https://github.com/kallemickelborg/agentic-ai
Stateful AI Agent for Knowledge Extraction
https://github.com/kallemickelborg/agentic-ai
agentic agentic-ai agentic-workflow ai-agents chain-of-thought dspy dspy-ai knowledge-extraction knowledge-retrieval state-workflow stateflow stateful
Last synced: about 1 month ago
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Stateful AI Agent for Knowledge Extraction
- Host: GitHub
- URL: https://github.com/kallemickelborg/agentic-ai
- Owner: kallemickelborg
- Created: 2024-09-25T21:38:04.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-05T18:52:28.000Z (6 months ago)
- Last Synced: 2025-05-06T21:08:35.605Z (about 2 months ago)
- Topics: agentic, agentic-ai, agentic-workflow, ai-agents, chain-of-thought, dspy, dspy-ai, knowledge-extraction, knowledge-retrieval, state-workflow, stateflow, stateful
- Language: Python
- Homepage: https://agentic-ai-frontend.onrender.com/
- Size: 283 KB
- Stars: 11
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Stateful AI Agent for Knowledge Extraction in Medical Research
A simple human-in-the-loop multi-state AI agent designed to answer medical research questions with research papers from PubMed. This project is based on the [StateFlow](https://arxiv.org/abs/2403.11322) research paper, using states with cascading function calling in a research pipeline. The benefit of using states is that it allows for a more structured and modular approach to the research process, making it easier to manage and scale. Using states is a different but highly effective approach for building AI agents, allowing for more deterministic and predictable behavior. The function calling is implemented using [FastAPI](https://fastapi.tiangolo.com/), [OpenAI API](https://openai.com/api/) and [DSPy](https://dspy.ai/) to process Chain-of-Thought reasoning for prompting the LLM. The backend is interfaced using a frontend implemented in [Next.js](https://nextjs.org/) and [Tailwind CSS](https://tailwindcss.com/).
## Table of Contents
- [Tech Stack](#tech-stack)
- [Overview](#overview)
- [Functions](#functions)
- [Roadmap](#roadmap)
- [Backlog](#backlog)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Development](#development)
- [Deployment](#deployment)
- [Contributing](#contributing)
- [Contact](#contact)
## Tech Stack
- Frontend: [Next.js](https://nextjs.org/) (React framework)
- Backend: [Python](https://www.python.org/) with [FastAPI](https://fastapi.tiangolo.com/) (Python framework) and [Uvicorn](https://www.uvicorn.org/) (ASGI server)
- Data Validation: [Pydantic](https://docs.pydantic.dev/) (Type checking)
- Language Model: [OpenAI API](https://openai.com/blog/openai-api) (GPT models)
- Prompting Framework: [DSPy](https://dspy.ai/) (Chain-of-Thought reasoning)
- Styling: [Tailwind CSS](https://tailwindcss.com/), [Radix UI](https://www.radix-ui.com/primitives) (UI components)
- Animation: [Framer Motion](https://www.framer.com/motion/)
- Deployment: [Render](https://render.com/)
## Overview
This project implements a number of different Python frameworks and libraries to create a multi-state AI agent for knowledge extraction in medical research. The agent has been designed with 5 states in mind; Start, Clarify, Research, Analyze, and Conclusion. Each state has a number of functions that are used to extract knowledge from the research papers. Below is a list of the functions for each state.
### Functions
#### State 1: Start
- `solve_task`: Initializes the research process and transitions to the Clarify state
#### State 2: Clarify
- `generate_clarifying_questions`: Generates relevant questions to better understand the user's research needs
- `ClarifyQuestions` (DSPy Signature): Processes the task description to generate targeted clarifying questions
#### State 3: Research
- `fetch_research_papers`: Retrieves research papers from PubMed based on the query
- `process_research_papers`: Processes and evaluates retrieved papers
- `enhance_search_query`: Optimizes the search query for better results
- `enhance_query_with_dspy`: Enhances the query using clarifying answers
- `check_paper_accessibility`: Checks if papers are openly accessible
- `PaperEvaluation` (DSPy Signature): Evaluates papers for relevance and scientific merit
- `relevancy_score`: Ranks each research paper based on its relevance to the user's query
- `citation_score`: Ranks each research paper based on its methdology, study design, and other factors
#### State 4: Analyze
- `analyze_papers`: Performs comprehensive analysis of selected papers, altogether
- `analyze_paper_content`: Analyzes individual paper content using full text or abstract
- `fetch_pdf_content`: Retrieves and extracts text from PDF papers using URL
- `fetch_pmc_paper_content`: Fetches paper content from PubMed Central
- `PaperAnalysis` (DSPy Signature): Extracts supporting and opposing evidence from papers
#### State 5: Conclude (WIP)
- `conclude_research`: Generates final conclusions based on analyzed papers
- `Conclude` (DSPy Signature): Processes all findings to create a comprehensive conclusion
## Roadmap
- [ ] Implement the Conclusion state
- [ ] Implement Embase API access
- [ ] Implement PICO Search Option for research paper literature review
- [ ] Implement paper access type (Open Access, Paywalled, etc.) tag
- [ ] Implement citation scoring of entire research paper pdf rather than abstract
- [ ] Improve the UI/UX on the frontend
### Potential Roadmaps
- [ ] Implement research question critique agent state
## Backlog
This project is a work in progress, and so needs more work to be fully functional. Below is a list of tasks.
- [x] Add state transition to move between states (02/01/2025)
- [x] Implement proper component structure and file path organization on the frontend (03/01/2025)
- [x] Create classes.ts to consolidate Tailwind classes (05/01/2025)
- [ ] Refactor functions for each state for better readability and maintainability
- [ ] Add more descriptive logging and error handling for debugging and troubleshooting
- [ ] Add proper DSPy instantiation of prompt optimization
- [ ] Break up main app.py (serverless function) into smaller callable functions
## Getting Started
### Prerequisites
- Node.js (v14 or later)
- Python (v3.7 or later)
- OpenAI API key
### Installation
1. Clone the repository:
```
git clone https://github.com/kallemickelborg/agentic-ai.git
cd agentic-ai
```
2. Set up the frontend:
```
cd frontend
npm install
```
3. Set up the backend:
```
cd backend
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
pip install -r requirements.txt
```
4. Create a `.env` file in the backend directory with your OpenAI API key:
```
OPENAI_API_KEY=your_api_key_here
```
## Development
1. Start the backend server:
```
cd backend
uvicorn app:app --reload
```
2. In a new terminal, start the frontend development server:
```
cd frontend
npm run dev
```
3. Open your browser and navigate to `http://localhost:3000`
## Deployment
This project is configured for deployment on Render. Follow these steps:
1. Fork this repository to your GitHub account.
2. Create a new Web Service on Render, connecting to your forked repository.
3. Set up the environment variables in Render, including your OpenAI API key.
4. Deploy the service on Render.
For detailed deployment instructions, refer to the [Render documentation](https://render.com/docs).
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request. If you are a medical researcher or student and know about the process of conducting research, please feel free to write to me and help me understand the medical research process better.
## Contact
If you have any questions or feedback, please feel free to contact me at [email protected]