LangSmith's Latest Feature: Grouped Monitoring Charts Tag and Metadata Grouping LangSmith has long supported monitoring charts to showcase important performance and feedback metrics overtime for your LLM applications (see the Monitoring
Benchmarking RAG on tables Key links LangChain public benchmark evaluation notebooks: * Long context LLMs here * Chunk size tuning here * Multi vector with ensemble here Motivation Retrieval augmented generation (RAG)
Multi-modal RAG on slide decks Key Links * LangChain public benchmark evaluation notebooks * LangChain template for multi-modal RAG on presentations Motivation Retrieval augmented generation (RAG) is one of the most important
Extraction Benchmarking Test confidence_level_similarity json_edit_distance json_schema off_topic_similarity programming_language_similarity question_category sentiment_similarity toxicity_similarity claude-2-xsd-to-xml-5689 0.97 0.
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
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
A Chunk by Any Other Name: Structured Text Splitting and Metadata-enhanced RAG There's something of a structural irony in the fact that building context-aware LLM applications typically begins with a systematic process of decontextualization, wherein
The Prompt Landscape Context Prompt Engineering can steer LLM behavior without updating the model weights. A variety of prompts for different uses-cases have emerged (e.g., see @dair_
Building Chat LangChain Hosted: https://chat.langchain.com Repo: https://github.com/langchain-ai/chat-langchain Intro LangChain packs the power of large language models and an entire ecosystem of
