langchain-core-workflow-a

# LangChain Core Workflow A: Chains & Prompts ## Overview Build production-ready chains using LangChain Expression Language (LCEL) with prompt templates, output parsers, and composition patterns. ## Prerequisites - Completed `langchain-install-auth` setup - Understanding of prompt engineering basics - Familiarity with Python type hints ## Instructions ### Step 1: Create Prompt Templates ```python from langchain_core.prompts import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder ) # Simple template simple_prompt = ChatPromptTemplate.from_template( "Translate '{text}' to {language}" ) # Chat-style template chat_prompt = ChatPromptTemplate.from_messages([ SystemMessagePromptTemplate.from_template( "You are a {role}. Respond in {style} style." ), MessagesPlaceholder(variable_name="history", optional=True), HumanMessagePromptTemplate.from_template("{input}") ]) ``` ### Step 2: Build LCEL Chains ```python from langchain_openai import ChatOpenAI from langchain_core.output_parsers import StrOutputParser, JsonOutputParser llm = ChatOpenAI(model="gpt-4o-mini") # Basic chain: prompt -> llm -> parser basic_chain = simple_prompt | llm | StrOutputParser() # Invoke the chain result = basic_chain.invoke({ "text": "Hello, world!", "language": "Spanish" }) print(result) # "Hola, mundo!" ``` ### Step 3: Chain Composition ```python from langchain_core.runnables import RunnablePassthrough, RunnableParallel # Sequential chain chain1 = prompt1 | llm | StrOutputParser() chain2 = prompt2 | llm | StrOutputParser() sequential = chain1 | (lambda x: {"summary": x}) | chain2 # Parallel execution parallel = RunnableParallel( summary=prompt1 | llm | StrOutputParser(), keywords=prompt2 | llm | StrOutputParser(), sentiment=prompt3 | llm | StrOutputParser() ) results = parallel.invoke({"text": "Your input text"}) # Returns: {"summary": "...", "keywords": "...", "sentiment": "..."} ``` ### Step 4: Branching Logic ```python from langchain_core.runnables import RunnableBranch # Conditional branching branch = RunnableBranch( (lambda x: x["type"] == "question", question_chain), (lambda x: x["type"] == "command", command_chain), default_chain # Fallback ) result = branch.invoke({"type": "question", "input": "What is AI?"}) ``` ## Output - Reusable prompt templates with variable substitution - Type-safe LCEL chains with clear data flow - Composable chain patterns (sequential, parallel, branching) - Consistent output parsing ## Examples ### Multi-Step Processing Chain ```python from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser llm = ChatOpenAI(model="gpt-4o-mini") # Step 1: Extract key points extract_prompt = ChatPromptTemplate.from_template( "Extract 3 key points from: {text}" ) # Step 2: Summarize summarize_prompt = ChatPromptTemplate.from_template( "Create a one-sentence summary from these points: {points}" ) # Compose the chain chain = ( {"points": extract_prompt | llm | StrOutputParser()} | summarize_prompt | llm | StrOutputParser() ) summary = chain.invoke({"text": "Long article text here..."}) ``` ### With Context Injection ```python from langchain_core.runnables import RunnablePassthrough def get_context(input_dict): """Fetch relevant context from database.""" return f"Context for: {input_dict['query']}" chain = ( RunnablePassthrough.assign(context=get_context) | prompt | llm | StrOutputParser() ) result = chain.invoke({"query": "user question"}) ``` ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | Missing Variable | Template variable not provided | Check input dict keys match template | | Type Error | Wrong input type | Ensure inputs match expected schema | | Parse Error | Output doesn't match parser | Use more specific prompts or fallback | ## Resources - [LCEL Conceptual Guide](https://python.langchain.com/docs/concepts/lcel/) - [Prompt Templates](https://python.langchain.com/docs/concepts/prompt_templates/) - [Runnables](https://python.langchain.com/docs/concepts/runnables/) ## Next Steps Proceed to `langchain-core-workflow-b` for agents and tools workflow.