diff --git a/365085a8-a90a-43f9-a779-f8769ec7eca1.html b/365085a8-a90a-43f9-a779-f8769ec7eca1.html index 966ea6b66b..1213a90d81 100644 --- a/365085a8-a90a-43f9-a779-f8769ec7eca1.html +++ b/365085a8-a90a-43f9-a779-f8769ec7eca1.html @@ -10,8 +10,8 @@ - - + +
diff --git a/404.html b/404.html index 4b1279ab08..9115e4a3c0 100644 --- a/404.html +++ b/404.html @@ -10,8 +10,8 @@ - - + + diff --git a/Starter-Projects/starter-projects-complex-agent.html b/Starter-Projects/starter-projects-complex-agent.html index 16d8c5c20d..e8c5d264db 100644 --- a/Starter-Projects/starter-projects-complex-agent.html +++ b/Starter-Projects/starter-projects-complex-agent.html @@ -10,8 +10,8 @@ - - + + diff --git a/Starter-Projects/starter-projects-hierarchical-crew.html b/Starter-Projects/starter-projects-hierarchical-crew.html index 88f9ae491c..81f08090de 100644 --- a/Starter-Projects/starter-projects-hierarchical-crew.html +++ b/Starter-Projects/starter-projects-hierarchical-crew.html @@ -10,8 +10,8 @@ - - + + diff --git a/Starter-Projects/starter-projects-sequential-crew.html b/Starter-Projects/starter-projects-sequential-crew.html index 09e76d4de1..8382caa093 100644 --- a/Starter-Projects/starter-projects-sequential-crew.html +++ b/Starter-Projects/starter-projects-sequential-crew.html @@ -10,8 +10,8 @@ - - + + diff --git a/assets/js/0be1d5fe.4524db73.js b/assets/js/0be1d5fe.4524db73.js deleted file mode 100644 index 6127e6900a..0000000000 --- a/assets/js/0be1d5fe.4524db73.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[145],{75:(e,n,s)=>{s.r(n),s.d(n,{assets:()=>d,contentTitle:()=>o,default:()=>h,frontMatter:()=>r,metadata:()=>l,toc:()=>c});var t=s(4848),i=s(8453);const r={title:"Models",sidebar_position:5,slug:"/components-models"},o=void 0,l={id:"Components/components-models",title:"Models",description:"This page may contain outdated information. It will be updated as soon as possible.",source:"@site/docs/Components/components-models.md",sourceDirName:"Components",slug:"/components-models",permalink:"/components-models",draft:!1,unlisted:!1,tags:[],version:"current",sidebarPosition:5,frontMatter:{title:"Models",sidebar_position:5,slug:"/components-models"},sidebar:"defaultSidebar",previous:{title:"Helpers",permalink:"/components-helpers"},next:{title:"Embedding Models",permalink:"/components-embedding-models"}},d={},c=[{value:"Amazon Bedrock",id:"3b8ceacef3424234814f95895a25bf43",level:2},{value:"Anthropic",id:"a6ae46f98c4c4d389d44b8408bf151a1",level:2},{value:"Azure OpenAI",id:"7e3bff29ce714479b07feeb4445680cd",level:2},{value:"Cohere",id:"706396a33bf94894966c95571252d78b",level:2},{value:"Google Generative AI",id:"074d9623463449f99d41b44699800e8a",level:2},{value:"Hugging Face API",id:"c1267b9a6b36487cb2ee127ce9b64dbb",level:2},{value:"LiteLLM Model",id:"9fb59dad3b294a05966320d39f483a50",level:2},{value:"Ollama",id:"14e8e411d28d4711add53bfc3e52c6cd",level:2},{value:"OpenAI",id:"fe6cd793446748eda6eaad72e30f70b3",level:2},{value:"Qianfan",id:"6e4a6b2370ee4b9f8beb899e7cf9c8f6",level:2},{value:"Vertex AI",id:"86b7d539e17c436fb758c47ec3ffb084",level:2}];function a(e){const n={a:"a",admonition:"admonition",code:"code",h2:"h2",hr:"hr",li:"li",p:"p",strong:"strong",ul:"ul",...(0,i.R)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(n.admonition,{type:"info",children:(0,t.jsx)(n.p,{children:"This page may contain outdated information. It will be updated as soon as possible."})}),"\n",(0,t.jsx)(n.h2,{id:"3b8ceacef3424234814f95895a25bf43",children:"Amazon Bedrock"}),"\n",(0,t.jsx)(n.p,{children:"This component facilitates the generation of text using the LLM (Large Language Model) model from Amazon Bedrock."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Params"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input Value:"}),"\xa0Specifies the input text for text generation."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message (Optional):"}),"\xa0A system message to pass to the model."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model ID (Optional):"}),"\xa0Specifies the model ID to be used for text generation. Defaults to\xa0",(0,t.jsx)(n.code,{children:'"anthropic.claude-instant-v1"'}),". Available options include:","\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"ai21.j2-grande-instruct"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"ai21.j2-jumbo-instruct"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"ai21.j2-mid"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"ai21.j2-mid-v1"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"ai21.j2-ultra"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"ai21.j2-ultra-v1"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"anthropic.claude-instant-v1"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"anthropic.claude-v1"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"anthropic.claude-v2"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"cohere.command-text-v14"'})}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Credentials Profile Name (Optional):"}),"\xa0Specifies the name of the credentials profile."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Region Name (Optional):"}),"\xa0Specifies the region name."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Kwargs (Optional):"}),"\xa0Additional keyword arguments for the model."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Endpoint URL (Optional):"}),"\xa0Specifies the endpoint URL."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Streaming (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Cache (Optional):"}),"\xa0Specifies whether to cache the response."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"NOTE"}),"\n",(0,t.jsx)(n.p,{children:"Ensure that necessary credentials are provided to connect to the Amazon Bedrock API. If connection fails, a ValueError will be raised."}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"a6ae46f98c4c4d389d44b8408bf151a1",children:"Anthropic"}),"\n",(0,t.jsx)(n.p,{children:"This component allows the generation of text using Anthropic Chat&Completion large language models."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Params"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Name:"}),"\xa0Specifies the name of the Anthropic model to be used for text generation. Available options include (and not limited to):","\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"claude-2.1"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"claude-2.0"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"claude-instant-1.2"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"claude-instant-1"'})}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Anthropic API Key:"}),"\xa0Your Anthropic API key."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Max Tokens (Optional):"}),"\xa0Specifies the maximum number of tokens to generate. Defaults to\xa0",(0,t.jsx)(n.code,{children:"256"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Temperature (Optional):"}),"\xa0Specifies the sampling temperature. Defaults to\xa0",(0,t.jsx)(n.code,{children:"0.7"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"API Endpoint (Optional):"}),"\xa0Specifies the endpoint of the Anthropic API. Defaults to\xa0",(0,t.jsx)(n.code,{children:'"https://api.anthropic.com"'}),"if not specified."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input Value:"}),"\xa0Specifies the input text for text generation."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message (Optional):"}),"\xa0A system message to pass to the model."]}),"\n"]}),"\n",(0,t.jsxs)(n.p,{children:["For detailed documentation and integration guides, please refer to the\xa0",(0,t.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/chat/anthropic",children:"Anthropic Component Documentation"}),"."]}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"7e3bff29ce714479b07feeb4445680cd",children:"Azure OpenAI"}),"\n",(0,t.jsx)(n.p,{children:"This component allows the generation of text using the LLM (Large Language Model) model from Azure OpenAI."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Params"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Name:"}),"\xa0Specifies the name of the Azure OpenAI model to be used for text generation. Available options include:","\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"gpt-35-turbo"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"gpt-35-turbo-16k"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"gpt-35-turbo-instruct"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"gpt-4"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"gpt-4-32k"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"gpt-4-vision"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"gpt-4o"'})}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Azure Endpoint:"}),"\xa0Your Azure endpoint, including the resource. Example:\xa0",(0,t.jsx)(n.code,{children:"https://example-resource.azure.openai.com/"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Deployment Name:"}),"\xa0Specifies the name of the deployment."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"API Version:"}),"\xa0Specifies the version of the Azure OpenAI API to be used. Available options include:","\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"2023-03-15-preview"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"2023-05-15"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"2023-06-01-preview"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"2023-07-01-preview"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"2023-08-01-preview"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"2023-09-01-preview"'})}),"\n",(0,t.jsx)(n.li,{children:(0,t.jsx)(n.code,{children:'"2023-12-01-preview"'})}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"API Key:"}),"\xa0Your Azure OpenAI API key."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Temperature (Optional):"}),"\xa0Specifies the sampling temperature. Defaults to\xa0",(0,t.jsx)(n.code,{children:"0.7"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Max Tokens (Optional):"}),"\xa0Specifies the maximum number of tokens to generate. Defaults to\xa0",(0,t.jsx)(n.code,{children:"1000"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input Value:"}),"\xa0Specifies the input text for text generation."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message (Optional):"}),"\xa0A system message to pass to the model."]}),"\n"]}),"\n",(0,t.jsxs)(n.p,{children:["For detailed documentation and integration guides, please refer to the\xa0",(0,t.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/llms/azure_openai",children:"Azure OpenAI Component Documentation"}),"."]}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"706396a33bf94894966c95571252d78b",children:"Cohere"}),"\n",(0,t.jsx)(n.p,{children:"This component enables text generation using Cohere large language models."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Params"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Cohere API Key:"}),"\xa0Your Cohere API key."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Max Tokens (Optional):"}),"\xa0Specifies the maximum number of tokens to generate. Defaults to\xa0",(0,t.jsx)(n.code,{children:"256"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Temperature (Optional):"}),"\xa0Specifies the sampling temperature. Defaults to\xa0",(0,t.jsx)(n.code,{children:"0.75"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input Value:"}),"\xa0Specifies the input text for text generation."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message (Optional):"}),"\xa0A system message to pass to the model."]}),"\n"]}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"074d9623463449f99d41b44699800e8a",children:"Google Generative AI"}),"\n",(0,t.jsx)(n.p,{children:"This component enables text generation using Google Generative AI."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Params"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Google API Key:"}),"\xa0Your Google API key to use for the Google Generative AI."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model:"}),"\xa0The name of the model to use. Supported examples are\xa0",(0,t.jsx)(n.code,{children:'"gemini-pro"'}),"\xa0and\xa0",(0,t.jsx)(n.code,{children:'"gemini-pro-vision"'}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Max Output Tokens (Optional):"}),"\xa0The maximum number of tokens to generate."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Temperature:"}),"\xa0Run inference with this temperature. Must be in the closed interval [0.0, 1.0]."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Top K (Optional):"}),"\xa0Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Top P (Optional):"}),"\xa0The maximum cumulative probability of tokens to consider when sampling."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"N (Optional):"}),"\xa0Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input Value:"}),"\xa0The input to the model."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message (Optional):"}),"\xa0A system message to pass to the model."]}),"\n"]}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"c1267b9a6b36487cb2ee127ce9b64dbb",children:"Hugging Face API"}),"\n",(0,t.jsx)(n.p,{children:"This component facilitates text generation using LLM models from the Hugging Face Inference API."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Params"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Endpoint URL:"}),"\xa0The URL of the Hugging Face Inference API endpoint. Should be provided along with necessary authentication credentials."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Task:"}),"\xa0Specifies the task for text generation. Options include\xa0",(0,t.jsx)(n.code,{children:'"text2text-generation"'}),",\xa0",(0,t.jsx)(n.code,{children:'"text-generation"'}),", and\xa0",(0,t.jsx)(n.code,{children:'"summarization"'}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"API Token:"}),"\xa0The API token required for authentication with the Hugging Face Hub."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Keyword Arguments (Optional):"}),"\xa0Additional keyword arguments for the model. Should be provided as a Python dictionary."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input Value:"}),"\xa0The input text for text generation."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message (Optional):"}),"\xa0A system message to pass to the model."]}),"\n"]}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"9fb59dad3b294a05966320d39f483a50",children:"LiteLLM Model"}),"\n",(0,t.jsxs)(n.p,{children:["Generates text using the\xa0",(0,t.jsx)(n.code,{children:"LiteLLM"}),"\xa0collection of large language models."]}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Parameters"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model name:"}),"\xa0The name of the model to use. For example,\xa0",(0,t.jsx)(n.code,{children:"gpt-3.5-turbo"}),". (Type: str)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"API key:"}),"\xa0The API key to use for accessing the provider's API. (Type: str, Optional)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Provider:"}),'\xa0The provider of the API key. (Type: str, Choices: "OpenAI", "Azure", "Anthropic", "Replicate", "Cohere", "OpenRouter")']}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Temperature:"}),"\xa0Controls the randomness of the text generation. (Type: float, Default: 0.7)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model kwargs:"}),"\xa0Additional keyword arguments for the model. (Type: Dict, Optional)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Top p:"}),"\xa0Filter responses to keep the cumulative probability within the top p tokens. (Type: float, Optional)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Top k:"}),"\xa0Filter responses to only include the top k tokens. (Type: int, Optional)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"N:"}),"\xa0Number of chat completions to generate for each prompt. (Type: int, Default: 1)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Max tokens:"}),"\xa0The maximum number of tokens to generate for each chat completion. (Type: int, Default: 256)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Max retries:"}),"\xa0Maximum number of retries for failed requests. (Type: int, Default: 6)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Verbose:"}),"\xa0Whether to print verbose output. (Type: bool, Default: False)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input:"}),"\xa0The input prompt for text generation. (Type: str)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream:"}),"\xa0Whether to stream the output. (Type: bool, Default: False)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System message:"}),"\xa0System message to pass to the model. (Type: str, Optional)"]}),"\n"]}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"14e8e411d28d4711add53bfc3e52c6cd",children:"Ollama"}),"\n",(0,t.jsx)(n.p,{children:"Generate text using Ollama Local LLMs."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Parameters"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Base URL:"}),"\xa0Endpoint of the Ollama API. Defaults to '",(0,t.jsx)(n.a,{href:"http://localhost:11434/",children:"http://localhost:11434"}),"' if not specified."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Name:"}),"\xa0The model name to use. Refer to\xa0",(0,t.jsx)(n.a,{href:"https://ollama.ai/library",children:"Ollama Library"}),"\xa0for more models."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Temperature:"}),"\xa0Controls the creativity of model responses. (Default: 0.8)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Cache:"}),"\xa0Enable or disable caching. (Default: False)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Format:"}),"\xa0Specify the format of the output (e.g., json). (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Metadata:"}),"\xa0Metadata to add to the run trace. (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Mirostat:"}),"\xa0Enable/disable Mirostat sampling for controlling perplexity. (Default: Disabled)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Mirostat Eta:"}),"\xa0Learning rate for Mirostat algorithm. 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(Default: None) (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Repeat Penalty:"}),"\xa0Penalty for repetitions in generated text. (Default: None) (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"TFS Z:"}),"\xa0Tail free sampling value. (Default: None) (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Timeout:"}),"\xa0Timeout for the request stream. (Default: None) (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Top K:"}),"\xa0Limits token selection to top K. (Default: None) (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Top P:"}),"\xa0Works together with top-k. (Default: None) (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Verbose:"}),"\xa0Whether to print out response text."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Tags:"}),"\xa0Tags to add to the run trace. (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stop Tokens:"}),"\xa0List of tokens to signal the model to stop generating text. (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System:"}),"\xa0System to use for generating text. (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Template:"}),"\xa0Template to use for generating text. (Advanced)"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Input:"}),"\xa0The input text."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream:"}),"\xa0Whether to stream the response."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message:"}),"\xa0System message to pass to the model. 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Defaults to\xa0",(0,t.jsx)(n.code,{children:"gpt-4-1106-preview"}),". Supported options include:\xa0",(0,t.jsx)(n.code,{children:"gpt-4-turbo-preview"}),",\xa0",(0,t.jsx)(n.code,{children:"gpt-4-0125-preview"}),",\xa0",(0,t.jsx)(n.code,{children:"gpt-4-1106-preview"}),",\xa0",(0,t.jsx)(n.code,{children:"gpt-4-vision-preview"}),",\xa0",(0,t.jsx)(n.code,{children:"gpt-3.5-turbo-0125"}),",\xa0",(0,t.jsx)(n.code,{children:"gpt-3.5-turbo-1106"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"OpenAI API Base (Optional):"}),"\xa0The base URL of the OpenAI API. Defaults to\xa0",(0,t.jsx)(n.code,{children:"https://api.openai.com/v1"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"OpenAI API Key (Optional):"}),"\xa0The API key for accessing the OpenAI API."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Temperature:"}),"\xa0Controls the creativity of model responses. Defaults to\xa0",(0,t.jsx)(n.code,{children:"0.7"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Stream (Optional):"}),"\xa0Specifies whether to stream the response from the model. Defaults to\xa0",(0,t.jsx)(n.code,{children:"False"}),"."]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"System Message (Optional):"}),"\xa0System message to pass to the model."]}),"\n"]}),"\n",(0,t.jsx)(n.hr,{}),"\n",(0,t.jsx)(n.h2,{id:"6e4a6b2370ee4b9f8beb899e7cf9c8f6",children:"Qianfan"}),"\n",(0,t.jsx)(n.p,{children:"This component facilitates the generation of text using Baidu Qianfan chat models."}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.strong,{children:"Params"})}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.strong,{children:"Model Name:"}),"\xa0Specifies the name of the Qianfan chat model to be used for text generation. 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They also dynamically change the Playground and can be renamed to facilitate building and maintaining your flows."}),"\n",(0,s.jsx)(n.h2,{id:"6b1421ec66994d5ebe9fcce000829328",children:"Inputs"}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.p,{children:"Inputs are components used to define where data enters your flow. They can receive data from the user, a database, or any other source that can be converted to Text or Data."}),"\n",(0,s.jsx)(n.p,{children:"The difference between Chat Input and other Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground."}),"\n",(0,s.jsxs)(n.p,{children:["Chat Input components can output\xa0",(0,s.jsx)(n.code,{children:"Text"}),"\xa0or\xa0",(0,s.jsx)(n.code,{children:"Data"}),". When you want to pass the sender name or sender to the next component, use the\xa0",(0,s.jsx)(n.code,{children:"Data"}),"\xa0output. To pass only the message, use the\xa0",(0,s.jsx)(n.code,{children:"Text"}),"\xa0output, useful when saving the message to a database or memory system like Zep."]}),"\n",(0,s.jsxs)(n.p,{children:["You can find out more about Chat Input and other Inputs\xa0",(0,s.jsx)(n.a,{href:"/components-io",children:"here"}),"."]}),"\n",(0,s.jsx)(n.h3,{id:"2a5f02262f364f8fb75bcfa246e7bb26",children:"Chat Input"}),"\n",(0,s.jsx)(n.p,{children:"This component collects user input from the chat."}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Sender Type:"}),"\xa0Specifies the sender type. Defaults to\xa0",(0,s.jsx)(n.code,{children:"User"}),". Options are\xa0",(0,s.jsx)(n.code,{children:"Machine"}),"\xa0and\xa0",(0,s.jsx)(n.code,{children:"User"}),"."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Sender Name:"}),"\xa0Specifies the name of the sender. 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This feature is particularly valuable for scenarios requiring user input to initiate or influence the flow."}),"\n",(0,s.jsx)(n.h3,{id:"260aef3726834896b496b56cdefb6d4a",children:"Text Input"}),"\n",(0,s.jsxs)(n.p,{children:["The\xa0",(0,s.jsx)(n.strong,{children:"Text Input"}),"\xa0component adds an\xa0",(0,s.jsx)(n.strong,{children:"Input"}),"\xa0field on the Playground. This enables you to define parameters while running and testing your flow."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Value:"}),"\xa0Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Data Template:"}),"\xa0Specifies how a\xa0",(0,s.jsx)(n.code,{children:"Data"}),"\xa0should be converted into\xa0",(0,s.jsx)(n.code,{children:"Text"}),"."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["The\xa0",(0,s.jsx)(n.strong,{children:"Data Template"}),"\xa0field is used to specify how a\xa0",(0,s.jsx)(n.code,{children:"Data"}),"\xa0should be converted into\xa0",(0,s.jsx)(n.code,{children:"Text"}),". This is particularly useful when you want to extract specific information from a\xa0",(0,s.jsx)(n.code,{children:"Data"}),"\xa0and pass it as text to the next component in the sequence."]}),"\n",(0,s.jsxs)(n.p,{children:["For example, if you have a\xa0",(0,s.jsx)(n.code,{children:"Data"}),"\xa0with the following structure:"]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.code,{children:'{ "name": "John Doe", "age": 30, "email": "johndoe@email.com"}'})}),"\n",(0,s.jsxs)(n.p,{children:["A template with\xa0",(0,s.jsx)(n.code,{children:"Name: {name}, Age: {age}"}),"\xa0will convert the\xa0",(0,s.jsx)(n.code,{children:"Data"}),"\xa0into a text string of\xa0",(0,s.jsx)(n.code,{children:"Name: John Doe, Age: 30"}),"."]}),"\n",(0,s.jsxs)(n.p,{children:["If you pass more than one\xa0",(0,s.jsx)(n.code,{children:"Data"}),", the text will be concatenated with a new line separator."]}),"\n",(0,s.jsx)(n.h2,{id:"f62c5ad37a6f45a39b463c9b35ce7842",children:"Outputs"}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.p,{children:"Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground."}),"\n",(0,s.jsx)(n.p,{children:"The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user."}),"\n",(0,s.jsxs)(n.p,{children:["You can find out more about it and the other Outputs\xa0",(0,s.jsx)(n.a,{href:"/components-io",children:"here"}),"."]}),"\n",(0,s.jsx)(n.h3,{id:"1edd49b72781432ea29d70acbda4e7e7",children:"Chat Output"}),"\n",(0,s.jsx)(n.p,{children:"This component sends a message to the chat."}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Sender Type:"}),"\xa0Specifies the sender type. Default is\xa0",(0,s.jsx)(n.code,{children:'"Machine"'}),". Options are\xa0",(0,s.jsx)(n.code,{children:'"Machine"'}),"\xa0and\xa0",(0,s.jsx)(n.code,{children:'"User"'}),"."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Sender Name:"}),"\xa0Specifies the sender's name. Default is\xa0",(0,s.jsx)(n.code,{children:'"AI"'}),"."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Session ID:"}),"\xa0Specifies the session ID of the chat history. If provided, messages are saved in the Message History."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Message:"}),"\xa0Specifies the text of the message."]}),"\n"]}),"\n",(0,s.jsx)(n.admonition,{type:"note",children:(0,s.jsxs)(n.p,{children:["If\xa0",(0,s.jsx)(n.code,{children:"As Data"}),"\xa0is\xa0",(0,s.jsx)(n.code,{children:"true"}),"\xa0and the\xa0",(0,s.jsx)(n.code,{children:"Message"}),"\xa0is a\xa0",(0,s.jsx)(n.code,{children:"Data"}),", the data in the\xa0",(0,s.jsx)(n.code,{children:"Data"}),"\xa0is updated with the\xa0",(0,s.jsx)(n.code,{children:"Sender"}),",\xa0",(0,s.jsx)(n.code,{children:"Sender Name"}),", and\xa0",(0,s.jsx)(n.code,{children:"Session ID"}),"."]})}),"\n",(0,s.jsx)(n.h3,{id:"b607000bc0c5402db0433c1a7d734d01",children:"Text Output"}),"\n",(0,s.jsx)(n.p,{children:"This component displays text data to the user. It is useful when you want to show text without sending it to the chat."}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Value:"}),"\xa0Specifies the text data to be displayed. Defaults to an empty string."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["The\xa0",(0,s.jsx)(n.code,{children:"TextOutput"}),"\xa0component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow."]})]})}function l(e={}){const{wrapper:n}={...(0,i.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(h,{...e})}):h(e)}},8453:(e,n,t)=>{t.d(n,{R:()=>a,x:()=>d});var s=t(6540);const i={},o=s.createContext(i);function a(e){const n=s.useContext(o);return s.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function d(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(i):e.components||i:a(e.components),s.createElement(o.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/3ae94ad4.95af4b45.js b/assets/js/3ae94ad4.95af4b45.js new file mode 100644 index 0000000000..6f46e7d15d --- /dev/null +++ b/assets/js/3ae94ad4.95af4b45.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[7408],{2553:(e,t,n)=>{n.r(t),n.d(t,{CH:()=>c,assets:()=>h,chCodeConfig:()=>l,contentTitle:()=>d,default:()=>x,frontMatter:()=>i,metadata:()=>a,toc:()=>u});var s=n(4848),r=n(8453),o=n(4754);const i={title:"Inputs & Outputs",sidebar_position:1,slug:"/components-io"},d="Inputs & Outputs",a={id:"Components/components-io",title:"Inputs & Outputs",description:"This category of components defines where data enters and exits your flow. 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They dynamically alter the Playground and can be renamed to facilitate building and maintaining your flows."}),"\n",(0,s.jsx)(t.h2,{id:"inputs",children:"Inputs"}),"\n",(0,s.jsx)(t.p,{children:"Inputs are components used to define where data enters your flow. They can receive data from various sources, such as users, databases, or any other source that can be converted to Text or Data."}),"\n",(0,s.jsx)(t.h3,{id:"chat-input",children:"Chat Input"}),"\n",(0,s.jsx)(t.p,{children:"This component collects user input from the chat."}),"\n",(0,s.jsx)(t.p,{children:"The difference between Chat Input and other Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground."}),"\n",(0,s.jsx)(t.p,{children:"Chat Input components can output Text or Data. When you want to pass the sender name or sender to the next component, use the Data output. To pass only the message, use the Text output. Passing only the message is useful when saving the message to a database or a memory system like Zep."}),"\n",(0,s.jsx)(t.h4,{id:"parameters",children:"Parameters"}),"\n",(0,s.jsxs)(t.table,{children:[(0,s.jsx)(t.thead,{children:(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.th,{children:"Name"}),(0,s.jsx)(t.th,{children:"Display Name"}),(0,s.jsx)(t.th,{children:"Info"})]})}),(0,s.jsxs)(t.tbody,{children:[(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Sender Type"}),(0,s.jsx)(t.td,{children:"Sender Type"}),(0,s.jsx)(t.td,{children:"Specifies the sender type (User or Machine). Defaults to User"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Sender Name"}),(0,s.jsx)(t.td,{children:"Sender Name"}),(0,s.jsx)(t.td,{children:"Specifies the name of the sender. Defaults to User"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Message"}),(0,s.jsx)(t.td,{children:"Message"}),(0,s.jsx)(t.td,{children:"Specifies the message text. Multiline text input"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Session ID"}),(0,s.jsx)(t.td,{children:"Session ID"}),(0,s.jsx)(t.td,{children:"Specifies the session ID of the chat history"})]})]})]}),"\n",(0,s.jsx)(t.admonition,{type:"note",children:(0,s.jsx)(t.p,{children:'If "As Data" is true and the "Message" is a Data, the data will be updated with the Sender, Sender Name, and Session ID.'})}),"\n",(0,s.jsx)(t.h3,{id:"text-input",children:"Text Input"}),"\n",(0,s.jsx)(t.p,{children:"This component adds an Input field on the Playground, allowing parameter definition while running and testing your flow."}),"\n",(0,s.jsx)(t.p,{children:"The Data Template field specifies how a Data should be converted into Text. 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