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110 lines
4.8 KiB
Plaintext
110 lines
4.8 KiB
Plaintext
---
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title: "OpenRouterChatGenerator"
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id: openrouterchatgenerator
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slug: "/openrouterchatgenerator"
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description: "This component enables chat completion with any model hosted on [OpenRouter](https://openrouter.ai/)."
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---
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# OpenRouterChatGenerator
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This component enables chat completion with any model hosted on [OpenRouter](https://openrouter.ai/).
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| | |
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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”: An OpenRouter API key. Can be set with `OPENROUTER_API_KEY` env variable or passed to `init()` method. |
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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** | [OpenRouter](/reference/integrations-openrouter) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/openrouter |
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## Overview
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The `OpenRouterChatGenerator` enables you to use models from multiple providers (such as `openai/gpt-4o`, `anthropic/claude-3.5-sonnet`, and others) by making chat completion calls to the [OpenRouter API](https://openrouter.ai/docs/quickstart).
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This generator also supports OpenRouter-specific features such as:
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- Provider routing and model fallback that are configurable with the `generation_kwargs` parameter during initialization or runtime.
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- Custom HTTP headers that can be supplied using the `extra_headers` parameter.
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This component uses the same `ChatMessage` format as other Haystack Chat Generators for structured input and output. For more information, see the [ChatMessage documentation](../../concepts/data-classes/chatmessage.mdx).
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It is also fully compatible with Haystack [Tools](../../tools/tool.mdx) and [Toolsets](../../tools/toolset.mdx) that allow function-calling capabilities with supported models.
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### Initialization
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To use this integration, you must have an active OpenRouter subscription with sufficient credits and an API key. You can provide it with the `OPENROUTER_API_KEY` environment variable or by using a [Secret](../../concepts/secret-management.mdx).
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Then, install the `openrouter-haystack` integration:
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```shell
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pip install openrouter-haystack
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```
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### Streaming
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`OpenRouterChatGenerator` supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) responses from the LLM, allowing tokens to be emitted as they are generated. To enable streaming, pass a callable to the `streaming_callback` parameter during initialization.
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## Usage
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### On its own
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```python
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from haystack.dataclasses import ChatMessage
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from haystack_integrations.components.generators.openrouter import (
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OpenRouterChatGenerator,
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)
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client = OpenRouterChatGenerator()
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response = client.run([ChatMessage.from_user("What are Agentic Pipelines? Be brief.")])
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print(response["replies"][0].text)
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```
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With streaming and model routing:
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```python
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from haystack.dataclasses import ChatMessage
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from haystack_integrations.components.generators.openrouter import (
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OpenRouterChatGenerator,
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)
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client = OpenRouterChatGenerator(
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model="openrouter/auto",
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streaming_callback=lambda chunk: print(chunk.content, end="", flush=True),
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)
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response = client.run([ChatMessage.from_user("What are Agentic Pipelines? Be brief.")])
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## check the model used for the response
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print("\n\n Model used: ", response["replies"][0].meta["model"])
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```
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### In a 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.openrouter import (
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OpenRouterChatGenerator,
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)
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prompt_builder = ChatPromptBuilder()
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llm = OpenRouterChatGenerator(model="openai/gpt-4o-mini")
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pipe = Pipeline()
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pipe.add_component("builder", prompt_builder)
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pipe.add_component("llm", llm)
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pipe.connect("builder.prompt", "llm.messages")
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messages = [
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ChatMessage.from_system("Give brief answers."),
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ChatMessage.from_user("Tell me about {{city}}"),
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]
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response = pipe.run(
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data={"builder": {"template": messages, "template_variables": {"city": "Berlin"}}},
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)
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print(response)
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```
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