diff --git a/404.html b/404.html index 4ac0f917ed..aa9dd5662e 100644 --- a/404.html +++ b/404.html @@ -21,7 +21,7 @@ - + diff --git a/agents-overview.html b/agents-overview.html index 17e5be582c..70cc088199 100644 --- a/agents-overview.html +++ b/agents-overview.html @@ -21,7 +21,7 @@ - + diff --git a/agents-tool-calling-agent-component.html b/agents-tool-calling-agent-component.html index a48a45f2d4..c16cb4060c 100644 --- a/agents-tool-calling-agent-component.html +++ b/agents-tool-calling-agent-component.html @@ -21,7 +21,7 @@ - + diff --git a/api-reference-api-examples.html b/api-reference-api-examples.html index 24b47282f8..334e80f877 100644 --- a/api-reference-api-examples.html +++ b/api-reference-api-examples.html @@ -21,7 +21,7 @@ - + diff --git a/api.html b/api.html index a1c1a6e0d7..882ffd0830 100644 --- a/api.html +++ b/api.html @@ -21,7 +21,7 @@ - + diff --git a/api/add-user.html b/api/add-user.html index 38a9631fb3..b69f4f0dce 100644 --- a/api/add-user.html +++ b/api/add-user.html @@ -21,7 +21,7 @@ - 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+ diff --git a/api/upload-file-2.html b/api/upload-file-2.html index 58766e705a..051b41adba 100644 --- a/api/upload-file-2.html +++ b/api/upload-file-2.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-file.html b/api/upload-file.html index c668d908f2..65bcf48d3d 100644 --- a/api/upload-file.html +++ b/api/upload-file.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-user-file-1.html b/api/upload-user-file-1.html index e063d90463..9174e95d9e 100644 --- a/api/upload-user-file-1.html +++ b/api/upload-user-file-1.html @@ -21,7 +21,7 @@ - + diff --git a/api/upload-user-file.html b/api/upload-user-file.html index 1e5f9fb7e3..e85499df09 100644 --- a/api/upload-user-file.html +++ b/api/upload-user-file.html @@ -21,7 +21,7 @@ - + diff --git a/api/webhook-run-flow.html b/api/webhook-run-flow.html index 2ef5ca5558..b49108a0c0 100644 --- a/api/webhook-run-flow.html +++ b/api/webhook-run-flow.html @@ -21,7 +21,7 @@ - + diff --git a/assets/js/2ab0d4f5.a7ee93ff.js b/assets/js/2ab0d4f5.a7ee93ff.js new file mode 100644 index 0000000000..1c584a5c5a --- /dev/null +++ b/assets/js/2ab0d4f5.a7ee93ff.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[1845],{28453:(e,n,t)=>{t.d(n,{R:()=>a,x:()=>o});var i=t(96540);const s={},r=i.createContext(s);function a(e){const n=i.useContext(r);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function o(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(s):e.components||s:a(e.components),i.createElement(r.Provider,{value:n},e.children)}},83640:(e,n,t)=>{t.r(n),t.d(n,{contentTitle:()=>o,default:()=>c,frontMatter:()=>a,metadata:()=>i,toc:()=>l});const i=JSON.parse('{"type":"api","id":"simplified-run-flow","title":"Simplified Run Flow","description":"","slug":"/simplified-run-flow","frontMatter":{},"api":{"tags":["Base"],"description":"Executes a specified flow by ID with support for streaming and telemetry.\\n\\nThis endpoint executes a flow identified by ID or name, with options for streaming the response\\nand tracking execution metrics. 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Local inference models do not require an API key."]}),"\n",(0,t.jsx)(n.p,{children:"Use this component to create embeddings with Hugging Face's hosted models, or to connect to your own locally hosted models."}),"\n",(0,t.jsx)(n.h3,{id:"inputs-9",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"API Key"}),(0,t.jsx)(n.td,{children:"API Key"}),(0,t.jsx)(n.td,{children:"The API key for accessing the Hugging Face Inference API."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"API URL"}),(0,t.jsx)(n.td,{children:"API URL"}),(0,t.jsx)(n.td,{children:"The URL of the Hugging Face Inference API."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"The name of the model to use for embeddings."})]})]})]}),"\n",(0,t.jsx)(n.h3,{id:"outputs-9",children:"Outputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsx)(n.tbody,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"embeddings"}),(0,t.jsx)(n.td,{children:"Embeddings"}),(0,t.jsx)(n.td,{children:"The generated embeddings."})]})})]}),"\n",(0,t.jsx)(n.h3,{id:"connect-the-hugging-face-component-to-a-local-embeddings-model",children:"Connect the Hugging Face component to a local embeddings model"}),"\n",(0,t.jsxs)(n.p,{children:["To run an embeddings inference locally, see the ",(0,t.jsx)(n.a,{href:"https://huggingface.co/docs/text-embeddings-inference/local_cpu",children:"HuggingFace documentation"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:["To connect the local Hugging Face model to the ",(0,t.jsx)(n.strong,{children:"Hugging Face embeddings inference"})," component and use it in a flow, follow these steps:"]}),"\n",(0,t.jsxs)(n.ol,{children:["\n",(0,t.jsxs)(n.li,{children:["Create a ",(0,t.jsx)(n.a,{href:"/starter-projects-vector-store-rag",children:"Vector store RAG flow"}),".\nThere are two embeddings models in this flow that you can replace with ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," embeddings inference components."]}),"\n",(0,t.jsxs)(n.li,{children:["Replace both ",(0,t.jsx)(n.strong,{children:"OpenAI"})," embeddings model components with ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," model components."]}),"\n",(0,t.jsxs)(n.li,{children:["Connect both ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," components to the ",(0,t.jsx)(n.strong,{children:"Embeddings"})," ports of the ",(0,t.jsx)(n.strong,{children:"Astra DB vector store"})," components."]}),"\n",(0,t.jsxs)(n.li,{children:["In the ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," components, set the ",(0,t.jsx)(n.strong,{children:"Inference Endpoint"})," field to the URL of your local inference model. ",(0,t.jsxs)(n.strong,{children:["The ",(0,t.jsx)(n.strong,{children:"API Key"})," field is not required for local inference."]})]}),"\n",(0,t.jsx)(n.li,{children:"Run the flow. The local inference models generate embeddings for the input text."}),"\n"]}),"\n",(0,t.jsx)(n.h2,{id:"ibm-watsonx-embeddings",children:"IBM watsonx embeddings"}),"\n",(0,t.jsxs)(n.p,{children:["This component generates text using ",(0,t.jsx)(n.a,{href:"https://www.ibm.com/watsonx",children:"IBM watsonx.ai"})," foundation models."]}),"\n",(0,t.jsxs)(n.p,{children:["To use ",(0,t.jsx)(n.strong,{children:"IBM watsonx.ai"})," embeddings components, replace an embeddings component with the IBM watsonx.ai component in a flow."]}),"\n",(0,t.jsx)(n.p,{children:"An example document processing flow looks like the following:"}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.img,{alt:"IBM watsonx embeddings model loading a chroma-db with split text",src:d(96425).A+"",width:"1714",height:"1486"})}),"\n",(0,t.jsx)(n.p,{children:"This flow loads a PDF file from local storage and splits the text into chunks."}),"\n",(0,t.jsxs)(n.p,{children:["The ",(0,t.jsx)(n.strong,{children:"IBM watsonx"})," embeddings component converts the text chunks into embeddings, which are then stored in a Chroma DB vector store."]}),"\n",(0,t.jsxs)(n.p,{children:["The values for ",(0,t.jsx)(n.strong,{children:"API endpoint"}),", ",(0,t.jsx)(n.strong,{children:"Project ID"}),", ",(0,t.jsx)(n.strong,{children:"API key"}),", and ",(0,t.jsx)(n.strong,{children:"Model Name"})," are found in your IBM watsonx.ai deployment.\nFor more information, see the ",(0,t.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/text_embedding/ibm_watsonx/",children:"Langchain documentation"}),"."]}),"\n",(0,t.jsx)(n.h3,{id:"default-models",children:"Default models"}),"\n",(0,t.jsx)(n.p,{children:"The component supports several default models with the following vector dimensions:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"sentence-transformers/all-minilm-l12-v2"}),": 384-dimensional embeddings"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"ibm/slate-125m-english-rtrvr-v2"}),": 768-dimensional embeddings"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"ibm/slate-30m-english-rtrvr-v2"}),": 768-dimensional embeddings"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"intfloat/multilingual-e5-large"}),": 1024-dimensional embeddings"]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"The component automatically fetches and updates the list of available models from your watsonx.ai instance when you provide your API endpoint and credentials."}),"\n",(0,t.jsx)(n.h3,{id:"inputs-10",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"url"}),(0,t.jsx)(n.td,{children:"watsonx API Endpoint"}),(0,t.jsx)(n.td,{children:"The base URL of the API."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"project_id"}),(0,t.jsx)(n.td,{children:"watsonx project id"}),(0,t.jsx)(n.td,{children:"The project ID for your watsonx.ai instance."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"api_key"}),(0,t.jsx)(n.td,{children:"API Key"}),(0,t.jsx)(n.td,{children:"The API Key to use for the model."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"model_name"}),(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"The name of the embedding model to use."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"truncate_input_tokens"}),(0,t.jsx)(n.td,{children:"Truncate Input Tokens"}),(0,t.jsxs)(n.td,{children:["The maximum number of tokens to process. Default: ",(0,t.jsx)(n.code,{children:"200"}),"."]})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"input_text"}),(0,t.jsx)(n.td,{children:"Include the original text in the output"}),(0,t.jsxs)(n.td,{children:["Determines if the original text is included in the output. Default: ",(0,t.jsx)(n.code,{children:"True"}),"."]})]})]})]}),"\n",(0,t.jsx)(n.h3,{id:"outputs-10",children:"Outputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsx)(n.tbody,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"embeddings"}),(0,t.jsx)(n.td,{children:"Embeddings"}),(0,t.jsx)(n.td,{children:"An instance for generating embeddings using watsonx.ai"})]})})]}),"\n",(0,t.jsx)(n.h2,{id:"lm-studio-embeddings",children:"LM Studio Embeddings"}),"\n",(0,t.jsxs)(n.p,{children:["This component generates embeddings using ",(0,t.jsx)(n.a,{href:"https://lmstudio.ai/docs",children:"LM Studio"})," models."]}),"\n",(0,t.jsx)(n.h3,{id:"inputs-11",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"model"}),(0,t.jsx)(n.td,{children:"Model"}),(0,t.jsx)(n.td,{children:"The LM Studio model to use for generating embeddings"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"base_url"}),(0,t.jsx)(n.td,{children:"LM Studio Base URL"}),(0,t.jsx)(n.td,{children:"The base URL for the LM Studio API"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"api_key"}),(0,t.jsx)(n.td,{children:"LM Studio API Key"}),(0,t.jsx)(n.td,{children:"API key for authentication with LM Studio"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"temperature"}),(0,t.jsx)(n.td,{children:"Model 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models."]}),"\n",(0,t.jsx)(n.h3,{id:"inputs-12",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Type"}),(0,t.jsx)(n.th,{children:"Description"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"model"}),(0,t.jsx)(n.td,{children:"String"}),(0,t.jsx)(n.td,{children:'The MistralAI model to use (default: "mistral-embed")'})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"mistral_api_key"}),(0,t.jsx)(n.td,{children:"SecretString"}),(0,t.jsx)(n.td,{children:"API key for authenticating with MistralAI"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"max_concurrent_requests"}),(0,t.jsx)(n.td,{children:"Integer"}),(0,t.jsx)(n.td,{children:"Maximum number of concurrent API requests (default: 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",(0,t.jsx)(n.code,{children:"https://api.mistral.ai/v1/"}),")"]})]})]})]}),"\n",(0,t.jsx)(n.h3,{id:"outputs-12",children:"Outputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Type"}),(0,t.jsx)(n.th,{children:"Description"})]})}),(0,t.jsx)(n.tbody,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"embeddings"}),(0,t.jsx)(n.td,{children:"Embeddings"}),(0,t.jsx)(n.td,{children:"MistralAIEmbeddings instance for generating embeddings"})]})})]}),"\n",(0,t.jsx)(n.h2,{id:"nvidia",children:"NVIDIA"}),"\n",(0,t.jsxs)(n.p,{children:["This component generates embeddings using ",(0,t.jsx)(n.a,{href:"https://docs.nvidia.com",children:"NVIDIA models"}),"."]}),"\n",(0,t.jsx)(n.h3,{id:"inputs-13",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Type"}),(0,t.jsx)(n.th,{children:"Description"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"model"}),(0,t.jsx)(n.td,{children:"String"}),(0,t.jsxs)(n.td,{children:["The NVIDIA model to use for embeddings (e.g., ",(0,t.jsx)(n.code,{children:"nvidia/nv-embed-v1"}),")"]})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"base_url"}),(0,t.jsx)(n.td,{children:"String"}),(0,t.jsxs)(n.td,{children:["Base URL for the NVIDIA API (default: ",(0,t.jsx)(n.code,{children:"https://integrate.api.nvidia.com/v1"}),")"]})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"nvidia_api_key"}),(0,t.jsx)(n.td,{children:"SecretString"}),(0,t.jsx)(n.td,{children:"API key for authenticating with NVIDIA's 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Ensure you have sufficient computational resources to run the models."}),"\n",(0,t.jsx)(n.h3,{id:"inputs-8",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Cache Folder"}),(0,t.jsx)(n.td,{children:"Cache Folder"}),(0,t.jsx)(n.td,{children:"Folder path to cache HuggingFace models"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Encode Kwargs"}),(0,t.jsx)(n.td,{children:"Encoding Arguments"}),(0,t.jsx)(n.td,{children:"Additional arguments for the encoding process"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Model Kwargs"}),(0,t.jsx)(n.td,{children:"Model Arguments"}),(0,t.jsx)(n.td,{children:"Additional arguments for the model"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"Name of the HuggingFace model to use"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Multi Process"}),(0,t.jsx)(n.td,{children:"Multi-Process"}),(0,t.jsx)(n.td,{children:"Whether to use multiple processes"})]})]})]}),"\n",(0,t.jsx)(n.h3,{id:"outputs-8",children:"Outputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsx)(n.tbody,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"embeddings"}),(0,t.jsx)(n.td,{children:"Embeddings"}),(0,t.jsx)(n.td,{children:"The generated embeddings"})]})})]}),"\n",(0,t.jsx)(n.h2,{id:"hugging-face-embeddings-inference",children:"Hugging Face embeddings inference"}),"\n",(0,t.jsxs)(n.p,{children:["This component generates embeddings using ",(0,t.jsx)(n.a,{href:"https://huggingface.co/",children:"Hugging Face Inference API models"})," and requires a ",(0,t.jsx)(n.a,{href:"https://huggingface.co/docs/hub/security-tokens",children:"Hugging Face API token"})," to authenticate. Local inference models do not require an API key."]}),"\n",(0,t.jsx)(n.p,{children:"Use this component to create embeddings with Hugging Face's hosted models, or to connect to your own locally hosted models."}),"\n",(0,t.jsx)(n.h3,{id:"inputs-9",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"API Key"}),(0,t.jsx)(n.td,{children:"API Key"}),(0,t.jsx)(n.td,{children:"The API key for accessing the Hugging Face Inference API."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"API URL"}),(0,t.jsx)(n.td,{children:"API URL"}),(0,t.jsx)(n.td,{children:"The URL of the Hugging Face Inference API."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"The name of the model to use for embeddings."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Cache Folder"}),(0,t.jsx)(n.td,{children:"Cache Folder"}),(0,t.jsx)(n.td,{children:"The folder path to cache Hugging Face models."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Encode Kwargs"}),(0,t.jsx)(n.td,{children:"Encoding Arguments"}),(0,t.jsx)(n.td,{children:"Additional arguments for the encoding process."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Model Kwargs"}),(0,t.jsx)(n.td,{children:"Model Arguments"}),(0,t.jsx)(n.td,{children:"Additional arguments for the model."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"Multi Process"}),(0,t.jsx)(n.td,{children:"Multi-Process"}),(0,t.jsx)(n.td,{children:"Whether to use multiple processes."})]})]})]}),"\n",(0,t.jsx)(n.h3,{id:"outputs-9",children:"Outputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsx)(n.tbody,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"embeddings"}),(0,t.jsx)(n.td,{children:"Embeddings"}),(0,t.jsx)(n.td,{children:"The generated embeddings."})]})})]}),"\n",(0,t.jsx)(n.h3,{id:"connect-the-hugging-face-component-to-a-local-embeddings-model",children:"Connect the Hugging Face component to a local embeddings model"}),"\n",(0,t.jsxs)(n.p,{children:["To run an embeddings inference locally, see the ",(0,t.jsx)(n.a,{href:"https://huggingface.co/docs/text-embeddings-inference/local_cpu",children:"HuggingFace documentation"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:["To connect the local Hugging Face model to the ",(0,t.jsx)(n.strong,{children:"Hugging Face embeddings inference"})," component and use it in a flow, follow these steps:"]}),"\n",(0,t.jsxs)(n.ol,{children:["\n",(0,t.jsxs)(n.li,{children:["Create a ",(0,t.jsx)(n.a,{href:"/starter-projects-vector-store-rag",children:"Vector store RAG flow"}),".\nThere are two embeddings models in this flow that you can replace with ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," embeddings inference components."]}),"\n",(0,t.jsxs)(n.li,{children:["Replace both ",(0,t.jsx)(n.strong,{children:"OpenAI"})," embeddings model components with ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," model components."]}),"\n",(0,t.jsxs)(n.li,{children:["Connect both ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," components to the ",(0,t.jsx)(n.strong,{children:"Embeddings"})," ports of the ",(0,t.jsx)(n.strong,{children:"Astra DB vector store"})," components."]}),"\n",(0,t.jsxs)(n.li,{children:["In the ",(0,t.jsx)(n.strong,{children:"Hugging Face"})," components, set the ",(0,t.jsx)(n.strong,{children:"Inference Endpoint"})," field to the URL of your local inference model. ",(0,t.jsxs)(n.strong,{children:["The ",(0,t.jsx)(n.strong,{children:"API Key"})," field is not required for local inference."]})]}),"\n",(0,t.jsx)(n.li,{children:"Run the flow. The local inference models generate embeddings for the input text."}),"\n"]}),"\n",(0,t.jsx)(n.h2,{id:"ibm-watsonx-embeddings",children:"IBM watsonx embeddings"}),"\n",(0,t.jsxs)(n.p,{children:["This component generates text using ",(0,t.jsx)(n.a,{href:"https://www.ibm.com/watsonx",children:"IBM watsonx.ai"})," foundation models."]}),"\n",(0,t.jsxs)(n.p,{children:["To use ",(0,t.jsx)(n.strong,{children:"IBM watsonx.ai"})," embeddings components, replace an embeddings component with the IBM watsonx.ai component in a flow."]}),"\n",(0,t.jsx)(n.p,{children:"An example document processing flow looks like the following:"}),"\n",(0,t.jsx)(n.p,{children:(0,t.jsx)(n.img,{alt:"IBM watsonx embeddings model loading a chroma-db with split text",src:d(96425).A+"",width:"1714",height:"1486"})}),"\n",(0,t.jsx)(n.p,{children:"This flow loads a PDF file from local storage and splits the text into chunks."}),"\n",(0,t.jsxs)(n.p,{children:["The ",(0,t.jsx)(n.strong,{children:"IBM watsonx"})," embeddings component converts the text chunks into embeddings, which are then stored in a Chroma DB vector store."]}),"\n",(0,t.jsxs)(n.p,{children:["The values for ",(0,t.jsx)(n.strong,{children:"API endpoint"}),", ",(0,t.jsx)(n.strong,{children:"Project ID"}),", ",(0,t.jsx)(n.strong,{children:"API key"}),", and ",(0,t.jsx)(n.strong,{children:"Model Name"})," are found in your IBM watsonx.ai deployment.\nFor more information, see the ",(0,t.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/text_embedding/ibm_watsonx/",children:"Langchain documentation"}),"."]}),"\n",(0,t.jsx)(n.h3,{id:"default-models",children:"Default models"}),"\n",(0,t.jsx)(n.p,{children:"The component supports several default models with the following vector dimensions:"}),"\n",(0,t.jsxs)(n.ul,{children:["\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"sentence-transformers/all-minilm-l12-v2"}),": 384-dimensional embeddings"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"ibm/slate-125m-english-rtrvr-v2"}),": 768-dimensional embeddings"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"ibm/slate-30m-english-rtrvr-v2"}),": 768-dimensional embeddings"]}),"\n",(0,t.jsxs)(n.li,{children:[(0,t.jsx)(n.code,{children:"intfloat/multilingual-e5-large"}),": 1024-dimensional embeddings"]}),"\n"]}),"\n",(0,t.jsx)(n.p,{children:"The component automatically fetches and updates the list of available models from your watsonx.ai instance when you provide your API endpoint and credentials."}),"\n",(0,t.jsx)(n.h3,{id:"inputs-10",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"url"}),(0,t.jsx)(n.td,{children:"watsonx API Endpoint"}),(0,t.jsx)(n.td,{children:"The base URL of the API."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"project_id"}),(0,t.jsx)(n.td,{children:"watsonx project id"}),(0,t.jsx)(n.td,{children:"The project ID for your watsonx.ai instance."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"api_key"}),(0,t.jsx)(n.td,{children:"API Key"}),(0,t.jsx)(n.td,{children:"The API Key to use for the model."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"model_name"}),(0,t.jsx)(n.td,{children:"Model Name"}),(0,t.jsx)(n.td,{children:"The name of the embedding model to use."})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"truncate_input_tokens"}),(0,t.jsx)(n.td,{children:"Truncate Input Tokens"}),(0,t.jsxs)(n.td,{children:["The maximum number of tokens to process. 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Default: ",(0,t.jsx)(n.code,{children:"True"}),"."]})]})]})]}),"\n",(0,t.jsx)(n.h3,{id:"outputs-10",children:"Outputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsx)(n.tbody,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"embeddings"}),(0,t.jsx)(n.td,{children:"Embeddings"}),(0,t.jsx)(n.td,{children:"An instance for generating embeddings using watsonx.ai"})]})})]}),"\n",(0,t.jsx)(n.h2,{id:"lm-studio-embeddings",children:"LM Studio Embeddings"}),"\n",(0,t.jsxs)(n.p,{children:["This component generates embeddings using ",(0,t.jsx)(n.a,{href:"https://lmstudio.ai/docs",children:"LM Studio"})," models."]}),"\n",(0,t.jsx)(n.h3,{id:"inputs-11",children:"Inputs"}),"\n",(0,t.jsxs)(n.table,{children:[(0,t.jsx)(n.thead,{children:(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.th,{children:"Name"}),(0,t.jsx)(n.th,{children:"Display Name"}),(0,t.jsx)(n.th,{children:"Info"})]})}),(0,t.jsxs)(n.tbody,{children:[(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"model"}),(0,t.jsx)(n.td,{children:"Model"}),(0,t.jsx)(n.td,{children:"The LM Studio model to use for generating embeddings"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"base_url"}),(0,t.jsx)(n.td,{children:"LM Studio Base URL"}),(0,t.jsx)(n.td,{children:"The base URL for the LM Studio API"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"api_key"}),(0,t.jsx)(n.td,{children:"LM Studio API Key"}),(0,t.jsx)(n.td,{children:"API key for authentication with LM Studio"})]}),(0,t.jsxs)(n.tr,{children:[(0,t.jsx)(n.td,{children:"temperature"}),(0,t.jsx)(n.td,{children:"Model 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e={5354:0,1869:0};r.f.j=(a,f)=>{var c=r.o(e,a)?e[a]:void 0;if(0!==c)if(c)f.push(c[2]);else if(/^(1869|5354)$/.test(a))e[a]=0;else{var b=new Promise(((f,b)=>c=e[a]=[f,b]));f.push(c[2]=b);var d=r.p+r.u(a),t=new Error;r.l(d,(f=>{if(r.o(e,a)&&(0!==(c=e[a])&&(e[a]=void 0),c)){var b=f&&("load"===f.type?"missing":f.type),d=f&&f.target&&f.target.src;t.message="Loading chunk "+a+" failed.\n("+b+": "+d+")",t.name="ChunkLoadError",t.type=b,t.request=d,c[1](t)}}),"chunk-"+a,a)}},r.O.j=a=>0===e[a];var a=(a,f)=>{var c,b,d=f[0],t=f[1],o=f[2],n=0;if(d.some((a=>0!==e[a]))){for(c in t)r.o(t,c)&&(r.m[c]=t[c]);if(o)var i=o(r)}for(a&&a(f);n - + diff --git a/components-agents.html b/components-agents.html index b58587bca5..f0a332c914 100644 --- a/components-agents.html +++ b/components-agents.html @@ -21,7 +21,7 @@ - + diff --git a/components-bundle-components.html b/components-bundle-components.html index c0f52f1d79..e80fe3807a 100644 --- a/components-bundle-components.html +++ b/components-bundle-components.html @@ -21,7 +21,7 @@ - + diff --git a/components-custom-components.html b/components-custom-components.html index 037597c470..124d4bc33d 100644 --- a/components-custom-components.html +++ b/components-custom-components.html @@ -21,7 +21,7 @@ - + diff --git a/components-data.html b/components-data.html index e814c059c5..b258bbf49d 100644 --- a/components-data.html +++ b/components-data.html @@ -21,7 +21,7 @@ - + diff --git a/components-embedding-models.html b/components-embedding-models.html index e08b77c2cf..17f8b34a2a 100644 --- a/components-embedding-models.html +++ b/components-embedding-models.html @@ -21,7 +21,7 @@ - + @@ -105,7 +105,7 @@ Instead, use the Hugging Face Embed

This component generates embeddings using Hugging Face Inference API models and requires a Hugging Face API token to authenticate. Local inference models do not require an API key.

Use this component to create embeddings with Hugging Face's hosted models, or to connect to your own locally hosted models.

Inputs​

-
NameDisplay NameInfo
API KeyAPI KeyThe API key for accessing the Hugging Face Inference API.
API URLAPI URLThe URL of the Hugging Face Inference API.
Model NameModel NameThe name of the model to use for embeddings.
Cache FolderCache FolderThe folder path to cache Hugging Face models.
Encode KwargsEncoding ArgumentsAdditional arguments for the encoding process.
Model KwargsModel ArgumentsAdditional arguments for the model.
Multi ProcessMulti-ProcessWhether to use multiple processes.
+
NameDisplay NameInfo
API KeyAPI KeyThe API key for accessing the Hugging Face Inference API.
API URLAPI URLThe URL of the Hugging Face Inference API.
Model NameModel NameThe name of the model to use for embeddings.

Outputs​

NameDisplay NameInfo
embeddingsEmbeddingsThe generated embeddings.

Connect the Hugging Face component to a local embeddings model​

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