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()
)