Announcing LangChain Templates

A collection of easily deployable reference architectures for a wide variety of tasks so you can get going fast.

• Learn more about LangChain Templates: video walkthrough, blog post, webinar recording.
• Explore the whole collection of templates here.
• Want to deploy templates with LangServe? Sign up for early access to Hosted LangServe.
• Check out more templates (and request ones you’d like to see) on our LangChain Templates community board.

Favorite LangChain Templates

• Private RAG: QA over your documents in private without any external API
• Text-to-SQL (via API or private): natural language to SQL query
• QA over semi-structured data: QA on documents with text and tables
• OpenAI function calling: Unstructured text to a desired output schema (e.g., JSON)
• Content moderation: Using Guardrails for chat filtering / monitoring
• Check out more templates (and request ones you’d like to see) on our LangChain Templates community board.

Data Annotation Queues

Newest feature in LangSmith–our SaaS platform for managing your LangChain applications.

• Easily move datapoints from your production logs into a data annotation queue so you (and your teammates) can inspect your data and build up intuition for where the chain is not performing well.
• Sign up for beta access to LangSmith.

In case you missed it

• DeepLearning AI Short Course: on Functions, Tools and Agents with LangChain. Covers the basics of OpenAI function calling and using it to do tagging, extraction, tool selection, and we even build up to a conversational agent!
• Scrimba courses: An interactive course on the latest in LangChainJS. Remix and run code mid-learning.
• We celebrated LangChain’s 1st Birthday with a big thank you to our community in this blog post.
• Reddit AMA: read through the community’s questions and Harrison’s, LangChain CEO and cofounders, replies
• Webinar Recordings
  • LangServe and LangChain Templates Webinar: over the past month we've released some big features around LangServe like Initial LangServe release (APIs), LangServe playground, and LangChain Templates (deploy w/ LangServe). We talked through them all!
  • Build Apps with the new GenAI Stack from Docker, LangChain, Ollama, and Neo4j
  • Path to Production: Skypoint's Journey with LangChain and Astra DB
• Blog Posts
  • Embeddings Drive the Quality of RAG: Voyage AI in Chat LangChain by the Voyage AI team
  • Query Transformations: a walkthrough of approaches to transform human questions in order to improve retrieval
  • Beyond Text: Making GenAI Applications Accessible to All: by Andres Torres and Dylan Brock from Norwegian Cruise Line.
  • Constructing knowledge graphs from text using OpenAI functions: Leveraging knowledge graphs to power LangChain Applications by Tomaz Bratanic.
  • LangChain + Robocorp Case Study: Robocorp is all in on code with their AI-powered developer assistant, Remark. Anyone who can speak natural language can use ReMark to translate ideas into Python bots.  Customers are building automations 4x faster and their team has saved hundreds of hours on support requests.
  • Multi-Vector Retriever for RAG on tables, text, and images: Seamless question-answering across diverse data types (images, text, tables) is one of the holy grails of RAG. A guide of 3 new cookbooks that will help you get there.
• From the Community
  • Step-Back Prompting: How to Make LLM Chatbots Smarter Than Ever: A Walkthrough for the Implementation of Step-back Prompting in LangChain by Yeyu Huang
  • How to implement Weaviate RAG applications with Local LLMs and Embedding models: Develop RAG applications and don’t share your private data with anyone by Tomaz Bratanic
  • Evaluate LLMs and RAG a practical example using LangChain and Hugging Face by Hugging Face
  • Chatting with your data overview: a 20 min guide by  Prince Krampah, repo here
  • Create a full-stack semantic search web app with custom documents by Dylan Babbs
  • LangChain + RAG Overview: a presentation by Sophia Yang
  • Why your next AI product needs RAG implemented in it by Avra
  • Cutting LLM Costs by 83% with LangSmith: by @DataheraldCo
  • The LangChain Implementation Of DeepMind’s Step-Back Prompting: run DeepMind’s Step-Back prompting in a notebook by Corbus Greyling
  • Retrieval Augmented Generation on audio data by Assembly AI
  • Step Back Prompting Tutorial by @1littlecoder
  • multi-vector retriever for handling semi-structured data tutorial by Sudarshan Koirala
  • Text to SQL for RAG apps: A short guide showing how LangChain helps you prompt your SQL database by @gswithai

Coming Soon

• Want to deploy templates with LangServe? Sign up for early access to Hosted LangServe.
• What templates would you like to see? Request them on our LangChain Templates community board and we’ll do our best to create them!

See you in a couple weeks!