# Query Engines and Response Synthesis — LangChain / LlamaIndex

Source: https://www.geekswithgeeks.com/en/langchain-llamaindex/i-engine

> Answer questions by retrieving nodes and synthesising a response.

## Retrieve, then synthesise

`index.as_query_engine()` wraps retrieval plus a **response synthesizer**: it retrieves the top nodes, builds a prompt with them and asks the LLM for the answer. The returned `Response` includes `source_nodes` so you can show citations. **Response modes** control how context is combined: `compact` stuffs as much as fits, `refine` improves an answer node by node, `tree_summarize` summarises hierarchically (good for summaries of many nodes). You can add **node postprocessors** (similarity cutoff, reranker, metadata filters) between retrieval and synthesis, and use `as_chat_engine()` for multi-turn chat with memory.

## A query engine with mock models, run

I ran this offline in a Python virtual environment with langchain-core 1.6.6, langchain-text-splitters 1.1.2 and llama-index-core 0.14.25. No API key or network call is needed because a fake model or a toy embedding stands in for the real one. `MockLLM` and `MockEmbedding` let the engine run offline, so the answer text is a placeholder, but the structure is real: the result is a `Response` object with one source node holding the policy text.

```python
from llama_index.core import Document, VectorStoreIndex, Settings
from llama_index.core.embeddings import MockEmbedding
from llama_index.core.llms import MockLLM

Settings.llm = MockLLM(max_tokens=20)
Settings.embed_model = MockEmbedding(embed_dim=8)

index = VectorStoreIndex.from_documents([Document(text="Employees get 24 days of paid leave per year.")])
engine = index.as_query_engine(similarity_top_k=1)
response = engine.query("How many leave days?")
print(type(response).__name__)
print("source nodes:", len(response.source_nodes))
print(response.source_nodes[0].node.get_content())

```

Output:

```
Response
source nodes: 1
Employees get 24 days of paid leave per year.
```

**Quiz:** Why does the Response include source_nodes?

- [ ] To train the LLM
- [x] So you can show which passages supported the answer
- [ ] To compress the index
- [ ] To remove citations

*Answer:* So you can show which passages supported the answer. Source nodes enable citations and debugging.