How to build AI apps on Vertex AI with LangChain

If you are interested in learning more about how you can utilize the power of artificial intelligence and specifically generative AI into your applications. You are sure to enjoy this quick overview guide on how to build AI apps on Vertex AI with LangChain. AI technology leverages large language models (LLMs) to generate content, is being utilized in a variety of applications, from chatbots to document summarization tools. Two key players in this field are Google’s Vertex AI and LangChain, which together provide a robust framework for building generative AI-driven applications.

Generative AI, as the name suggests, is a type of AI that can generate new content. This can range from text and chat to images and code. The power of generative AI lies in its ability to create content that is not only coherent but also contextually relevant, making it a valuable tool for developers. Google’s Vertex AI provides access to generative AI models, also known as foundation models, which are categorized by the type of content they are designed to generate. These models are exposed through a publisher endpoint that is specific to a Google Cloud project, eliminating the need for developers to deploy the foundation model unless it needs to be fine-tuned for a specific use case.

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PaLM 2

One of the key generative AI models available in Vertex AI is the Pathways Language Model 2 (PaLM 2), which is the driving force behind the PaLM API. PaLM 2 is a state-of-the-art language model with improved multilingual, reasoning, and coding capabilities. The PaLM API for text is fine-tuned for language tasks such as classification, summarization, and entity extraction, while the PaLM API for chat is fine-tuned for multi-turn chat, where the model keeps track of previous messages in the chat and uses it as context for generating new responses. Google explains more about PaLM 2 :

“PaLM 2 is our next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than our previous state-of-the-art LLMs, including PaLM. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements.”

However, using these large language models in applications is not without its challenges. For instance, dealing with complex use cases, such as leveraging real-time data or combining multiple large language models, can be difficult. Furthermore, large language models only have access to the knowledge in their training data, which can be a limitation for certain use cases.

LangChain

To overcome these challenges, developers can use design patterns such as including data from external sources, making multiple calls to large language models, and chaining multiple models together. These patterns can be implemented by writing boilerplate code or using a framework like LangChain for abstraction and orchestration.

LangChain is a framework that offers flexible abstractions for building LLM-driven applications. It enables data-aware and agentic applications, simplifying the process of integrating external data and chaining multiple models. LangChain’s Python SDK integrates with Vertex AI, making it easier to build applications on top of Vertex AI. This integration extends to services like the Vertex AI PaLM API for text, chat, and embeddings, Vertex AI vector search, and Vertex AI search.

A practical example of using Vertex AI and LangChain for document summarization involves installing the Vertex AI and LangChain SDK in your environment, importing the libraries, setting the text bias model as default, creating prompt templates, initializing the associated MapReduce chain, and generating summaries using the chain. This process illustrates how Vertex AI and LangChain can accelerate the generative AI development life cycle.

The combination of Vertex AI and LangChain provides a powerful tool for developers looking to leverage the power of generative AI in their applications. From improving customer support through a chat interface to exploring unstructured data through conversational interfaces and summarizing large documents, the potential use cases for generative AI development are vast and varied. As such, understanding how to utilize Vertex AI and LangChain effectively is crucial for developers looking to stay at the forefront of AI-driven application development.

Other Generative AI offerings

The Codey APIs generate code. The Codey APIs include three models that generate code, suggest code for code completion, and let developers chat to get help with code-related questions. For more information, see Code models overview.
The Text Embedding API generates vector embeddings for input text. You can use embeddings for tasks like semantic search, recommendation, classification, and outlier detection.
Multimodal embeddings generates embedding vectors based on image and text inputs. These embeddings can later be used for other subsequent tasks like image classification or content recommendations. For more information, see the multimodal embeddings page.
Imagen, our text-to-image foundation model, lets organizations generate and customize studio-grade images at scale for any business need. For more information, see the Imagen on Vertex AI overview.

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