Tavily Use Cases in Jupyter Notebooks

Welcome to the repository showcasing various use cases of Tavily in simple Jupyter notebooks. This collection provides practical examples and applications of Tavily's advanced search capabilities with other state-of-the-art frameworks and language models.

Notebooks Included

1. Data Enrichment

   The notebook data_enrichment_agent.ipynb demonstrates how to enrich data by populating missing values using Tavily, LangGraph Framework, and OpenAI. It accepts CSV, Excel files, or Google Sheets as input and generates an updated dataset with the missing data filled in.

2. Company Research

   The notebook company_research.ipynb demonstrates how to generate company research reports by using Tavily for up-to-date information, the LangGraph Framework for data processing, and OpenAI for content generation. It gathers and validates information with citations, then compiles it into a detailed PDF report, streamlining company analysis.

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Feel free to explore each notebook to understand how Tavily's capabilities can be applied to various data processing and analysis tasks. Each notebook provides a step-by-step guide and practical examples to help you get started quickly.