Ollama integration tile (#108)

* add logo

* add ollama tile

* add sachin

* Update integrations/ollama.md

Co-authored-by: Stefano Fiorucci <[email protected]>

* Update integrations/ollama.md

Co-authored-by: Stefano Fiorucci <[email protected]>

* Update integrations/ollama.md

Co-authored-by: Stefano Fiorucci <[email protected]>

* update page with docker helpers

* add example heading

* add a nice colon

* Update integrations/ollama.md

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

* Update integrations/ollama.md

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

* Update integrations/ollama.md

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

* Update integrations/ollama.md

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

* add url to generator and move query closer to response

* use logo with fixed background

---------

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

integrations/ollama.md

@@ -0,0 +1,119 @@
+---
+layout: integration
+name: Ollama
+description: Use Ollama models with Haystack. Ollama allows you to get up and running with large language models, locally. 
+authors:
+    - name: Alistair Rogers
+      socials:
+        github: alistairlr112
+        linkedin: https://www.linkedin.com/in/alistairlr/
+    - name: Sachin Sachdeva
+      socials:
+        github: sachinsachdeva
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/ollama-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/ollama
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/ollama.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Introduction](#introduction)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Example](#example)
+
+## Introduction
+
+You can use [Ollama Models](https://ollama.ai/library) in your Haystack 2.0 pipelines with the OllamaGenerator.
+
+[Ollama](https://ollama.ai/) is a project focused on running Large Language Models locally. Internally it uses the quantized GGUF format by default. This means it is possible to run LLMs on standard machines (even without GPUs), without having to handle complex installation procedures.
+
+## Installation
+
+```bash
+pip install ollama-haystack
+```
+
+## Usage
+
+You can leverage Ollama models through the OllamaGenerator Component
+
+To use an Ollama model for text generation:
+
+1. Follow instructions on the [Ollama Github Page](https://github.com/jmorganca/ollama) to pull and serve your model of choice 
+2. Initialize an `OllamaGenerator` with the name of the model served in your Ollama instance and you can then use the `OllamaGenerator` instance in a question answering pipeline after the `PromptBuilder`.  
+
+
+### Example
+To run the example, you may choose to run a docker container serving an Ollama model of your choice. 
+Here are some commands that work with this example:
+
+```bash
+docker run -d -p 11434:11434 --name ollama ollama/ollama:latest
+docker exec ollama ollama pull orca-mini
+```
+
+Below is the example of generative questions answering pipeline using RAG with `PromptBuilder` and  `OllamaGenerator`:
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+from ollama_haystack import OllamaGenerator
+
+document_store = InMemoryDocumentStore()
+document_store.write_documents(
+    [
+        Document(content="Super Mario was an important politician"),
+        Document(content="Mario owns several castles and uses them to conduct important political business"),
+        Document(
+            content="Super Mario was a successful military leader who fought off several invasion attempts by "
+            "his arch rival - Bowser"
+        ),
+    ]
+)
+
+template = """
+Given only the following information, answer the question.
+Ignore your own knowledge.
+
+Context:
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+
+Question: {{ query }}?
+"""
+
+pipe = Pipeline()
+
+pipe.add_component("retriever", InMemoryBM25Retriever(document_store=document_store))
+pipe.add_component("prompt_builder", PromptBuilder(template=template))
+pipe.add_component("llm", OllamaGenerator(model="orca-mini", url="http://localhost:11434/api/generate"))
+pipe.connect("retriever", "prompt_builder.documents")
+pipe.connect("prompt_builder", "llm")
+
+query = "Who is Super Mario?"
+
+response = pipe.run({"prompt_builder": {"query": query}, "retriever": {"query": query}})
+
+print(response["llm"]["replies"])
+```
+You should receive an output like (output is not deterministic):
+```
+['Based on the information provided, Super Mario is a successful military leader who fought
+off several invasion attempts by his arch rival - Bowser. He is also an important politician and owns several
+castles where he conducts political business. ' 'Therefore, it can be inferred that Super Mario is a combination of
+both a military leader and an important politician.']
+```