Skip to main content

Llama-stack integration for Next Gen UI Agent

Project description

Next Gen UI Llama Stack Integration

This module is part of the Next Gen UI Agent project.

Module Category Module Status

Support for Llama Stack framework.

Provides

  • NextGenUILlamaStackAgent - takes all tool messages from provided conversation turn steps (Llama Stack Agent API), and process data from them into UI components.
    • Tool name is used as InputData.type for the UI Agent, so distinct configurations can be applied based on it, like input data transformations, defined UI components (dynamic ot Hand Build) etc.
    • Two event types can be emitted during the processing:
      • success with output from the UI Agent processing. Payload is array of UIBlock.
      • error with error from the UI Agent processing. Payload is Exception.
    • Depending on the agent's execution_mode, processing is performed as:
      • stream: Process individual data in parallel, yield each result as an independent event immediatelly as processing completes/fails. So overall number of produced events is the same as number of data pieces. Each success event contains exactly one UIBlock in the payload array. (default)
      • batch: Process individual data in parallel, yield all results as one event containing results for all the data, or one error event if processing of any data fails.
  • LlamaStackAgentInference and LlamaStackAsyncAgentInference to use LLM hosted in Llama Stack server (Llama Stack Chat Completion API)

Installation

pip install -U next_gen_ui_llama_stack

Example

Integrate Next Gen UI with your assistent

Let's have your ReAct Agent e.g. Movies agent like this:

from llama_stack_client.lib.agents.react.agent import ReActAgent

client = LlamaStackClient(
    base_url=f"http://{LLAMA_STACK_HOST}:{LLAMA_STACK_PORT}",
)
INFERENCE_MODEL = "meta-llama/Llama-3.2-3B-Instruct"
movies_agent = ReActAgent(
    client=client,
    model=INFERENCE_MODEL,
    client_tools=[
        movies,
    ],
    json_response_format=True,
)

session_id = movies_agent.create_session("test-session")

# Send a query to your agent
response = movies_agent.create_turn(
    messages=[{"role": "user", "content": user_input}],
    session_id=session_id,
    stream=False,
)

Use NextGenUILlamaStackAgent class and just pass llama stack client and model name and pass steps from your movies agent to Next Gen UI Agent.

from next_gen_ui_llama_stack import NextGenUILlamaStackAgent

# Pass steps to Next Gen UI Agent for processing. Events with results are emitted.
ngui_agent = NextGenUILlamaStackAgent(client, INFERENCE_MODEL)
result = await ngui_agent.create_turn(user_input, steps=response.steps)

Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

next_gen_ui_llama_stack-0.4.0-py3-none-any.whl (7.3 kB view details)

Uploaded Python 3

File details

Details for the file next_gen_ui_llama_stack-0.4.0-py3-none-any.whl.

File metadata

File hashes

Hashes for next_gen_ui_llama_stack-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 0aca5eeec7458e54daf7704fe1b8c7962d5f7131d09e7681d9ad60e5fe3257dc
MD5 84266e07d4c6c57aef21ea8657505d16
BLAKE2b-256 f8b01e3b0e66686ccfa072a71a2b04e7b56ee2c352acd26fd51d975e8819adfb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page