LangChain is a tool (library) that helps developers create robust applications using large language models (GPT-3, GPT-4, etc) that can really do about anything...
Its purpose is to make it easier for developers to chain together different prompts & actions to create interactive applications.
With LangChain, developers can build more complex applications where the output of one prompt can be used as context for the next prompt combined with the actions of prompts.
For example, instead of asking an LLM to bake a cake, LangChain can be used to prompt the LLM for the ingredients needed to bake a cake and then prompt for the steps to bake the cake. LangChain simplifies the process of working with LLMs and provides a framework for building more sophisticated applications.
Essentially, LangChain is a tool for orchestrating a series of prompts to achieve a desired outcome, making it easier for developers to use LLMs and apply them to existing & new business’s.
LLMs are highly efficient in generating completions for single prompts. However, they do not reason like humans, and their completions rely on predicting the next most likely word. Humans, on the other hand, can reason by using existing knowledge to break down a problem into smaller sub-tasks. This is where LangChain comes in – it enables developers to create agents that can reason and chain together multiple commands to solve a problem.
With using agents, your application can now communicate with softwares/integrations and perform actions on behalf of the users request.
You could for example have your language model communicate with Slack with to send a message to your team about an email that you just received.
The prompt generated from the user could look like the following…
“Grab my latest email from my account and message my slack team the summary of what was said in the #leads channel”
Assuming the LLM is set up correctly (chained together) it can provide it’s own logic & reasoning then perform the following actions if the users has provided the login credentials for both accounts.
The bot’s logic would look something like this (keep in mind it’s doing this on it’s own just from the users prompt that was inputed
1. “I need grab the most recent email from person X’s inbox”
2. “I need to then summarize the body of the email”
3. “I will then locate the #leads channel in slack and send the summarized message
By relying on LLMs to generate each step, LangChain eliminates the need for manual human input to order the steps.
This makes LangChain a powerful tool for building complex, interactive applications that can reason about problems and break them into smaller, more manageable sub-tasks.
LangChain offers a powerful way to enhance LLMs by providing memory and context, allowing for more complex problem-solving with greater accuracy. Developers are particularly excited about LangChain because it offers a new way to build user interfaces.
Model's such as OpenAI's GPT-3/4 are used as sort of base layer. They're very powerful on their own, however coupled with external data via APIs & databases, allow for more complex and intuitive use-cases.
This process allows ChatGPT to generate more accurate and helpful code completions for developers. Additionally, ChatGPT's ability to explain the code after it has been generated by the LLM demonstrates the power of the model in providing context and memory to its completions.
If you want to find out more and connect with individuals who are already using this technology to implement in their business, join our discord community and introduce yourself!
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