Class LlamaIndexQueryPipelineAgent (1.88.0)

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.

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