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

Next Steps

Proceed to langchain-core-workflow-b for agents and tools workflow.