调用 AWS Bedrock Converse Agent 的函数¶

本笔记本将向您展示如何使用基于函数调用功能的 AWS Bedrock Converse 代理。

初始设置¶

让我们从导入一些基础组件开始。

我们主要需要以下内容：

1. 具备访问 Bedrock 和 Claude Haiku LLM 权限的 AWS 凭证
2. 存储对话历史记录的位置
3. 为智能体定义可使用的工具集

如果您在 Colab 上打开此 Notebook，可能需要安装 LlamaIndex 🦙。

In [ ]:

%pip install llama-index
%pip install llama-index-llms-bedrock-converse
%pip install llama-index-embeddings-huggingface

%pip install llama-index %pip install llama-index-llms-bedrock-converse %pip install llama-index-embeddings-huggingface

让我们为智能体定义一些非常简单的计算器工具。

In [ ]:

def multiply(a: int, b: int) -> int:
    """Multiple two integers and returns the result integer"""
    return a * b


def add(a: int, b: int) -> int:
    """Add two integers and returns the result integer"""
    return a + b

def multiply(a: int, b: int) -> int: """Multiple two integers and returns the result integer""" return a * b def add(a: int, b: int) -> int: """Add two integers and returns the result integer""" return a + b

请确保设置您的 AWS 认证凭证，可以是 profile_name 或以下密钥。

In [ ]:

from llama_index.llms.bedrock_converse import BedrockConverse

llm = BedrockConverse(
    model="anthropic.claude-3-haiku-20240307-v1:0",
    # NOTE replace with your own AWS credentials
    aws_access_key_id="AWS Access Key ID to use",
    aws_secret_access_key="AWS Secret Access Key to use",
    aws_session_token="AWS Session Token to use",
    region_name="AWS Region to use, eg. us-east-1",
)

from llama_index.llms.bedrock_converse import BedrockConverse llm = BedrockConverse( model="anthropic.claude-3-haiku-20240307-v1:0", # NOTE replace with your own AWS credentials aws_access_key_id="AWS Access Key ID to use", aws_secret_access_key="AWS Secret Access Key to use", aws_session_token="AWS Session Token to use", region_name="AWS Region to use, eg. us-east-1", )

初始化 AWS Bedrock Converse 代理¶

这里我们初始化了一个带有计算器功能的简单 AWS Bedrock Converse 代理。

In [ ]:

from llama_index.core.agent.workflow import FunctionAgent

agent = FunctionAgent(
    tools=[multiply, add],
    llm=llm,
)

from llama_index.core.agent.workflow import FunctionAgent agent = FunctionAgent( tools=[multiply, add], llm=llm, )

聊天¶

In [ ]:

response = await agent.run("What is (121 + 2) * 5?")
print(str(response))

response = await agent.run("What is (121 + 2) * 5?") print(str(response))

In [ ]:

# inspect sources
print(response.tool_calls)

# inspect sources print(response.tool_calls)

基于RAG管道的AWS Bedrock Converse智能体构建¶

在10K文档规模上构建AWS Bedrock Converse智能体。我们同时采用HuggingFace嵌入技术和BAAI/bge-small-en-v1.5模型构建RAG（检索增强生成）管道，并将其作为工具集成至AWS Bedrock Converse智能体。

In [ ]:

!mkdir -p 'data/10k/'
!curl -o 'data/10k/uber_2021.pdf' 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf'

!mkdir -p 'data/10k/' !curl -o 'data/10k/uber_2021.pdf' 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf'

In [ ]:

from llama_index.core.tools import QueryEngineTool
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.bedrock_converse import BedrockConverse

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
query_llm = BedrockConverse(
    model="anthropic.claude-3-haiku-20240307-v1:0",
    # NOTE replace with your own AWS credentials
    aws_access_key_id="AWS Access Key ID to use",
    aws_secret_access_key="AWS Secret Access Key to use",
    aws_session_token="AWS Session Token to use",
    region_name="AWS Region to use, eg. us-east-1",
)

# load data
uber_docs = SimpleDirectoryReader(
    input_files=["./data/10k/uber_2021.pdf"]
).load_data()

# build index
uber_index = VectorStoreIndex.from_documents(
    uber_docs, embed_model=embed_model
)
uber_engine = uber_index.as_query_engine(similarity_top_k=3, llm=query_llm)
query_engine_tool = QueryEngineTool.from_defaults(
    query_engine=uber_engine,
    name="uber_10k",
    description=(
        "Provides information about Uber financials for year 2021. "
        "Use a detailed plain text question as input to the tool."
    ),
)

from llama_index.core.tools import QueryEngineTool from llama_index.core import SimpleDirectoryReader, VectorStoreIndex from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.llms.bedrock_converse import BedrockConverse embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") query_llm = BedrockConverse( model="anthropic.claude-3-haiku-20240307-v1:0", # NOTE replace with your own AWS credentials aws_access_key_id="AWS Access Key ID to use", aws_secret_access_key="AWS Secret Access Key to use", aws_session_token="AWS Session Token to use", region_name="AWS Region to use, eg. us-east-1", ) # load data uber_docs = SimpleDirectoryReader( input_files=["./data/10k/uber_2021.pdf"] ).load_data() # build index uber_index = VectorStoreIndex.from_documents( uber_docs, embed_model=embed_model ) uber_engine = uber_index.as_query_engine(similarity_top_k=3, llm=query_llm) query_engine_tool = QueryEngineTool.from_defaults( query_engine=uber_engine, name="uber_10k", description=( "Provides information about Uber financials for year 2021. " "Use a detailed plain text question as input to the tool." ), )

In [ ]:

from llama_index.core.agent.workflow import FunctionAgent

agent = FunctionAgent(
    tools=[query_engine_tool],
    llm=llm,
)

from llama_index.core.agent.workflow import FunctionAgent agent = FunctionAgent( tools=[query_engine_tool], llm=llm, )

In [ ]:

response = await agent.run(
    "Tell me both the risk factors and tailwinds for Uber? Do two parallel tool calls."
)

response = await agent.run( "Tell me both the risk factors and tailwinds for Uber? Do two parallel tool calls." )

In [ ]:

print(str(response))

print(str(response))