Graph-based metadata filtering for improving vector search in RAG applications Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Editor's Note: the following is a guest blog post from Tomaz
Announcing LangSmith is now a transactable offering in the Azure Marketplace Today, we’re thrilled to announce that enterprises can purchase LangSmith in the Azure Marketplace as an Azure Kubernetes Application. LangSmith is a unified DevOps
Empowering Development with FlowTestAI: Bridging APIs and LLMs for Enhanced Testing and Privacy Editor's note: we're excited to highlight this blog from FlowTestAI. FlowTestAI is an exciting startup building on top of LangChain. Specifically,
Tool Calling with LangChain TLDR: We are introducing a new tool_calls attribute on AIMessage. More and more LLM providers are exposing API’s for reliable tool calling. The
Rethinking Our Documentation LangChain has seen some incredible growth in the last year and half. The Python open-source library is now downloaded over 7 million times per month,
LangSmith: Production Monitoring & Automations Key Links: * YouTube Walkthrough * Sign up for LangSmith here If 2023 was a breakthrough year for LLMs, then 2024 is shaping up to be the
LangFriend: a Journal with Long-Term Memory One of the concepts we are most interested in at LangChain is memory. Whenever we are interested in a concept, we like to build an
Open Source Extraction Service Earlier this month we announced our most recent OSS use-case accelerant: a service for extracting structured data from unstructured sources, such as text and PDF
Using Feedback to Improve Your Application: Self Learning GPTs We built and hosted a simple demo app to show how applications can learn and improve from feedback over time. The app is called "
LangChain Integrates NVIDIA NIM for GPU-optimized LLM Inference in RAG Roughly a year and a half ago, OpenAI launched ChatGPT and the generative AI era really kicked off. Since then we’ve seen rapid growth
Enhancing RAG-based application accuracy by constructing and leveraging knowledge graphs A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain Editor's Note: the following is
Benchmarking Query Analysis in High Cardinality Situations Several key use cases for LLMs involve returning data in a structured format. Extraction is one such use case - we recently highlighted this with
