Choosing a Vector Store for LangChain

It can be challenging to shepherd GenAI apps from prototype to production. Vector stores and LangChain are technologies that, used together, can increase response accuracy and speed up release times. 

In this post, you’ll learn what vector stores and LangChain do, how they work together, and how to choose a vector store that integrates seamlessly with LangChain. 

Using a vector store with LangChain

First, let’s take a quick look at what vector store and LangChain each contribute to building an accurate and reliable GenAI app. 

LangChain 

A GenAI app typically consists of multiple components. It may use one or more large language models (LLMs) - generalized AI systems trained on large volumes of data - to respond to different types of queries. It will also commonly include components such as response parsers, verifiers, external data stores, cached data, agents (e.g., chatbots), and integrations with third-party APIs. 

LangChain is a framework, available in both Python and JavaScript, that’s designed to streamline AI application development. It represents all of the components of a GenAI application as objects and provides a simple language to assemble (chain) them into a request/response processing pipeline. 

Using LangChain, you can create complex applications using hundreds of components - sophisticated chatbots, code renderers, etc. - with just a few dozen lines of code. LangChain also implements operational robustness features,  such as LLM streaming and parallelization, so you don’t have to code them yourself. 

Vector stores

LLMs are very good at processing natural language queries and creating responses based on their training set. However, that training set is generalized and usually a couple of years old. Obtaining accurate and timely responses requires supplying additional context with your prompts. 

Retrieval-augmented generation (RAG) is a technique that takes a user’s query and gathers additional context from external, domain-specific data stores - e.g., your product catalog and manuals, and your customer support logs. It then includes the most relevant context in the prompt to the LLM. 

A vector database, or vector store, has become the go-to method for implementing such searches because vector databases excel at storing high-dimensional data with retrieval via semantic search. A vector store represents multi-modal data as mathematical vectors and then retrieves similar instances (nearest neighbors) based on these calculations. 

Vector stores can process queries and return nearest neighbors with low latency - a key requirement for a multi-step AI processing pipeline. Including this additional data in your GenAI prompts results in timely, more accurate, domain-specific LLM responses with fewer hallucinations. 

Considerations when selecting a vector store  for LangChain

Because LangChain aims to be an “everything framework” for AI apps, it supports a number of different vector stores. However, not all vector databases are created equal. When choosing one, we recommend keeping the following factors in mind. 

Ease of use 

One consideration for a vector store is how easy it is to store and retrieve data, particularly for application developers who may be new to the technology.  Do users have to understand the underlying storage model in detail to load data? Or does it provide an easy API to initialize? Similarly, is it easy to turn a user query into a vector embedding you can use to perform lookups? 

Performance

“Performance” can mean a few different things for a vector store:

Throughput - GenAI use cases require access to large volumes of recent and relative data to ensure accuracy. Make sure to select a vector store with rapid data ingestion and indexing as measured by industry benchmarks.

Query execution time - A typical interactive online application should respond quickly to feel seamless and to keep a user engaged. For chatbots - a popular GenAI use case - the pipeline should respond within the same amount of time a user feels it would take a human operator to respond. When adding a vector store to your GenAI app stack, choose one that provides the lowest latency and fastest query execution time for your use case.

Accuracy and relevancy - Data isn’t any good if it’s the wrong data. Make sure to use a metric such as an F1-Score to gauge the accuracy of responses for your vector database queries. 

System reliability 

Finally, consider system reliability. Adding a vector store to your app means adding yet another architectural component you have to ensure has high reliability and can scale to meet demand. 

The easiest way to address reliability concerns is by using a serverless vector store. A serverless database is fully operated and managed by a third-party provider and scales automatically to meet your storage and interactive user requirements. 

Vector stores that integrate with LangChain 

Which vector store you use with LangChain will highly depend on your requirements. At DataStax, we’ve put a lot of effort into creating solutions that enable GenAI app developers to add RAG functionality with minimal effort. Here are two ways to take advantage of it. 

Apache Cassandra 

Apache Cassandra® is a popular NoSQL distributed database system used by companies such as Uber, Netflix, and Priceline. Using its custom query language (CQL, a variant of SQL), developers can access large volumes of data reliably and with industry-leading query performance. 

Cassandra Version 5.0 incorporates work done by our team at DataStax to add support for approximate nearest neighbor vector search and Storage-Attached Indexes, bringing the power of vector storage to all Cassandra users. LangChain users can integrate Cassandra easily into their GenAI pipelines using the LangChain Cassandra provider.

Astra DB

The easiest way to add a vector store to your application is to leverage a serverless provider. That’s where DataStax comes in. 

Astra DB is a zero-friction drop-in replacement for Cassandra made available as a fully managed service. Astra DB provides petabyte scalability along with fast query response times, low latency, and strong security and compliance. It’s also affordable - up to 5x cheaper than hosting a Cassandra cluster yourself. 

You can add Astra DB easily to both LangChain Python and LangChain JS applications. DataStax makes this integration even easier with Langflow, a no-code Integrated Developer Environment (IDE) you can use to assemble and configure your LangChain GenAI apps visually. 

Conclusion

Using a vector store with LangChain eliminates a lot of the heavy lifting involved in creating a highly reliable, high-performance, and accurate GenAI application. DataStax reduces that overhead even further by providing a full-stack AI platform to bring your apps quickly from prototype to production.

Want to try it for yourself? Sign up for a free DataStax account today.