Build an Autonomous Researcher

Are you running out of ideas what to write on your twitter? Or maybe too many ideas but no time to do your research?

Our focus will be on creating an Autonomous Researcher utilizing the powerful capabilities of the General Purpose Transformer (GPT). We will leverage a tool named Langchain to streamline the process and deliver a no-code solution. Let's get started.

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Step 1: Conceiving the Idea

In our pursuit of designing an AI researcher, the primary objective is to build an agent that can carry out research on any given topic and produce high-quality content based on the research conducted. With this tool, you can write regular Twitter threads about how famous people amassed their fortunes, conduct market or competitor analysis, and even monitor discourse on competitor channels.

Step 2: Choosing the Right Tools

Our initial plan was to utilize tools like Flowise and Landflow to construct an autonomous research agent. However, due to some limitations in controlling the search results and URLs returned by these tools, we decided to opt for Langchain instead. Langchain allows us to quickly construct a user interface for our autonomous agent.

Step 3: Gathering Relevant Articles

The first practical step in building our autonomous researcher involves using a service called SERP. SERP allows us to search the internet for relevant articles, which can be later used to extract information about our research topic. We retrieve a list of articles via SERP, feed this list to the large language model, and prompt it to choose the best articles.

Step 4: Summarizing Each Article

Once we have a list of the best articles, we extract the content of each article and use GPT to generate summaries. To make this process more efficient, we employ a tool from Langchain called DocLoader, which loads HTML docs from a list of URLs. These contents are then divided into smaller chunks using TextSplitter and are passed onto GPT for summarization.

Step 5: Generating a Twitter Thread

With a comprehensive summary of all the articles at our disposal, the next step is to create engaging Twitter threads from this information. We feed these summary chunks into a large language model and generate viral Twitter threads on the topic of interest.

Step 6: Creating the User Interface

The final step in creating our autonomous researcher is to construct a user interface for our tool. For this, we use Streamlit, which allows us to create an easy-to-use UI quickly. This UI enables users to input the topic they are interested in, and the AI researcher will provide a comprehensive Twitter thread based on the topic.

Alternative No-Code Solution: AI

To build a no-code autonomous researcher, consider using AI. It is a flexible platform that enables users to construct complex AI applications quickly. AI provides various building blocks similar to Langchain, offering a UI that is more adaptable than Flowwise. Building an autonomous researcher on AI can be achieved in around ten minutes due to its user-friendly design.

Conclusion

Creating an Autonomous Researcher using GPT can provide a wide range of possibilities for content generation, market analysis, and competitor insights. Whether you choose to go down the path of coding your solution or opt for a no-code platform like AI, the opportunities are abundant. With Langchain, SERP, GPT, and Streamlit at your disposal, creating an AI-powered researcher has never been easier.

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