streaming.md

Streaming

To use a guardrails configuration in streaming mode, the following must be met:

1. The main LLM must support streaming.
2. There are no output rails.

Configuration

To activate streaming on a guardrails configuration, add the following to your config.yml:

streaming: True

Usage

Chat CLI

You can enable streaming when launching the NeMo Guardrails chat CLI by using the --streaming option:

nemoguardrails chat --config=examples/configs/streaming --streaming

Python API

You can use the streaming directly from the python API in two ways:

1. Simple: receive just the chunks (tokens).
2. Full: receive both the chunks as they are generated and the full response at the end.

For the simple usage, you need to call the stream_async method on the LLMRails instance:

from nemoguardrails import LLMRails

app = LLMRails(config)

history = [{"role": "user", "content": "What is the capital of France?"}]

async for chunk in app.stream_async(messages=history):
    print(f"CHUNK: {chunk}")
    # Or do something else with the token

For the full usage, you need to provide a StreamingHandler instance to the generate_async method on the LLMRails instance:

from nemoguardrails import LLMRails
from nemoguardrails.streaming import StreamingHandler

app = LLMRails(config)

history = [{"role": "user", "content": "What is the capital of France?"}]

streaming_handler = StreamingHandler()

async def process_tokens():
    async for chunk in streaming_handler:
        print(f"CHUNK: {chunk}")
        # Or do something else with the token

asyncio.create_task(process_tokens())

result = await app.generate_async(
    messages=history, streaming_handler=streaming_handler
)
print(result)

For the complete working example, check out this demo script.

Server API

To make a call to the NeMo Guardrails Server in streaming mode, you have to set the stream parameter to True inside the JSON body. For example, to get the completion for a chat session using the /v1/chat/completions endpoint:

POST /v1/chat/completions

{
    "config_id": "some_config_id",
    "messages": [{
      "role":"user",
      "content":"Hello! What can you do for me?"
    }],
    "stream": true
}

Streaming for LLMs deployed using HuggingFacePipeline

We also support streaming for LLMs deployed using HuggingFacePipeline. One example is provided in the HF Pipeline Dolly configuration.

To use streaming for HF Pipeline LLMs, you first need to set the streaming flag in your config.yml.

streaming: True

Then you need to create an nemoguardrails.llm.providers.huggingface.AsyncTextIteratorStreamer streamer object, add it to the kwargs of the pipeline and to the model_kwargs of the HuggingFacePipelineCompatible object.

from nemoguardrails.llm.providers.huggingface import AsyncTextIteratorStreamer

# instantiate tokenizer object required by LLM
streamer = AsyncTextIteratorStreamer(tokenizer, skip_prompt=True)
params = {"temperature": 0.01, "max_new_tokens": 100, "streamer": streamer}

pipe = pipeline(
    # all other parameters
    **params,
)

llm = HuggingFacePipelineCompatible(pipeline=pipe, model_kwargs=params)