LangChain vs LlamaIndex Comparison

# LangChain — RAG pipeline (Python)
from langchain_anthropic import ChatAnthropic
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA

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

# RAG chain
llm = ChatAnthropic(model="claude-opus-4-7")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
    return_source_documents=True
)

result = qa_chain.invoke({"query": "What is the company policy?"})
print(result["result"])
print("Sources:", [doc.metadata for doc in result["source_documents"]])

# LlamaIndex — document-based question-answering system
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings

# Configure LLM
Settings.llm = Anthropic(model="claude-opus-4-7")

# Load and index documents
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)

# Query engine
query_engine = index.as_query_engine(
    similarity_top_k=5,
    response_mode="tree_summarize"
)

response = query_engine.query(
    "What are user rights under KVKK?"
)
print(response)
print("Source nodes:", response.source_nodes)