Matt Kennedy

Building a <a title="What is Generative AI" href="https://www.datastax.com/guides/what-is-generative-ai">generative AI</a> app requires a rock-solid database. But building on that stable foundation can be a complex and laborious process. That&rsquo;s exactly why we support <a title="LangChain" href="https://www.langchain.com/">LangChain</a>, a framework that simplifies building GenAI applications.
Integrated with the highly scalable Apache Cassandra&reg; and DataStax Astra DB, LangChain enables developers to work with a GenAI-centric framework and enjoy all the advantages of a vector database, without the need to code to the API of a particular data store.&nbsp;
GenAI apps and agents need speed and scale. Consider a <a href="https://totalrecall.click/">browser plugin</a> that is constantly writing vectors of what&rsquo;s been browsed, and constantly reading vectors to discover related topics. You&rsquo;ll need a <a href="https://www.datastax.com/guides/what-is-a-vector-database">vector database</a> that can ingest and index data in real time from a global user base, support synchronous operations, and deliver low-latency responses.&nbsp;
The Cassandra community spent four years building a new indexing capability for Cassandra that now supports our vector implementation. While most vendors are still using the HNSW libraries from Lucene, we knew that to support high-growth use cases we&rsquo;d have to deliver something more robust that adds parallelism and real-time indexing alongside other significant performance improvements. We <a href="https://github.com/jbellis/jvector">introduced JVector</a> to address those needs, and deliver a monumental leap in <a title="Vector Search" href="https://www.datastax.com/products/vector-search?utm_source=LangChain_Astra_blog&amp;utm_medium=blog&amp;utm_campaign=blogpost">vector search performance</a>. JVector implements state-of-the-art algorithms inspired by DiskANN and related research and takes full advantage of <a href="https://openjdk.org/projects/panama/">JVM SIMD acceleration</a>.
With the construction of a solid foundation complete, we just needed that LangChain house atop it.&nbsp;
LangChain is a framework for developing applications powered by large language models (LLMs), and is specifically designed for GenAI applications that require:&nbsp;
<ul>
<li style="font-weight: 400;" aria-level="1">An interface between apps and LLMs</li>
<li style="font-weight: 400;" aria-level="1">An interface between data and the tools that prepare and store that data for the application</li>
<li style="font-weight: 400;" aria-level="1">Composability, in that it can generate and integrate with AI agents that interact with the environment and ecosystem.&nbsp;</li>
</ul>
The building blocks LangChain delivers to developers are: &ldquo;components,&rdquo; which are abstractions between apps and language models; and &ldquo;chains,&rdquo; which assemble the components needed for specific use cases, and are also designed to be customizable
LangChain provides a <a href="https://integrations.langchain.com/vectorstores">plugin architecture to add vector stores</a>. We have contributed a connector to Cassandra and added it to Astra DB so that LangChain developers can easily and naturally add Cassandra or Astra DB as a vector store in new and existing <a href="https://www.datastax.com/guides/what-is-retrieval-augmented-generation">retrieval augmented generation</a> (<a href="https://www.youtube.com/watch?v=TsmQHXEyDZ4">RAG</a>) applications. In a RAG application, a model is provided with additional data or context from other sources&mdash;most often a database that can store vectors.
If you're building a high-growth generative AI app or already store your data in Cassandra, you'll require a powerful Python library to abstract the process of accessing Cassandra; we developed <a href="https://cassio.org/">CassIO</a> to help with this (check out <a href="https://colab.research.google.com/github/CassioML/cassio-website/blob/main/docs/frameworks/langchain/.colab/colab_qa-basic.ipynb">this notebook</a> for a code-centric quickstart).
The enterprises we work with are already using this design pattern to deliver chatbots, recommendation systems, and more. We can&rsquo;t wait to see what you create.
You can get started with a <a href="https://cassio.org/start_here/">code example</a> straight away and, as always, reach out through the chat function on <a href="https://astra.datastax.com">astra.datastax.com</a> if you have questions.&nbsp;
<a href="https://www.datastax.com/resources/webinar/path-to-production-skypoint-s-journey-with-langchain-and-cassandra?filter=%7B%7D">Join us</a> on October 26 at 9am PT for a live webinar where LangChain founder and CEO Harrison Chase and SkyPoint founder and CEO Tisson Mathew discuss their experience building production RAG apps that use customer data with LangChain, <a title="What is a Large Language Model" href="https://www.datastax.com/guides/what-is-a-large-language-model">LLMs</a>, and vector search.

LangChain and Astra DB: A Rock-Solid Foundation for Building Generative AI Applications

Matt KennedyProduct Strategy, DataStax

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