本頁說明如何使用 LlamaIndex Query Pipelines 範本 (Vertex AI SDK for Python 中的 LlamaIndexQueryPipelineAgent 類別) 開發代理。這個代理程式的設計目的是使用檢索增強生成 (RAG) 技術回答問題，例如「Paul Graham 的大學生活如何？」這類查詢。
請按照下列步驟，使用 LlamaIndex Query Pipelines 開發代理：
- 定義及設定模型
- 定義及使用擷取器
- 定義及使用回覆合成器
- (選用) 自訂提示範本
- (選用) 自訂協調流程
事前準備
請按照「設定環境」一文中的步驟，確認環境已設定完成。
定義及設定模型
定義及設定 LlamaIndex Query Pipeline 代理程式要使用的模型。
定義模型版本：
model = "gemini-2.0-flash"(選用) 指定模型參數：
model_kwargs = { # vertexai_config (dict): By providing the region and project_id parameters, # you can enable model usage through Vertex AI. "vertexai_config": { "project": "PROJECT_ID", "location": "LOCATION" }, # temperature (float): The sampling temperature controls the degree of # randomness in token selection. "temperature": 0.28, # 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, }使用下列模型設定建立
LlamaIndexQueryPipelineAgent：from vertexai.preview import reasoning_engines agent = reasoning_engines.LlamaIndexQueryPipelineAgent( model=model, # Required. model_kwargs=model_kwargs, # Optional. )如果您在互動式環境 (例如終端機或 Colab 筆記本) 中執行，可以查詢代理程式：
response = agent.query(input="What is Paul Graham's life in college?") print(response)您應該會收到類似以下的回應：
{'message': {'role': 'assistant', 'additional_kwargs': {}, 'blocks': [{'block_type': 'text', 'text': "Unfortunately, there's not a lot of publicly available information about Paul Graham's personal life in college. ..."}]}, 'raw': {'content': {'parts': [{'video_metadata': None, 'thought': None, 'code_execution_result': None, 'executable_code': None, 'file_data': None, 'function_call': None, 'function_response': None, 'inline_data': None, 'text': "Unfortunately, there's not a lot of publicly available information about Paul Graham's personal life in college. ..."}], 'role': 'model'}, 'citation_metadata': None, 'finish_message': None, 'token_count': None, 'avg_logprobs': -0.1468650027438327, 'finish_reason': 'STOP', 'grounding_metadata': None, 'index': None, 'logprobs_result': None, 'safety_ratings': [{'blocked': None, 'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'probability_score': 0.022949219, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.014038086}, {'blocked': None, 'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'probability_score': 0.056640625, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.029296875}, {'blocked': None, 'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'probability_score': 0.071777344, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.024047852}, {'blocked': None, 'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'probability_score': 0.103515625, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.05102539}], 'usage_metadata': {'cached_content_token_count': None, 'candidates_token_count': 222, 'prompt_token_count': 10, 'total_token_count': 232}}, 'delta': None, 'logprobs': None, 'additional_kwargs': {}}
(選用) 自訂模型
LlamaIndexQueryPipelineAgent 範本預設會使用 Google GenAI，以便存取 Google Cloud中所有可用的基礎模型。如要使用 Google GenAI 無法提供的模型，請定義 model_builder=，如下所示：
from typing import Optional
def model_builder(
*,
model_name: str, # Required. The name of the model
model_kwargs: Optional[dict] = None, # Optional. The model keyword arguments.
**kwargs, # Optional. The remaining keyword arguments to be ignored.
):
如要查看 LlamaIndexQueryPipeline 支援的即時通訊模型及其功能，請參閱「可用的 LLM 整合」。每個即時通訊模型都有一組專屬的 model= 和 model_kwargs= 支援值。
Google 生成式 AI
設定環境時，系統會預設安裝 Google GenAI，並在您省略 model_builder 時，自動在 LlamaIndexQueryPipelineAgent 範本中使用。
from vertexai.preview import reasoning_engines
agent = reasoning_engines.LlamaIndexQueryPipelineAgent(
model=model, # Required.
model_kwargs=model_kwargs, # Optional.
)
Anthropic
請參閱 Anthropic 文件，設定帳戶並安裝
llama-index-llms-anthropic套件。定義
model_builder以傳回Anthropic模型：def model_builder(*, model_name: str, model_kwargs = None, **kwargs): from llama_index.llms.anthropic import Anthropic return Anthropic(model=model_name, **model_kwargs)在
LlamaIndexQueryPipelineAgent範本中使用 Anthropic 模型：from vertexai.preview import reasoning_engines agent = reasoning_engines.LlamaIndexQueryPipelineAgent( model="claude-3-opus-20240229", # Required. model_builder=model_builder, # Required. model_kwargs={ "api_key": "ANTHROPIC_API_KEY", # Required. "temperature": 0.28, # Optional. }, )
OpenAILike
你可以搭配 Gemini 的 ChatCompletions API 使用 OpenAILike。
按照
OpenAILike說明文件安裝套件：pip install llama-index-llms-openai-like定義會傳回
OpenAILike模型的model_builder：def model_builder( *, model_name: str, model_kwargs = None, project: str, # Specified via vertexai.init location: str, # Specified via vertexai.init **kwargs, ): import google.auth from llama_index.llms.openai_like import OpenAILike # Note: the credential lives for 1 hour by default. # After expiration, it must be refreshed. creds, _ = google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) auth_req = google.auth.transport.requests.Request() creds.refresh(auth_req) if model_kwargs is None: model_kwargs = {} endpoint = f"https://{location}-aiplatform.googleapis.com" api_base = f'{endpoint}/v1beta1/projects/{project}/locations/{location}/endpoints/openapi' return OpenAILike( model=model_name, api_base=api_base, api_key=creds.token, **model_kwargs, )在
LlamaIndexQueryPipelineAgent範本中使用模型：from vertexai.preview import reasoning_engines agent = reasoning_engines.LlamaIndexQueryPipelineAgent( model="google/gemini-2.0-flash", # Or "meta/llama3-405b-instruct-maas" model_builder=model_builder, # Required. model_kwargs={ "temperature": 0, # Optional. "max_retries": 2, # Optional. }, )
定義及使用擷取器
定義模型後，請定義模型用於推理的擷取器。retriever可以建構在索引之上，但也可以全面定義。您應在本地測試檢索器。
定義檢索器，傳回相關文件和相似度分數：
def retriever_builder(model, retriever_kwargs=None): import os import requests from llama_index.core import ( StorageContext, VectorStoreIndex, load_index_from_storage, ) from llama_index.core import SimpleDirectoryReader from llama_index.embeddings.vertex import VertexTextEmbedding import google.auth credentials, _ = google.auth.default() embed_model = VertexTextEmbedding( model_name="text-embedding-005", project="PROJECT_ID", credentials=credentials ) data_dir = "data/paul_graham" essay_file = os.path.join(data_dir, "paul_graham_essay.txt") storage_dir = "storage" # --- Simple Download (if needed) --- if not os.path.exists(essay_file): os.makedirs(data_dir, exist_ok=True) # Make sure the directory exists essay_url = "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt" try: response = requests.get(essay_url) response.raise_for_status() # Check for download errors with open(essay_file, "wb") as f: f.write(response.content) print("Essay downloaded.") except requests.exceptions.RequestException as e: print(f"Download failed: {e}") # --- Build/Load Index --- if not os.path.exists(storage_dir): print("Creating new index...") # --- Load Data --- reader = SimpleDirectoryReader(data_dir) docs = reader.load_data() index = VectorStoreIndex.from_documents(docs, model=model, embed_model=embed_model) index.storage_context.persist(persist_dir=storage_dir) else: print("Loading existing index...") storage_context = StorageContext.from_defaults(persist_dir=storage_dir) index = load_index_from_storage(storage_context, embed_model=embed_model) return index.as_retriever()測試擷取器：
from llama_index.llms.google_genai import GoogleGenAI model = GoogleGenAI( model=model, **model_kwargs ) retriever = retriever_builder(model) retrieved_response = retriever.retrieve("What is Paul Graham's life in College?")擷取的回應應類似如下：
[ NodeWithScore( node=TextNode( id_='692a5d5c-cd56-4ed0-8e29-ecadf6eb9933', embedding=None, metadata={'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-24', 'last_modified_date': '2025-03-24'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={ <NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='3e1c4d73-1e1d-4e83-bd16-2dae24abb231', node_type='4', metadata={'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-24', 'last_modified_date': '2025-03-24'}, hash='0c3c3f46cac874b495d944dfc4b920f6b68817dbbb1699ecc955d1fafb2bf87b'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='782c5787-8753-4f65-85ed-c2833ea6d4d8', node_type='1', metadata={'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-24', 'last_modified_date': '2025-03-24'}, hash='b8e6463833887a8a2b13f1b5a623672819faedc1b725d9565ba003223628db0e'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='f7d2cb7e-fa0c-40bf-b8e7-b888e36b87f9', node_type='1', metadata={}, hash='db7cc1a67fa3afd1e5f24c8c61583781ce6a00c444da8f25a5374468c17b7de0') }, metadata_template='{key}: {value}', metadata_separator='\n', text='So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp...', mimetype='text/plain', start_char_idx=7166, end_char_idx=11549, metadata_separator='\n', text_template='{metadata_str}\n\n{content}' ), score=0.7403571819090398 ) ]如要在
LlamaIndexQueryPipelineAgent範本中使用擷取器，請在retriever_builder=引數下方新增擷取器：from vertexai.preview import reasoning_engines agent = reasoning_engines.LlamaIndexQueryPipelineAgent( model=model, # Required. model_kwargs=model_kwargs, # Optional. retriever_builder=retriever_builder, # Optional. )執行測試查詢，在本機測試代理程式：
response = agent.query( input="What is Paul Graham's life in College?" )回應是可進行 JSON 序列化的節點清單，其中包含分數。
[{'node': {'id_': '692a5d5c-cd56-4ed0-8e29-ecadf6eb9933', 'embedding': None, 'metadata': {'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-12', 'last_modified_date': '2025-03-12'}, 'excluded_embed_metadata_keys': ['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], 'excluded_llm_metadata_keys': ['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], 'relationships': {'1': {'node_id': '07ee9574-04c8-46c7-b023-b22ba9558a1f', 'node_type': '1', 'metadata': {}, 'hash': '44136fa355b3678a1146ad16f7e8649e94fb4fc21fe77e8310c060f61caaff8a', 'class_name': 'RelatedNodeInfo'}, '2': {'node_id': 'ac7e54aa-6fff-40b5-a15e-89c5eb234936', 'node_type': '1', 'metadata': {'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-12', 'last_modified_date': '2025-03-12'}, 'hash': '755327a01efe7104db771e4e6f9683417884ea6895d878da882d2b21a6b66442', 'class_name': 'RelatedNodeInfo'}, '3': {'node_id': '3a04be27-ac46-4acd-a8c6-031689508982', 'node_type': '1', 'metadata': {}, 'hash': 'db7cc1a67fa3afd1e5f24c8c61583781ce6a00c444da8f25a5374468c17b7de0', 'class_name': 'RelatedNodeInfo'}}, 'metadata_template': '{key}: {value}', 'metadata_separator': '\n', 'text': 'So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp...', 'mimetype': 'text/plain', 'start_char_idx': 7164, 'end_char_idx': 11547, 'metadata_separator': '\n', 'text_template': '{metadata_str}\n\n{content}', 'class_name': 'TextNode'}, 'score': 0.25325886336265013, 'class_name': 'NodeWithScore'} ]
定義及使用回覆合成器
定義模型和擷取器後，請定義 response-synthesizer，該合成器會使用使用者查詢和一組指定的文字區塊，從 LLM 生成回應。 你可以使用預設的 get_response_synthesizer，或設定回應模式。
定義傳回答案的回應合成器：
def response_synthesizer_builder(model, response_synthesizer_kwargs=None): from llama_index.core.response_synthesizers import SimpleSummarize return SimpleSummarize(llm=model)測試函式：
response_synthesizer = response_synthesizer_builder(model=model) response = response_synthesizer.get_response( "What is Paul Graham's life in College?", [node.model_dump_json() for node in retrieved_response], )回覆應類似如下：
"While in a PhD program for computer science, he took art classes and worked on a book about Lisp hacking. He applied to art schools, got accepted to RISD, and later got an invitation to take the entrance exam at the Accademia di Belli Arti in Florence. He was accepted to both. He attended the Accademia, but was disappointed by the lack of instruction."如要在
LlamaIndexQueryPipeline範本中使用回應合成器，請在response_synthesizer_builder=引數下方新增：from vertexai.preview import reasoning_engines agent = reasoning_engines.LlamaIndexQueryPipelineAgent( model=model, # Required. model_kwargs=model_kwargs, # Optional. retriever_builder=retriever_builder, # Optional. response_synthesizer_builder=response_synthesizer_builder, # Optional. )執行測試查詢，在本機測試完整的 RAG 查詢管道：
response = agent.query( input="What is Paul Graham's life in College?" )回應是類似下列內容的字典：
{ 'response': "While in college, he was drawn to McCarthy's 1960 Lisp, although he didn't fully grasp the reasons for his interest at the time. He also had a brief encounter with surplus Xerox Dandelions in the computer lab but found them too slow for his liking. \n", 'source_nodes': [ '{"node":{"id_":"95889c30-53c7-43d0-bf91-930dbb23bde6"...,"score":0.7077213268404997,"class_name":"NodeWithScore"}' ], 'metadata': { '95889c30-53c7-43d0-bf91-930dbb23bde6': { 'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-25', 'last_modified_date': '2025-03-25' } } }
(選用) 自訂提示範本
提示範本會將使用者輸入內容轉換為模型指令，引導模型生成符合情境且連貫的回覆。詳情請參閱「提示」一文。
預設提示範本會依序分為下列幾個部分：
| 區段 | 說明 |
|---|---|
| (選用) 系統指令 | 適用於所有查詢的代理程式指令。 |
| 使用者輸入內容 | 使用者查詢，代理程式會據此回覆。 |
如果您建立代理程式時未指定自己的提示範本，系統就會產生預設提示範本，如下所示：
from llama_index.core import prompts
from llama_index.core.base.llms import types
message_templates = [
types.ChatMessage(role=types.MessageRole.SYSTEM, content=system_instruction),
types.ChatMessage(role=types.MessageRole.USER, content="{input}"),
]
prompts.ChatPromptTemplate(message_templates=message_templates)
在下列範例中，您可以在例項化代理程式時使用完整提示範本：
from vertexai.preview import reasoning_engines
system_instruction = "I help to find what is Paul Graham's life in College"
agent = reasoning_engines.LlamaIndexQueryPipelineAgent(
model=model,
system_instruction=system_instruction,
)
您可以覆寫預設提示範本，並在建構服務專員時使用：
prompt_str = "Please answer {question} about {name}"
prompt_tmpl = PromptTemplate(prompt_str)
from vertexai.preview import reasoning_engines
agent = reasoning_engines.LlamaIndexQueryPipelineAgent(
model = model,
prompt = prompt_tmpl,
)
agent.query(
input={
"name": "Paul Graham",
"question": "What is the life in college?",
}
)
(選用) 自訂自動化調度管理
所有 LlamaIndexQueryPipeline 元件都會實作 Query Component 介面，提供用於協調的輸入和輸出結構定義。LlamaIndexQueryPipelineAgent
需要建構可執行的項目，才能回應查詢。根據預設，LlamaIndexQueryPipelineAgent 會使用 Query Pipeline 建構循序鏈結或有向非循環圖 (DAG)。
如果您打算執行下列任一操作，可能需要自訂協調流程：
實作可擴充 RAG 管道的代理程式 (例如擴充現有的提示、模型、檢索器、回應合成器模組至查詢引擎、查詢轉換器、輸出剖析器、後處理器/重新排序器或自訂查詢元件)。
使用 ReAct 提示代理程式執行工具，並為每個步驟加上註解，說明執行該步驟的原因。如要執行此操作，請在建立
LlamaIndexQueryPipelineAgent時指定runnable_builder=引數，覆寫預設的可執行檔：from typing import Optional from llama_index.core.llms import function_calling def runnable_builder( model: function_calling.FunctionCallingLLM, *, system_instruction: Optional[str] = None, prompt: Optional[query.QUERY_COMPONENT_TYPE] = None, retriever: Optional[query.QUERY_COMPONENT_TYPE] = None, response_synthesizer: Optional[query.QUERY_COMPONENT_TYPE] = None, runnable_kwargs: Optional[Mapping[str, Any]] = None, ):其中：
您可以使用自訂管道或 ReAct 自訂協調邏輯：
自訂管道
如要為代理程式提供額外模組 (例如後處理器)，請覆寫 LlamaIndexQueryPipelineAgent 的 runnable_builder。
定義後處理器：
def post_processor_builder(): from llama_index.core.postprocessor import SimilarityPostprocessor # similarity postprocessor: filter nodes below 0.7 similarity score return SimilarityPostprocessor(similarity_cutoff=0.7) def runnable_with_postprocessor_builder( model, runnable_kwargs, **kwargs ): from llama_index.core.query_pipeline import QueryPipeline pipeline = QueryPipeline(**runnable_kwargs) pipeline_modules = { "retriever": retriever_builder(model), "postprocessor": post_processor_builder(), } pipeline.add_modules(pipeline_modules) pipeline.add_link("retriever", "postprocessor") return pipeline agent = reasoning_engines.LlamaIndexQueryPipelineAgent( model=model, runnable_builder=runnable_with_postprocessor_builder, )查詢代理程式：
result = agent.query(input="What is Paul Graham's life in College?")畫面會顯示如下的輸出內容：
[ { 'node': {'id_': 'bb7d2942-213d-4fb3-a7cb-1a664642a7ff', 'embedding': None, 'metadata': { 'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-25', 'last_modified_date': '2025-03-25' }, 'excluded_embed_metadata_keys': [ 'file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date' ], 'excluded_llm_metadata_keys': [ 'file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date' ], 'relationships': {'1': {'node_id': 'c508cee5-5ef2-4fdf-a33d-0427dcb78b5c', 'node_type': '4', 'metadata': {'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-25', 'last_modified_date': '2025-03-25'}, 'hash': '0c3c3f46cac874b495d944dfc4b920f6b68817dbbb1699ecc955d1fafb2bf87b', 'class_name': 'RelatedNodeInfo'}, '2': {'node_id': '97a84b41-62bf-4959-acae-cfd4bdfbd4d9', 'node_type': '1', 'metadata': {'file_path': '/content/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2025-03-25', 'last_modified_date': '2025-03-25'}, 'hash': 'a7dd352be97e47e8e553ceda3d2d2c9e9d5c54adb298063c94da06167938d583', 'class_name': 'RelatedNodeInfo'}, '3': {'node_id': 'b984eea1-f0bc-4880-812e-3f49f1e304b8', 'node_type': '1', 'metadata': {}, 'hash': 'db7cc1a67fa3afd1e5f24c8c61583781ce6a00c444da8f25a5374468c17b7de0', 'class_name': 'RelatedNodeInfo'}}, 'metadata_template': '{key}: {value}', 'metadata_separator': '\n', 'text': 'So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp...', 'mimetype': 'text/plain', 'start_char_idx': 7166, 'end_char_idx': 11549, 'metadata_separator': '\n', 'text_template': '{metadata_str}\n\n{content}', 'class_name': 'TextNode'}, 'score': 0.7403571819090398, 'class_name': 'NodeWithScore' }, { 'node': {'id_': 'b984eea1-f0bc-4880-812e-3f49f1e304b8...'} 'score': 0.7297395567513889, 'class_name': 'NodeWithScore' } ]
ReAct 代理程式
如要使用自己的 ReAct 代理程式提供工具呼叫行為，請覆寫 LlamaIndexQueryPipelineAgent 的 runnable_builder。
定義會傳回匯率的範例函式：
def get_exchange_rate( currency_from: str = "USD", currency_to: str = "EUR", currency_date: str = "latest", ): """Retrieves the exchange rate between two currencies on a specified date. Uses the Frankfurter API (https://api.frankfurter.app/) to obtain exchange rate data. Args: currency_from: The base currency (3-letter currency code). Defaults to "USD" (US Dollar). currency_to: The target currency (3-letter currency code). Defaults to "EUR" (Euro). currency_date: The date for which to retrieve the exchange rate. Defaults to "latest" for the most recent exchange rate data. Can be specified in YYYY-MM-DD format for historical rates. Returns: dict: A dictionary containing the exchange rate information. Example: {"amount": 1.0, "base": "USD", "date": "2023-11-24", "rates": {"EUR": 0.95534}} """ import requests response = requests.get( f"https://api.frankfurter.app/{currency_date}", params={"from": currency_from, "to": currency_to}, ) return response.json()使用工具建立自訂 ReAct 代理程式：
def runnable_with_tools_builder(model, runnable_kwargs=None, **kwargs): from llama_index.core.query_pipeline import QueryPipeline from llama_index.core.tools import FunctionTool from llama_index.core.agent import ReActAgent llama_index_tools = [] for tool in runnable_kwargs.get("tools"): llama_index_tools.append(FunctionTool.from_defaults(tool)) agent = ReActAgent.from_tools(llama_index_tools, llm=model, verbose=True) return QueryPipeline(modules = {"agent": agent}) agent = reasoning_engines.LlamaIndexQueryPipelineAgent( model="gemini-2.0-flash", runnable_kwargs={"tools": [get_exchange_rate]}, runnable_builder=runnable_with_tools_builder, )查詢代理程式：
result = agent.query(input="What is the exchange rate between US and EURO today?")輸出內容應如下所示：
{ 'response': 'The exchange rate between US and EURO today, 2025-03-19, is 1 USD to 0.91768 EUR.', 'source_nodes': [], 'metadata': None }
後續步驟
- 評估代理程式。
- 部署代理程式。
- 排解開發代理程式的問題。
- 取得支援。