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Week13/Day1/simple_lcel.py

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+# LangChain Chain Types Tutorial
+# This tutorial demonstrates Simple Chain, Sequential Chain, and Simple Sequential Chain
+
+import os
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain.chains import LLMChain, SequentialChain, SimpleSequentialChain
+
+# Set your OpenAI API key
+# os.environ["OPENAI_API_KEY"] = "your-api-key-here"
+
+# Initialize the LLM
+llm = ChatOpenAI(
+    model="gpt-3.5-turbo",
+    temperature=0.7,
+    max_tokens=500
+)
+
+print("🚀 LangChain Chain Types Tutorial")
+print("=" * 50)
+
+# ============================================================================
+# 1. SIMPLE CHAIN (Single Input/Output)
+# ============================================================================
+print("\n1️⃣ SIMPLE CHAIN")
+print("-" * 20)
+
+# Create a prompt template for generating a story
+story_prompt = ChatPromptTemplate.from_template(
+    "Write a short creative story (2-3 sentences) about {topic}. Make it interesting and engaging."
+)
+
+# Create a simple chain: Prompt → LLM → Output Parser
+simple_chain = story_prompt | llm 
+
+
+# Create a simple LLMChain
+simple_chain = LLMChain(
+    llm=llm,
+    prompt=story_prompt,
+)
+
+# Execute the simple chain
+topic = "a robot learning to paint"
+story_result = simple_chain.invoke({"topic": topic})
+print(f"Topic: {topic}")
+print(f"Generated Story: {story_result}")
+
+# ============================================================================
+# 2. SIMPLE SEQUENTIAL CHAIN (Output of Chain 1 → Input of Chain 2)
+# ============================================================================
+print("\n2️⃣ SIMPLE SEQUENTIAL CHAIN")
+print("-" * 25)
+
+# Chain 1: Generate a business idea
+idea_prompt = ChatPromptTemplate.from_template(
+    "Generate a creative business idea for the industry: {industry}. "
+    "Provide just the business idea in one sentence."
+)
+idea_chain = idea_prompt | llm | StrOutputParser()
+
+# Chain 2: Create a marketing slogan for the business idea
+slogan_prompt = ChatPromptTemplate.from_template(
+    "Create a catchy marketing slogan for this business idea: {business_idea}. "
+    "Make it memorable and under 10 words."
+)
+slogan_chain = slogan_prompt | llm | StrOutputParser()
+
+# Create Simple Sequential Chain
+# Output from idea_chain automatically becomes input for slogan_chain
+simple_sequential_chain = SimpleSequentialChain(
+    chains=[idea_chain, slogan_chain],
+    verbose=True  # Shows intermediate outputs
+)
+
+# Execute the chain
+industry = "sustainable technology"
+final_result = simple_sequential_chain.invoke({"input": industry})
+print(f"Industry: {industry}")
+print(f"Final Marketing Slogan: {final_result['output']}")
+
+# ============================================================================
+# 3. SEQUENTIAL CHAIN (Multiple Inputs/Outputs with Named Variables)
+# ============================================================================
+print("\n3️⃣ SEQUENTIAL CHAIN")
+print("-" * 18)
+
+# Chain 1: Analyze a product concept
+analysis_prompt = PromptTemplate(
+    input_variables=["product_name", "target_market"],
+    template="""
+    Analyze this product concept:
+    Product: {product_name}
+    Target Market: {target_market}
+
+    Provide a brief market analysis (2-3 sentences).
+    """
+)
+analysis_chain = LLMChain(
+    llm=llm,
+    prompt=analysis_prompt,
+    output_key="market_analysis"  # Named output
+)
+
+# Chain 2: Generate pricing strategy
+pricing_prompt = PromptTemplate(
+    input_variables=["product_name", "market_analysis"],
+    template="""
+    Based on this market analysis: {market_analysis}
+
+    Suggest a pricing strategy for {product_name}.
+    Include price range and reasoning (2-3 sentences).
+    """
+)
+pricing_chain = LLMChain(
+    llm=llm,
+    prompt=pricing_prompt,
+    output_key="pricing_strategy"  # Named output
+)
+
+# Chain 3: Create final business plan summary
+business_plan_prompt = PromptTemplate(
+    input_variables=["product_name", "target_market", "market_analysis", "pricing_strategy"],
+    template="""
+    Create a concise business plan summary using:
+
+    Product: {product_name}
+    Target Market: {target_market}
+    Market Analysis: {market_analysis}
+    Pricing Strategy: {pricing_strategy}
+
+    Summarize in 3-4 sentences focusing on key opportunities.
+    """
+)
+business_plan_chain = LLMChain(
+    llm=llm,
+    prompt=business_plan_prompt,
+    output_key="business_plan"  # Named output
+)
+
+# Create Sequential Chain with multiple named inputs/outputs
+sequential_chain = SequentialChain(
+    chains=[analysis_chain, pricing_chain, business_plan_chain],
+    input_variables=["product_name", "target_market"],  # Initial inputs
+    output_variables=["market_analysis", "pricing_strategy", "business_plan"],  # All outputs
+    verbose=True  # Shows all intermediate steps
+)
+
+# Execute the sequential chain
+inputs = {
+    "product_name": "Smart Fitness Mirror",
+    "target_market": "health-conscious millennials"
+}
+
+sequential_result = sequential_chain.invoke(inputs)
+
+print("📊 SEQUENTIAL CHAIN RESULTS:")
+print(f"Product: {inputs['product_name']}")
+print(f"Target Market: {inputs['target_market']}")
+print(f"\n📈 Market Analysis:\n{sequential_result['market_analysis']}")
+print(f"\n💰 Pricing Strategy:\n{sequential_result['pricing_strategy']}")
+print(f"\n📋 Business Plan:\n{sequential_result['business_plan']}")
+
+# ============================================================================
+# SUMMARY OF DIFFERENCES
+# ============================================================================
+print("\n" + "=" * 60)
+print("📚 SUMMARY OF CHAIN TYPES")
+print("=" * 60)
+
+print("""
+1️⃣ SIMPLE CHAIN:
+   • Single operation: Prompt → LLM → Output
+   • Uses: prompt | llm | parser syntax
+   • Best for: Individual tasks
+
+2️⃣ SIMPLE SEQUENTIAL CHAIN:
+   • Multiple chains in sequence
+   • Output of Chain 1 → Input of Chain 2
+   • Single input/output flow
+   • Best for: Linear workflows
+
+3️⃣ SEQUENTIAL CHAIN:
+   • Multiple chains with named variables
+   • Can handle multiple inputs/outputs
+   • More complex data flow
+   • Best for: Complex multi-step processes
+""")
+
+# ============================================================================
+# PRACTICAL EXAMPLE: Content Creation Pipeline
+# ============================================================================
+print("\n🎯 PRACTICAL EXAMPLE: Blog Post Creation Pipeline")
+print("-" * 50)
+
+# Step 1: Generate blog topics
+topic_generator = ChatPromptTemplate.from_template(
+    "Generate 3 engaging blog post topics about {subject}. List them numbered 1-3."
+) | llm | StrOutputParser()
+
+# Step 2: Pick first topic and create outline
+outline_generator = ChatPromptTemplate.from_template(
+    "From these topics: {topics}\n\nPick the first topic and create a detailed outline with 4 main points."
+) | llm | StrOutputParser()
+
+# Step 3: Write introduction
+intro_writer = ChatPromptTemplate.from_template(
+    "Based on this outline: {outline}\n\nWrite an engaging introduction paragraph (3-4 sentences)."
+) | llm | StrOutputParser()
+
+# Create content pipeline
+content_pipeline = SimpleSequentialChain(
+    chains=[topic_generator, outline_generator, intro_writer],
+    verbose=True
+)
+
+# Execute content creation pipeline
+subject = "artificial intelligence in healthcare"
+content_result = content_pipeline.invoke({"input": subject})
+
+print(f"Subject: {subject}")
+print(f"Final Blog Introduction:\n{content_result['output']}")
+
+print("\n✅ Tutorial Complete! You've learned all three chain types in LangChain.")