Office Hours 5: LangChain/LangSmith

This Q&A session on LangChain/LangSmith covers topics like product differentiation, features, use cases, agent workflows, data set creation, and full-stack development for ML engineers. .

Christian Mills


July 6, 2024

This post is part of the following series:
  • Mastering LLMs Course Notes: My notes from the course Mastering LLMs: A Conference For Developers & Data Scientists by Hamel Husain and Dan Becker.

LangSmith’s Position in the Observability Market

  • Question: How does LangSmith differentiate itself from other observability tools in the market?
  • Answer:
    • LLM Application Focus: LangSmith is specifically designed for LLM applications, offering specialized features like message and document visualization for debugging.
    • Chains of LLM Calls: It emphasizes visualizing and analyzing entire chains of LLM calls and retrieval steps, which is crucial for complex applications.
    • Human-in-the-Loop Features: LangSmith prioritizes human interaction with features like:
      • Data visualization
      • Annotation queues for collaboration with subject matter experts
      • Side-by-side comparisons for evaluating improvements
      • Alignment of evaluators with human preferences
      • How-to guides: Human feedback
    • Pairwise Evaluation: LangSmith enables pairwise evaluation of models, leading to more stable results.
    • Strong Support and Openness: LangSmith is praised for its excellent support, responsive team, and open APIs that allow integration with other tools.

LangSmith’s Support for Human Annotation and Action Items

  • Question: What support does LangSmith offer for human annotation, annotation queues, and taking action on user feedback?
  • Answer:
    • Annotation Queues:
      • Data can be sent to annotation queues programmatically (e.g., based on user thumbs down) or manually.
      • Annotators can provide feedback, edit outputs, and add corrected examples to datasets.
      • Guide: Use annotation queues
    • Datasets for Improvement: Corrected examples in datasets can be used for testing and future model improvement.
    • Few-Shot Learning: LangSmith aims to be a platform for gathering few-shot example datasets, which can be used for personalization by pulling down the most similar examples during runtime.

Understanding the LangChain “Lang” Namespace

  • Question: What’s the difference between Langchain, Langsmith, Langgraph, Langflow, and Langserve?
  • Answer:
    • LangChain: The foundational open-source package for building LLM apps, offering a runtime, abstractions, integrations, and off-the-shelf chains.
    • LangFlow (Not Langchain Company): A low-code/no-code UI built on top of LangChain.
    • LangServe: Simplifies deploying LangChain applications by exposing them as FastAPI endpoints.
    • LangGraph: An extension of LangChain specifically designed for building and managing highly controllable agent-based workflows.
    • LangSmith: A standalone observability and testing tool for LLM apps, usable with or without LangChain.

When to Use LangChain vs. LangGraph

  • Question: When would you choose LangChain, and when is LangGraph the better option?
  • Answer:
    • LangChain: Ideal for beginners and for rapidly prototyping simple LLM applications with single LLM calls.
    • LangGraph: Suited for advanced teams building complex, agentic workflows that require:
      • Cyclical agent execution
      • Fine-grained control
      • Built-in persistence
      • Streaming and background modes

Popularity of TypeScript vs. Python in LLM Tools

  • Question: How does the usage of TypeScript APIs compare to Python APIs in LangChain and related tools?
  • Answer:
    • Python Dominates: Python remains more popular overall, possibly due to:
      • A larger community focused on LLM application prototyping.
      • Stronger ecosystem for data engineering tasks related to retrieval.
    • TypeScript for Generative UI: TypeScript is gaining traction, especially for applications involving generative UI, which is more challenging to implement in Python.

Generative UI Explained

  • Question: What is generative UI, and how does it work?
  • Answer:
    • Beyond Simple Chat: Generative UI enables LLMs to return more than text; they send UI components to create richer interfaces.
    • Example: Instead of a list of weather data, an LLM might return a dynamic graph component with zoom and interaction capabilities.
    • Vercel AI SDK Integration: LangChain now integrates with Vercel’s AI SDK for easier development of generative UI experiences.
      • Vercel AI SDK: TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js, and more.

Defining “Agentic” in the Context of LLMs

  • Question: What does “agentic” mean in the context of LLMs, and is it a significant distinction?
  • Answer:
    • LLM in Control: An agentic system is one where the LLM controls the application’s control flow and decision-making process.
    • More Than Function Calling: While related to function calling, agentic systems go further by enabling LLMs to loop, adapt, and make dynamic decisions about the next steps.
    • Implications for Development: This distinction introduces new challenges and considerations in UX design, observability, and testing.

LangChain/LangSmith Features for Agentic Workflows

  • Question: What features in LangChain/LangSmith specifically aid in developing and managing agentic workflows?
  • Answer:
    • LangGraph’s Strengths:
      • Controllability: LangGraph’s low-level design provides a high degree of control, which is essential for managing complex agents.
      • Persistence and Human-in-the-Loop: Built-in persistence and easy access to execution history enable checkpointing, resuming from specific states, and human intervention when needed.
    • LangSmith’s Role: While not agent-specific, LangSmith’s observability features are particularly valuable for debugging and understanding complex, agentic applications.

Multiple LLM Collaboration in Practice

  • Question: Is the idea of using multiple LLMs with different strengths in a single application realistic?
  • Answer:
    • Planning and Execution: A common pattern involves a powerful LLM (e.g., GPT-4) for high-level planning and decision-making, while specialized or more cost-effective models (e.g., specialized code generation models) handle specific tasks.

Building Evaluation Sets with LangSmith

  • Question: What’s the most effective way to use LangSmith for creating evaluation sets?
  • Answer:
    1. Manual Seeding: Begin with a small set (5-10) of manually crafted examples.
    2. Production Feedback Loop: Integrate with production logs to capture real-user interactions and identify edge cases.
    3. Iterative Refinement:
      • Manually add challenging or interesting cases to the dataset.
      • Encourage user feedback and incorporate relevant examples.
      • Consider synthetic data generation to expand the dataset, but prioritize human review and labeling.
  • Concepts: Evaluation
  • How-to guides: Evaluation
  • Tutorial: Evaluate your LLM application