In the rapidly evolving world of AI, staying ahead of the curve is essential. In this talk, we explored how to harness the power of Large Language Models (LLMs) using a tool that's changing the game: Langchain.
The guest speaker for this presentation was Abhishek Raj, a software engineer with extensive experience in AI applications. Currently a Software Engineer at Observe.ai, one of the leading AI startups with over $210M in funding, Abhishek has previously worked at Zamp (a finance platform that raised over $25M) and Swiggy (where he spent 2.5 years).
Abhishek shared his insights on Langchain, a powerful open-source library simplifying the development of LLM-powered applications. This talk provided a practical overview of Langchain, covering its core concepts, real-world applications, and key considerations when building with it. We explored how Langchain acts as a versatile tool for developers to leverage advancements in LLMs while addressing challenges and exploring exciting future directions in this space.
Langchain is an open-source library designed to simplify building applications with Large Language Models (LLMs). One of its standout features is being LLM-agnostic, allowing developers to interact with various LLMs like ChatGPT, Claude, and Gemini without being tied to a single one.
This flexibility is essential in an ever-evolving field, enabling developers to switch between LLMs seamlessly by changing import statements. This adaptability ensures access to the best models available while maintaining development efficiency.
Use Cases and Real-World Examples
Langchain has practical applications across various domains. A compelling example discussed in the talk is Hermes, a tool built at Swiggy and now open source. Hermes enables product managers to query the company’s database using natural language, eliminating the need for data analysts. It converts natural language queries into SQL, retrieves the requested data, and presents it graphically.
This showcases how Langchain can democratize access to complex data for non-technical users.
Pro Tip: Explore more about Hermes.
Key Concepts
Tokenization
- LLMs convert natural language into numerical representations (tokens). These tokens enable relationships and comparisons, but developers must be mindful of the token limitations of each LLM.
Prompt Engineering
Crafting specific instructions (prompts) is vital for desired LLM outputs.
Use prompt templates to provide context and guide behavior, e.g., setting an LLM as a SQL agent by including metadata and example inputs.
Journey of a Prompt
Parsers
- Tools like Pydantic parse LLM outputs (e.g., JSON queries) into structured objects for further processing.
Embeddings
- Storing data as embeddings (numerical representations preserving relationships) optimizes querying for large datasets.
No-Code Options
- Tools like Flowise, built on Langchain, enable drag-and-drop interfaces for quickly prototyping AI-powered workflows without coding expertise. Do not miss out on this interesting tool, go and explore Flowise.
Challenges and Important Considerations
While Langchain provides a strong foundation, the talk highlighted some challenges:
Dependency Management: Frequent library updates can complicate dependency tracking.
Documentation: The rapid pace of development makes navigating documentation a challenge.
LLM Selection: Choose LLMs based on use cases and benchmarks, balancing cost and performance.
Language Choice: Use a programming language (Python/TypeScript) that you are most comfortable with.
Context and Memory: Storing conversation history is costly. Instead, summarize interactions or use vector databases to manage context more efficiently.
Production Deployments: Begin with Docker on EC2 for early-stage deployments, but consider on-premise servers for compliance in established environments.
Top Questions from the Talk (and Abhishek’s Insights)
Question: How do I choose the best LLM for my use case, considering cost?
Answer: Start by Googling benchmarks of different LLMs and comparing how they scored for specific tasks. For example, if you need to do math, look for benchmarks related to math. Also, consider the cost and whether a smaller, faster model would be sufficient for your task.
Question: Is there any difference when using Langchain with Python or TypeScript?
Answer: The specific language is not that important. Use whichever language you are comfortable with.
Question: What are some problems developers face when using Langchain?
Answer: Dependency management can be challenging, and the documentation can be difficult to navigate because of how rapidly the libraries are updated.
Question: How can I manage context without storing all the text?
Answer: Summarize previous interactions and use that as context rather than storing every piece of text. You can also use vector databases, but cost is still a factor.
Question: What's one area in AI that excites you to explore next?
Answer: I am currently very interested in audio models and how they work.
This talk by Abhishek Raj illuminated the potential of Langchain for building innovative LLM-powered applications. By understanding its core concepts and addressing practical challenges, developers can unlock new possibilities and streamline workflows in AI development.
Want to learn more? Check out the full video on TechKareer’s YouTube channel.