Class LlamaIndexQueryPipelineAgent (1.95.1) 

LlamaIndexQueryPipelineAgent(
    model: str,
    *,
    system_instruction: typing.Optional[str] = None,
    prompt: typing.Optional[QueryComponent] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_builder: typing.Optional[typing.Callable[[...], FunctionCallingLLM]] = None,
    retriever_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    retriever_builder: typing.Optional[typing.Callable[[...], QueryComponent]] = None,
    response_synthesizer_kwargs: typing.Optional[
        typing.Mapping[str, typing.Any]
    ] = None,
    response_synthesizer_builder: typing.Optional[
        typing.Callable[[...], QueryComponent]
    ] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_builder: typing.Optional[typing.Callable[[...], QueryPipeline]] = None,
    enable_tracing: bool = False
)

A LlamaIndex Query Pipeline Agent.

This agent uses a query pipeline for LLAIndex, including prompt, model, retrieval and summarization steps. More details can be found in https://docs.llamaindex.ai/en/stable/module_guides/querying/pipeline/.

Methods

LlamaIndexQueryPipelineAgent

LlamaIndexQueryPipelineAgent(
    model: str,
    *,
    system_instruction: typing.Optional[str] = None,
    prompt: typing.Optional[QueryComponent] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_builder: typing.Optional[typing.Callable[[...], FunctionCallingLLM]] = None,
    retriever_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    retriever_builder: typing.Optional[typing.Callable[[...], QueryComponent]] = None,
    response_synthesizer_kwargs: typing.Optional[
        typing.Mapping[str, typing.Any]
    ] = None,
    response_synthesizer_builder: typing.Optional[
        typing.Callable[[...], QueryComponent]
    ] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_builder: typing.Optional[typing.Callable[[...], QueryPipeline]] = None,
    enable_tracing: bool = False
)

Initializes the LlamaIndexQueryPipelineAgent.

Under-the-hood, assuming .set_up() is called, this will correspond to

# model_builder
model = model_builder(model_name, project, location, model_kwargs)

# runnable_builder
runnable = runnable_builder(
    prompt=prompt,
    model=model,
    retriever=retriever_builder(model, retriever_kwargs),
    response_synthesizer=response_synthesizer_builder(
        model, response_synthesizer_kwargs
    ),
    runnable_kwargs=runnable_kwargs,
)

When everything is based on their default values, this corresponds to a query pipeline Prompt - Model:

# Default Model Builder
model = google_genai.GoogleGenAI(
    model=model_name,
    vertexai_config={
        "project": initializer.global_config.project,
        "location": initializer.global_config.location,
    },
)

# Default Prompt Builder
prompt = prompts.ChatPromptTemplate(
    message_templates=[
        types.ChatMessage(
            role=types.MessageRole.USER,
            content="{input}",
        ),
    ],
)

# Default Runnable Builder
runnable = QueryPipeline(
    modules = {
        "prompt": prompt,
        "model": model,
    },
)
pipeline.add_link("prompt", "model")

When system_instruction is specified, the prompt will be updated to include the system instruction.

# Updated Prompt Builder
prompt = prompts.ChatPromptTemplate(
    message_templates=[
        types.ChatMessage(
            role=types.MessageRole.SYSTEM,
            content=system_instruction,
        ),
        types.ChatMessage(
            role=types.MessageRole.USER,
            content="{input}",
        ),
    ],
)

When all inputs are specified, this corresponds to a query pipeline Prompt - Model - Retriever - Summarizer:

runnable = QueryPipeline(
    modules = {
        "prompt": prompt,
        "model": model,
        "retriever": retriever_builder(retriever_kwargs),
        "response_synthesizer": response_synthesizer_builder(
            response_synthesizer_kwargs
        ),
    },
)
pipeline.add_link("prompt", "model")
pipeline.add_link("model", "retriever")
pipeline.add_link("model", "response_synthesizer", dest_key="query_str")
pipeline.add_link("retriever", "response_synthesizer", dest_key="nodes")
Parameters
Name Description
model str

The name of the model (e.g. "gemini-1.0-pro").
system_instruction str

Optional. The system instruction to use for the agent.
prompt llama_index.core.base.query_pipeline.query.QUERY_COMPONENT_TYPE

Optional. The prompt template for the model.
model_kwargs Mapping[str, Any]

Optional. Keyword arguments for the model constructor of the google_genai.GoogleGenAI. An example of a model_kwargs is: 

python { # api_key (string): The API key for the GoogleGenAI model. # The API can also be fetched from the GOOGLE_API_KEY # environment variable. If vertexai_config is provided, # the API key is ignored. "api_key": "your_api_key", # temperature (float): Sampling temperature, it controls the # degree of randomness in token selection. If not provided, # the default temperature is 0.1. "temperature": 0.1, # context_window (int): The context window of the model. # If not provided, the default context window is 200000. "context_window": 200000, # max_tokens (int): Token limit determines the maximum # amount of text output from one prompt. If not provided, # the default max_tokens is 256. "max_tokens": 256, # is_function_calling_model (bool): Whether the model is a # function calling model. If not provided, the default # is_function_calling_model is True. "is_function_calling_model": True, }

model_builder Callable

Optional. Callable that returns a language model.
retriever_kwargs Mapping[str, Any]

Optional. Keyword arguments for the retriever constructor.
retriever_builder Callable

Optional. Callable that returns a retriever object.
response_synthesizer_kwargs Mapping[str, Any]

Optional. Keyword arguments for the response synthesizer constructor.
response_synthesizer_builder Callable

Optional. Callable that returns a response_synthesizer object.
runnable_kwargs Mapping[str, Any]

Optional. Keyword arguments for the runnable constructor.
runnable_builder Callable

Optional. Callable that returns a runnable (query pipeline).
enable_tracing bool

Optional. Whether to enable tracing. Defaults to False.

clone

clone() -> (
    vertexai.preview.reasoning_engines.templates.llama_index.LlamaIndexQueryPipelineAgent
)

Returns a clone of the LlamaIndexQueryPipelineAgent.

query

query(
    input: typing.Union[str, typing.Mapping[str, typing.Any]], **kwargs: typing.Any
) -> typing.Union[
    str,
    typing.Dict[str, typing.Any],
    typing.Sequence[typing.Union[str, typing.Dict[str, typing.Any]]],
]

Queries the Agent with the given input and config.

Parameter
Name Description
input Union[str, Mapping[str, Any]]

Required. The input to be passed to the Agent.

set_up

set_up()

Sets up the agent for execution of queries at runtime.

It initializes the model, connects it with the prompt template, retriever and response_synthesizer.

This method should not be called for an object that being passed to the ReasoningEngine service for deployment, as it initializes clients that can not be serialized.