https://github.com/anirbanbasu/dqa
Language models attempting multi-hop question answering (MHQA)
https://github.com/anirbanbasu/dqa
agentic-ai agents artificial-intelligence dspy generative-ai generative-ai-tools large-language-models llms mhqa multi-hop-question-answering questions-and-answers reinforcement-learning
Last synced: 5 months ago
JSON representation
Language models attempting multi-hop question answering (MHQA)
- Host: GitHub
- URL: https://github.com/anirbanbasu/dqa
- Owner: anirbanbasu
- License: mit
- Created: 2024-08-29T08:00:38.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-06T05:00:45.000Z (7 months ago)
- Last Synced: 2025-05-06T21:09:05.105Z (5 months ago)
- Topics: agentic-ai, agents, artificial-intelligence, dspy, generative-ai, generative-ai-tools, large-language-models, llms, mhqa, multi-hop-question-answering, questions-and-answers, reinforcement-learning
- Language: Python
- Homepage:
- Size: 727 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://www.python.org/downloads/release/python-3120/)
[](#)
# DQA: Difficult Questions Attempted
## Overview
The DQA aka _difficult questions attempted_ project utilises large language models (LLMs) to perform _multi-hop question answering_ (MHQA). This project has been inspired by the tutorial [^1] and the article [^2], both by [Dean Sacoransky](https://www.linkedin.com/in/dean-sacoransky-6a671119a/). Unlike both the tutorial and the article, which use the [LangGraph framework from LangChain](https://langchain-ai.github.io/langgraph/) for building agents, this project makes use of [DSPy](https://dspy.ai/).
[^1]: Sacoransky, D., 2024. Build a RAG agent to answer complex questions. IBM Developer Tutorial. [URL](https://developer.ibm.com/tutorials/awb-build-rag-llm-agents/).
[^2]: Sacoransky, D., 2025. Reasoning & Recursive Retrieval With Deepseek-r1, Tavily, and LangGraph. Medium article. [URL](https://medium.com/@deansaco/reasoning-recursive-retrieval-with-deepseek-r1-tavily-and-langgraph-79b3336731e2).
## Project status
Following is a table of some updates regarding the project status. Note that these do not correspond to specific commits or milestones.
| Date | Status | Notes or observations |
|----------|:-------------:|----------------------|
| February 15, 2025 | active | Custom adapter added for Deepseek models. |
| January 26, 2025 | active | LlamaIndex Workflows replaced by DSPy. |
| September 21, 2024 | active | Workflows made selectable. |
| September 13, 2024 | active | Low parameter LLMs perform badly in unnecessary self-discovery, query refinements and ReAct tool selections. |
| September 10, 2024 | active | Query decomposition may generate unnecessary sub-workflows. |
| August 31, 2024 | active | Using built-in ReAct agent. |
| August 29, 2024 | active | Project started. |
## Installation
Install and activate a Python virtual environment in the directory where you have cloned this repository. Let us refer to this directory as the _working directory_ or _WD_ (interchangeably) hereonafter. Install [poetry](https://python-poetry.org/docs/). Make sure you use Python 3.12.0 or later. To install the project with its dependencies in a virtual environment, run the following in the _WD_.
```bash
poetry install
```
In addition to Python dependencies, see the installation instructions of [Ollama](https://ollama.com/download). You can install it on a separate machine. Download the [tool calling model of Ollama](https://ollama.com/search?c=tools) that you want to use, e.g., `llama3.1` or `mistral-nemo`. Reinforcement learning based models such as `deepseek-r1:7b` will also work.
## Environment variables
Following is a list of environment variables that can be used to configure the DQA application. All environment variables should be supplied as quoted strings. They will be interpreted as the correct type as necessary.
For environment variables starting with `GRADIO_`, See [Gradio documentation for environment variables](https://www.gradio.app/guides/environment-variables).
| Variable | [Default value] and description |
|--------------|----------------|
| `OLLAMA_URL` | [http://localhost:11434] URL of your intended Ollama host. |
| `LLM__OLLAMA_MODEL` | [mistral-nemo] See the [available models](https://ollama.com/library). The model must be available on the selected Ollama server. The model must [support tool calling]((https://ollama.com/search?c=tools)). |
## Usage (local)
Create a `.env` file in the _working directory_, to set the environment variables as above. Then, run the following in the _WD_ to start the web server.
```bash
poetry run dqa-webapp
```
The web UI will be available at [http://localhost:7860](http://localhost:7860). To exit the server, use the Ctrl+C key combination.
## Contributing
Install [`pre-commit`](https://pre-commit.com/) for Git and [`ruff`](https://docs.astral.sh/ruff/installation/). Then enable `pre-commit` by running the following in the _WD_.
```bash
pre-commit install
```
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## License
[MIT](https://choosealicense.com/licenses/mit/).