Amazon Neptune（支持SPARQL）

Amazon Neptune is a high-performance graph analytics and serverless database for superior scalability and availability.

This example shows the QA chain that queries Resource Description Framework (RDF) data in an Amazon Neptune graph database using the SPARQL query language and returns a human-readable response.

SPARQL is a standard query language for RDF graphs.

此示例使用一个 NeptuneRdfGraph 类，该类连接到 Neptune 数据库并加载其模式。 create_neptune_sparql_qa_chain 用于将图和 LLM 连接起来，以提出自然语言问题。

本笔记本演示了使用组织数据的示例。

运行此笔记本的先决条件：

此笔记本可访问Neptune 1.2.x集群
Python 3.9 或更高版本的内核
对于Bedrock访问，请确保IAM角色具有此策略。

{
        "Action": [
            "bedrock:ListFoundationModels",
            "bedrock:InvokeModel"
        ],
        "Resource": "*",
        "Effect": "Allow"
}

用于暂存样本数据的 S3 存储桶。该存储桶应与 Neptune 处于同一账户/区域。

设置

Seed the W3C organizational data

播种W3C组织数据，W3C组织本体以及一些实例。

你需要一个与Neptune集群在同一区域和账户中的S3存储桶。将该存储桶命名为STAGE_BUCKET。

STAGE_BUCKET = "<bucket-name>"

%%bash  -s "$STAGE_BUCKET"

rm -rf data
mkdir -p data
cd data
echo getting org ontology and sample org instances
wget http://www.w3.org/ns/org.ttl 
wget https://raw.githubusercontent.com/aws-samples/amazon-neptune-ontology-example-blog/main/data/example_org.ttl 

echo Copying org ttl to S3
aws s3 cp org.ttl s3://$1/org.ttl
aws s3 cp example_org.ttl s3://$1/example_org.ttl

我们将使用 %load 魔术命令从 graph-notebook 包中插入 W3C 数据到 Neptune 图中。在运行 %load 之前，请使用 %%graph_notebook_config 设置图连接参数。

!pip install --upgrade --quiet graph-notebook

%load_ext graph_notebook.magics

%%graph_notebook_config
{
    "host": "<neptune-endpoint>",
    "neptune_service": "neptune-db",
    "port": 8182,
    "auth_mode": "<[DEFAULT|IAM]>",
    "load_from_s3_arn": "<neptune-cluster-load-role-arn>",
    "ssl": true,
    "aws_region": "<region>"
}

批量加载组织ttl - 包括本体和实例。

%load -s s3://{STAGE_BUCKET} -f turtle --store-to loadres --run

%load_status {loadres['payload']['loadId']} --errors --details

设置链

!pip install --upgrade --quiet langchain-aws

重启内核

准备一个示例

EXAMPLES = """

<question>
Find organizations.
</question>

<sparql>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> 
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> 
PREFIX org: <http://www.w3.org/ns/org#> 

select ?org ?orgName where {{
    ?org rdfs:label ?orgName .
}} 
</sparql>

<question>
Find sites of an organization
</question>

<sparql>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> 
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> 
PREFIX org: <http://www.w3.org/ns/org#> 

select ?org ?orgName ?siteName where {{
    ?org rdfs:label ?orgName .
    ?org org:hasSite/rdfs:label ?siteName . 
}} 
</sparql>

<question>
Find suborganizations of an organization
</question>

<sparql>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> 
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> 
PREFIX org: <http://www.w3.org/ns/org#> 

select ?org ?orgName ?subName where {{
    ?org rdfs:label ?orgName .
    ?org org:hasSubOrganization/rdfs:label ?subName  .
}} 
</sparql>

<question>
Find organizational units of an organization
</question>

<sparql>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> 
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> 
PREFIX org: <http://www.w3.org/ns/org#> 

select ?org ?orgName ?unitName where {{
    ?org rdfs:label ?orgName .
    ?org org:hasUnit/rdfs:label ?unitName . 
}} 
</sparql>

<question>
Find members of an organization. Also find their manager, or the member they report to.
</question>

<sparql>
PREFIX org: <http://www.w3.org/ns/org#> 
PREFIX foaf: <http://xmlns.com/foaf/0.1/> 

select * where {{
    ?person rdf:type foaf:Person .
    ?person  org:memberOf ?org .
    OPTIONAL {{ ?person foaf:firstName ?firstName . }}
    OPTIONAL {{ ?person foaf:family_name ?lastName . }}
    OPTIONAL {{ ?person  org:reportsTo ??manager }} .
}}
</sparql>


<question>
Find change events, such as mergers and acquisitions, of an organization
</question>

<sparql>
PREFIX org: <http://www.w3.org/ns/org#> 

select ?event ?prop ?obj where {{
    ?org rdfs:label ?orgName .
    ?event rdf:type org:ChangeEvent .
    ?event org:originalOrganization ?origOrg .
    ?event org:resultingOrganization ?resultingOrg .
}}
</sparql>

"""

创建Neptune数据库RDF图

from langchain_aws.graphs import NeptuneRdfGraph

host = "<your host>"
port = 8182  # change if different
region = "us-east-1"  # change if different
graph = NeptuneRdfGraph(host=host, port=port, use_iam_auth=True, region_name=region)

# Optionally, change the schema
# elems = graph.get_schema_elements
# change elems ...
# graph.load_schema(elems)

API 参考：NeptuneRdfGraph

使用Neptune SPARQL QA链

这个问答链使用 SPARQL 查询 Neptune 图数据库，并返回一个易于理解的响应。

from langchain_aws import ChatBedrockConverse
from langchain_aws.chains import create_neptune_sparql_qa_chain

MODEL_ID = "anthropic.claude-3-5-sonnet-20241022-v2:0"
llm = ChatBedrockConverse(
    model_id=MODEL_ID,
    temperature=0,
)

chain = create_neptune_sparql_qa_chain(
    llm=llm,
    graph=graph,
    examples=EXAMPLES,
)

result = chain.invoke("How many organizations are in the graph?")
print(result["result"].content)

API 参考：ChatBedrockConverse | create_neptune_sparql_qa_chain

以下是几个可以在已导入的图数据上尝试的提示。

result = chain.invoke("Are there any mergers or acquisitions?")
print(result["result"].content)

result = chain.invoke("Find organizations.")
print(result["result"].content)

result = chain.invoke("Find sites of MegaSystems or MegaFinancial.")
print(result["result"].content)

result = chain.invoke("Find a member who is a manager of one or more members.")
print(result["result"].content)

result = chain.invoke("Find five members and their managers.")
print(result["result"].content)

result = chain.invoke(
    "Find org units or suborganizations of The Mega Group. What are the sites of those units?"
)
print(result["result"].content)

添加消息历史

Neptune SPARQL QA 链能够被 RunnableWithMessageHistory 包装。这为链添加了消息历史记录，使我们能够创建一个聊天机器人，使其在多次调用之间保持对话状态。

首先，我们需要一种方法来存储和加载消息历史记录。为此，每个线程将被创建为 InMemoryChatMessageHistory 的一个实例，并存储到字典中以便重复访问。

(另请参阅： https://python.langchain.com/docs/versions/migrating_memory/chat_history/#chatmessagehistory)

from langchain_core.chat_history import InMemoryChatMessageHistory

chats_by_session_id = {}


def get_chat_history(session_id: str) -> InMemoryChatMessageHistory:
    chat_history = chats_by_session_id.get(session_id)
    if chat_history is None:
        chat_history = InMemoryChatMessageHistory()
        chats_by_session_id[session_id] = chat_history
    return chat_history

API 参考：InMemoryChatMessageHistory

现在，问答链和消息历史存储可以用于创建新的 RunnableWithMessageHistory。请注意，我们必须将 query 设置为输入键，以匹配基础链所期望的格式。

from langchain_core.runnables.history import RunnableWithMessageHistory

runnable_with_history = RunnableWithMessageHistory(
    chain,
    get_chat_history,
    input_messages_key="query",
)

API 参考：RunnableWithMessageHistory

在调用链之前，需要为新 InMemoryChatMessageHistory 将记住的对话生成一个唯一的 session_id。

import uuid

session_id = uuid.uuid4()

最后，使用启用消息历史记录的链调用 session_id。

result = runnable_with_history.invoke(
    {"query": "How many org units or suborganizations does the The Mega Group have?"},
    config={"configurable": {"session_id": session_id}},
)
print(result["result"].content)

当链继续使用相同的 session_id 调用时，响应将基于对话中之前的查询上下文返回。

result = runnable_with_history.invoke(
    {"query": "List the sites for each of the units."},
    config={"configurable": {"session_id": session_id}},
)
print(result["result"].content)

设置​

Seed the W3C organizational data​

设置链​

准备一个示例​

创建Neptune数据库RDF图​

使用Neptune SPARQL QA链​

添加消息历史​

设置

Seed the W3C organizational data

设置链

准备一个示例

创建Neptune数据库RDF图

使用Neptune SPARQL QA链

添加消息历史