Add NVIDIA integration tile (#204)

* add nvidia integration

* add nvidia logo

* add the rest of the examples

* add link to generate NVIDIA api key

* Update integrations/nvidia.md

Co-authored-by: Silvano Cerza <[email protected]>

* Update integrations/nvidia.md

Co-authored-by: Silvano Cerza <[email protected]>

* Update integrations/nvidia.md

Co-authored-by: Bilge Yücel <[email protected]>

* Update code

* Mention `NVIDIA Inference Microservices`

* Update integrations/nvidia.md

---------

Co-authored-by: Silvano Cerza <[email protected]>
Co-authored-by: Bilge Yücel <[email protected]>
Co-authored-by: Madeesh Kannan <[email protected]>

integrations/nvidia.md

@@ -0,0 +1,192 @@
+---
+layout: integration
+name: NVIDIA
+description: Use NVIDIA models with Haystack.
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/nvidia-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/nvidia.png
+version: Haystack 2.0
+toc: true
+---
+### **Table of Contents**
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+[NVIDIA AI Foundation Models](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) and [NVIDIA Inference Microservices](https://build.nvidia.com/explore/discover) allow you to reach optimal performance on NVIDIA accelerated infrastructure. With pretrained generative AI models, enterprises can create custom models faster and take advantage of the latest training and inference techniques. 
+
+This integration allows you to use NVIDIA Foundation Models and NVIDIA Inference Microservices in your Haystack pipelines.
+
+
+In order to use this integration, you'll need a [NVIDIA API key](https://org.ngc.nvidia.com/setup). Set it as an environment variable, `NVIDIA_API_KEY`.
+
+## Installation
+
+```bash
+pip install nvidia-haystack
+```
+
+## Usage
+### Components
+This integration introduces the following components:
+
+- `NvidiaTextEmbedder`: 
+    A component for embedding strings, using NVIDIA AI Foundation and NVIDIA Inference Microservices embedding models.
+
+    For models that differentiate between query and document inputs,
+    this component embeds the input string as a query.
+
+- `NvidiaDocumentEmbedder`:
+    A component for embedding documents, using NVIDIA AI Foundation and NVIDIA Inference Microservices embedding models.
+
+- `NvidiaGenerator`:     A component for generating text using generative models provided by NVIDIA AI Foundation Endpoints and NVIDIA Inference Microservices.
+
+## Use the components on their own:
+
+### `NvidiaTextEmbedder`:
+
+```python
+from haystack_integrations.components.embedders.nvidia import NvidiaTextEmbedder
+
+text_to_embed = "I love pizza!"
+
+text_embedder = NvidiaTextEmbedder(model="nvolveqa_40k")
+text_embedder.warm_up()
+
+print(text_embedder.run(text_to_embed))
+# {'embedding': [-0.02264290489256382, -0.03457780182361603, ...}
+
+```
+### `NvidiaDocumentEmbedder`:
+```python
+from haystack.dataclasses import Document
+from haystack_integrations.components.embedders.nvidia import NvidiaDocumentEmbedder
+
+documents = [Document(content="Pizza is made with dough and cheese"),
+             Document(content="Cake is made with floud and sugar"),
+             Document(content="Omlette is made with eggs")]
+
+
+
+document_embedder = NvidiaDocumentEmbedder(model="nvolveqa_40k")
+document_embedder.warm_up()
+document_embedder.run(documents=documents)
+#{'documents': [Document(id=2136941caed9b4667d83f906a80d9a2fad1ce34861392889016830ac8738e6c4, content: 'Pizza is made with dough and cheese', embedding: vector of size 1024), ... 'meta': {'usage': {'prompt_tokens': 36, 'total_tokens': 36}}}
+```
+
+
+### `NvidiaGenerator`:
+
+```python
+from haystack_integrations.components.generators.nvidia import NvidiaGenerator
+
+generator = NvidiaGenerator(
+    model="nv_llama2_rlhf_70b",
+    model_arguments={
+        "temperature": 0.2,
+        "top_p": 0.7,
+        "max_tokens": 1024,
+        "seed": None,
+        "bad": None,
+        "stop": None,
+    },
+)
+generator.warm_up()
+
+result = generator.run(prompt="When was the Golden Gate Bridge built?")
+print(result["replies"])
+print(result["meta"])
+# ['The Golden Gate Bridge was built in 1937 and was completed and opened to the public on May 29, 1937....'[{'role': 'assistant', 'finish_reason': 'stop'}]
+```
+
+## Use NVIDIA components in Haystack pipelines
+
+### Indexing pipeline
+```python
+from haystack_integrations.components.generators.nvidia import NvidiaGenerator
+from haystack_integrations.components.embedders.nvidia import NvidiaDocumentEmbedder
+from haystack import Pipeline
+from haystack.dataclasses import Document
+from haystack.components.writers import DocumentWriter
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+documents = [Document(content="Tilde lives in San Francisco"),
+             Document(content="Tuana lives in Amsterdam"),
+             Document(content="Bilge lives in Istanbul")]
+
+document_store = InMemoryDocumentStore()
+
+document_embedder = NvidiaDocumentEmbedder(model="nvolveqa_40k")
+writer = DocumentWriter(document_store=document_store)
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component(instance=document_embedder, name="document_embedder")
+indexing_pipeline.add_component(instance=writer, name="writer")
+
+indexing_pipeline.connect("document_embedder.documents", "writer.documents")
+indexing_pipeline.run(data={"document_embedder":{"documents": documents}})
+
+# Calling filter with no arguments will print the contents of the document store
+document_store.filter_documents({})
+
+```
+
+### RAG Query pipeline
+```python
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import PromptBuilder
+from haystack_integrations.components.generators.nvidia import NvidiaGenerator
+from haystack_integrations.components.embedders.nvidia import NvidiaTextEmbedder
+
+prompt = """ Answer the query, based on the
+content in the documents.
+If you can't answer based on the given documents, say so.
+
+Documents:
+{% for doc in documents %}
+  {{doc.content}}
+{% endfor %}
+
+Query: {{query}}
+"""
+
+text_embedder = NvidiaTextEmbedder(model="nvolveqa_40k")
+retriever = InMemoryEmbeddingRetriever(document_store=document_store)
+prompt_builder = PromptBuilder(template=prompt)
+generator = NvidiaGenerator(model="nv_llama2_rlhf_70b")
+generator.warm_up()
+
+rag_pipeline = Pipeline()
+
+rag_pipeline.add_component(instance=text_embedder, name="text_embedder")
+rag_pipeline.add_component(instance=retriever, name="retriever")
+rag_pipeline.add_component(instance=prompt_builder, name="prompt_builder")
+rag_pipeline.add_component(instance=generator, name="generator")
+
+rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder", "generator")
+
+question = "Who lives in San Francisco?"
+result = rag_pipeline.run(data={"text_embedder":{"text": question},
+                                "prompt_builder":{"query": question}})
+print(result)
+# {'text_embedder': {'meta': {'usage': {'prompt_tokens': 10, 'total_tokens': 10}}}, 'generator': {'replies': ['Tilde'], 'meta': [{'role': 'assistant', 'finish_reason': 'stop'}], 'usage': {'completion_tokens': 3, 'prompt_tokens': 101, 'total_tokens': 104}}}
+
+```
+
+### License
+
+`nvidia-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.