- Includes: Input/output schema, /docs endpoint, invoke/batch/stream endpoints, /stream_log endpoint for streaming intermediate steps, LangSmith integration
- LangServe Playground & Configurability: The playground is designed to be an easy-to-use UI that you can easily share with your team members to let them best interact with your LangChains. Also built on core LangChain code so you can easily configure fields and alternatives (docs here).
- Improvements to LangChain Expression Language: LangServe is made possible by improvements to LangChain Expression Language, our new syntax for writing chains. This includes better streaming, input/output schemas, intermediate results and more. Docs here.
- LangServe webinar: 11/2 at 9amPT
New in LangSmith
- Test Run Comparisons: Great AI engineers often manually inspect data to gain deep insights about problems. Score runs and compare with past runs in a straightforward UI showing inputs, reference/actual outputs, metrics, & more.
- Semi-structured RAG Cookbook: apply multi-vector retriever best practices to text and tables parsed from pdfs using @UnstructuredIO
- Semi-structured and Multi-modal RAG with LLaVA and LLaMA2 Cookbook: Multi-modal LLMs unlock RAG on images. Local RAG stack (M2 max 32gb) with a bunch of OSS models!
In case you missed it
- Work with a LangChain Partner to accelerate your LLM app development. Shoutout to BCG, DeepsenseAI, Datatonic, and Rubric Labs for being such fantastic early partners. If you want to become a LangChain partner, say hello and tell us about the LLM apps you’ve been working on.
- LangChain’s DeepLearningAI course is now on Coursera!
- Webinar Recordings
- Theory of Mind Webinar with Plastic Labs: a deep-dive on memory, a really interesting but under-explored topic in LLM apps
- Cognitive Architectures for Language Models: Attempts like the COALA paper to categorize and breakdown complex architectures will help make it more feasible to collaborate on complex agent systems. We go deep with industry experts.
- Data Privacy for LLM applications: with @deepsense_ai and @opaquesys
- LangChain’s Jacob Lee talks at GoogleAI WebSummit: Local LLMs are incredible, but their current reach is just engineers. How do we change that? The punchline: we need a new browser API. Blog version here.
- Blog Posts
- Finetuning on "Chain of Density"Summarization: @__Charlie_G finetuned GPT 3.5 Turbo on these outputs, and produced a finetuned model that beats raw GPT-4 (and is nearly at same performance as Chain-of-Density GPT-4) at a fraction of the cost and latency
- Integrating with You.com’s Search API: Blog. Notebook.
- How to design an Agent for Production by Rubric Labs: writing about launching cal.com
- Using a Knowledge Graph to implement a DevOps RAG application and Constructing knowledge graphs from text using OpenAI functions from Neo4j
- A new GenAI stack: with Docker, Neo4j, and Ollama.
- A Chunk by Any Other Name: Structured Text Splitting and Metadata-enhanced RAG: by Martin Zirulnik
- From the Community:
- Struggling to get good results with traditional RAG? Mohamed Azharudeen wrote an awesome article on a new retrieval technique, Parent Document Retrievers.
- Harry Zhang’s (updated) diagram: of the LangChain ecosystem
- LangChain 101 Course: from Ivan Reznikov
- Augmenting LLMs with RAG: An end-to-end example of seeing how well an LLM model can answer Amazon SageMaker related questions. A good overview of a lot of different components in LangChain.
- ‘Talk’ to Your SQL Database Using LangChain and Azure OpenAI: by Satwiki De
- Building with LangChain Expression Language: LCEL is a great way to develop sophisticated LLM applications, even for beginners. We’re going to help more people get try it out in creative ways.
- Path to Production Webinar (10/26): with Datastax and Skypoint
- Reddit AMA (10/24) in r/langchain
- LangServe Webinar (11/2): come learn about the recent introduction and features of LangServe, the best way to deploy LangChain apps