streaming-responses
Use when streaming tokens incrementally from an LLM via liter-llm over SSE or async iterators. Covers chat_stream, delta handling, and null-content chunks.
Datos de origen
- Repositorio
- xberg-io/liter-llm
- Última actividad en el origen
- 10 de octubre de 2026 a las 07:47
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 262
- Forks
- 23
Opciones de instalación
De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.
Revisa los archivos de origen
Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.
Mostrando SKILL.md
SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- streaming-responses
- description
- Use when streaming tokens incrementally from an LLM via liter-llm over SSE or async iterators. Covers chat_stream, delta handling, and null-content chunks.
<!--
AI-RULEZ :: GENERATED FILE — DO NOT EDIT
Content-Hash: blake3:13410510899be94ef40712ffcb27a284ef4ac4977b6e45b38b014d60e913f995
Source-Hash: blake3:3cb955aedbbbd51d9b5f60a411d90b9386534c5f14bf20a056bef60f7dace49e
Schema-Version: v1
-->
# Streaming Responses
Use `chat_stream(...)` to receive tokens as they are produced instead of waiting
for the full completion. The proxy streams over SSE; bindings expose async
iterators.
## Python
```python
import asyncio, os
from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest
async def main() -> None:
client = create_client(api_key=os.environ["OPENAI_API_KEY"])
request = ChatCompletionRequest.from_json(
'{"model":"openai/gpt-4o","messages":[{"role":"user","content":"Tell me a story"}],"stream":true}'
)
async for chunk in client.chat_stream(request):
if chunk.choices and chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
print()
asyncio.run(main())
```
## TypeScript
```typescript
import { createClient } from "@xberg-io/liter-llm";
const client = createClient(process.env.OPENAI_API_KEY!);
const chunks = await client.chatStream({
model: "openai/gpt-4o",
messages: [{ role: "user", content: "Tell me a story" }],
});
for await (const chunk of chunks) {
process.stdout.write(chunk.choices?.[0]?.delta?.content ?? "");
}
```
## Notes
- The first and last chunks often carry null content. Always null-check
`chunk.choices[0].delta.content` (Python) or
`chunk.choices[0]?.delta?.content` (TypeScript) before using it.
- Tool-call deltas arrive in `delta.tool_calls` (Python) /
`delta.toolCalls` (TypeScript); accumulate `function.arguments` fragments
across chunks before parsing.
- Through the proxy, request streaming with `"stream": true` on
`/v1/chat/completions`.
Ver en GitHub