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+---
+layout: integration
+name: Cohere
+description: Use Cohere with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/farm-haystack
+repo: https://github.com/deepset-ai/haystack
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack/issues
+logo: /logos/cohere.png
+---
+
+You can use [Cohere Models](https://cohere.com/) in your Haystack pipelines with the [EmbeddingRetriever](https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended), [PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node), and [CohereRanker](https://docs.haystack.deepset.ai/docs/ranker#cohereranker).
+
+## Installation
+
+```bash
+pip install farm-haystack
+```
+
+## Usage
+
+You can use Cohere models in various ways:
+
+### Embedding Models
+
+To use `/embed` models from Cohere, initialize an `EmbeddingRetriever` with the model name and Cohere API key. You can then use this `EmbeddingRetriever` in an indexing pipeline to create Cohere embeddings for documents and index them to a document store. 
+
+Below is the example indexing pipeline with `PreProcessor`, `InMemoryDocumentStore` and  `EmbeddingRetriever`:
+
+```python
+from haystack.nodes import EmbeddingRetriever
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.pipelines import Pipeline
+from haystack.schema import Document
+
+document_store = InMemoryDocumentStore(embedding_dim=768)
+preprocessor = PreProcessor()
+retriever = EmbeddingRetriever(
+    embedding_model="embed-multilingual-v2.0", document_store=document_store, api_key=COHERE_API_KEY
+)
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_node(component=preprocessor, name="Preprocessor", inputs=["File"])
+indexing_pipeline.add_node(component=retriever, name="Retriever", inputs=["Preprocessor"])
+indexing_pipeline.add_node(component=document_store, name="document_store", inputs=["Retriever"])
+indexing_pipeline.run(documents=[Document("This is my document")])
+```
+
+### Generative Models (LLMs) 
+
+To use `/generate` models from Cohere, initialize a `PromptNode` with the model name, Cohere API key and the prompt template. You can then use this `PromptNode` in a question answering pipeline to generate answers based on the given context.  
+
+Below is the example of generative questions answering pipeline using RAG with `EmbeddingRetriever` and  `PromptNode`:
+
+```python
+from haystack.nodes import PromptNode, EmbeddingRetriever
+from haystack.pipelines import Pipeline
+
+retriever = EmbeddingRetriever(
+    embedding_model="embed-english-v2.0", document_store=document_store, api_key=COHERE_API_KEY
+)
+prompt_node = PromptNode(model_name_or_path="command", api_key=COHERE_API_KEY, default_prompt_template="deepset/question-answering")
+
+query_pipeline = Pipeline()
+query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])
+query_pipeline.run("YOUR_QUERY")
+```
+
+### Ranker Models
+
+To use `/rerank` models from Cohere, initialize a `CohereRanker` with the model name, and Cohere API key. You can then use this `CohereRanker` to sort documents based on their relevancy to the query.
+
+Below is the example of document retrieval pipeline with `BM25Retriever` and  `CohereRanker`:
+
+```python
+from haystack.nodes import CohereRanker, BM25Retriever
+from haystack.pipelines import Pipeline
+
+retriever = BM25Retriever(document_store=document_store)
+ranker = CohereRanker(api_key=COHERE_API_KEY, model_name_or_path="rerank-english-v2.0")
+
+document_retrieval_pipeline = Pipeline()
+document_retrieval_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+document_retrieval_pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"])
+document_retrieval_pipeline.run("YOUR_QUERY")
+```
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