{"id":26418617,"url":"https://github.com/yasharthbajpai/research-associate","last_synced_at":"2025-10-29T04:24:09.979Z","repository":{"id":282687565,"uuid":"949360604","full_name":"yasharthbajpai/Research-Associate","owner":"yasharthbajpai","description":"A Python-based research assistant that leverages Perplexity AI, web search, and Wikipedia to generate comprehensive research summaries on user-specified topics and saves the results as structured text files.","archived":false,"fork":false,"pushed_at":"2025-03-16T09:50:19.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-16T10:26:34.726Z","etag":null,"topics":["api","perplexity-api","python3"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/yasharthbajpai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-16T09:27:37.000Z","updated_at":"2025-03-16T10:18:53.000Z","dependencies_parsed_at":"2025-03-16T10:36:42.022Z","dependency_job_id":null,"html_url":"https://github.com/yasharthbajpai/Research-Associate","commit_stats":null,"previous_names":["yasharthbajpai/research-associate"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yasharthbajpai%2FResearch-Associate","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yasharthbajpai%2FResearch-Associate/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yasharthbajpai%2FResearch-Associate/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yasharthbajpai%2FResearch-Associate/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yasharthbajpai","download_url":"https://codeload.github.com/yasharthbajpai/Research-Associate/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244141576,"owners_count":20404835,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["api","perplexity-api","python3"],"created_at":"2025-03-18T01:48:50.496Z","updated_at":"2025-10-29T04:24:09.897Z","avatar_url":"https://github.com/yasharthbajpai.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Research Associate\n\nA Python-based research tool that leverages Perplexity AI, web search, and Wikipedia to generate comprehensive research summaries on user-specified topics.\n\n## Overview\n\nResearch Associate is an intelligent assistant that combines multiple data sources to create well-structured research summaries. The application uses the Perplexity AI Sonar Pro model to process information gathered from web searches and Wikipedia, delivering organized research results saved as text files.\n\n## Features\n\n- **AI-Powered Research**: Utilizes Perplexity AI's Sonar Pro model for intelligent summary generation\n- **Multiple Data Sources**: Integrates DuckDuckGo web search and Wikipedia for comprehensive information gathering\n- **Structured Output**: Presents research in a well-organized format with topic, summary, sources, and tools used\n- **Progress Tracking**: Provides detailed status updates throughout the research process\n- **Error Handling**: Implements robust fallback mechanisms for reliability\n- **Local Storage**: Saves all research results as text files for future reference\n\n## Project Structure\n\n```\nRESEARCH_ASSOCIATE/\n├── __pycache__/\n├── .env                  # Environment variables (API keys)\n├── Include/\n├── Lib/\n├── Scripts/\n├── desktop.ini\n├── pyvenv.cfg\n├── results/              # Directory for saved research results\n│   ├── desktop.ini\n│   ├── Narendra Modi.txt\n│   ├── Raspberry.txt\n│   ├── raw_Narendra Modi.txt\n│   └── raw_Raspberry.txt\n├── .gitignore\n├── main.py               # Main application file\n├── requirements.txt      # Project dependencies\n├── tools.py              # Tool definitions for search and saving\n```\n\n## Installation\n\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/yourusername/research-associate.git\n   cd research-associate\n   ```\n\n2. Create and activate a virtual environment (recommended):\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n   ```\n\n3. Install the required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. Create a `.env` file with your Perplexity API key:\n   ```\n   PPLX_API_KEY=your_api_key_here\n   ```\n\n## Usage\n\nRun the main script:\n```bash\npython main.py\n```\n\nWhen prompted, enter a research topic. The Research Associate will:\n1. Search the web for relevant information\n2. Query Wikipedia for additional content\n3. Generate a comprehensive research summary\n4. Save the results to a text file in the `results` directory\n\n## Demo\n\nWatch the demo video: [Research Associate Demo](https://drive.google.com/file/d/1hBrBrNKlVpLzCLnnJBNk0gXAEGTde6Pb/view?usp=sharing)\n\n## Example Output\n\n```\n[14:30:25] Initializing research assistant...\n[14:30:25] Research will be saved to: results/Artificial Intelligence.txt\n[14:30:25] Connecting to Perplexity AI...\n[14:30:26] Searching the web for information...\n[14:30:28] Web search completed successfully\n[14:30:28] Querying Wikipedia...\n[14:30:30] Wikipedia query completed successfully\n[14:30:30] Data sources combined\n[14:30:30] Creating structured output model...\n[14:30:30] Creating research prompt...\n[14:30:30] Prompt formatted successfully\n[14:30:30] Generating research summary with AI...\n[14:30:35] Research summary generated successfully\n[14:30:35] Saving research to results/Artificial Intelligence.txt...\n[14:30:35] Research successfully saved to results/Artificial Intelligence.txt\n\n=================================================\n--- Research Results ---\n=================================================\nTopic: Artificial Intelligence\n\nSummary: Artificial Intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence...\n\nSources: Web Search, Wikipedia\n\nTools Used: search, wikipedia\n\nResearch saved to: results/Artificial Intelligence.txt\n```\n\n## Dependencies\n\n- python-dotenv==1.0.1\n- langchain-community==0.3.19\n- langchain-core==0.3.45\n- openai\n- langchain\n- wikipedia\n- langchain-openai\n- langchain-anthropic\n- pydantic\n- duckduckgo-search\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nMIT\n\n---\n\n© 2025 Yasharth Bajpai  \nAll rights reserved\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyasharthbajpai%2Fresearch-associate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyasharthbajpai%2Fresearch-associate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyasharthbajpai%2Fresearch-associate/lists"}