LangChain vs LlamaIndex Vergleich

# 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

# Vektorspeicher erstellen
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": "Was ist die Unternehmensrichtlinie?"})
print(result["result"])
print("Quellen:", [doc.metadata for doc in result["source_documents"]])

# LlamaIndex — dokumentenbasiertes Frage-Antwort-System
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings

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

# Dokumente laden und indexieren
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)

# Abfrage-Engine
query_engine = index.as_query_engine(
    similarity_top_k=5,
    response_mode="tree_summarize"
)

response = query_engine.query(
    "Welche Rechte haben Nutzer laut Datenschutzgesetz?"
)
print(response)
print("Quellknoten:", response.source_nodes)