Cassandra Database Toolkit

Apache Cassandra® is a widely used database for storing transactional application data. The introduction of functions and >tooling in Large Language Models has opened up some exciting use cases for existing data in Generative AI applications.

The Cassandra Database toolkit enables AI engineers to integrate agents with Cassandra data efficiently, offering the following features:

• Fast data access through optimized queries. Most queries should run in single-digit ms or less.
• Schema introspection to enhance LLM reasoning capabilities
• Compatibility with various Cassandra deployments, including Apache Cassandra®, DataStax Enterprise™, and DataStax Astra™
• Currently, the toolkit is limited to SELECT queries and schema introspection operations. (Safety first)

For more information on creating a Cassandra DB agent see the CQL agent cookbook

Quick Start

• Install the cassio library
• Set environment variables for the Cassandra database you are connecting to
• Initialize CassandraDatabase
• Pass the tools to your agent with toolkit.get_tools()
• Sit back and watch it do all your work for you

Theory of Operation

Cassandra Query Language (CQL) is the primary human-centric way of interacting with a Cassandra database. While offering some flexibility when generating queries, it requires knowledge of Cassandra data modeling best practices. LLM function calling gives an agent the ability to reason and then choose a tool to satisfy the request. Agents using LLMs should reason using Cassandra-specific logic when choosing the appropriate toolkit or chain of toolkits. This reduces the randomness introduced when LLMs are forced to provide a top-down solution. Do you want an LLM to have complete unfettered access to your database? Yeah. Probably not. To accomplish this, we provide a prompt for use when constructing questions for the agent:

You are an Apache Cassandra expert query analysis bot with the following features and rules:

• You will take a question from the end user about finding specific data in the database.
• You will examine the schema of the database and create a query path.
• You will provide the user with the correct query to find the data they are looking for, showing the steps provided by the query path.
• You will use best practices for querying Apache Cassandra using partition keys and clustering columns.
• Avoid using ALLOW FILTERING in the query.
• The goal is to find a query path, so it may take querying other tables to get to the final answer.

The following is an example of a query path in JSON format:

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

Tools Provided

cassandra_db_schema

Gathers all schema information for the connected database or a specific schema. Critical for the agent when determining actions.

cassandra_db_select_table_data

Selects data from a specific keyspace and table. The agent can pass parameters for a predicate and limits on the number of returned records.

cassandra_db_query

Expiriemental alternative to cassandra_db_select_table_data which takes a query string completely formed by the agent instead of parameters. Warning: This can lead to unusual queries that may not be as performant(or even work). This may be removed in future releases. If it does something cool, we want to know about that too. You never know!

Environment Setup

Install the following Python modules:

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

.env file

Connection is via cassio using auto=True parameter, and the notebook uses OpenAI. You should create a .env file accordingly.

For Casssandra, set:

CASSANDRA_CONTACT_POINTS
CASSANDRA_USERNAME
CASSANDRA_PASSWORD
CASSANDRA_KEYSPACE

For Astra, set:

ASTRA_DB_APPLICATION_TOKEN
ASTRA_DB_DATABASE_ID
ASTRA_DB_KEYSPACE

For example:

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

# Also set
OPENAI_API_KEY=sk-....

(You may also modify the below code to directly connect with 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

Connect to a Cassandra Database

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}, '[e-mail]');
"""
)

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

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 '[e-mail]' 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 = '[e-mail]'", 'limit': 1}`


Row(user_email='[e-mail]', 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 '[e-mail]' 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 '[e-mail]'.
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 = '[e-mail]';"
        },
        {
          "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 '[e-mail]' 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 '[e-mail]'.
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 = '[e-mail]';"
        },
        {
          "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.