LangChain - LangChain Blog (Page 7)

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

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