"advanced": false,
|
||||
|
|
@ -475,17 +464,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -506,7 +484,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 StrInput(\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 StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n StrInput(\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 Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\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 TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n TextInput(\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -516,7 +494,7 @@
|
|||
"file_path": "",
|
||||
"info": "Message to be passed as output.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -564,7 +542,7 @@
|
|||
"file_path": "",
|
||||
"info": "Name of the sender.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -586,7 +564,7 @@
|
|||
"file_path": "",
|
||||
"info": "Session ID for the message.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -659,9 +637,9 @@
|
|||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text_output",
|
||||
"selected": "Text",
|
||||
"selected": "Message",
|
||||
"types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
|
|
@ -695,7 +673,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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 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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = self.json_mode\n seed = self.seed\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n response_format = None\n if json_mode:\n response_format = {\"type\": \"json_object\"}\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n response_format=response_format,\n seed=seed,\n )\n\n return output\n"
|
||||
"value": "from langchain_openai import ChatOpenAI\nfrom langflow.schema.message import Message\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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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=\"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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Message:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(self.schema)\n seed = self.seed\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 or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n seed=seed,\n )\n if json_mode:\n output = output.with_structured_output(schema=self.schema, method=\"json_mode\")\n\n return output\n"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -971,7 +949,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass URLComponent(Component):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n inputs = [\n TextInput(\n name=\"urls\",\n display_name=\"URLs\",\n info=\"Enter one or more URLs, separated by commas.\",\n value=\"\",\n is_list=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n ]\n\n def ensure_url(self, string: str) -> str:\n \"\"\"\n Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n Raises an error if the string is not a valid URL.\n\n Parameters:\n string (str): The string to be checked and possibly modified.\n\n Returns:\n str: The modified string that is ensured to be a URL.\n\n Raises:\n ValueError: If the string is not a valid URL.\n \"\"\"\n if not string.startswith((\"http://\", \"https://\")):\n string = \"http://\" + string\n\n # Basic URL validation regex\n url_regex = re.compile(\n r\"^(http://|https://)?\" # http:// or https://\n r\"(([a-zA-Z0-9\\.-]+)\" # domain\n r\"(\\.[a-zA-Z]{2,}))\" # top-level domain\n r\"(:[0-9]{1,5})?\" # optional port\n r\"(\\/.*)?$\" # optional path\n )\n\n if not re.match(url_regex, string):\n raise ValueError(f\"Invalid URL: {string}\")\n\n return string\n\n def fetch_content(self) -> Data:\n urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n docs = loader.load()\n data = [Data(text_key=\"text\", content=doc.page_content, **doc.metadata) for doc in docs]\n self.status = data\n return data\n"
|
||||
"value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass URLComponent(Component):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n inputs = [\n TextInput(\n name=\"urls\",\n display_name=\"URLs\",\n info=\"Enter one or more URLs, separated by commas.\",\n value=\"\",\n is_list=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n ]\n\n def ensure_url(self, string: str) -> str:\n \"\"\"\n Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n Raises an error if the string is not a valid URL.\n\n Parameters:\n string (str): The string to be checked and possibly modified.\n\n Returns:\n str: The modified string that is ensured to be a URL.\n\n Raises:\n ValueError: If the string is not a valid URL.\n \"\"\"\n if not string.startswith((\"http://\", \"https://\")):\n string = \"http://\" + string\n\n # Basic URL validation regex\n url_regex = re.compile(\n r\"^(http://|https://)?\" # http:// or https://\n r\"(([a-zA-Z0-9\\.-]+)\" # domain\n r\"(\\.[a-zA-Z]{2,}))\" # top-level domain\n r\"(:[0-9]{1,5})?\" # optional port\n r\"(\\/.*)?$\" # optional path\n )\n\n if not re.match(url_regex, string):\n raise ValueError(f\"Invalid URL: {string}\")\n\n return string\n\n def fetch_content(self) -> Data:\n urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n docs = loader.load()\n data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n self.status = data\n return data\n"
|
||||
},
|
||||
"urls": {
|
||||
"advanced": false,
|
||||
|
|
|
|||
|
|
@ -89,23 +89,23 @@
|
|||
"id": "OpenAIModel-8b6nG",
|
||||
"name": "text_output",
|
||||
"output_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
]
|
||||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
"id": "ChatOutput-y4SCS",
|
||||
"inputTypes": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"type": "str"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-OpenAIModel-8b6nG{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-8b6nGœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œTextœ]}-ChatOutput-y4SCS{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-y4SCSœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
|
||||
"source": "OpenAIModel-8b6nG",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-8b6nGœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œTextœ]}",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-8b6nGœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
|
||||
"target": "ChatOutput-y4SCS",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-y4SCSœ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-y4SCSœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
}
|
||||
],
|
||||
"nodes": [
|
||||
|
|
@ -144,7 +144,7 @@
|
|||
"outputs": [
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Prompt",
|
||||
"display_name": "Prompt Message",
|
||||
"method": "build_prompt",
|
||||
"name": "prompt",
|
||||
"selected": "Message",
|
||||
|
|
@ -152,17 +152,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "format_prompt",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"pinned": false,
|
||||
|
|
@ -236,7 +225,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Message:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n"
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\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 kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n prompt_message = Message(text=prompt.format_text(), **kwargs)\n self.status = prompt_message\n return prompt_message\n"
|
||||
},
|
||||
"template": {
|
||||
"advanced": false,
|
||||
|
|
@ -316,17 +305,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -347,7 +325,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\n input_types=[],\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=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 )\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -356,7 +334,9 @@
|
|||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"info": "Message to be passed as input.",
|
||||
"input_types": [],
|
||||
"input_types": [
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
"multiline": true,
|
||||
|
|
@ -495,17 +475,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -526,7 +495,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 StrInput(\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 StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n StrInput(\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 Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\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 TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n TextInput(\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -536,7 +505,7 @@
|
|||
"file_path": "",
|
||||
"info": "Message to be passed as output.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -584,7 +553,7 @@
|
|||
"file_path": "",
|
||||
"info": "Name of the sender.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -606,7 +575,7 @@
|
|||
"file_path": "",
|
||||
"info": "Session ID for the message.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -798,9 +767,9 @@
|
|||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text_output",
|
||||
"selected": "Text",
|
||||
"selected": "Message",
|
||||
"types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
|
|
@ -835,7 +804,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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 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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = self.json_mode\n seed = self.seed\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n response_format = None\n if json_mode:\n response_format = {\"type\": \"json_object\"}\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n response_format=response_format,\n seed=seed,\n )\n\n return output\n"
|
||||
"value": "from langchain_openai import ChatOpenAI\nfrom langflow.schema.message import Message\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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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=\"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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Message:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(self.schema)\n seed = self.seed\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 or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n seed=seed,\n )\n if json_mode:\n output = output.with_structured_output(schema=self.schema, method=\"json_mode\")\n\n return output\n"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
|
|||
|
|
@ -104,26 +104,26 @@
|
|||
"id": "OpenAIModel-9RykF",
|
||||
"name": "text_output",
|
||||
"output_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
]
|
||||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
"id": "ChatOutput-P1jEe",
|
||||
"inputTypes": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"type": "str"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-OpenAIModel-9RykF{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}-ChatOutput-P1jEe{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
|
||||
"source": "OpenAIModel-9RykF",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-9RykFœ, œoutput_typesœ: [œTextœ], œnameœ: œtext_outputœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-9RykFœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
"target": "ChatOutput-P1jEe",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-P1jEeœ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-P1jEeœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
},
|
||||
{
|
||||
"className": "stroke-foreground stroke-connection",
|
||||
|
|
@ -194,17 +194,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -225,7 +214,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\n input_types=[],\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=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 )\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -234,7 +223,9 @@
|
|||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"info": "Message to be passed as input.",
|
||||
"input_types": [],
|
||||
"input_types": [
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
"multiline": true,
|
||||
|
|
@ -373,17 +364,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -404,7 +384,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 StrInput(\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 StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n StrInput(\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 Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\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 TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n TextInput(\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -414,7 +394,7 @@
|
|||
"file_path": "",
|
||||
"info": "Message to be passed as output.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -462,7 +442,7 @@
|
|||
"file_path": "",
|
||||
"info": "Name of the sender.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -484,7 +464,7 @@
|
|||
"file_path": "",
|
||||
"info": "Session ID for the message.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -750,7 +730,7 @@
|
|||
"outputs": [
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Prompt",
|
||||
"display_name": "Prompt Message",
|
||||
"method": "build_prompt",
|
||||
"name": "prompt",
|
||||
"selected": "Message",
|
||||
|
|
@ -758,17 +738,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "format_prompt",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -789,7 +758,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Message:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n"
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\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 kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n prompt_message = Message(text=prompt.format_text(), **kwargs)\n self.status = prompt_message\n return prompt_message\n"
|
||||
},
|
||||
"context": {
|
||||
"advanced": false,
|
||||
|
|
@ -929,9 +898,9 @@
|
|||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text_output",
|
||||
"selected": "Text",
|
||||
"selected": "Message",
|
||||
"types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
|
|
@ -965,7 +934,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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 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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = self.json_mode\n seed = self.seed\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n response_format = None\n if json_mode:\n response_format = {\"type\": \"json_object\"}\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n response_format=response_format,\n seed=seed,\n )\n\n return output\n"
|
||||
"value": "from langchain_openai import ChatOpenAI\nfrom langflow.schema.message import Message\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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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=\"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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Message:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(self.schema)\n seed = self.seed\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 or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n seed=seed,\n )\n if json_mode:\n output = output.with_structured_output(schema=self.schema, method=\"json_mode\")\n\n return output\n"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@
|
|||
"id": "TextInput-sptaH",
|
||||
"name": "text",
|
||||
"output_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
]
|
||||
},
|
||||
"targetHandle": {
|
||||
|
|
@ -26,7 +26,7 @@
|
|||
},
|
||||
"id": "reactflow__edge-TextInput-sptaH{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}-Prompt-amqBu{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
|
||||
"source": "TextInput-sptaH",
|
||||
"sourceHandle": "{œdataTypeœ: œTextInputœ, œidœ: œTextInput-sptaHœ, œoutput_typesœ: [œTextœ], œnameœ: œtextœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œTextInputœ, œidœ: œTextInput-sptaHœ, œoutput_typesœ: [œMessageœ], œnameœ: œtextœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
|
|
@ -40,9 +40,7 @@
|
|||
"dataType": "Prompt",
|
||||
"id": "Prompt-amqBu",
|
||||
"name": "text",
|
||||
"output_types": [
|
||||
"Text"
|
||||
]
|
||||
"output_types": []
|
||||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
|
|
@ -56,7 +54,7 @@
|
|||
},
|
||||
"id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-TextOutput-2MS4a{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
|
||||
"source": "Prompt-amqBu",
|
||||
"sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ, œoutput_typesœ: [œTextœ], œnameœ: œtextœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ, œoutput_typesœ: [], œnameœ: œtextœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
|
|
@ -100,7 +98,7 @@
|
|||
"id": "OpenAIModel-uYXZJ",
|
||||
"name": "text_output",
|
||||
"output_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
]
|
||||
},
|
||||
"targetHandle": {
|
||||
|
|
@ -117,7 +115,7 @@
|
|||
},
|
||||
"id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-Prompt-gTNiz{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
|
||||
"source": "OpenAIModel-uYXZJ",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ, œoutput_typesœ: [œTextœ], œnameœ: œtext_outputœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
|
|
@ -132,26 +130,26 @@
|
|||
"id": "OpenAIModel-uYXZJ",
|
||||
"name": "text_output",
|
||||
"output_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
]
|
||||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
"id": "ChatOutput-EJkG3",
|
||||
"inputTypes": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"type": "str"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-ChatOutput-EJkG3{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
|
||||
"source": "OpenAIModel-uYXZJ",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ, œoutput_typesœ: [œTextœ], œnameœ: œtext_outputœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
"target": "ChatOutput-EJkG3",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-EJkG3œ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-EJkG3œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
},
|
||||
{
|
||||
"className": "stroke-gray-900 stroke-connection",
|
||||
|
|
@ -160,9 +158,7 @@
|
|||
"dataType": "Prompt",
|
||||
"id": "Prompt-gTNiz",
|
||||
"name": "text",
|
||||
"output_types": [
|
||||
"Text"
|
||||
]
|
||||
"output_types": []
|
||||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
|
|
@ -176,7 +172,7 @@
|
|||
},
|
||||
"id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-TextOutput-MUDOR{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
|
||||
"source": "Prompt-gTNiz",
|
||||
"sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ, œoutput_typesœ: [œTextœ], œnameœ: œtextœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ, œoutput_typesœ: [], œnameœ: œtextœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
|
|
@ -220,26 +216,26 @@
|
|||
"id": "OpenAIModel-XawYB",
|
||||
"name": "text_output",
|
||||
"output_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
]
|
||||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
"id": "ChatOutput-DNmvg",
|
||||
"inputTypes": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"type": "str"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-OpenAIModel-XawYB{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}-ChatOutput-DNmvg{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
|
||||
"source": "OpenAIModel-XawYB",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-XawYBœ, œoutput_typesœ: [œTextœ], œnameœ: œtext_outputœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-XawYBœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
"target": "ChatOutput-DNmvg",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-DNmvgœ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-DNmvgœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
}
|
||||
],
|
||||
"nodes": [
|
||||
|
|
@ -277,7 +273,7 @@
|
|||
"outputs": [
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Prompt",
|
||||
"display_name": "Prompt Message",
|
||||
"method": "build_prompt",
|
||||
"name": "prompt",
|
||||
"selected": "Message",
|
||||
|
|
@ -285,17 +281,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "format_prompt",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -316,7 +301,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Message:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n"
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\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 kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n prompt_message = Message(text=prompt.format_text(), **kwargs)\n self.status = prompt_message\n return prompt_message\n"
|
||||
},
|
||||
"document": {
|
||||
"advanced": false,
|
||||
|
|
@ -419,7 +404,7 @@
|
|||
"outputs": [
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Prompt",
|
||||
"display_name": "Prompt Message",
|
||||
"method": "build_prompt",
|
||||
"name": "prompt",
|
||||
"selected": "Message",
|
||||
|
|
@ -427,17 +412,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "format_prompt",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -458,7 +432,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Message:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n"
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\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 kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n prompt_message = Message(text=prompt.format_text(), **kwargs)\n self.status = prompt_message\n return prompt_message\n"
|
||||
},
|
||||
"summary": {
|
||||
"advanced": false,
|
||||
|
|
@ -561,17 +535,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -592,7 +555,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 StrInput(\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 StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n StrInput(\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 Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\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 TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n TextInput(\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -602,7 +565,7 @@
|
|||
"file_path": "",
|
||||
"info": "Message to be passed as output.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -650,7 +613,7 @@
|
|||
"file_path": "",
|
||||
"info": "Name of the sender.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -672,7 +635,7 @@
|
|||
"file_path": "",
|
||||
"info": "Session ID for the message.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -739,17 +702,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -770,7 +722,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 StrInput(\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 StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n StrInput(\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 Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\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 TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n TextInput(\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -780,7 +732,7 @@
|
|||
"file_path": "",
|
||||
"info": "Message to be passed as output.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -828,7 +780,7 @@
|
|||
"file_path": "",
|
||||
"info": "Name of the sender.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -850,7 +802,7 @@
|
|||
"file_path": "",
|
||||
"info": "Session ID for the message.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -906,9 +858,9 @@
|
|||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"selected": "Message",
|
||||
"types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
|
|
@ -931,7 +883,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import TextInput\nfrom langflow.template import Output\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n inputs = [\n TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Text:\n return self.build(input_value=self.input_value)\n"
|
||||
"value": "from langflow.base.io.text import TextComponent\nfrom langflow.inputs import TextInput\nfrom langflow.template import Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n inputs = [\n TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n message = Message(\n text=self.input_value,\n )\n return message\n"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -1127,9 +1079,9 @@
|
|||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text_output",
|
||||
"selected": "Text",
|
||||
"selected": "Message",
|
||||
"types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
|
|
@ -1163,7 +1115,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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 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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = self.json_mode\n seed = self.seed\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n response_format = None\n if json_mode:\n response_format = {\"type\": \"json_object\"}\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n response_format=response_format,\n seed=seed,\n )\n\n return output\n"
|
||||
"value": "from langchain_openai import ChatOpenAI\nfrom langflow.schema.message import Message\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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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=\"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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Message:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(self.schema)\n seed = self.seed\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 or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n seed=seed,\n )\n if json_mode:\n output = output.with_structured_output(schema=self.schema, method=\"json_mode\")\n\n return output\n"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -1542,9 +1494,9 @@
|
|||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text_output",
|
||||
"selected": "Text",
|
||||
"selected": "Message",
|
||||
"types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
|
|
@ -1578,7 +1530,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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 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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = self.json_mode\n seed = self.seed\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n response_format = None\n if json_mode:\n response_format = {\"type\": \"json_object\"}\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n response_format=response_format,\n seed=seed,\n )\n\n return output\n"
|
||||
"value": "from langchain_openai import ChatOpenAI\nfrom langflow.schema.message import Message\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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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=\"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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Message:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(self.schema)\n seed = self.seed\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 or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n seed=seed,\n )\n if json_mode:\n output = output.with_structured_output(schema=self.schema, method=\"json_mode\")\n\n return output\n"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
|
|||
|
|
@ -105,14 +105,14 @@
|
|||
"id": "OpenAIModel-EjXlN",
|
||||
"name": "text_output",
|
||||
"output_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
]
|
||||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
"id": "ChatOutput-Q39I8",
|
||||
"inputTypes": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"type": "str"
|
||||
}
|
||||
|
|
@ -120,12 +120,12 @@
|
|||
"id": "reactflow__edge-OpenAIModel-EjXlN{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}-ChatOutput-Q39I8{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
|
||||
"selected": false,
|
||||
"source": "OpenAIModel-EjXlN",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-EjXlNœ, œoutput_typesœ: [œTextœ], œnameœ: œtext_outputœ}",
|
||||
"sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-EjXlNœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
|
||||
"style": {
|
||||
"stroke": "#555"
|
||||
},
|
||||
"target": "ChatOutput-Q39I8",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-Q39I8œ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-Q39I8œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
},
|
||||
{
|
||||
"className": "stroke-gray-900 stroke-connection",
|
||||
|
|
@ -333,17 +333,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -364,7 +353,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\n input_types=[],\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\n name=\"input_value\",\n display_name=\"Text\",\n multiline=True,\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 TextInput(\n name=\"sender_name\",\n type=str,\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n TextInput(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=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 )\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -373,7 +362,9 @@
|
|||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"info": "Message to be passed as input.",
|
||||
"input_types": [],
|
||||
"input_types": [
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
"multiline": true,
|
||||
|
|
@ -1040,9 +1031,9 @@
|
|||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text_output",
|
||||
"selected": "Text",
|
||||
"selected": "Message",
|
||||
"types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
|
|
@ -1076,7 +1067,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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 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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Text:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = self.json_mode\n seed = self.seed\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n response_format = None\n if json_mode:\n response_format = {\"type\": \"json_object\"}\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n response_format=response_format,\n seed=seed,\n )\n\n return output\n"
|
||||
"value": "from langchain_openai import ChatOpenAI\nfrom langflow.schema.message import Message\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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.template import Output\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=\"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 BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n info=\"Enable JSON mode for the model output.\",\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 outputs = [\n Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n ]\n\n def text_response(self) -> Message:\n input_value = self.input_value\n stream = self.stream\n system_message = self.system_message\n output = self.build_model()\n result = self.get_chat_result(output, stream, input_value, system_message)\n self.status = result\n return result\n\n def build_model(self) -> BaseLanguageModel:\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(self.schema)\n seed = self.seed\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 or {},\n model=model_name or None,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n seed=seed,\n )\n if json_mode:\n output = output.with_structured_output(schema=self.schema, method=\"json_mode\")\n\n return output\n"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -1337,7 +1328,7 @@
|
|||
"outputs": [
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Prompt",
|
||||
"display_name": "Prompt Message",
|
||||
"method": "build_prompt",
|
||||
"name": "prompt",
|
||||
"selected": "Message",
|
||||
|
|
@ -1345,17 +1336,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "format_prompt",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -1376,7 +1356,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n ]\n\n async def format_prompt(self) -> str:\n prompt = await self.build_prompt()\n formatted_text = prompt.format_text()\n self.status = formatted_text\n return formatted_text\n\n async def build_prompt(\n self,\n ) -> Message:\n kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n self.status = prompt.format_text()\n return prompt\n"
|
||||
"value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\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 kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n prompt = await Message.from_template_and_variables(self.template, kwargs)\n prompt_message = Message(text=prompt.format_text(), **kwargs)\n self.status = prompt_message\n return prompt_message\n"
|
||||
},
|
||||
"context": {
|
||||
"advanced": false,
|
||||
|
|
@ -1509,17 +1489,6 @@
|
|||
"Message"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Text",
|
||||
"method": "text_response",
|
||||
"name": "text",
|
||||
"selected": "Text",
|
||||
"types": [
|
||||
"Text"
|
||||
],
|
||||
"value": "__UNDEFINED__"
|
||||
}
|
||||
],
|
||||
"template": {
|
||||
|
|
@ -1540,7 +1509,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 StrInput(\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 StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n StrInput(\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 Output(display_name=\"Text\", name=\"text\", method=\"text_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\n def text_response(self) -> Text:\n text = self.message_response().text\n return text\n"
|
||||
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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 TextInput(\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 TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n TextInput(\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"
|
||||
},
|
||||
"input_value": {
|
||||
"advanced": false,
|
||||
|
|
@ -1550,7 +1519,7 @@
|
|||
"file_path": "",
|
||||
"info": "Message to be passed as output.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -1598,7 +1567,7 @@
|
|||
"file_path": "",
|
||||
"info": "Name of the sender.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -1620,7 +1589,7 @@
|
|||
"file_path": "",
|
||||
"info": "Session ID for the message.",
|
||||
"input_types": [
|
||||
"Text"
|
||||
"Message"
|
||||
],
|
||||
"list": false,
|
||||
"load_from_db": false,
|
||||
|
|
@ -1980,15 +1949,7 @@
|
|||
"outputs": [
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Vector Store",
|
||||
"method": "build_vector_store",
|
||||
"name": "vector_store",
|
||||
"types": [],
|
||||
"value": "__UNDEFINED__"
|
||||
},
|
||||
{
|
||||
"cache": true,
|
||||
"display_name": "Base Retriever",
|
||||
"display_name": "Retriever",
|
||||
"method": "build_base_retriever",
|
||||
"name": "base_retriever",
|
||||
"types": [],
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue