Adding Titan Takeoff Integration and TitanML logo (#198)

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integrations/titanml-takeoff.md

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+---
+layout: integration
+name: Titan Takeoff Inference Server
+description: Use Titan Takeoff to run local open-source LLMs with Haystack. Titan Takeoff allows you to run the latest models from Meta, Mistral and Alphabet directly in your laptop.
+authors:
+    - name: Fergus Finn
+      socials:
+        github: fergusbarratt
+        twitter: BarrattFergus
+        linkedin: https://www.linkedin.com/in/fergusfinn/
+    - name: Rod Rivera
+      socials:
+        github: rorcde
+        twitter: rorcde
+        linkedin: https://www.linkedin.com/in/aiengineer/
+pypi: https://pypi.org/project/takeoff-haystack/
+repo: https://github.com/titanml/takeoff-haystack
+type: Model Provider
+report_issue: https://github.com/titanml/takeoff-haystack/issues
+logo: /logos/titanml.png
+version: Haystack 2.0
+toc: true
+---
+### **Table of Contents**
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Example](#example)
+
+## Overview
+You can use the Takeoff inference server to deploy local models efficiently in your Haystack 2.0 pipelines. Takeoff is a state-of-the art inference server focused on deploying openly available language models at scale. It can run LLMs on local machines with consumer GPUs, and on cloud infrastructure. 
+
+The TakeoffGenerator component in Haystack 2.0 is a wrapper around the Takeoff server API, and can be used to serve takeoff-deployed models efficiently in Haystack pipelines.
+
+## Installation
+
+```bash
+pip install takeoff_haystack
+```
+
+## Usage
+You can interact with takeoff deployed models using the `TakeoffGenerator` component in Haystack. To do so, you must have a takeoff model deployed. For information on how to do so, please read the takeoff docs [here](https://docs.titanml.co/docs/Docs/launching/).
+
+The following example deploys a Llama-2-7B-Chat-AWQ model using takeoff locally on port 3000. You can get a free license [here](https://www.titanml.co/contact).
+
+```bash
+docker run --gpus all -e TAKEOFF_MODEL_NAME=TheBloke/Llama-2-7B-Chat-AWQ \
+                      -e TAKEOFF_DEVICE=cuda \
+                      -e TAKEOFF_MAX_SEQUENCE_LENGTH=256 \
+                      -it \
+                      -p 3000:3000 tytn/takeoff-pro:0.11.0-gpu
+```
+
+## Example
+### Daily News Digest Generation
+
+Below is an example of using takeoff models in a Haystack RAG pipeline. It summarizes headlines from popular news sites in technology such as Techcrunch, TheVerge, Engadget and others.
+
+```bash
+from typing import Dict, List
+from haystack import Document, Pipeline
+from haystack.components.builders.prompt_builder import PromptBuilder  
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+import feedparser
+from takeoff_haystack import TakeoffGenerator
+
+# Dict of website RSS feeds  
+urls = {
+  'theverge': 'https://www.theverge.com/rss/frontpage/',
+  'techcrunch': 'https://techcrunch.com/feed',
+  'mashable': 'https://mashable.com/feeds/rss/all',
+  'cnet': 'https://cnet.com/rss/news',
+  'engadget': 'https://engadget.com/rss.xml',
+  'zdnet': 'https://zdnet.com/news/rss.xml',
+  'venturebeat': 'https://feeds.feedburner.com/venturebeat/SZYF',
+  'readwrite': 'https://readwrite.com/feed/',    
+  'wired': 'https://wired.com/feed/rss',
+  'gizmodo': 'https://gizmodo.com/rss',
+}
+
+# Configurable parameters
+NUM_WEBSITES = 3  
+NUM_TITLES = 1
+
+def get_titles(urls: Dict[str, str], num_sites: int, num_titles: int) -> List[str]:
+  titles: List[str] = []
+  sites = list(urls.keys())[:num_sites]
+
+  for site in sites:
+    feed = feedparser.parse(urls[site])  
+    entries = feed.entries[:num_titles]
+
+    for entry in entries:
+      titles.append(entry.title)
+
+  return titles
+
+titles = get_titles(urls, NUM_WEBSITES, NUM_TITLES)
+
+document_store = InMemoryDocumentStore()
+document_store.write_documents([Document(content=title) for title in titles])
+
+template = """
+HEADLINES:  
+{% for document in documents %}
+  {{ document.content }}  
+{% endfor %}
+REQUEST: {{ query }}
+"""
+
+pipe = Pipeline()
+pipe.add_component("retriever", InMemoryBM25Retriever(document_store=document_store))
+pipe.add_component("prompt_builder", PromptBuilder(template=template))
+pipe.add_component("llm", TakeoffGenerator(base_url="http://localhost", port="3000"))
+pipe.connect("retriever", "prompt_builder.documents")
+pipe.connect("prompt_builder", "llm")
+
+query = f"Summarize each of the {NUM_WEBSITES * NUM_TITLES} provided headlines in three words."
+response = pipe.run({"prompt_builder": {"query": query}, "retriever": {"query": query}})
+print(response["llm"]["replies"])
+```
+
+You should see a response like the following
+```
+['\n\n\nANSWER:\n\n1. Poker Roguelike - Exciting gameplay\n2. AI-powered news reader - Personalized feed\n3. Best laptops MWC 2024 - Powerful devices']
+```