# 1) Load and index data
loader = LlamaIndex.loaders.GoogleDrive
docs = loader.load
chunks = chunk(docs, strategy="semantic", size=800)
index = LlamaIndex.VectorIndex(chunks, embedder="bge-large", hybrid=True)
# 2) Configure retriever with reranker
retriever = index.as_retriever(k=8, reranker="colbert", weights={"bm25":0.4,"dense":0.6})
# 3) Query engine with synthesis
qe = LlamaIndex.QueryEngine(
retriever=retriever,
synth="tree_summarize",
citations=True
)
answer = qe.query("Summarize policy exceptions for EU customers")

# 1) Build retriever tool
vectorstore = Chroma.from_documents(docs, embedding=OpenAIEmbeddings)
retriever = vectorstore.as_retriever(search_type="mmr", k=6)
rag_tool = create_retrieval_tool(retriever)
# 2) Define tools and agent
tools = ,,.
## Where [Sider.AI](https://sider.ai) Fits
- Value: Side-by-side experimentation across prompts, retrievers, and chain designs helps you converge faster on a winning RAG stack.
- Use case: Compare LlamaIndex’s hybrid retrieval + reranking vs. LangChain’s agentic RAG in one workspace. Track which setup yields better grounded answers for your dataset.
- Link: Check out [Sider.AI](https://sider.ai) here:
## Key Takeaways
- LlamaIndex is ideal when retrieval quality over private, complex datasets is your north star.
- LangChain is best when you need agentic flexibility, broad integrations, and production observability.
- Both are top-tier in 2025. Your choice should mirror your bottleneck: retrieval fidelity vs. orchestration and monitoring.
- Start simple: baseline RAG with reranking, then layer agents or advanced retrieval as needed.
### FAQ
Q1:Is LlamaIndex or LangChain better for enterprise RAG in 2025?
If your priority is high-quality retrieval over large private corpora, LlamaIndex often wins. For complex agents, integrations, and production observability, LangChain with LangSmith is hard to beat.
Q2:Which is easier for beginners: LlamaIndex vs LangChain?
For retrieval-first apps, LlamaIndex can feel more straightforward due to opinionated RAG abstractions. If you’re building agents with many tools, LangChain’s modular design becomes easier over time.
Q3:How do I choose between LlamaIndex and LangChain for RAG pipelines?
Decide based on your bottleneck: retrieval fidelity (LlamaIndex) vs. orchestration and monitoring (LangChain). Prototype both with your real data and evaluate groundedness, latency, and cost.
Q4:Can I combine LlamaIndex and LangChain in one application?
Yes. Teams often use LlamaIndex for indexing/retrieval while orchestrating agents with LangChain, connected via simple tool interfaces. Just ensure tracing and evaluation cover both layers.
Q5:What are the latest updates influencing LlamaIndex vs LangChain in 2025?
Guides highlight LlamaIndex’s gains in retrieval accuracy and LangChain’s expanding agent and observability ecosystem. Both remain top picks in 2025 RAG framework comparisons.