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Mendon Kissling 2411d8036e docs: build OpenAPI spec and cut version 1.10 (#13537) 
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---
title: IBM
slug: /bundles-ibm
---
import Icon from "@site/src/components/icon";
import PartialParams from '@site/docs/_partial-hidden-params.mdx';
import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
<Icon name="Blocks" aria-hidden="true" /> [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
The **IBM** bundle provides access to IBM watsonx.ai models for text and embedding generation, plus an IBM Db2 Vector Store.
These components require an IBM watsonx.ai deployment with API credentials, and/or a reachable IBM Db2 instance with the `ibm-db` driver.
## Install the IBM bundle
The **IBM** bundle is included in the `lfx-ibm` Extension bundle, which is installed automatically as part of `uv pip install langflow`.
If you need to install it separately, run:
```bash
uv pip install lfx-ibm
uv run langflow run
```
To verify the bundle is loaded in your environment:
```bash
lfx extension list
```
## IBM watsonx.ai
The **IBM watsonx.ai** component generates text using [supported foundation models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx) in [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai).
To use gateway models, use the [**OpenAI** text generation component](/bundles-openai) with the gateway model's OpenAI-compatible endpoint.
You can use the **IBM watsonx.ai** component anywhere you need a language model in a flow.
![A basic prompting flow using the IBM watsonx.ai component as the central Language Model component.](/img/component-watsonx-model.png)
### IBM watsonx.ai parameters {#ibm-watsonxai-parameters}
<PartialParams />
| Name | Type | Description |
|------|------|-------------|
| url | String | Input parameter. The [watsonx API base URL](https://cloud.ibm.com/apidocs/watsonx-ai#endpoint-url) for your deployment and region. |
| project_id | String | Input parameter. Your [watsonx Project ID](https://www.ibm.com/docs/en/watsonx/saas?topic=projects). |
| api_key | SecretString | Input parameter. A [watsonx API key](https://www.ibm.com/docs/en/watsonx/saas?topic=administration-managing-user-api-key) to authenticate watsonx API access to the specified watsonx.ai deployment and model. |
| model_name | String | Input parameter. The name of the watsonx model to use. Options are dynamically fetched from the API. |
| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Default: `1000`. |
| stop_sequence | String | Input parameter. The sequence where generation should stop. |
| temperature | Float | Input parameter. Controls randomness in the output. Default: `0.1`. |
| top_p | Float | Input parameter. Controls nucleus sampling, which limits the model to tokens whose probability is below the `top_p` value. Range: Default: `0.9`. |
| frequency_penalty | Float | Input parameter. Controls frequency penalty. A positive value decreases the probability of repeating tokens, and a negative value increases the probability. Range: Default: `0.5`. |
| presence_penalty | Float | Input parameter. Controls presence penalty. A positive value increases the likelihood of new topics being introduced. Default: `0.3`. |
| seed | Integer | Input parameter. A random seed for the model. Default: `8`. |
| logprobs | Boolean | Input parameter. Whether to return log probabilities of output tokens or not. Default: `true`. |
| top_logprobs | Integer | Input parameter. The number of most likely tokens to return at each position. Default: `3`. |
| logit_bias | String | Input parameter. A JSON string of token IDs to bias or suppress. |
### IBM watsonx.ai output
The **IBM watsonx.ai** component 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 IBM watsonx.ai 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).
The `LanguageModel` output from the **IBM watsonx.ai** component is an instance of `[ChatWatsonx](https://docs.langchain.com/oss/python/integrations/chat/ibm_watsonx)` configured according to the [component's parameters](#ibm-watsonxai-parameters).
## IBM watsonx.ai Embeddings
The **IBM watsonx.ai Embeddings** component uses the [supported foundation models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx) in [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai) for embedding generation.
The output is [`Embeddings`](/data-types#embeddings) generated with [`WatsonxEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/ibm_watsonx/).
For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
![A basic embedding generation flow using the IBM watsonx.ai Embeddings component](/img/component-watsonx-embeddings-chroma.png)
### IBM watsonx.ai Embeddings parameters
<PartialParams />
| Name | Display Name | Info |
|------|--------------|------|
| url | watsonx API Endpoint | Input parameter. The [watsonx API base URL](https://cloud.ibm.com/apidocs/watsonx-ai#endpoint-url) for your deployment and region. |
| project_id | watsonx project id | Input parameter. Your [watsonx Project ID](https://www.ibm.com/docs/en/watsonx/saas?topic=projects). |
| api_key | API Key | Input parameter. A [watsonx API key](https://www.ibm.com/docs/en/watsonx/saas?topic=administration-managing-user-api-key) to authenticate watsonx API access to the specified watsonx.ai deployment and model. |
| model_name | Model Name | Input parameter. The name of the embedding model to use. Supports [default embedding models](#default-embedding-models) and automatically updates after connecting to your watsonx.ai deployment. |
| truncate_input_tokens | Truncate Input Tokens | Input parameter. The maximum number of tokens to process. Default: `200`. |
| input_text | Include the original text in the output | Input parameter. Determines if the original text is included in the output. Default: `true`. |
### Default embedding models
By default, the **IBM watsonx.ai Embeddings** component supports the following default models:
- `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
After entering your API endpoint and credentials, the component automatically fetches the list of available models from your watsonx.ai deployment.
## IBM Db2 Vector Store
You can use the **IBM Db2 Vector Store** component to read and write to an IBM Db2 database using an instance of `DB2VS` vector store.
Includes support for remote Db2 instances with enterprise-grade security and performance.
<details>
<summary>About vector store instances</summary>
<PartialVectorStoreInstance />
</details>
When writing, the component can create a new table at the specified location.
:::tip
IBM Db2 Vector Store provides enterprise-grade vector search capabilities with built-in security validation and support for multiple distance strategies.
:::
<PartialVectorSearchResults />
### Use the IBM Db2 Vector Store component in a flow
The **IBM Db2 Vector Store** component can be used for both reads and writes:
* When writing, it splits `JSON` from a [**URL** component](/url) into chunks, computes embeddings with attached **Embedding Model** component, and then loads the chunks and embeddings into the Db2 vector store.
To trigger writes, click <Icon name="Play" aria-hidden="true"/> **Run component** on the **IBM Db2 Vector Store** component.
* When reading, it uses chat input to perform a similarity search on the vector store, and then print the search results to the chat.
To trigger reads, open the **Playground** and enter a chat message.
After running the flow once, you can click <Icon name="TextSearch" aria-hidden="true"/> **Inspect Output** on each component to understand how the data transformed as it passed from component to component.
### IBM Db2 Vector Store parameters
You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
<PartialParams />
<PartialConditionalParams />
For information about accepted values and functionality, see the provider's documentation or inspect [component code](/concepts-components#component-code).
| Name | Type | Description |
|------|------|-------------|
| **Table Name** (`collection_name`) | String | Input parameter. The name of your Db2 table to store vectors. Default: `LANGFLOW_VECTORS`. The table will be created if it doesn't exist. |
| **Database Name** (`database`) | String | Input parameter. Name of the Db2 database. Use a Generic-typed global variable or direct input. Credential-typed variables are not allowed for database names. |
| **Hostname** (`hostname`) | String | Input parameter. Db2 server hostname or IP address. Use a Generic-typed global variable or direct input. |
| **Port** (`port`) | Integer | Input parameter. Db2 server port. Default: `50000`. |
| **Username** (`username`) | String | Input parameter. Db2 database username. Use a Generic-typed global variable or direct input. |
| **Password** (`password`) | String | Input parameter. Db2 database password. This should use a Credential-typed global variable for security. |
| **Ingest Data** (`ingest_data`) | JSON or Table | Input parameter. `JSON` or `Table` input containing the records to write to the vector store. Only relevant for writes. |
| **Search Query** (`search_query`) | String | Input parameter. The query to use for vector search. Only relevant for reads. |
| **Cache Vector Store** (`should_cache_vector_store`) | Boolean | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
| **Embedding** (`embedding`) | Embeddings | Input parameter. The embedding function to use for the vector store. You must attach an **Embedding Model** component to generate embeddings for your data. |
| **Allow Duplicates** (`allow_duplicates`) | Boolean | Input parameter. If `true` (default), writes don't check for existing duplicates in the collection, allowing you to store multiple copies of the same content. If `false`, writes won't add documents that match existing documents already present in the collection. Only relevant for writes.|
| **Search Type** (`search_type`) | String | Input parameter. The type of search to perform: `Similarity`, `MMR`, or `similarity_score_threshold`. Only relevant for reads. |
| **Number of Results** (`number_of_results`) | Integer | Input parameter. The number of search results to return. Default: `4`. Only relevant for reads. |
| **Distance Strategy** (`distance_strategy`) | String | Input parameter. Distance calculation strategy: `COSINE`, `EUCLIDEAN_DISTANCE`, or `DOT_PRODUCT`. Default: `COSINE`. |
## See also
* [IBM documentation](https://cloud.ibm.com/docs)
* [**Local DB** component](/components-bundle-components#vector-stores-bundle)
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