> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# Amazon Neptune 与 Cypher 集成

> 使用 LangChain Python 集成 Amazon Neptune 的 Cypher 图。

> [Amazon Neptune](https://aws.amazon.com/neptune/) 是一款高性能的图分析和服务端无数据库，具有卓越的扩展性和可用性。
>
> 此示例展示了使用 openCypher 查询 Neptune 图数据库并返回人类可读响应的 QA 链。
>
> [Cypher](https://en.wikipedia.org/wiki/Cypher_\(query_language\)) 是一种声明式图查询语言，允许在属性图中进行表达力强且高效的数据查询。
>
> [openCypher](https://opencypher.org/) 是 Cypher 的开源实现。

# Neptune Open Cypher QA 链

此 QA 链使用 openCypher 查询 Amazon Neptune 并返回人类可读的响应

LangChain 支持通过 `create_neptune_opencypher_qa_chain` 同时使用 [Neptune Database](https://docs.aws.amazon.com/neptune/latest/userguide/intro.html) 和 [Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html)。

Neptune Database 是一款专为最佳扩展性和可用性设计的无服务器图数据库。它为需要扩展到每秒 100,000 次查询、多可用区高可用性和多区域部署的图数据库工作负载提供解决方案。您可以将 Neptune Database 用于社交网络、欺诈警报和客户 360 应用程序。

Neptune Analytics 是一个分析数据库引擎，可以快速在内存中分析大量图数据以获取见解并发现趋势。Neptune Analytics 是快速分析存储在数据湖中的现有图数据库或图数据集的解决方案。它使用流行的图分析算法和低延迟分析查询。

## 使用 Neptune Database

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws.graphs import NeptuneGraph

host = "<neptune-host>"
port = 8182
use_https = True

graph = NeptuneGraph(host=host, port=port, use_https=use_https)
```

### 使用 Neptune Analytics

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws.graphs import NeptuneAnalyticsGraph

graph = NeptuneAnalyticsGraph(graph_identifier="<neptune-analytics-graph-id>")
```

## 使用 Neptune openCypher QA 链

此 QA 链使用 openCypher 查询 Neptune 图数据库并返回人类可读的响应。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws import ChatBedrockConverse
from langchain_aws.chains import create_neptune_opencypher_qa_chain

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

chain = create_neptune_opencypher_qa_chain(llm=llm, graph=graph)

result = chain.invoke("How many outgoing routes does the Austin airport have?")
print(result["result"].content)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
Austin airport has 98 outgoing routes.
```

### 添加消息历史

Neptune openCypher QA 链能够被 [`RunnableWithMessageHistory`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html#langchain_core.runnables.history.RunnableWithMessageHistory) 包装。这会将消息历史添加到链中，使我们能够创建一个在多次调用之间保留对话状态的聊天机器人。

首先，我们需要一种存储和加载消息历史的方法。为此，每个线程都将作为 [`InMemoryChatMessageHistory`](https://reference.langchain.com/python/langchain-core/chat_history/InMemoryChatMessageHistory) 的实例创建，并存储在字典中以供重复访问。

(另见：[python.langchain.com/docs/versions/migrating\_memory/chat\_history/#chatmessagehistory](https://python.langchain.com/docs/versions/migrating_memory/chat_history/#chatmessagehistory))

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
```

现在，QA 链和消息历史存储可用于创建新的 `RunnableWithMessageHistory`。请注意，我们必须将 `query` 设置为输入键，以匹配基础链预期的格式。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.runnables.history import RunnableWithMessageHistory

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

在调用链之前，需要为新的 `InMemoryChatMessageHistory` 要记住的对话生成唯一的 `session_id`。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import uuid

session_id = uuid.uuid4()
```

最后，使用 `session_id` 调用启用了消息历史的链。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = runnable_with_history.invoke(
    {"query": "How many destinations can I fly to directly from Austin airport?"},
    config={"configurable": {"session_id": session_id}},
)
print(result["result"].content)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
You can fly directly to 98 destinations from Austin airport.
```

随着链继续使用相同的 `session_id` 被调用，响应将在对话中先前查询的上下文中返回。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = runnable_with_history.invoke(
    {"query": "Out of those destinations, how many are in Europe?"},
    config={"configurable": {"session_id": session_id}},
)
print(result["result"].content)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
You can fly directly to 4 destinations in Europe from Austin airport.
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = runnable_with_history.invoke(
    {"query": "Give me the codes and names of those airports."},
    config={"configurable": {"session_id": session_id}},
)
print(result["result"].content)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
The four European destinations you can fly to directly from Austin airport are:
- AMS (Amsterdam Airport Schiphol)
- FRA (Frankfurt am Main)
- LGW (London Gatwick)
- LHR (London Heathrow)
```

***

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