LangChain & RAG Frameworks - Interactive Guide

🎯 Why Learn LangChain & RAG?

🏗️ Build AI Applications

LangChain provides the building blocks for creating sophisticated AI applications that connect LLMs with tools, data, and APIs.

📚 Accurate Responses

RAG (Retrieval-Augmented Generation) grounds AI responses in your actual data, reducing hallucinations and improving accuracy.

🚀 Production Ready

These frameworks handle the complexity of building production AI systems with memory, agents, and tool integration.

🔧 Extensible

Modular architecture allows you to swap components, add custom tools, and integrate with any LLM or data source.

Real-World Applications

💬 Intelligent Chatbots - Context-aware assistants with memory
📊 Data Analysis - Query databases with natural language
📝 Document Q&A - Answer questions from your documents
🤖 Autonomous Agents - AI that can use tools and APIs
🔍 Semantic Search - Find information by meaning, not keywords

🦜 Core Frameworks

🦜 LangChain

The most popular LLM framework

✅ Chains & Agents
✅ Memory Systems
✅ Tool Integration
✅ Multiple LLM Support

🦙 LlamaIndex

Specialized for data indexing

✅ Advanced Indexing
✅ Query Engines
✅ Document Processing
✅ Hybrid Search

🌾 Haystack

Production NLP pipelines

✅ Pipeline Architecture
✅ Neural Search
✅ Question Answering
✅ Enterprise Ready

🔧 Framework Selector

Describe your use case and get a framework recommendation:

Your framework recommendation will appear here...

⛓️ Chain Patterns

Sequential Chains

Connect multiple LLM calls in sequence, where each output feeds into the next input.

# Sequential chain example
from langchain import LLMChain, PromptTemplate
from langchain.chains import SimpleSequentialChain

# First chain: Summarize
summarize_template = "Summarize this text: {text}"
summarize_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate.from_template(summarize_template)
)

# Second chain: Translate
translate_template = "Translate to Spanish: {text}"
translate_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate.from_template(translate_template)
)

# Combine chains
sequential_chain = SimpleSequentialChain(
    chains=[summarize_chain, translate_chain]
)

result = sequential_chain.run(long_text)

Parallel Chains

Run multiple chains simultaneously for faster processing.

Map-Reduce

Process documents in parallel then combine results.

Router Chains

Route inputs to different chains based on content.

Conditional Chains

Execute chains based on conditions or rules.

📚 RAG Systems

How RAG Works

Document Processing - Split documents into chunks
Embedding Generation - Convert chunks to vectors
Vector Storage - Store in vector database
Query Processing - Convert query to vector
Similarity Search - Find relevant chunks
Context Injection - Add chunks to prompt
Response Generation - LLM generates answer

Implementation Example

# RAG implementation example
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA

# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    documents=docs,
    embedding=embeddings
)

# Create retrieval chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

# Query the system
result = qa_chain({"query": "What is the refund policy?"})
print(result["result"])
print("Sources:", result["source_documents"])

Vector Databases

🔷 Pinecone

Managed vector database with high performance.

🟢 Weaviate

Open-source with hybrid search capabilities.

🎨 ChromaDB

Lightweight, perfect for development.

💻 Hands-On Practice

Build Your First Chain

Create a simple LangChain application:

# Step 1: Install LangChain
pip install langchain openai

# Step 2: Basic setup
from langchain import OpenAI, LLMChain
from langchain.prompts import PromptTemplate

# Step 3: Create LLM
llm = OpenAI(temperature=0.7)

# Step 4: Create prompt
prompt = PromptTemplate(
    input_variables=["product"],
    template="Create a tagline for {product}:"
)

# Step 5: Create chain
chain = LLMChain(llm=llm, prompt=prompt)

# Step 6: Run chain
result = chain.run("eco-friendly water bottles")
print(result)

Try It Yourself

Enter a product name to generate a tagline:

Your AI-generated tagline will appear here...

📖 Quick Reference

Essential Imports

# LangChain essentials
from langchain import OpenAI, LLMChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.agents import initialize_agent, Tool
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

Common Patterns

Memory Chain

memory = ConversationBufferMemory()
chain = ConversationChain(
    llm=llm,
    memory=memory
)

Agent with Tools

tools = [SearchTool(), CalculatorTool()]
agent = initialize_agent(
    tools, llm,
    agent="zero-shot-react"
)

RAG Pipeline

qa = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever()
)

🔗 LangChain & RAG Frameworks