chore: Project initiation with updated README.

Author: anirbanbasu

README.md

@@ -1,21 +1,18 @@
 [![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue?logo=python&logoColor=3776ab&labelColor=e4e4e4)](https://www.python.org/downloads/release/python-3120/)
-[![Experimental status](https://img.shields.io/badge/Status-experimental-orange)](#) [![pytest](https://github.com/anirbanbasu/py-a2a-dapr/actions/workflows/uv-pytest.yml/badge.svg)](https://github.com/anirbanbasu/py-a2a-dapr/actions/workflows/uv-pytest.yml)
+[![Experimental status](https://img.shields.io/badge/Status-experimental-orange)](#) [![pytest](https://github.com/anirbanbasu/dqa/actions/workflows/uv-pytest.yml/badge.svg)](https://github.com/anirbanbasu/dqa/actions/workflows/uv-pytest.yml)
 
-# py-a2a-dapr
+# DQA: Difficult Questions Attempted
 
-The Agent2Agent (A2A) protocol is [described](https://a2a-protocol.org/latest/) as:
-> The Agent2Agent (A2A) Protocol is an open standard developed by Google and donated to the Linux Foundation designed to enable seamless communication and collaboration between AI agents.
+<p align="center">
+  <img width="400" height="200" src="https://raw.githubusercontent.com/anirbanbasu/dqa/master/docs/images/logo.svg" alt="dqa logo" style="filter: invert(0.5)">
+</p>
 
-Dapr actors are described in the [documentation](https://docs.dapr.io/developing-applications/building-blocks/actors/actors-overview/) as:
-> The [actor pattern](https://en.wikipedia.org/wiki/Actor_model) describes actors as the lowest-level “unit of computation”. [...] Dapr includes a runtime that specifically implements the [Virtual Actor pattern](https://www.microsoft.com/research/project/orleans-virtual-actors/).
+The DQA aka _difficult questions attempted_ project utilises large language model (LLM) agent(s) to perform _multi-hop question answering_ (MHQA).
 
-**py-a2a-dapr** is a template repository for developing Dapr managed Agent2Agent (A2A) systems in Python.
+**Note that this repository is undergoing a complete overhaul of the [older, now obsolete, version of DQA](https://github.com/anirbanbasu/dqa-obsolete). The purpose of this overhaul is to standardise agent communication using the A2A protocol and to use the Dapr virtual actors to manage the backend logic.**
 
 ## Overview
-
-The components and their interactions in py-a2a-dapr is shown in the figure below. The template exposed in this project helps construct A2A endpoints that invoke Dapr actors. The endpoints and the Dapr actors, both run as Dapr applications with their respective Dapr sidecars. The JSON-RPC clients (e.g., a web or a CLI application, or just `curl`) interact with the A2A endpoints and are oblivous to the underlying actors.
-
-![img-components-overview](https://raw.githubusercontent.com/anirbanbasu/py-a2a-dapr/master/docs/images/components-overview.svg)
+TBA
 
 ## Installation
 
@@ -27,8 +24,8 @@ The components and their interactions in py-a2a-dapr is shown in the figure belo
 ## Usage
 
 - Start the Dapr actor service and the A2A endpoints by running `./start_dapr_multi.sh`. (This will send the dapr sidecar processes in the background.)
-- Invoke the A2A agent using JSON-RPC by calling `uv run a2a-client --help` to learn about the various skills-based A2A endpoint invocations.
-- Or, start the Gradio web app by running `uv run web-app` and then browse to http://localhost:7860.
+- Invoke the A2A agent using JSON-RPC by calling `uv run dqa-cli --help` to learn about the various skills-based A2A endpoint invocations.
+- Or, start the Gradio web app by running `uv run dqa-web-app` and then browse to http://localhost:7860.
 - Once done, stop the dapr sidecars by running `./stop_dapr_multi.sh`.
 
 ## Tests and coverage