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* fix: nightly now properly gets 1.9.0 branch (#12215) before it was attempting to pull release-notes as letters are alphanumerically after numbers when we sort -V then grab tail now we only look at branch names that follow the pattern '^release-[0-9]+\.[0-9]+\.[0-9]+$' * docs: add search icon (#12216) add-back-svg * initial-content * cut-1.8-release-and-include-next-version * stage-1.8.0-and-next --------- Co-authored-by: Adam-Aghili <149833988+Adam-Aghili@users.noreply.github.com>
66 lines
3.6 KiB
Plaintext
66 lines
3.6 KiB
Plaintext
---
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title: Ollama
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slug: /bundles-ollama
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---
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import Icon from "@site/src/components/icon";
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<Icon name="Blocks" aria-hidden="true" /> [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
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This page describes the components that are available in the **Ollama** bundle.
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For more information about Ollama features and functionality used by Ollama components, see the [Ollama documentation](https://ollama.com/).
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## Ollama text generation
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This component generates text using [Ollama's language models](https://ollama.com/library).
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To use the **Ollama** component in a flow, connect Langflow to your locally running Ollama server and select a model:
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1. Add the **Ollama** component to your flow.
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2. In the **Base URL** field, enter the address for your locally running Ollama server.
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This value is set as the `OLLAMA_HOST` environment variable in Ollama.
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The default base URL is `http://127.0.0.1:11434`.
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3. Once the connection is established, select a model in the **Model Name** field, such as `llama3.2:latest`.
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To refresh the server's list of models, click <Icon name="RefreshCw" aria-hidden="true"/> **Refresh**.
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4. Optional: To configure additional parameters, such as temperature or max tokens, click the component to open the [component inspection panel](/concepts-components#component-menus).
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5. Connect the **Ollama** component to other components in the flow, depending on how you want to use the model.
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Language model components can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)). Use the **Language Model** output when you want to use an Ollama model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. For more information, see [Language model components](/components-models).
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In the following example, the flow uses `LanguageModel` output to use an Ollama model as the LLM for an [**Agent** component](/components-agents).
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## Ollama Embeddings
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The **Ollama Embeddings** component generates embeddings using [Ollama embedding models](https://ollama.com/search?c=embedding).
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To use this component in a flow, connect Langflow to your locally running Ollama server and select an embeddings model:
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1. Add the **Ollama Embeddings** component to your flow.
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2. In the **Ollama Base URL** field, enter the address for your locally running Ollama server.
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This value is set as the `OLLAMA_HOST` environment variable in Ollama.
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The default base URL is `http://127.0.0.1:11434`.
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3. Once the connection is established, select a model in the **Ollama Model** field, such as `all-minilm:latest`.
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To refresh the server's list of models, click <Icon name="RefreshCw" aria-hidden="true"/> **Refresh**.
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4. Optional: To configure additional parameters, such as temperature or max tokens, click the component to open the [component inspection panel](/concepts-components#component-menus).
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Available parameters depend on the selected model.
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5. Connect the **Ollama Embeddings** component to other components in the flow.
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For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
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This example connects the **Ollama Embeddings** component to generate embeddings for text chunks extracted from a PDF file, and then stores the embeddings and chunks in a Chroma DB vector store.
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