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+ diff --git a/assets/js/0be1d5fe.a18b93ad.js b/assets/js/0be1d5fe.a18b93ad.js new file mode 100644 index 0000000000..2c30c849e1 --- /dev/null +++ b/assets/js/0be1d5fe.a18b93ad.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[145],{75:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>h,contentTitle:()=>i,default:()=>a,frontMatter:()=>r,metadata:()=>l,toc:()=>c});var s=n(74848),d=n(28453);const r={title:"Models",slug:"/components-models"},i="Model components in Langflow",l={id:"Components/components-models",title:"Models",description:"Model components generate text using large language models.",source:"@site/docs/Components/components-models.md",sourceDirName:"Components",slug:"/components-models",permalink:"/components-models",draft:!1,unlisted:!1,tags:[],version:"current",frontMatter:{title:"Models",slug:"/components-models"},sidebar:"docs",previous:{title:"Memories",permalink:"/components-memories"},next:{title:"Processing",permalink:"/components-processing"}},h={},c=[{value:"Use a model component in a flow",id:"use-a-model-component-in-a-flow",level:2},{value:"AI/ML API",id:"aiml-api",level:2},{value:"Inputs",id:"inputs",level:3},{value:"Outputs",id:"outputs",level:3},{value:"Amazon Bedrock",id:"amazon-bedrock",level:2},{value:"Inputs",id:"inputs-1",level:3},{value:"Outputs",id:"outputs-1",level:3},{value:"Anthropic",id:"anthropic",level:2},{value:"Inputs",id:"inputs-2",level:3},{value:"Outputs",id:"outputs-2",level:3},{value:"Azure OpenAI",id:"azure-openai",level:2},{value:"Inputs",id:"inputs-3",level:3},{value:"Cohere",id:"cohere",level:2},{value:"Inputs",id:"inputs-4",level:3},{value:"Google Generative AI",id:"google-generative-ai",level:2},{value:"Inputs",id:"inputs-5",level:3},{value:"Groq",id:"groq",level:2},{value:"Inputs",id:"inputs-6",level:3},{value:"Outputs",id:"outputs-3",level:3},{value:"Hugging Face API",id:"hugging-face-api",level:2},{value:"Inputs",id:"inputs-7",level:3},{value:"Maritalk",id:"maritalk",level:2},{value:"Inputs",id:"inputs-8",level:3},{value:"Outputs",id:"outputs-4",level:3},{value:"Mistral",id:"mistral",level:2},{value:"Inputs",id:"inputs-9",level:3},{value:"Outputs",id:"outputs-5",level:3},{value:"NVIDIA",id:"nvidia",level:2},{value:"Inputs",id:"inputs-10",level:3},{value:"Outputs",id:"outputs-6",level:3},{value:"Ollama",id:"ollama",level:2},{value:"Inputs",id:"inputs-11",level:3},{value:"OpenAI",id:"openai",level:2},{value:"Inputs",id:"inputs-12",level:3},{value:"Outputs",id:"outputs-7",level:3},{value:"Qianfan",id:"qianfan",level:2},{value:"Perplexity",id:"perplexity",level:2},{value:"Inputs",id:"inputs-13",level:3},{value:"Outputs",id:"outputs-8",level:3},{value:"SambaNova",id:"sambanova",level:2},{value:"Inputs",id:"inputs-14",level:3},{value:"Outputs",id:"outputs-9",level:3},{value:"VertexAI",id:"vertexai",level:2},{value:"Inputs",id:"inputs-15",level:3},{value:"Outputs",id:"outputs-10",level:3},{value:"Novita AI",id:"novita-ai",level:2},{value:"Parameters",id:"parameters",level:3},{value:"Inputs",id:"inputs-16",level:4}];function o(e){const t={a:"a",code:"code",h1:"h1",h2:"h2",h3:"h3",h4:"h4",header:"header",img:"img",p:"p",strong:"strong",table:"table",tbody:"tbody",td:"td",th:"th",thead:"thead",tr:"tr",...(0,d.R)(),...e.components};return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(t.header,{children:(0,s.jsx)(t.h1,{id:"model-components-in-langflow",children:"Model components in Langflow"})}),"\n",(0,s.jsx)(t.p,{children:"Model components generate text using large language models."}),"\n",(0,s.jsx)(t.p,{children:"Refer to your specific component's documentation for more information on parameters."}),"\n",(0,s.jsx)(t.h2,{id:"use-a-model-component-in-a-flow",children:"Use a model component in a flow"}),"\n",(0,s.jsx)(t.p,{children:"Model components receive inputs and prompts for generating text, and the generated text is sent to an output component."}),"\n",(0,s.jsxs)(t.p,{children:["The model output can also be sent to the ",(0,s.jsx)(t.strong,{children:"Language Model"})," port and on to a ",(0,s.jsx)(t.strong,{children:"Parse Data"})," component, where the output can be parsed into structured ",(0,s.jsx)(t.a,{href:"/configuration-objects",children:"Data"})," objects."]}),"\n",(0,s.jsxs)(t.p,{children:["This example has the OpenAI model in a chatbot flow. 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e={5354:0,1869:0};r.f.j=(a,c)=>{var d=r.o(e,a)?e[a]:void 0;if(0!==d)if(d)c.push(d[2]);else if(/^(1869|5354)$/.test(a))e[a]=0;else{var f=new Promise(((c,f)=>d=e[a]=[c,f]));c.push(d[2]=f);var b=r.p+r.u(a),t=new Error;r.l(b,(c=>{if(r.o(e,a)&&(0!==(d=e[a])&&(e[a]=void 0),d)){var f=c&&("load"===c.type?"missing":c.type),b=c&&c.target&&c.target.src;t.message="Loading chunk "+a+" failed.\n("+f+": "+b+")",t.name="ChunkLoadError",t.type=f,t.request=b,d[1](t)}}),"chunk-"+a,a)}},r.O.j=a=>0===e[a];var a=(a,c)=>{var d,f,b=c[0],t=c[1],o=c[2],n=0;if(b.some((a=>0!==e[a]))){for(d in t)r.o(t,d)&&(r.m[d]=t[d]);if(o)var i=o(r)}for(a&&a(c);n - + diff --git a/components-custom-components.html b/components-custom-components.html index 74bdec224a..d2ddb2e0b8 100644 --- a/components-custom-components.html +++ b/components-custom-components.html @@ -10,7 +10,7 @@ - + diff --git a/components-data.html b/components-data.html index 549d8a26d9..f8559e8623 100644 --- a/components-data.html +++ b/components-data.html @@ -10,7 +10,7 @@ - + diff --git a/components-embedding-models.html b/components-embedding-models.html index 1d35bd55bf..db57f38ff0 100644 --- a/components-embedding-models.html +++ b/components-embedding-models.html @@ -10,7 +10,7 @@ - + diff --git a/components-helpers.html b/components-helpers.html index 633f4b077f..f5f17a11e2 100644 --- a/components-helpers.html +++ b/components-helpers.html @@ -10,7 +10,7 @@ - + diff --git a/components-io.html b/components-io.html index 9fc1b65869..a7fed18260 100644 --- a/components-io.html +++ b/components-io.html @@ -10,7 +10,7 @@ - + diff --git a/components-loaders.html b/components-loaders.html index 1132aa2ea4..b1f85349bd 100644 --- a/components-loaders.html +++ b/components-loaders.html @@ -10,7 +10,7 @@ - + diff --git a/components-logic.html b/components-logic.html index 1dd9b2603e..b9d69cc0a5 100644 --- a/components-logic.html +++ b/components-logic.html @@ -10,7 +10,7 @@ - + diff --git a/components-memories.html b/components-memories.html index ead1a403af..a1b9dd5bbe 100644 --- a/components-memories.html +++ b/components-memories.html @@ -10,7 +10,7 @@ - + diff --git a/components-models.html b/components-models.html index 764ad81ae3..2025ee30bd 100644 --- a/components-models.html +++ b/components-models.html @@ -10,7 +10,7 @@ - + @@ -130,6 +130,12 @@

Inputs​

NameTypeDescription
credentialsFileJSON credentials file. Leave empty to fallback to environment variables. File type: JSON.
model_nameStringThe name of the Vertex AI model to use. Default: "gemini-1.5-pro".
projectStringThe project ID (advanced).
locationStringThe location for the Vertex AI API. Default: "us-central1" (advanced).
max_output_tokensIntegerThe maximum number of tokens to generate (advanced).
max_retriesIntegerMaximum number of retries for API calls. Default: 1 (advanced).
temperatureFloatControls randomness in the output. Default: 0.0.
top_kIntegerThe number of highest probability vocabulary tokens to keep for top-k-filtering (advanced).
top_pFloatThe cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Default: 0.95 (advanced).
verboseBooleanWhether to print verbose output. Default: False (advanced).

Outputs​

-
NameTypeDescription
modelLanguageModelAn instance of ChatVertexAI configured with the specified parameters.

Hi, how can I help you?

+
NameTypeDescription
modelLanguageModelAn instance of ChatVertexAI configured with the specified parameters.
+

Novita AI​

+

This component generates text using Novita AI's language models.

+

For more information, see Novita AI documentation.

+

Parameters​

+

Inputs​

+
NameTypeDescription
api_keySecretStringYour Novita AI API Key.
modelStringThe id of the Novita AI model to use.
max_tokensIntegerThe maximum number of tokens to generate. Set to 0 for unlimited tokens.
temperatureFloatControls randomness in the output. Range: [0.0, 1.0]. Default: 0.7.
top_pFloatControls the nucleus sampling. Range: [0.0, 1.0]. Default: 1.0.
frequency_penaltyFloatControls the frequency penalty. Range: [0.0, 2.0]. Default: 0.0.
presence_penaltyFloatControls the presence penalty. Range: [0.0, 2.0]. Default: 0.0.

Hi, how can I help you?

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