Adding pypi packaging support

README.md

@@ -7,8 +7,10 @@
     <p>Build and explore efficient retrieval-augmented generative models and applications</p>
 </h4>
 
-:round_pushpin: <a href="#round_pushpin-installation">Installation</a> • :rocket: <a href="components.md">Components</a> • :books: <a href="examples.md">Examples</a> • :red_car: <a href="getting_started.md">Getting Started</a> • :pill: <a href="Demo.md">Demos</a> • :pencil2: <a href="scripts/README.md">Scripts</a> • :bar_chart: <a href="benchmarks/README.md">Benchmarks</a>
+![PyPI - Version](https://img.shields.io/pypi/v/fastrag)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/fastrag)
 
+:round_pushpin: <a href="#round_pushpin-installation">Installation</a> • :rocket: <a href="components.md">Components</a> • :books: <a href="examples.md">Examples</a> • :red_car: <a href="getting_started.md">Getting Started</a> • :pill: <a href="Demo.md">Demos</a> • :pencil2: <a href="scripts/README.md">Scripts</a> • :bar_chart: <a href="benchmarks/README.md">Benchmarks</a>
 
 </div>
 
@@ -105,13 +107,21 @@ Preliminary requirements:
 - **Python** 3.8 or higher.
 - **PyTorch** 2.0 or higher.
 
-To set up the software, clone the project and run the following, preferably in a newly created virtual environment:
+To set up the software, install from `pip` or clone the project for the bleeding-edge updates. Run the following, preferably in a newly created virtual environment:
+
+via `pip` pypi:
+
+```bash
+pip install fastrag
+```
+
+or from a local clone:
 
 ```bash
 pip install .
 ```
 
-There are several dependencies to consider, depending on your specific usage:
+There are several dependencies to consider, depending on your specific usage (also works with `pip install fastrag[*]` package):
 
 ```bash
 # Additional engines/components
@@ -122,9 +132,6 @@ pip install .[colbert]             # Support for ColBERT+PLAID; requires FAISS
 pip install .[faiss-cpu]           # CPU-based Faiss library
 pip install .[faiss-gpu]           # GPU-based Faiss library
 
-# Benchmarking
-pip install .[benchmark]
-
 # Development tools
 pip install .[dev]
 ```

benchmarks/README.md

@@ -4,6 +4,15 @@ We provide scripts for running some well known benchmarks with fastRAG. The goal
 
 We report results as compared with the original benchmark papers.
 
+## Installation
+
+Running the benchmarks require some additional packages:
+
+``` sh
+pip install beir
+pip install git+https://github.com/facebookresearch/KILT.git
+```
+
 ## :beers: BeIR Benchmark
 
 [BeIR](https://github.com/beir-cellar/beir) contains diverse IR tasks ([Thakur et al. 2021](#org29ef2fc)); here we focus on MSMARCO and NaturalQuestions (NQ). For each task, we test two pipelines:

fastrag/__init__.py

@@ -1,4 +1,4 @@
 ### Essential imports for loading components and modifications to original Haystack components
 from fastrag import generators, rankers, retrievers, stores
 
-__version__ = "3.0.0"
+__version__ = "3.0.1"

setup.cfg

@@ -1,6 +1,4 @@
 [options]
-packages = find:
-
 install_requires =
     haystack-ai==2.1.2
     transformers>=4.35.2
@@ -29,10 +27,6 @@ dev =
     pytest
     pre-commit
 
-benchmark =
-    beir
-    kilt @ git+https://github.com/facebookresearch/KILT.git
-
 elastic =
     elasticsearch-haystack
 

setup.py

@@ -1,14 +1,13 @@
-import codecs
 import os
 import pathlib
 
-from setuptools import setup
+from setuptools import find_namespace_packages, find_packages, setup
 
 here = pathlib.Path(__file__).parent.resolve()
 
 
 def read(rel_path):
-    with codecs.open(os.path.join(here, rel_path), "r") as fp:
+    with open(os.path.join(here, rel_path), "r", encoding="utf-8") as fp:
         return fp.read()
 
 
@@ -21,15 +20,22 @@ def get_version(rel_path):
     raise RuntimeError("Unable to find version string.")
 
 
-# Get the long description from the README file
-long_description = (here / "README.md").read_text(encoding="utf-8")
-
 setup(
     name="fastrag",
     author="Intel Labs",
     version=get_version("fastrag/__init__.py"),
-    description="A research framework for building and evaluating neural information retrieval and generative models.",
-    long_description=long_description,
+    packages=find_namespace_packages(include=["fastrag*"]),
+    description="An Efficient Retrieval Augmentation and Generation Framework for Intel Hardware.",
+    long_description=read("README.md"),
     long_description_content_type="text/markdown",
+    url="https://github.com/IntelLabs/fastRAG",
+    license="Apache-2.0",
     python_requires=">=3.8, <4",
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: Apache Software License",
+        "Operating System :: OS Independent",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: Software Development :: Libraries",
+    ],
 )