Research-Navigator

Research Navigator is a project designed to automate research tasks using a combination of a FastAPI backend and a sophisticated multi-agent system powered by crewAI. It streamlines the process of gathering, filtering, and synthesizing information based on user queries.

The system leverages specialized AI agents, each responsible for a specific part of the research workflow, from determining query relevance and planning the research approach to retrieving data from various sources (web search, RAG) and synthesizing the final results.

Features

• FastAPI Backend: Provides an API interface for interacting with the research agents (potential future feature).
• crewAI Integration: Utilizes a multi-agent framework for complex task decomposition and execution.
• Modular Agent Design: Employs distinct agents for relevance checking, research planning, query generation, data retrieval, and synthesis.
• Asynchronous Operations: Supports asynchronous tasks like RAG retrieval and web searching for improved performance.
• Development Tools: Integrated with uv, ruff for dependency management, linting, and formatting.

Installation

Install dependencies using uv:

make install
# or
uv sync

Running the Application

Run the FastAPI development server:

make run
# or
uv run fastapi dev --app app --reload

Running the Crew

Run the main crew script directly:

make run-crew
# or
uv run run_crew

Development Commands

• Lint: make lint or uv run ruff check .
• Format: make format or uv run ruff format .
• Test: make test or uv run test
• Clean: make clean
• Build (Docker): make build or uv run dockerpyze

Project Structure

.
├── app/                # Main application code (FastAPI, crewAI logic)
│   ├── agents/         # Agent definitions
│   ├── tasks/          # Task definitions
│   ├── tools/          # Custom tools for agents
│   ├── main.py         # FastAPI application entrypoint (if applicable)
│   └── ...
├── tests/              # Unit and integration tests
├── scripts/            # Utility scripts (e.g., run_crew.py)
├── .env.example        # Example environment variables
├── Dockerfile          # Docker configuration
├── Makefile            # Make commands for development tasks
├── pyproject.toml      # Project metadata and dependencies (for uv)
├── ruff.toml           # Ruff linter/formatter configuration
└── README.md           # This file

Agents & Tasks Overview

Agents

• Relevancy Agent
• Research Agent
• Query Agent
• Retrieval Agent
• Synthesizer Agent

Tasks

• Is Relevant Question? (Relevancy Agent)
• Create Research Approach (Research Agent)
• Search Query Generation (Dynamic number) (Query Agent)
• RAG Retrieval (Async) (Retrieval Agent)
• Web Search (Async) (Retrieval Agent)
• Keep Relevant Data for Question (Relevancy Agent)
• Summarize Everything (Synthesizer Agent)

Contributing

Contributions are welcome! Please follow standard fork-and-pull-request workflows. Ensure your code adheres to the project's linting and formatting standards (make lint and make format).

License

This project is licensed under the MIT License - see the LICENSE file for details (assuming MIT, add a LICENSE file if needed).