---
title: Frequently Asked Questions (FAQ) | Developer Documentation
---

Tip

If you haven’t already, [install LlamaIndex](/python/framework/getting_started/installation/index.md) and complete the [starter tutorial](/python/framework/getting_started/starter_example/index.md). If you run into terms you don’t recognize, check out the [high-level concepts](/python/framework/getting_started/concepts/index.md).

In this section, we start with the code you wrote for the [starter example](/python/framework/getting_started/starter_example/index.md) and show you the most common ways you might want to customize it for your use case:

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)
```

---

## **“I want to parse my documents into smaller chunks"**

```
# Global settings
from llama_index.core import Settings


Settings.chunk_size = 512


# Local settings
from llama_index.core.node_parser import SentenceSplitter


index = VectorStoreIndex.from_documents(
    documents, transformations=[SentenceSplitter(chunk_size=512)]
)
```

---

## **"I want to use a different vector store”**

First, you can install the vector store you want to use. For example, to use Chroma as the vector store, you can install it using pip:

Terminal window

```
pip install llama-index-vector-stores-chroma
```

To learn more about all integrations available, check out [LlamaHub](https://llamahub.ai).

Then, you can use it in your code:

```
import chromadb
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext


chroma_client = chromadb.PersistentClient()
chroma_collection = chroma_client.create_collection("quickstart")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
```

`StorageContext` defines the storage backend for where the documents, embeddings, and indexes are stored. You can learn more about [storage](/python/framework/module_guides/storing/index.md) and [how to customize it](/python/framework/module_guides/storing/customization/index.md).

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)
```

---

## **”I want to retrieve more context when I query”**

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)
response = query_engine.query("What did the author do growing up?")
print(response)
```

`as_query_engine` builds a default `retriever` and `query engine` on top of the index. You can configure the retriever and query engine by passing in keyword arguments. Here, we configure the retriever to return the top 5 most similar documents (instead of the default of 2). You can learn more about [retrievers](/python/framework/module_guides/querying/retriever/retrievers/index.md) and [query engines](/python/framework/module_guides/querying/retriever/index.md).

---

## **”I want to use a different LLM”**

```
# Global settings
from llama_index.core import Settings
from llama_index.llms.ollama import Ollama


Settings.llm = Ollama(
    model="mistral",
    request_timeout=60.0,
    # Manually set the context window to limit memory usage
    context_window=8000,
)


# Local settings
index.as_query_engine(
    llm=Ollama(
        model="mistral",
        request_timeout=60.0,
        # Manually set the context window to limit memory usage
        context_window=8000,
    )
)
```

You can learn more about [customizing LLMs](/python/framework/module_guides/models/llms/index.md).

---

## **”I want to use a different response mode”**

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(response_mode="tree_summarize")
response = query_engine.query("What did the author do growing up?")
print(response)
```

You can learn more about [query engines](/python/framework/module_guides/querying/index.md) and [response modes](/python/framework/module_guides/deploying/query_engine/response_modes/index.md).

---

## **”I want to stream the response back”**

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(streaming=True)
response = query_engine.query("What did the author do growing up?")
response.print_response_stream()
```

You can learn more about [streaming responses](/python/framework/module_guides/deploying/query_engine/streaming/index.md).

---

## **”I want a chatbot instead of Q\&A”**

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_chat_engine()
response = query_engine.chat("What did the author do growing up?")
print(response)


response = query_engine.chat("Oh interesting, tell me more.")
print(response)
```

Learn more about the [chat engine](/python/framework/module_guides/deploying/chat_engines/usage_pattern/index.md).

---

## Next Steps

- Want a thorough walkthrough of (almost) everything you can configure? Get started with [Understanding LlamaIndex](/python/framework/understanding/index.md).
- Want more in-depth understanding of specific modules? Check out the [component guides](/python/framework/module_guides/index.md).