If you would like to build your very own autonomous AI research agent that is capable of running 24-hours a day seven days a week scouring the web for anything you ask it. You might be interested in a new tutorial by Michael Borman who has kindly uploaded the code to GitHub for you to download and tweak to your exact requirements.
Building a research agent using LangChain and integrating it into a product is a complex yet rewarding process. This process involves several steps, including building a tool for search and scraping, creating a Lang chain agent, hosting the web app using Streamlit, and using Fast API to turn the code into an API. The API can then be integrated into a product through Zapier automation, hosted as an API using Fast API, and connected to Google Sheets or a CRM using Zapier.
The first step in building a research agent using Lang chain code is to create a tool for search and scraping. This tool uses a web search tool to find links and a web scraper tool to extract data from those pages. The agent iterates these steps until it has enough research to answer the question, then passes the output back. The video demonstrates how to build the search and scraping tools, using the SerpApi for web search and the BrowseList API for web scraping.
How to build an autonomous AI research agent running 24/7
Other articles you may find of interest on the subject of LangChain and autonomous AI agents :
Once the search and scraping tools are built, the next step is to create a Lang chain agent. This agent is instantiated with a list of tools, a language model, and a type of agent. The agent is given a general system message that instructs it on its task and how many iterations to perform. The Python library Beautiful Soup is used to clean the data and extract the text from the HTML page. The agent then uses a summarizer to reduce the data to the necessary information, using a method called Map Reduce.
After the Lang chain agent is created, the next step is to host the web app using Streamlit. A simple front-end that can quickly get the application up and running. The agent can be tested using Streamlit to ensure it is functioning correctly. Once the web app is hosted and tested, the next step is to use Fast API to turn the code into an API. This allows the agent to be hosted as an API, which can be queried from anywhere on the web. The API can be hosted on Render, a platform that allows for easy hosting of applications.
The final step in building a research agent using LangChain code is to integrate the API into a product through Zapier automation. This allows the API to be connected to Google Sheets or a CRM using Zapier. This integration allows for automated research on leads, making the research agent a valuable tool for identifying the best prospects for a product.
Building a research agent using Lang chain code and integrating it into a product involves several steps, but the end result is a powerful tool for automated research. By following these steps, one can create a research agent that can scrape the web, provide research on any topic, and be integrated into a product for automated lead research.
You might be interested in other automation systems created using AI and Zapier :
Filed Under: Guides, Top News
Latest Geeky Gadgets Deals
Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Credit: Source link