# Streaming and Async — LangChain / LlamaIndex

Source: https://www.geekswithgeeks.com/en/langchain-llamaindex/c-stream

> Show output as it is produced and run calls concurrently.

## Faster to first word

Language models generate token by token, so waiting for the whole answer feels slow. Every runnable has **`stream`** (and async **`astream`**) that yields pieces as they arrive, letting a UI show text immediately; perceived latency drops even though total time is similar. The async methods (`ainvoke`, `abatch`, `astream`) let a server handle many requests concurrently without blocking threads. When streaming through a parser, make sure the parser supports partial output (`StrOutputParser` does; a JSON parser may emit partial objects). Remember to handle errors that occur mid-stream.

## Streaming pieces, 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. The fake model streams its canned reply one character at a time (15 pieces here); real models stream tokens. Joining the pieces gives the full text.

```python
from langchain_core.language_models.fake_chat_models import FakeListChatModel
from langchain_core.output_parsers import StrOutputParser

chain = FakeListChatModel(responses=["streaming works"]) | StrOutputParser()
pieces = list(chain.stream("hi"))
print(pieces[:5], "...", len(pieces), "pieces")
print("".join(pieces))

```

Output:

```
['s', 't', 'r', 'e', 'a'] ... 15 pieces
streaming works
```

**Quiz:** What does streaming improve?

- [ ] The vocabulary size
- [ ] The model's accuracy
- [x] Perceived latency: users see text sooner
- [ ] The training data

*Answer:* Perceived latency: users see text sooner. Streaming shows partial output immediately.