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+ diff --git a/api/upload-user-file.html b/api/upload-user-file.html index d5949172e5..21024a4163 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 df1e8b0772..311323fc76 100644 --- a/api/webhook-run-flow.html +++ b/api/webhook-run-flow.html @@ -21,7 +21,7 @@ - + diff --git a/assets/images/component-watsonx-embeddings-chroma-591e45d59ab635d1e1e68ab8036cfed7.png b/assets/images/component-watsonx-embeddings-chroma-591e45d59ab635d1e1e68ab8036cfed7.png new file mode 100644 index 0000000000..a117eb73ce Binary files /dev/null and b/assets/images/component-watsonx-embeddings-chroma-591e45d59ab635d1e1e68ab8036cfed7.png differ diff --git a/assets/js/2ab0d4f5.c98bf358.js b/assets/js/2ab0d4f5.1a12f90a.js similarity index 51% rename from assets/js/2ab0d4f5.c98bf358.js rename to assets/js/2ab0d4f5.1a12f90a.js index 8936f39522..eda6db0cf8 100644 --- a/assets/js/2ab0d4f5.c98bf358.js +++ b/assets/js/2ab0d4f5.1a12f90a.js @@ -1 +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)(d.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)(d.h3,{id:"inputs-9",children:"Inputs"}),"\n",(0,t.jsxs)(d.table,{children:[(0,t.jsx)(d.thead,{children:(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.th,{children:"Name"}),(0,t.jsx)(d.th,{children:"Display Name"}),(0,t.jsx)(d.th,{children:"Info"})]})}),(0,t.jsxs)(d.tbody,{children:[(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"API Key"}),(0,t.jsx)(d.td,{children:"API Key"}),(0,t.jsx)(d.td,{children:"The API key for accessing the Hugging Face Inference API."})]}),(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"API URL"}),(0,t.jsx)(d.td,{children:"API URL"}),(0,t.jsx)(d.td,{children:"The URL of the Hugging Face Inference API."})]}),(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"Model Name"}),(0,t.jsx)(d.td,{children:"Model Name"}),(0,t.jsx)(d.td,{children:"The name of the model to use for embeddings."})]}),(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"Cache Folder"}),(0,t.jsx)(d.td,{children:"Cache Folder"}),(0,t.jsx)(d.td,{children:"The folder path to cache Hugging Face models."})]}),(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"Encode Kwargs"}),(0,t.jsx)(d.td,{children:"Encoding Arguments"}),(0,t.jsx)(d.td,{children:"Additional arguments for the encoding process."})]}),(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"Model Kwargs"}),(0,t.jsx)(d.td,{children:"Model Arguments"}),(0,t.jsx)(d.td,{children:"Additional arguments for the model."})]}),(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"Multi Process"}),(0,t.jsx)(d.td,{children:"Multi-Process"}),(0,t.jsx)(d.td,{children:"Whether to use multiple processes."})]})]})]}),"\n",(0,t.jsx)(d.h3,{id:"outputs-9",children:"Outputs"}),"\n",(0,t.jsxs)(d.table,{children:[(0,t.jsx)(d.thead,{children:(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.th,{children:"Name"}),(0,t.jsx)(d.th,{children:"Display Name"}),(0,t.jsx)(d.th,{children:"Info"})]})}),(0,t.jsx)(d.tbody,{children:(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"embeddings"}),(0,t.jsx)(d.td,{children:"Embeddings"}),(0,t.jsx)(d.td,{children:"The generated embeddings."})]})})]}),"\n",(0,t.jsx)(d.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)(d.p,{children:["To run an embeddings inference locally, see the ",(0,t.jsx)(d.a,{href:"https://huggingface.co/docs/text-embeddings-inference/local_cpu",children:"HuggingFace documentation"}),"."]}),"\n",(0,t.jsxs)(d.p,{children:["To connect the local Hugging Face model to the ",(0,t.jsx)(d.strong,{children:"Hugging Face embeddings inference"})," component and use it in a flow, follow these steps:"]}),"\n",(0,t.jsxs)(d.ol,{children:["\n",(0,t.jsxs)(d.li,{children:["Create a ",(0,t.jsx)(d.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)(d.strong,{children:"Hugging Face"})," embeddings inference components."]}),"\n",(0,t.jsxs)(d.li,{children:["Replace both ",(0,t.jsx)(d.strong,{children:"OpenAI"})," embeddings model components with ",(0,t.jsx)(d.strong,{children:"Hugging Face"})," model components."]}),"\n",(0,t.jsxs)(d.li,{children:["Connect both ",(0,t.jsx)(d.strong,{children:"Hugging Face"})," components to the ",(0,t.jsx)(d.strong,{children:"Embeddings"})," ports of the ",(0,t.jsx)(d.strong,{children:"Astra DB vector store"})," components."]}),"\n",(0,t.jsxs)(d.li,{children:["In the ",(0,t.jsx)(d.strong,{children:"Hugging Face"})," components, set the ",(0,t.jsx)(d.strong,{children:"Inference Endpoint"})," field to the URL of your local inference model. ",(0,t.jsxs)(d.strong,{children:["The ",(0,t.jsx)(d.strong,{children:"API Key"})," field is not required for local inference."]})]}),"\n",(0,t.jsx)(d.li,{children:"Run the flow. 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Default: ",(0,t.jsx)(d.code,{children:"True"}),"."]})]})]})]}),"\n",(0,t.jsx)(d.h3,{id:"outputs-10",children:"Outputs"}),"\n",(0,t.jsxs)(d.table,{children:[(0,t.jsx)(d.thead,{children:(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.th,{children:"Name"}),(0,t.jsx)(d.th,{children:"Display Name"}),(0,t.jsx)(d.th,{children:"Info"})]})}),(0,t.jsx)(d.tbody,{children:(0,t.jsxs)(d.tr,{children:[(0,t.jsx)(d.td,{children:"embeddings"}),(0,t.jsx)(d.td,{children:"Embeddings"}),(0,t.jsx)(d.td,{children:"An instance for generating embeddings using watsonx.ai"})]})})]}),"\n",(0,t.jsx)(d.h2,{id:"lm-studio-embeddings",children:"LM Studio Embeddings"}),"\n",(0,t.jsxs)(d.p,{children:["This component generates embeddings using ",(0,t.jsx)(d.a,{href:"https://lmstudio.ai/docs",children:"LM Studio"})," 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b/components-bundle-components.html @@ -21,7 +21,7 @@ - + diff --git a/components-custom-components.html b/components-custom-components.html index bb77f80776..edaee44bca 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 df35021616..e039dd968f 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 457783c1fb..8f943a145f 100644 --- a/components-embedding-models.html +++ b/components-embedding-models.html @@ -21,7 +21,7 @@ - + @@ -119,48 +119,70 @@ There are two embeddings models in this flow that you can replace with H
  • In the Hugging Face components, set the Inference Endpoint field to the URL of your local inference model. The API Key field is not required for local inference.
  • Run the flow. The local inference models generate embeddings for the input text.
    • +

    IBM watsonx embeddings​

    +

    This component generates text using IBM watsonx.ai foundation models.

    +

    To use IBM watsonx.ai embeddings components, replace an embeddings component with the IBM watsonx.ai component in a flow.

    +

    An example document processing flow looks like the following:

    +

    IBM watsonx embeddings model loading a chroma-db with split text

    +

    This flow loads a PDF file from local storage and splits the text into chunks.

    +

    The IBM watsonx embeddings component converts the text chunks into embeddings, which are then stored in a Chroma DB vector store.

    +

    The values for API endpoint, Project ID, API key, and Model Name are found in your IBM watsonx.ai deployment. +For more information, see the Langchain documentation.

    +

    Default models​

    +

    The component supports several default models with the following vector dimensions:

    +
      +
    • sentence-transformers/all-minilm-l12-v2: 384-dimensional embeddings
      • +
    • ibm/slate-125m-english-rtrvr-v2: 768-dimensional embeddings
      • +
    • ibm/slate-30m-english-rtrvr-v2: 768-dimensional embeddings
      • +
    • intfloat/multilingual-e5-large: 1024-dimensional embeddings
      • +
    +

    The component automatically fetches and updates the list of available models from your watsonx.ai instance when you provide your API endpoint and credentials.

    +

    Inputs​

    +
    NameDisplay NameInfo
    urlwatsonx API EndpointThe base URL of the API.
    project_idwatsonx project idThe project ID for your watsonx.ai instance.
    api_keyAPI KeyThe API Key to use for the model.
    model_nameModel NameThe name of the embedding model to use.
    truncate_input_tokensTruncate Input TokensThe maximum number of tokens to process. Default: 200.
    input_textInclude the original text in the outputDetermines if the original text is included in the output. Default: True.
    +

    Outputs​

    +
    NameDisplay NameInfo
    embeddingsEmbeddingsAn instance for generating embeddings using watsonx.ai

    LM Studio Embeddings​

    This component generates embeddings using LM Studio models.

    -

    Inputs​

    +

    Inputs​

    NameDisplay NameInfo
    modelModelThe LM Studio model to use for generating embeddings
    base_urlLM Studio Base URLThe base URL for the LM Studio API
    api_keyLM Studio API KeyAPI key for authentication with LM Studio
    temperatureModel TemperatureTemperature setting for the model
    -

    Outputs​

    +

    Outputs​

    NameDisplay NameInfo
    embeddingsEmbeddingsThe generated embeddings

    MistralAI​

    This component generates embeddings using MistralAI models.

    -

    Inputs​

    +

    Inputs​

    NameTypeDescription
    modelStringThe MistralAI model to use (default: "mistral-embed")
    mistral_api_keySecretStringAPI key for authenticating with MistralAI
    max_concurrent_requestsIntegerMaximum number of concurrent API requests (default: 64)
    max_retriesIntegerMaximum number of retry attempts for failed requests (default: 5)
    timeoutIntegerRequest timeout in seconds (default: 120)
    endpointStringCustom API endpoint URL (default: https://api.mistral.ai/v1/)
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    Outputs​

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    Outputs​

    NameTypeDescription
    embeddingsEmbeddingsMistralAIEmbeddings instance for generating embeddings

    NVIDIA​

    This component generates embeddings using NVIDIA models.

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    Inputs​

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    Inputs​

    NameTypeDescription
    modelStringThe NVIDIA model to use for embeddings (e.g., nvidia/nv-embed-v1)
    base_urlStringBase URL for the NVIDIA API (default: https://integrate.api.nvidia.com/v1)
    nvidia_api_keySecretStringAPI key for authenticating with NVIDIA's service
    temperatureFloatModel temperature for embedding generation (default: 0.1)
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    Outputs​

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    Outputs​

    NameTypeDescription
    embeddingsEmbeddingsNVIDIAEmbeddings instance for generating embeddings

    Ollama Embeddings​

    This component generates embeddings using Ollama models.

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    Inputs​

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    Inputs​

    NameTypeDescription
    Ollama ModelStringName of the Ollama model to use (default: llama2)
    Ollama Base URLStringBase URL of the Ollama API (default: http://localhost:11434)
    Model TemperatureFloatTemperature parameter for the model. Adjusts the randomness in the generated embeddings
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    Outputs​

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    Outputs​

    NameTypeDescription
    embeddingsEmbeddingsAn instance for generating embeddings using Ollama

    OpenAI Embeddings​

    This component is used to load embedding models from OpenAI.

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    Inputs​

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    Inputs​

    NameTypeDescription
    OpenAI API KeyStringThe API key to use for accessing the OpenAI API
    Default HeadersDictDefault headers for the HTTP requests
    Default QueryNestedDictDefault query parameters for the HTTP requests
    Allowed SpecialListSpecial tokens allowed for processing (default: [])
    Disallowed SpecialListSpecial tokens disallowed for processing (default: ["all"])
    Chunk SizeIntegerChunk size for processing (default: 1000)
    ClientAnyHTTP client for making requests
    DeploymentStringDeployment name for the model (default: text-embedding-3-small)
    Embedding Context LengthIntegerLength of embedding context (default: 8191)
    Max RetriesIntegerMaximum number of retries for failed requests (default: 6)
    ModelStringName of the model to use (default: text-embedding-3-small)
    Model KwargsNestedDictAdditional keyword arguments for the model
    OpenAI API BaseStringBase URL of the OpenAI API
    OpenAI API TypeStringType of the OpenAI API
    OpenAI API VersionStringVersion of the OpenAI API
    OpenAI OrganizationStringOrganization associated with the API key
    OpenAI ProxyStringProxy server for the requests
    Request TimeoutFloatTimeout for the HTTP requests
    Show Progress BarBooleanWhether to show a progress bar for processing (default: False)
    Skip EmptyBooleanWhether to skip empty inputs (default: False)
    TikToken EnableBooleanWhether to enable TikToken (default: True)
    TikToken Model NameStringName of the TikToken model
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    Outputs​

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    Outputs​

    NameTypeDescription
    embeddingsEmbeddingsAn instance for generating embeddings using OpenAI

    Text embedder​

    This component generates embeddings for a given message using a specified embedding model.

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    Inputs​

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    Inputs​

    NameDisplay NameInfo
    embedding_modelEmbedding ModelThe embedding model to use for generating embeddings.
    messageMessageThe message for which to generate embeddings.
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    Outputs​

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    Outputs​

    NameDisplay NameInfo
    embeddingsEmbedding DataData object containing the original text and its embedding vector.

    VertexAI Embeddings​

    This component is a wrapper around Google Vertex AI Embeddings API.

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    Inputs​

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    Inputs​

    NameTypeDescription
    credentialsCredentialsThe default custom credentials to use
    locationStringThe default location to use when making API calls (default: us-central1)
    max_output_tokensIntegerToken limit determines the maximum amount of text output from one prompt (default: 128)
    model_nameStringThe name of the Vertex AI large language model (default: text-bison)
    projectStringThe default GCP project to use when making Vertex API calls
    request_parallelismIntegerThe amount of parallelism allowed for requests issued to VertexAI models (default: 5)
    temperatureFloatTunes the degree of randomness in text generations. Should be a non-negative value (default: 0)
    top_kIntegerHow the model selects tokens for output, the next token is selected from the top k tokens (default: 40)
    top_pFloatTokens are selected from the most probable to least until the sum of their probabilities exceeds the top p value (default: 0.95)
    tuned_model_nameStringThe name of a tuned model. If provided, model_name is ignored
    verboseBooleanThis parameter controls the level of detail in the output. When set to True, it prints internal states of the chain to help debug (default: False)
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    Outputs​

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    NameTypeDescription
    embeddingsEmbeddingsAn instance for generating embeddings using VertexAI