Artificial Intelligence (AI) is transforming every industry and sector, from healthcare to finance, from manufacturing to retail. However, not all AI solutions are created equal. Many of them suffer from limitations such as poor scalability, low accuracy, high latency, and lack of explainability.
That’s why we’re excited to announce the integration of KDB.AI and LangChain, two cutting-edge technologies designed to overcome these challenges and deliver unparalleled capabilities for enterprise AI via a simple and intuitive architecture that doesn’t require complex infrastructure or costly expertise.
In this blog post, I’ll give you a brief overview of each technology, discuss typical use cases, and then show you how to get started. Let’s begin.
What is KDB.AI?
KDB.AI is an enterprise grade vector database and analytics platform that enables real-time processing of both structured and unstructured time-oriented data. It’s based on kdb+, the world’s fastest time-series database, which is widely used by leading financial institutions for high-frequency trading and market data analysis.
With KDB.AI, developers can seamlessly scale from billions to trillions of vectors without performance degradation, thanks to its distributed architecture and efficient compression algorithms. It also supports various data formats, such as text, images, audio, video, and more.
With KDB.AI you can:
- Create an index of vectors (Flat, IVF, IVFPQ, or HNSW).
- Append vectors to an index.
- Perform fast vector similarity search with optional metadata filtering.
- Persist an index to disk.
- Load an index from disk.
To learn more about KDB.AI, visit our documentation site.
What is LangChain?
LangChain is an open-source framework designed to simplify the creation of applications powered by language models. At its core, LangChain enables you to “chain” together components, acting as the building blocks for natural language applications such as Chatbots, Virtual Agents and document summarization.
LangChain doesn’t rely on traditional NLP pipelines, such as tokenization, lemmatization, or dependency parsing, instead, it uses vector representations of natural language, such as word embeddings, sentence embeddings, or document embeddings, which capture the semantic and syntactic information of natural language in a compact and universal way.
To learn more about LangChain, visit their documentation site.
How KDB.AI and LangChain work together
The integration of KDB.AI and LangChain empowers developers with real-time vector processing capability and state-of-the-art NLP models. This combination opens new possibilities and use cases for enterprise AI, such as:
- Enterprise search: You can use LangChain to encode text documents into vectors, and then use KDB.AI to index and query them using advanced quad-search capabilities, combining keyword, fuzzy, semantic, and time-based search. This way, you can create a powerful and flexible enterprise search capability that can handle any type of query and return the most relevant results.
- RAG at scale: You can use LangChain to implement Retrieval Augmented Generation (RAG), a novel technique that combines a retriever and generator to produce rich and diverse text outputs. You can then use KDB.AI to store and retrieve the vectors of the documents that are used by the retriever, enabling you to scale RAG to large and complex domains and applications.
- Anomaly detection: You can use LangChain to detect anomalies in text data, such as spam, fraud, or cyberattacks, using pre-trained or fine-tuned models. You can then use KDB.AI to store and analyze the vectors of the anomalous texts, using clustering, classification, or regression techniques, to identify the root causes and patterns.
- Sentiment Analysis: You can use LangChain to perform sentiment analysis on text data, such as customer reviews, social media posts, or news articles, using pre-trained or fine-tuned models. You can then use KDB.AI to store and visualize the vectors of the texts, using dashboarding, charting, or reporting tools, to gain insights into the opinions and emotions of customers, users, or audiences.
- Text summarization: You can use LangChain to generate concise and informative summaries of long text documents, such as reports, articles, or books, using pre-trained or fine-tuned models. You can then use KDB.AI to store and compare the vectors of the original and summarized texts, using similarity or distance metrics, to evaluate the quality and accuracy of the summaries.
How to get started with KDB.AI and LangChain
If you’re interested in trying out KDB.AI on LangChain, I invite you to follow these simple steps.
- Sign up for a free trial of KDB.AI.
- Set up your environment and configure pre-requisites.
- Work through the sample integration.
We also have some great resources from our evangelism team, including samples over on the KDB.AI learning hub and regular livestreams. And should you have any feedback, questions, or issues a dedicated team over on our Slack community.