LangChain code samples from multiple contributors, covering fundamentals through advanced RAG and agent patterns.
conda env create -f environment.yml
conda activate langchainlangchain Cookbook Part 1 - Fundamentals_gkamradt.ipynb— chains, prompts, models, output parserslangchain Cookbook Part 2 - Use Cases_gkamradt.ipynb— QA, summarization, extraction, evaluation
Notebooks langchain_101-1 through langchain_103 build a streaming document-search chat UI step by step.
Scripts prefixed langchain_v1_ use the current LangChain v0.2+ API (LCEL, RunnablePassthrough, etc.):
langchain_v1_lcel.py— LangChain Expression Language chainslangchain_v1_models.py— LLM and chat model wrapperslangchain_v1_memory.py— conversation memorylangchain_v1_agent.py/langchain_v1_createagent.py— ReAct agent creation
langchain_Advanced_RAG_lucifertrj.ipynb— hybrid search, rerankinglangchain_Embedchain_RAG_lucifertrj.ipynb— Embedchain-based RAGlangchain_Agents_SQL_Database_Agent_Nichite.ipynb— SQL agentlangchain_dair-ai-prompt_engg-lecture.ipynb— prompt engineering patterns
Files named langchain_v1_* use the current LCEL-based API. Older notebooks (colinmcnamara series) use the legacy LLMChain pattern — they still run but the API is deprecated.