LangChain Blog (Page 16)

♠️ 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

LangChain Templates

Today we're excited to announce the release of LangChain Templates. LangChain Templates offers a collection of easily deployable reference architectures that anyone can

6 min read

Announcing Data Annotation Queues

💡Data Annotation Queues are a new feature in LangSmith, our developer platform aimed at helping bring LLM applications from prototype to production. Sign up for

4 min read

Query Transformations

Naive RAG typically splits documents into chunks, embeds them, and retrieves chunks with high semantic similarity to a user question. But, this present a few

4 min read

LangChain's First Birthday

It’s LangChain’s first birthday! It’s been a really exciting year! We worked with thousands of developers building LLM applications and tooling. We

By LangChain 15 min read

Beyond Text: Making GenAI Applications Accessible to All

Editor's Note: This post was written by Andres Torres and Dylan Brock from Norwegian Cruise Line. Building UI/UX for AI applications is

8 min read

Robocorp’s code generation assistant makes building Python automation easy for developers

Challenge Robocorp was founded in 2019 out of frustration that the promise of developers being able to automate monotonous work hadn’t been realized. Right

Case Studies 2 min read

Multi-Vector Retriever for RAG on tables, text, and images

Summary Seamless question-answering across diverse data types (images, text, tables) is one of the holy grails of RAG. We’re releasing three new cookbooks that

5 min read

LangServe Playground and Configurability

Last week we launched LangServe, a way to easily deploy chains and agents in a production-ready manner. Specifically, it takes a chain and easily spins

3 min read

Constructing knowledge graphs from text using OpenAI functions: Leveraging knowledge graphs to power LangChain Applications

Editor's Note: This post was written by Tomaz Bratanic from the Neo4j team. Extracting structured information from unstructured data like text has been

10 min read