Add cohere integration for Haystack 2.0 (#78)

integrations/cohere.md

@@ -1,33 +1,127 @@
 ---
 layout: integration
 name: Cohere
-description: Use Cohere with Haystack
+description: Use Cohere models 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
+pypi: https://pypi.org/project/cohere-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere
 type: Model Provider
-report_issue: https://github.com/deepset-ai/haystack/issues
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
 logo: /logos/cohere.png
+version: Haystack 2.0
+toc: true
 ---
 
+### **Table of Contents**
+
+- [Haystack 2.0](#haystack-20)
+  - [Installation](#installation)
+  - [Usage](#usage)
+- [Haystack 1.x](#haystack-1x)
+  - [Installation (1.x)](#installation-1x)
+  - [Usage (1.x)](#usage-1x)
+
+## Haystack 2.0
+
+You can use [Cohere Models](https://cohere.com/) in your Haystack 2.0 pipelines with the [Generators](https://docs.haystack.deepset.ai/v2.0/docs/generators) and [Embedders](https://docs.haystack.deepset.ai/v2.0/docs/embedders).
+
+### Installation
+
+```bash
+pip install cohere-haystack
+```
+
+### Usage
+
+You can use Cohere models in various ways:
+
+#### Embedding Models
+
+You can leverage `/embed` models from Cohere through two components: [CohereTextEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/cohere/src/cohere_haystack/embedders/text_embedder.py) and [CohereDocumentEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/cohere/src/cohere_haystack/embedders/document_embedder.py).
+
+To create semantic embeddings for documents, use `CohereDocumentEmbedder` in your indexing pipeline. For generating embeddings for queries, use `CohereTextEmbedder`. Once you've selected the suitable component for your specific use case, initialize the component with the model name and Cohere API key.
+
+Below is the example indexing pipeline with `InMemoryDocumentStore`, `CohereDocumentEmbedder` and  `DocumentWriter`:
+
+```python
+from haystack import Document, Pipeline
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.components.writers import DocumentWriter
+from cohere_haystack.embedders.document_embedder import CohereDocumentEmbedder
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [Document(content="My name is Wolfgang and I live in Berlin"),
+             Document(content="I saw a black horse running"),
+             Document(content="People speak French in France"),
+             Document(content="Germany has many big cities")]
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component("embedder", CohereDocumentEmbedder(api_key="COHERE_API_KEY", model_name="embed-multilingual-v2.0"))
+indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
+indexing_pipeline.connect("embedder", "writer")
+
+indexing_pipeline.run({"embedder": {"documents": documents}})
+```
+
+#### Generative Models (LLMs) 
+
+To use `/generate` models from Cohere, initialize a `CohereGenerator` with the model name, Cohere API key and the prompt template. You can then use this `CohereGenerator` in a question answering pipeline after the `PromptBuilder`.   
+
+Below is the example of generative questions answering pipeline using RAG with `PromptBuilder` and  `CohereGenerator`:
+
+```python
+from haystack import Pipeline
+from haystack.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.components.builders.prompt_builder import PromptBuilder
+from cohere_haystack.embedders.text_embedder import CohereTextEmbedder
+from cohere_haystack.generator import CohereGenerator
+
+template = """
+Given the following information, answer the question.
+
+Context: 
+{% for document in documents %}
+    {{ document.text }}
+{% endfor %}
+
+Question: What's the official language of {{ country }}?
+"""
+pipe = Pipeline()
+pipe.add_component("embedder", CohereTextEmbedder(api_key=api_key, model_name="embed-multilingual-v2.0"))
+pipe.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
+pipe.add_component("prompt_builder", PromptBuilder(template=template))
+pipe.add_component("llm", CohereGenerator(api_key=api_key, model_name="command-light"))
+pipe.connect("embedder.embedding", "retriever.query_embedding")
+pipe.connect("retriever", "prompt_builder.documents")
+pipe.connect("prompt_builder", "llm")
+
+pipe.run({
+    "embedder": {"text": "France"},
+    "prompt_builder": {"country": "France"}
+})  
+```
+
+## Haystack 1.x  
+
 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
+### Installation (1.x)
 
 ```bash
 pip install farm-haystack
 ```
 
-## Usage
+### Usage (1.x)
 
 You can use Cohere models in various ways:
 
-### Embedding Models
+#### 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. 
 
@@ -52,7 +146,7 @@ indexing_pipeline.add_node(component=document_store, name="document_store", inpu
 indexing_pipeline.run(documents=[Document("This is my document")])
 ```
 
-### Generative Models (LLMs) 
+#### 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.  
 
@@ -73,7 +167,7 @@ query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retri
 query_pipeline.run("YOUR_QUERY")
 ```
 
-### Ranker Models
+#### 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.