Cassandra 数据库工具包

Apache Cassandra®是用于存储事务性应用程序数据的广泛使用的数据库。在大型语言模型中引入函数和>工具，为生成式 AI 应用程序中的现有数据开辟了一些令人兴奋的用例。

这Cassandra Database工具包使 AI 工程师能够有效地将代理与 Cassandra 数据集成，从而提供具有以下特点：

通过优化的查询快速访问数据。大多数查询应以个位数毫秒或更短的时间运行。

Schema 自省以增强 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 查询和架构自省作。（安全第一）

有关创建 Cassandra 数据库代理的更多信息，请参阅 CQL 代理说明书

快速开始

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

工作原理

Cassandra Query Language (CQL)是与 Cassandra 数据库交互的主要以人为中心的方式。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，然后选择满足请求的工具。使用 LLM 的代理在选择适当的工具包或工具包链时，应使用特定于 Cassandra 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 能够完全不受限制地访问您的数据库？是的。可能不是。为此，我们提供了一个提示，供在为代理构建问题时使用：

您是具有以下功能的 Apache Cassandra 专家查询分析机器人和规则：

您将回答最终用户关于查找特定 data 的 data 中。
您将检查数据库的架构并创建查询路径。
您将为用户提供正确的查询以查找他们正在查找的数据 for，显示查询路径提供的步骤。
您将使用使用分区键查询 Apache Cassandra 的最佳实践和 clustering columns 的 Clustering Columns 中。
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到最终答案。

以下是 JSON 格式的查询路径示例：

 {
  "query_paths": [
    {
      "description": "Direct query to users table using email",
      "steps": [
        {
          "table": "user_credentials",
          "query": 
             "SELECT userid FROM user_credentials WHERE email = 'example@example.com';"
        },
        {
          "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架构的所有架构信息。在确定作时，对代理至关重要。

`cassandra_db_select_table_data`

从特定键空间和表中选择数据。代理可以传递谓词的参数和对返回记录数量的限制。

`cassandra_db_query`

过期的替代方案cassandra_db_select_table_data它采用完全由代理组成的查询字符串，而不是参数。警告：这可能会导致异常查询，这些查询的性能可能不那么好（甚至不起作用）。这可能会在将来的发行版中删除。如果它做了一些很酷的事情，我们也想知道它。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

连接是通过cassio用auto=True参数，并且笔记本使用 OpenAI。您应该创建一个.env相应地提交。

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-....

（您也可以修改下面的代码，直接与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

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}, 'patrick@datastax.com');
"""
)

session.execute(
    f"""
    INSERT INTO langchain_agent_test.users (id, name, email) 
    VALUES ({user_id}, 'Patrick McFadin', 'patrick@datastax.com');
"""
)

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 'patrick@datastax.com' 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"])

[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m
Invoking: `cassandra_db_schema` with `{'keyspace': 'langchain_agent_test'}`


[0m[36;1m[1;3mTable 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: 

[0m[32;1m[1;3m
Invoking: `cassandra_db_select_table_data` with `{'keyspace': 'langchain_agent_test', 'table': 'user_credentials', 'predicate': "user_email = 'patrick@datastax.com'", 'limit': 1}`


[0m[38;5;200m[1;3mRow(user_email='patrick@datastax.com', password=None, user_id=UUID('522b1fe2-2e36-4cef-a667-cd4237d08b89'))[0m[32;1m[1;3m
Invoking: `cassandra_db_select_table_data` with `{'keyspace': 'langchain_agent_test', 'table': 'user_videos', 'predicate': 'user_id = 522b1fe2-2e36-4cef-a667-cd4237d08b89', 'limit': 10}`


[0m[38;5;200m[1;3mRow(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')[0m[32;1m[1;3mTo find all the videos that the user with the email address 'patrick@datastax.com' 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 'patrick@datastax.com'.
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 = 'patrick@datastax.com';"
        },
        {
          "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.[0m

[1m> Finished chain.[0m
To find all the videos that the user with the email address 'patrick@datastax.com' 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 'patrick@datastax.com'.
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 = 'patrick@datastax.com';"
        },
        {
          "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.

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