Chaining your LLM

Lidiia Melnyk | NLP
July 25, 2023

tl;dr ⚡️

LangChain is a tool that enables chaining of large language models (LLMs) to improve performance and flexibility in AI prompting. It offers benefits like fallback models, improved prompting techniques, asynchronous processing, and enhanced performance with memory capacity. LangChain helps us to optimize memory management and facilitates parallel processing of user interactions, resulting in faster AI responses and an enhanced user experience.

Why LangChain? ⛓️

At Spoke, we quickly realized that relying solely on e.g. OpenAI or Cohere didn't fully meet our performance expectations and lacked the desired flexibility. We found ourselves spending a lot of time on prompting and coding when introducing multiple prompts for different use cases. That's when we recognized the need for a quick and elegant solution.

Enter LangChain – the game-changer we were looking for to organize our code, improve speed, introduce fallbacks, and enhance overall performance. Let's dive into the benefits it brings:

Fallback Models

LangChain offers a unified interface that supports various use cases. If you've developed a custom workflow and want to switch to using an LLM (Language Model) from Hugging Face or Cohere instead of OpenAI, LangChain makes the transition seamless. With just a few variable changes, you can adapt your specialized workflow to incorporate any LLM it supports, without major modifications.

Improved Prompting

With LangChain, you can implement the same powerful prompting techniques as with OpenAI, including zero-shot and few-shot prompts. Additionally, you can provide extra context, AI agent style.

LangChain helps in structuring and parsing the output from LLMs, delivering the desired output tailored to your specific needs. It also helps to manage the LLM's token limit for summarization tasks, by leveraging clever text splitters.

Asynchronous Processing

Speed is crucial when working with LLM APIs. That's why LangChain incorporates the asyncio library, enabling asynchronous support for Chains. This capability is currently available for LLMChain, LLMMathChain, ChatVectorDBChain, and QA chains, with plans to expand support to other chains.

Asynchronous processing allows for concurrent execution of multiple requests, boosting efficiency and responsiveness. It maximizes resource utilization, avoids blocking operations, and facilitates parallel processing of user interactions, resulting in faster AI responses and an enhanced user experience.

Enhanced Performance with Memory Capacity

LangChain provides powerful frameworks and tools to optimize memory management. Through classes like ChatMessageHistory and ConversationBufferMemory, you effortlessly capture and retain user interactions with the AI.

This stored information becomes a valuable resource to shape and enhance future AI responses, providing a more contextual and personalized conversational experience. LangChain empowers you to effectively manage and utilize memory for improved AI interactions.

Conclusion 🤝

In summary, LangChain is a powerful tool to amplify the performance of your LLMs. Its diverse support for databases and language models makes it an invaluable asset in speeding up and simplifying the development process. With LangChain, you can unlock the full potential of your language models while enjoying a smoother and more efficient workflow. Want to read about some other innovative projects harnessing the power of LangChain? Check out this article (overview of different projects) or this article (deep dive into a LangSmith application) written by our friends over at CommandBar!

If you want to learn more about how we implemented this technology into our infrastructure to improve our users' experience, or have any other topic you'd like to chat to us about, always feel free to reach out to us!

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