LangChain Blog (Page 19)

Sharing LangSmith Benchmarks

The single biggest pain point we hear from developers taking their apps into production is around testing and evaluation. This difficulty is felt more acutely

9 min read

Introducing Dream – an AI no-code tool to build fully functional web apps and components with natural language

I’m Calix, and I’m currently a sophomore at USC Iovine & Young Academy. For my hacker residency at LangChain, I continued working on

6 min read

Introducing Tuna - A Tool for Rapidly Generating Synthetic Fine-Tuning Datasets

Editor's Note: This post was written by Andrew Kean Gao through LangChain's Student Hacker in Residence Program. Brief Overview Tuna is

16 min read

Applying OpenAI's RAG Strategies

Context At their demo day, Open AI reported a series of RAG experiments for a customer that they worked with. While evaluation metics will depend

4 min read

"Research Assistant": Exploring UXs Besides Chat

TLDR: We’re excited to announce a new LangChain template for helping with research, heavily inspired by and in collaboration with the GPT Researcher team.

6 min read

LangChain Expands Collaboration with Microsoft

Today, we’re thrilled to announce a collaboration between LangChain and Microsoft. LangChain helps developers build context-aware reasoning applications and powers some of the most

Case Studies 2 min read

Query Construction

Key Links * Text-to-metadata: Updated self-query docs and template * Text-to-SQL+semantic: Cookbook and template There's great interest in seamlessly connecting natural language with diverse

7 min read

Morningstar Intelligence Engine puts personalized investment insights at analysts' fingertips

Challenge Financial services is one of the most data-driven industries and financial professionals are always hungry for more data and better tools to drive value

Case Studies 2 min read

Parallel Function Calling for Structured Data Extraction

Important Links: * Cookbook for extraction using parallel function calling One of the biggest use cases for language models that we see is in extraction. This

4 min read

♠️ SPADE: Automatically Digging up Evals based on Prompt Refinements

Written by Shreya Shankar (UC Berkeley) in collaboration with Haotian Li (HKUST), Will Fu-Hinthorn (LangChain), Harrison Chase (LangChain), J.D. Zamfirescu-Pereira (UC Berkeley), Yiming Lin

6 min read

Implementing advanced RAG strategies with Neo4j

Editor's note: We're excited to share this blogpost as it covers several of the advanced retrieval strategies we introduced in the

7 min read

Embeddings Drive the Quality of RAG: Voyage AI in Chat LangChain

Editor's Note: This post was written by the Voyage AI team. This post demonstrates that the choice of embedding models significantly impacts the

6 min read