How to Batch LangChain Requests in Python
Learn how to run multiple LangChain model requests with batch and abatch in Python, compare them with invoke, and control concurrency for practical AI apps.
LangChain
Master the creation of intelligent agents with LangChain. Explore tutorials, best practices, and practical guides for building data-driven applications powered by large language models.
15 posts
How to Cache LangChain Responses in Python
Learn how to cache LangChain chat model responses in Python with InMemoryCache and SQLiteCache so repeated prompts can return faster and avoid unnecessary model calls.
How to Stream LangChain Responses in Python
Learn how to stream LangChain model responses in Python so users can see output chunks as they are generated instead of waiting for the full answer.
LangChain Tools Explained for Beginners: Add Function Calling to Your AI Agent
Learn how LangChain tools let AI agents call Python functions, use structured inputs, and go beyond plain text generation.
LangChain Vector Stores Explained for Beginners: Store and Search Embeddings for RAG
Learn how LangChain vector stores keep embeddings searchable for RAG, semantic search, document retrieval, and beginner-friendly AI applications.
LangChain Embeddings Explained for Beginners: Turn Text into Vectors for RAG
Learn how LangChain embeddings turn text into vectors for RAG, semantic search, document retrieval, clustering, and similarity workflows.
LangChain Text Splitters Explained for Beginners: Split Documents for RAG
Learn how LangChain text splitters break long documents into useful chunks for RAG, semantic search, summarization, and retrieval workflows.
How to Build a Simple LCEL Chain in LangChain with Python
Learn how to build a simple LangChain LCEL chain with Python using ChatPromptTemplate, ChatOpenAI, and StrOutputParser in a beginner-friendly tutorial.
How to Build a RAG Chatbot with LangChain, Python, and FAISS
Build a beginner-friendly RAG chatbot with LangChain, Python, FAISS, and OpenAI by loading local text, creating embeddings, retrieving context, and answering questions from your own data.