LangChain

LANGCHAIN COMPONENT HIERARCHY: ┌─────────────────────────────────────────────────┐ │ APPLICATION │ │ ┌───────────────────────────────────────────┐ │ │ │ AGENTS / CHAINS │ │ │ │ Orchestrate LLM calls, tools, retrieval │ │ │ └─────────────────┬─────────────────────────┘ │ │ │ │ │ ┌───────────────┼───────────────┐ │ │ ▼ ▼ ▼ │ │ ┌──────┐ ┌──────┐ ┌──────────┐ │ │ │ LLMs │ │Tools │ │Retrievers│ │ │ └──────┘ └──────┘ └──────────┘ │ │ │ │ │ ┌───────────────┼───────────────┐ │ │ ▼ ▼ ▼ │ │ ┌──────┐ ┌──────┐ ┌──────────┐ │ │ │Memory│ │Prompts│ │VectorStore│ │ │ └──────┘ └──────┘ └──────────┘ │ └─────────────────────────────────────────────────┘ KEY COMPONENTS: 1. MODELS - LLM wrappers (OpenAI, Anthropic, local) - Chat models vs completion models - Embedding models 2. PROMPTS - Templates with variables - Few-shot examples - Output parsers 3. CHAINS - Sequential: A → B → C - Conditional: if X then A else B - Parallel: A and B simultaneously 4. AGENTS - ReAct pattern: Reason → Act → Observe - Tool selection and execution - Multi-step planning 5. MEMORY - Conversation buffer - Summary memory - Entity memory 6. RETRIEVAL - Document loaders - Text splitters - Vector stores - Retrievers

LANGCHAIN EXAMPLES: 1. SIMPLE CHAIN: from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate # Create components llm = ChatOpenAI(model="gpt-4") prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant."), ("user", "{question}") ]) # Create chain using LCEL (LangChain Expression Language) chain = prompt | llm # Invoke response = chain.invoke({"question": "What is RAG?"}) --- 2. RAG APPLICATION: from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores import Chroma from langchain_core.runnables import RunnablePassthrough # Setup retriever embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(docs, embeddings) retriever = vectorstore.as_retriever(k=3) # RAG chain rag_prompt = ChatPromptTemplate.from_template(""" Answer based on context: {context} Question: {question} """) def format_docs(docs): return "\n\n".join(d.page_content for d in docs) rag_chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | rag_prompt | llm ) answer = rag_chain.invoke("What is our refund policy?") --- 3. AGENT WITH TOOLS: from langchain.agents import create_react_agent, AgentExecutor from langchain.tools import tool @tool def search_web(query: str) -> str: """Search the web for current information.""" # Implementation return search_results @tool def calculate(expression: str) -> str: """Evaluate a math expression.""" return str(eval(expression)) tools = [search_web, calculate] agent = create_react_agent(llm, tools, prompt) executor = AgentExecutor(agent=agent, tools=tools) result = executor.invoke({ "input": "What's the population of Tokyo times 2?" }) # Agent: searches for population, then calculates --- LCEL (LangChain Expression Language): # Pipe syntax for chaining chain = prompt | llm | output_parser # Parallel execution chain = { "summary": summarize_chain, "keywords": keyword_chain } | combine_results # Conditional routing chain = RunnableBranch( (lambda x: "code" in x, code_chain), (lambda x: "math" in x, math_chain), default_chain )