chore: import upstream snapshot with attribution

docs-website/versioned_docs/version-2.19/pipeline-components/generators/coheregenerator.mdx

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+---
+title: "CohereGenerator"
+id: coheregenerator
+slug: "/coheregenerator"
+description: "`CohereGenerator` enables text generation using Cohere's large language models (LLMs)."
+---
+
+# CohereGenerator
+
+`CohereGenerator` enables text generation using Cohere's large language models (LLMs).
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | After a [`PromptBuilder`](../builders/promptbuilder.mdx)  |
+| **Mandatory init variables** | `api_key`: The Cohere API key. Can be set with `COHERE_API_KEY` or `CO_API_KEY` env var. |
+| **Mandatory run variables** | `prompt`: A string containing the prompt for the LLM |
+| **Output variables** | `replies`: A list of strings with all the replies generated by the LLM  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
+| **API reference** | [Cohere](/reference/integrations-cohere) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere |
+
+</div>
+
+ This integration supports Cohere models such as `command`, `command-r` and `comman-r-plus`. Check out the most recent full list in [Cohere documentation](https://docs.cohere.com/reference/chat).
+
+## Overview
+
+`CohereGenerator` needs a Cohere API key to work. You can write this key in:
+
+- The `api_key` init parameter using [Secret API](../../concepts/secret-management.mdx)
+- The `COHERE_API_KEY` environment variable (recommended)
+
+Then, the component needs a prompt to operate, but you can pass any text generation parameters directly to this component using the `generation_kwargs` parameter at initialization. For more details on the parameters supported by the Cohere API, refer to the [Cohere documentation](https://docs.cohere.com/reference/chat).
+
+### Streaming
+
+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.
+
+## Usage
+
+You need to install `cohere-haystack` package to use the  `CohereGenerator`:
+
+```shell
+pip install cohere-haystack
+```
+
+### On its own
+
+Basic usage:
+
+```python
+from haystack_integrations.components.generators.cohere import CohereGenerator
+
+client = CohereGenerator()
+response = client.run("Briefly explain what NLP is in one sentence.")
+print(response)
+
+ 'meta': [{'finish_reason': 'COMPLETE'}]}
+```
+
+With streaming:
+
+```python
+from haystack_integrations.components.generators.cohere import CohereGenerator
+
+client = CohereGenerator(
+    streaming_callback=lambda chunk: print(chunk.content, end="", flush=True),
+)
+response = client.run("Briefly explain what NLP is in one sentence.")
+print(response)
+```
+
+### In a pipeline
+
+In a RAG pipeline:
+
+```python
+from haystack import Pipeline
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.generators.cohere import CohereGenerator
+from haystack import Document
+
+docstore = InMemoryDocumentStore()
+docstore.write_documents(
+    [
+        Document(content="Rome is the capital of Italy"),
+        Document(content="Paris is the capital of France"),
+    ],
+)
+
+query = "What is the capital of France?"
+
+template = """
+Given the following information, answer the question.
+
+Context:
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+
+Question: {{ query }}?
+"""
+pipe = Pipeline()
+
+pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
+pipe.add_component("prompt_builder", PromptBuilder(template=template))
+pipe.add_component("llm", CohereGenerator())
+pipe.connect("retriever", "prompt_builder.documents")
+pipe.connect("prompt_builder", "llm")
+
+res = pipe.run({"prompt_builder": {"query": query}, "retriever": {"query": query}})
+
+print(res)
+```