XML parser | 🦜️🔗 LangChain
Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed XML. In the following example we use Claude model (https://docs.anthropic.com/claude/docs) which works really well with XML tags. Let's start with the simple request to the model. Now we will use the XMLOutputParser in order to get the structured output. Finally, let's add some tags to tailor the output to our needs. Find out api documentation for XMLOutputParser.
LangChain overview - Docs by LangChain Documentation Index Fetch the complete documentation index at: /llms.txt Use this file to discover all available pages before exploring further. Skip to main content Agent = Model + Harness. LangChain provides create_agent : a minimal, highly configurable harness. The harness is everything around the model loop: the prompt, the tools, and any middleware that shapes behavior. Start with the primitives and compose exactly what your use case needs. Supports OpenAI, Anthropic, Google, and more . LangChain vs. LangGraph vs. Deep Agents Start with Deep Agents f
related reading
- LangChain: the open agent platform to own your intelligencelangchain.com
- Building Effective AI Agents \ Anthropicanthropic.com
- LangChain Tutorial in Python - Crash Course - Python Engineerpython-engineer.com
- LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
- The Problem With LangChain | Max Woolf's Blogminimaxir.com
- Building Effective AI Agents \ Anthropicanthropic.com
- RAG with Deep Agents - Docs by LangChainpython.langchain.com
- Best practices for Claude Code - Claude Code Docsanthropic.com
- Re-implementing LangChain in 100 lines of codeblog.scottlogic.com
- GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4.github.com
- Building A RAG Pipeline for Semi-structured Data with Langchain - Analytics Vidhyaanalyticsvidhya.com
- What are agents? 🤔 · Hugging Facehuggingface.co