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- **For LLM, we recommend [OpenAI GPT](https://platform.openai.com/docs/guides/gpt)**, but you can use any other LLM model (including open sources) supported by [Langchain Adapter](https://python.langchain.com/docs/guides/adapters/openai), simply change the llm model and provider in config/config.py. Follow [this guide](https://python.langchain.com/docs/integrations/llms/) to learn how to integrate LLMs with Langchain.
- **For search engine, we recommend [Tavily Search API](https://app.tavily.com) (optimized for LLMs)**, but you can also refer to other search engines of your choice by changing the search provider in config/config.py to `"duckduckgo"`, `"googleAPI"`, `"googleSerp"`, or `"searx"`. Then add the corresponding env API key as seen in the config.py file.
- **For search engine, we recommend [Tavily Search API](https://app.tavily.com)**, but you can also refer to other search engines of your choice by changing the search provider in config/config.py to `"duckduckgo"`, `"bing"`, `"google"`, `"googleSerp"`, `"searx"` and more. Then add the corresponding env API key as seen in the config.py file.
- **We highly recommend using [OpenAI GPT](https://platform.openai.com/docs/guides/gpt) models and [Tavily Search API](https://app.tavily.com) for optimal performance.**
> **Step 4** - Run the agent with FastAPI

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# Introduction
We know that each AI application requires a different set of realtime data.
For example, a chatbot that answers questions about the coding tasks needs connect to rich code documentation.
A chatbot that answers questions about the stock market needs to connect stock market-related data, news and people.
For this we're excited to introduce Tavily Search Topics. You can now search for data based on a topic.

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# Code

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# Finance

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# News

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# People
### Coming soon! Stay up to date by signing up to Tavily at https://app.tavily.com.