Cassandra 数据库工具包

Apache Cassandra® 是一个广泛使用的数据库，用于存储事务应用程序数据。大型语言模型中函数和>工具的引入为生成式 AI 应用程序中现有数据开辟了一些令人兴奋的用例。

Cassandra 数据库工具包使 AI 工程师能够高效地将代理与 Cassandra 数据集成，提供以下功能

• 通过优化的查询实现快速数据访问。大多数查询应该在不到 10 毫秒的时间内完成。
• 模式内省以增强 LLM 推理能力
• 与各种 Cassandra 部署兼容，包括 Apache Cassandra®、DataStax Enterprise™ 和 DataStax Astra™
• 目前，该工具包仅限于 SELECT 查询和模式内省操作。（安全第一）

有关创建 Cassandra DB 代理的更多信息，请参阅CQL 代理食谱

快速入门

• 安装cassio库
• 设置要连接到的 Cassandra 数据库的环境变量
• 初始化CassandraDatabase
• 使用toolkit.get_tools()将工具传递给您的代理
• 坐下来，看着它为您完成所有工作

操作理论

Cassandra 查询语言 (CQL) 是与 Cassandra 数据库交互的主要以人为本方式。虽然在生成查询时提供了一些灵活性，但它需要了解 Cassandra 数据建模最佳实践。LLM 函数调用使代理能够进行推理，然后选择一个工具来满足请求。使用 LLM 的代理在选择合适的工具包或工具包链时应使用 Cassandra 特定的逻辑进行推理。这减少了 LLM 被迫提供自上而下解决方案时引入的随机性。您是否希望 LLM 完全不受限制地访问您的数据库？是的。可能不会。为了实现这一点，我们提供了一个提示，在为代理构建问题时使用

您是 Apache Cassandra 专家查询分析机器人，具有以下功能和规则

• 您将接收来自最终用户有关在数据库中查找特定数据的问题。
• 您将检查数据库的模式并创建查询路径。
• 您将向用户提供正确的查询以查找他们要查找的数据，并显示查询路径提供的步骤。
• 您将使用查询 Apache Cassandra 的最佳实践，使用分区键和聚类列。
• 避免在查询中使用 ALLOW FILTERING。
• 目标是找到查询路径，因此可能需要查询其他表才能获得最终答案。

以下是用 JSON 格式表示的查询路径示例

 {
  "query_paths": [
    {
      "description": "Direct query to users table using email",
      "steps": [
        {
          "table": "user_credentials",
          "query": 
             "SELECT userid FROM user_credentials WHERE email = '[email protected]';"
        },
        {
          "table": "users",
          "query": "SELECT * FROM users WHERE userid = ?;"
        }
      ]
    }
  ]
}

提供的工具

cassandra_db_schema

收集连接的数据库或特定模式的所有模式信息。对于代理在确定操作时至关重要。

cassandra_db_select_table_data

从特定的键空间和表中选择数据。代理可以传递谓词的参数以及返回记录数量的限制。

cassandra_db_query

cassandra_db_select_table_data的实验性替代方案，它接受代理完全形成的查询字符串而不是参数。警告：这可能导致不寻常的查询，这些查询可能效率不高（甚至不起作用）。这可能会在将来的版本中删除。如果它做了很酷的事情，我们也想知道。你永远不知道！

环境设置

安装以下 Python 模块

pip install ipykernel python-dotenv cassio langchain_openai langchain langchain-community langchainhub

.env 文件

连接通过使用auto=True参数的cassio进行，笔记本使用 OpenAI。您应该相应地创建一个.env文件。

对于 Casssandra，设置

CASSANDRA_CONTACT_POINTS
CASSANDRA_USERNAME
CASSANDRA_PASSWORD
CASSANDRA_KEYSPACE

对于 Astra，设置

ASTRA_DB_APPLICATION_TOKEN
ASTRA_DB_DATABASE_ID
ASTRA_DB_KEYSPACE

例如

# Connection to Astra:
ASTRA_DB_DATABASE_ID=a1b2c3d4-...
ASTRA_DB_APPLICATION_TOKEN=AstraCS:...
ASTRA_DB_KEYSPACE=notebooks

# Also set 
OPENAI_API_KEY=sk-....

（您也可以修改以下代码以直接连接到cassio。）

from dotenv import load_dotenv

load_dotenv(override=True)

# Import necessary libraries
import os

import cassio
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_community.agent_toolkits.cassandra_database.toolkit import (
    CassandraDatabaseToolkit,
)
from langchain_community.tools.cassandra_database.prompt import QUERY_PATH_PROMPT
from langchain_community.utilities.cassandra_database import CassandraDatabase
from langchain_openai import ChatOpenAI

API 参考：AgentExecutor | create_openai_tools_agent | CassandraDatabaseToolkit | CassandraDatabase | ChatOpenAI

连接到 Cassandra 数据库

cassio.init(auto=True)
session = cassio.config.resolve_session()
if not session:
    raise Exception(
        "Check environment configuration or manually configure cassio connection parameters"
    )

# Test data pep

session = cassio.config.resolve_session()

session.execute("""DROP KEYSPACE IF EXISTS langchain_agent_test; """)

session.execute(
    """
CREATE KEYSPACE if not exists langchain_agent_test 
WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1};
"""
)

session.execute(
    """
    CREATE TABLE IF NOT EXISTS langchain_agent_test.user_credentials (
    user_email text PRIMARY KEY,
    user_id UUID,
    password TEXT
);
"""
)

session.execute(
    """
    CREATE TABLE IF NOT EXISTS langchain_agent_test.users (
    id UUID PRIMARY KEY,
    name TEXT,
    email TEXT
);"""
)

session.execute(
    """
    CREATE TABLE IF NOT EXISTS langchain_agent_test.user_videos ( 
    user_id UUID,
    video_id UUID,
    title TEXT,
    description TEXT,
    PRIMARY KEY (user_id, video_id)
);
"""
)

user_id = "522b1fe2-2e36-4cef-a667-cd4237d08b89"
video_id = "27066014-bad7-9f58-5a30-f63fe03718f6"

session.execute(
    f"""
    INSERT INTO langchain_agent_test.user_credentials (user_id, user_email) 
    VALUES ({user_id}, '[email protected]');
"""
)

session.execute(
    f"""
    INSERT INTO langchain_agent_test.users (id, name, email) 
    VALUES ({user_id}, 'Patrick McFadin', '[email protected]');
"""
)

session.execute(
    f"""
    INSERT INTO langchain_agent_test.user_videos (user_id, video_id, title)
    VALUES ({user_id}, {video_id}, 'Use Langflow to Build a LangChain LLM Application in 5 Minutes');
"""
)

session.set_keyspace("langchain_agent_test")

# Create a CassandraDatabase instance
# Uses the cassio session to connect to the database
db = CassandraDatabase()

# Choose the LLM that will drive the agent
# Only certain models support this
llm = ChatOpenAI(temperature=0, model="gpt-4-1106-preview")
toolkit = CassandraDatabaseToolkit(db=db)

tools = toolkit.get_tools()

print("Available tools:")
for tool in tools:
    print(tool.name + "\t- " + tool.description)

Available tools:
cassandra_db_schema	- 
    Input to this tool is a keyspace name, output is a table description 
    of Apache Cassandra tables.
    If the query is not correct, an error message will be returned.
    If an error is returned, report back to the user that the keyspace 
    doesn't exist and stop.
    
cassandra_db_query	- 
    Execute a CQL query against the database and get back the result.
    If the query is not correct, an error message will be returned.
    If an error is returned, rewrite the query, check the query, and try again.
    
cassandra_db_select_table_data	- 
    Tool for getting data from a table in an Apache Cassandra database. 
    Use the WHERE clause to specify the predicate for the query that uses the 
    primary key. A blank predicate will return all rows. Avoid this if possible. 
    Use the limit to specify the number of rows to return. A blank limit will 
    return all rows.

prompt = hub.pull("hwchase17/openai-tools-agent")

# Construct the OpenAI Tools agent
agent = create_openai_tools_agent(llm, tools, prompt)

input = (
    QUERY_PATH_PROMPT
    + "\n\nHere is your task: Find all the videos that the user with the email address '[email protected]' has uploaded to the langchain_agent_test keyspace."
)

agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

response = agent_executor.invoke({"input": input})

print(response["output"])

> Entering new AgentExecutor chain...

Invoking: `cassandra_db_schema` with `{'keyspace': 'langchain_agent_test'}`


Table Name: user_credentials
- Keyspace: langchain_agent_test
- Columns
  - password (text)
  - user_email (text)
  - user_id (uuid)
- Partition Keys: (user_email)
- Clustering Keys: 

Table Name: user_videos
- Keyspace: langchain_agent_test
- Columns
  - description (text)
  - title (text)
  - user_id (uuid)
  - video_id (uuid)
- Partition Keys: (user_id)
- Clustering Keys: (video_id asc)


Table Name: users
- Keyspace: langchain_agent_test
- Columns
  - email (text)
  - id (uuid)
  - name (text)
- Partition Keys: (id)
- Clustering Keys: 


Invoking: `cassandra_db_select_table_data` with `{'keyspace': 'langchain_agent_test', 'table': 'user_credentials', 'predicate': "user_email = '[email protected]'", 'limit': 1}`


Row(user_email='[email protected]', password=None, user_id=UUID('522b1fe2-2e36-4cef-a667-cd4237d08b89'))
Invoking: `cassandra_db_select_table_data` with `{'keyspace': 'langchain_agent_test', 'table': 'user_videos', 'predicate': 'user_id = 522b1fe2-2e36-4cef-a667-cd4237d08b89', 'limit': 10}`


Row(user_id=UUID('522b1fe2-2e36-4cef-a667-cd4237d08b89'), video_id=UUID('27066014-bad7-9f58-5a30-f63fe03718f6'), description='DataStax Academy is a free resource for learning Apache Cassandra.', title='DataStax Academy')To find all the videos that the user with the email address '[email protected]' has uploaded to the `langchain_agent_test` keyspace, we can follow these steps:

1. Query the `user_credentials` table to find the `user_id` associated with the email '[email protected]'.
2. Use the `user_id` obtained from the first step to query the `user_videos` table to retrieve all the videos uploaded by the user.

Here is the query path in JSON format:

\`\`\`json
{
  "query_paths": [
    {
      "description": "Find user_id from user_credentials and then query user_videos for all videos uploaded by the user",
      "steps": [
        {
          "table": "user_credentials",
          "query": "SELECT user_id FROM user_credentials WHERE user_email = '[email protected]';"
        },
        {
          "table": "user_videos",
          "query": "SELECT * FROM user_videos WHERE user_id = 522b1fe2-2e36-4cef-a667-cd4237d08b89;"
        }
      ]
    }
  ]
}
\`\`\`

Following this query path, we found that the user with the user_id `522b1fe2-2e36-4cef-a667-cd4237d08b89` has uploaded at least one video with the title 'DataStax Academy' and the description 'DataStax Academy is a free resource for learning Apache Cassandra.' The video_id for this video is `27066014-bad7-9f58-5a30-f63fe03718f6`. If there are more videos, the same query can be used to retrieve them, possibly with an increased limit if necessary.

> Finished chain.
To find all the videos that the user with the email address '[email protected]' has uploaded to the `langchain_agent_test` keyspace, we can follow these steps:

1. Query the `user_credentials` table to find the `user_id` associated with the email '[email protected]'.
2. Use the `user_id` obtained from the first step to query the `user_videos` table to retrieve all the videos uploaded by the user.

Here is the query path in JSON format:

\`\`\`json
{
  "query_paths": [
    {
      "description": "Find user_id from user_credentials and then query user_videos for all videos uploaded by the user",
      "steps": [
        {
          "table": "user_credentials",
          "query": "SELECT user_id FROM user_credentials WHERE user_email = '[email protected]';"
        },
        {
          "table": "user_videos",
          "query": "SELECT * FROM user_videos WHERE user_id = 522b1fe2-2e36-4cef-a667-cd4237d08b89;"
        }
      ]
    }
  ]
}
\`\`\`

Following this query path, we found that the user with the user_id `522b1fe2-2e36-4cef-a667-cd4237d08b89` has uploaded at least one video with the title 'DataStax Academy' and the description 'DataStax Academy is a free resource for learning Apache Cassandra.' The video_id for this video is `27066014-bad7-9f58-5a30-f63fe03718f6`. If there are more videos, the same query can be used to retrieve them, possibly with an increased limit if necessary.

相关

• 工具 概念指南
• 工具 操作指南