LangDB

Great to see Gradient Ventures portfolio companies Gynger, CentML, and LangDB on this list!

We're #hiring a new Site Reliability Engineer (ClickHouse Specialist) - Remote in Greater Bengaluru Area. Apply today or share this post with your network.

Text2SQL is not enough! Introducing TAG (Table-Augmented Generation) TAG is a method that seamlessly combines the precision of databases with the reasoning power of Large Language Models (LLMs) to answer complex natural language questions. 💡 How Does TAG Work? 📝 Query Synthesis: Converts a natural language question into a database query. ⚙️ Query Execution: Retrieves the relevant data from the database. 💬 Answer Generation: Uses an LM to generate a comprehensive, context-aware answer. TAG is perfect for tackling questions that require both detailed data and sophisticated reasoning, making it an essential tool for advanced data-driven tasks. Why TAG is Effective ? 🎯 Precision: TAG accurately identifies the key factors affecting customer satisfaction by pulling and analyzing the most relevant data. 🧠 Contextual Insight: The Language Model adds value by interpreting customer feedback and generating a clear, actionable summary. Full paper here: https://lnkd.in/gJtCAaU6 #AI #NLP #LLM #RAG #DataScience #Innovation #Databases

We're #hiring a new Site Reliability Engineer (ClickHouse Specialist) - Remote, USA in San Francisco Bay Area. Apply today or share this post with your network.

We're #hiring a new Sales Manager/Director in Bengaluru, Karnataka. Apply today or share this post with your network.

At LangDB, we're pioneering a native #GenAI data platform, and after exploring various options, we found that nothing could meet our needs better than #ClickHouse. When developing #RAG applications, it's crucial to have: ✔️ Efficient vector and analytical query capabilities ⏱️ Real-time support 🔗 Seamless connectivity with upstream data sources No other database offers all of these features in one package! That’s why LangDB is all-in on #ClickHouse.

#RAG applications often leads to complex execution flows, making it difficult to track and understand the processes involved. When working with diverse data types, agents must frequently interact with the #LLM and underlying databases, executing various queries such as #vector, #analytical, #graph queries. In such scenarios, it is crucial to have the ability to inspect these flows to ensure clear understanding and facilitate debugging when necessary. Check out how LangDB can help with that.

Building #RAG applications often leads to complex execution flows, making it difficult to track and understand the processes involved. When working with diverse data types, agents must frequently interact with the #LLM and underlying databases, executing various queries such as #vector, #analytical, and #graph queries. In such scenarios, it is crucial to have the ability to inspect these flows to ensure clear understanding and facilitate debugging when necessary.

We will be introducing LangDB at the Singapore Data Engineering Meetup! Join us here: https://lnkd.in/gBWm-7V6

Extracting embeddings to be used in a GenAI application should be handled in the database itself. Here is how LangDB can handle it: ➨ An embedding model (using an arbitrary embedding provider) is created ➨ A periodic job running every minute is setup. The job automatically detects all newly inserted PDF files, extract embeddings and updates the embeddings table. All in less than 20 lines of code! #genai #langdb #llm