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Abstract

mation, summarize sources, and aggregate the information into a research report. It leverages various language models, such as gpt3.5-turbo-16k and gpt-4, to accomplish research tasks.</p><h2 id="e7ad">Integrating with LangChain</h2><p id="bc80">GPT Researcher is integrated with LangChain, allowing users to take advantage of different chat model integrations and LangSmith, a debugging/logging/monitoring platform.</p><p id="bfc0">The integration involves an OpenAI adapter that serves as a drop-in replacement for OpenAI. With this adapter, users can easily swap the code to use LangChain’s features. Below is an example of how to use the OpenAI adapter:</p><div id="cedc"><pre><span class="hljs-meta"># Before integration</span> <span class="hljs-keyword">import</span> openai

<span class="hljs-meta"># After integration</span> <span class="hljs-title">from</span> langchain.adapters <span class="hljs-keyword">import</span> openai</pre></div><p id="72d1">By passing in parameters such as <code>provider="ChatAnthropic"</code> and <code>model="claude-2"</code>, users can easily utilize different language models provided by LangChain.</p><h2 id="0fd4">Benefits of Integration</h2><p id="e16d">The integration provides two main benefits:</p><ol><li><b>Easy Usage of Other Models</b>: Users can seamlessly switch between different language models by passing the appropriate parameters.</li><li><b>Seamless Integration with LangSmith</b>: LangSmith allows users to track the calls made to the language model, making it easier to debug and understand the process.</li></ol><h2 id="d8c6">LangSmith Traces</h2><p id="e741">Lang

Options

Smith provides traces for the calls made to the language model. For example, the following traces are available:</p><ol><li><b>Agent Description Generation</b>: Trace for the call to the language model when generating an agent description.</li><li><b>Final Report Generation</b>: Trace for the final call to the language model when asking it to write the final report.</li></ol><h2 id="db19">Conclusion</h2><p id="773d">The integration of GPT Researcher with LangChain provides a powerful tool for web research using language models. By leveraging the OpenAI adapter and LangSmith, users can seamlessly switch between models and easily track the calls made to the language model.</p><div id="2895" class="link-block"> <a href="https://readmedium.com/langchain-can-multion-x-langchain-power-next-gen-web-automation-navigation-with-ai-2da2c3ca303f"> <div> <div> <h2>LANGCHAIN — Can Multion-X Langchain Power Next-Gen Web Automation Navigation with AI?</h2> <div><h3>Software is a great combination between artistry and engineering. — Bill Gates</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*nu7ZXSdSXeo6aCLEJYoZpg.jpeg)"></div> </div> </div> </a> </div><p id="86f7">In summary, GPT Researcher x LangChain offers a comprehensive solution for leveraging language models in web research tasks, making it a valuable addition to the LangChain ecosystem.</p></article></body>

LANGCHAIN — What Is GPT Researcher X Langchain?

Computers are good at following instructions, but not at reading your mind. — Donald Knuth

LANGCHAIN — From Foundation Models to Fine-Tuned Applications Using Label Studio

The function of good software is to make the complex appear to be simple. — Grady Booch

medium.com

GPT Researcher x LangChain integrates LangChain’s OpenAI adapter, LangSmith, and GPT Researcher’s planner and execution agents to facilitate seamless web research using various language models. Below is an in-depth tutorial on using GPT Researcher x LangChain, including code snippets and examples.

What is GPT Researcher?

GPT Researcher is an open-source implementation of a research assistant that utilizes a planner and execution agents to generate research questions, seek relevant information, summarize sources, and aggregate the information into a research report. It leverages various language models, such as gpt3.5-turbo-16k and gpt-4, to accomplish research tasks.

Integrating with LangChain

GPT Researcher is integrated with LangChain, allowing users to take advantage of different chat model integrations and LangSmith, a debugging/logging/monitoring platform.

The integration involves an OpenAI adapter that serves as a drop-in replacement for OpenAI. With this adapter, users can easily swap the code to use LangChain’s features. Below is an example of how to use the OpenAI adapter:

# Before integration
import openai

# After integration
from langchain.adapters import openai

By passing in parameters such as provider="ChatAnthropic" and model="claude-2", users can easily utilize different language models provided by LangChain.

Benefits of Integration

The integration provides two main benefits:

Easy Usage of Other Models: Users can seamlessly switch between different language models by passing the appropriate parameters.
Seamless Integration with LangSmith: LangSmith allows users to track the calls made to the language model, making it easier to debug and understand the process.

LangSmith Traces

LangSmith provides traces for the calls made to the language model. For example, the following traces are available:

Agent Description Generation: Trace for the call to the language model when generating an agent description.
Final Report Generation: Trace for the final call to the language model when asking it to write the final report.

Conclusion

The integration of GPT Researcher with LangChain provides a powerful tool for web research using language models. By leveraging the OpenAI adapter and LangSmith, users can seamlessly switch between models and easily track the calls made to the language model.

LANGCHAIN — Can Multion-X Langchain Power Next-Gen Web Automation Navigation with AI?

Software is a great combination between artistry and engineering. — Bill Gates

medium.com

In summary, GPT Researcher x LangChain offers a comprehensive solution for leveraging language models in web research tasks, making it a valuable addition to the LangChain ecosystem.