Competitor Analysis

This repository contains a system for analyzing competitors using data from various sources like Google, Wikipedia, LinkedIn and Reddit. It dynamically generates SWOT analyses and PDF reports using advanced AI-driven techniques.

Features

• Multi-Source Data Integration: Aggregates data from Google Search, Wikipedia, LinkedIn and Reddit.
• AI-Driven SWOT Analysis: Uses state-of-the-art LLMs (e.g., Llama-2, Gemma 2) to extract insights and generate SWOT analyses.
• Automated PDF Reporting: Produces professional reports summarizing the analysis.
• Modular Multi-Agent System: Designed for scalability and easy extensibility.

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Architecture

The system follows a modular, multi-agent architecture:

1. Data Retrieval Agent: Aggregates data from multiple external sources.
2. NLP Processing Agent: Normalizes and preprocesses raw data for downstream tasks.
3. SWOT Analysis Agent: Uses an LLM to generate Strengths, Weaknesses, Opportunities, and Threats.
4. Report Generator: Creates a polished PDF report for the final analysis.
5. Orchestrator: Manages the workflow between agents.

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Setup

1. Clone the Repository

git clone [email protected]:FardinHash/competitor-analysis.git
cd competitor-analysis

2. Create a Virtual Environment

python3 -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Set Up Environment Variables

Create a .env file in the root directory and populate it with the following variables:

OPENAI_API_KEY=your-openai-api-key
CRUNCHBASE_API_KEY=your-crunchbase-api-key
REDDIT_CLIENT_ID=your-reddit-client-id
REDDIT_CLIENT_SECRET=your-reddit-client-secret
REDDIT_USER_AGENT=your-reddit-user-agent
HUGGINGFACE_HUB_TOKEN=your-huggingface-hub-token

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Usage

Running the System

To run the system, execute the following command:

python src/main.py

Example

1. Enter a company name or product query (e.g., Tesla).
2. The system retrieves and processes data, generates a SWOT analysis, and creates a PDF report.
3. The report is saved in the outputs/reports/ directory.

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Test Cases (Development)

To ensure the system works as expected, use the following example test cases:

1. Data Retrieval

• Test Input: Query for a company like OpenAI.
• Expected Output: Retrieved summaries from Google, Wikipedia, and Reddit in structured format.

2. NLP Processing

• Test Input: Raw data from multiple sources.
• Expected Output: Normalized and cleaned data ready for SWOT analysis.

3. SWOT Analysis Generation

• Test Input: Normalized data for Tesla.
• Expected Output: A coherent SWOT analysis with structured Strengths, Weaknesses, Opportunities, and Threats.

4. PDF Report Generation

• Test Input: SWOT analysis for SpaceX.
• Expected Output: A PDF file saved in outputs/reports/SpaceX_analysis_report.pdf.

5. End-to-End Workflow

• Test Input: Query for NVIDIA.
• Expected Output: Complete analysis with a generated PDF report containing SWOT insights.

Running Tests

To run the test scripts, use:

pytest tests/

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License

This project is licensed under the MIT License. See the LICENSE file for details.