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115 lines
4.9 KiB
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115 lines
4.9 KiB
Plaintext
---
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title: "WatsonxChatGenerator"
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id: watsonxchatgenerator
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slug: "/watsonxchatgenerator"
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description: "Use this component with IBM watsonx models like `granite-3-2b-instruct` for chat generation."
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---
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# WatsonxChatGenerator
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Use this component with IBM watsonx models like `granite-3-2b-instruct` for chat generation.
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<div className="key-value-table">
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| --- | --- |
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| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx) |
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| **Mandatory init variables** | `api_key`: The IBM Cloud API key. Can be set with `WATSONX_API_KEY` env var. <br /> <br />`project_id`: The IBM Cloud project ID. Can be set with `WATSONX_PROJECT_ID` env var. |
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| **Mandatory run variables** | `messages` A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects |
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| **Output variables** | `replies`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects |
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| **API reference** | [Watsonx](/reference/integrations-watsonx) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/watsonx |
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</div>
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This integration supports IBM watsonx.ai foundation models such as `ibm/granite-13b-chat-v2`, `ibm/llama-2-70b-chat`, `ibm/llama-3-70b-instruct`, and similar. These models provide high-quality chat completion capabilities through IBM's cloud platform. Check out the most recent full list in the [IBM watsonx.ai documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-ibm.html?context=wx).
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## Overview
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`WatsonxChatGenerator` needs IBM Cloud credentials to work. You can set these in:
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- The `api_key` and `project_id` init parameters using [Secret API](../../concepts/secret-management.mdx)
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- The `WATSONX_API_KEY` and `WATSONX_PROJECT_ID` environment variables (recommended)
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Then, the component needs a prompt to operate, but you can pass any text generation parameters valid for the IBM watsonx.ai API directly to this component using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the parameters supported by the IBM watsonx.ai API, refer to the [IBM watsonx.ai documentation](https://cloud.ibm.com/apidocs/watsonx-ai).
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Finally, the component needs a list of `ChatMessage` objects to operate. `ChatMessage` is a data class that contains a message, a role (who generated the message, such as `user`, `assistant`, `system`, `function`), and optional metadata.
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### Streaming
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This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.
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## Usage
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You need to install `watsonx-haystack` package to use the `WatsonxChatGenerator`:
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```shell
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pip install watsonx-haystack
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```
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#### On its own
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```python
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from haystack_integrations.components.generators.watsonx.chat.chat_generator import WatsonxChatGenerator
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from haystack.dataclasses import ChatMessage
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from haystack.utils import Secret
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generator = WatsonxChatGenerator(
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api_key=Secret.from_env_var("WATSONX_API_KEY"),
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project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
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model="ibm/granite-13b-instruct-v2"
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)
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message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
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print(generator.run([message]))
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```
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With multimodal inputs:
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```python
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from haystack.dataclasses import ChatMessage, ImageContent
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from haystack_integrations.components.generators.watsonx.chat.chat_generator import WatsonxChatGenerator
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# Use a multimodal model
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llm = WatsonxChatGenerator(model="meta-llama/llama-3-2-11b-vision-instruct")
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image = ImageContent.from_file_path("apple.jpg")
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user_message = ChatMessage.from_user(content_parts=[
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"What does the image show? Max 5 words.",
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image
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])
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response = llm.run([user_message])["replies"][0].text
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print(response)
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# Red apple on straw.
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```
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#### In a Pipeline
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You can also use `WatsonxChatGenerator` to use IBM watsonx.ai chat models in your pipeline.
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```python
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from haystack import Pipeline
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from haystack.components.builders import ChatPromptBuilder
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from haystack.dataclasses import ChatMessage
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from haystack_integrations.components.generators.watsonx.chat.chat_generator import WatsonxChatGenerator
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from haystack.utils import Secret
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pipe = Pipeline()
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pipe.add_component("prompt_builder", ChatPromptBuilder())
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pipe.add_component("llm", WatsonxChatGenerator(
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api_key=Secret.from_env_var("WATSONX_API_KEY"),
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project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
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model="ibm/granite-13b-instruct-v2"
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))
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pipe.connect("prompt_builder", "llm")
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country = "Germany"
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system_message = ChatMessage.from_system("You are an assistant giving out valuable information to language learners.")
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messages = [system_message, ChatMessage.from_user("What's the official language of {{ country }}?")]
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res = pipe.run(data={"prompt_builder": {"template_variables": {"country": country}, "template": messages}})
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print(res)
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```
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