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How to Query Live Elasticsearch Data in Natural Language in Python using LlamaIndex


Use LlamaIndex to query live Elasticsearch data data in natural language using Python.

Start querying live data from Elasticsearch using the CData Python Connector for Elasticsearch. Leverage the power of AI with LlamaIndex and retrieve insights using simple English, eliminating the need for complex SQL queries. Benefit from real-time data access that enhances your decision-making process, while easily integrating with your existing Python applications.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Elasticsearch data in Python. When you issue complex SQL queries from Python, the driver pushes supported SQL operations, like filters and aggregations, directly to Elasticsearch and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Whether you're analyzing trends, generating reports, or visualizing data, our Python connectors enable you to harness the full potential of your live data source with ease.

About Elasticsearch Data Integration

Accessing and integrating live data from Elasticsearch has never been easier with CData. Customers rely on CData connectivity to:

  • Access both the SQL endpoints and REST endpoints, optimizing connectivity and offering more options when it comes to reading and writing Elasticsearch data.
  • Connect to virtually every Elasticsearch instance starting with v2.2 and Open Source Elasticsearch subscriptions.
  • Always receive a relevance score for the query results without explicitly requiring the SCORE() function, simplifying access from 3rd party tools and easily seeing how the query results rank in text relevance.
  • Search through multiple indices, relying on Elasticsearch to manage and process the query and results instead of the client machine.

Users frequently integrate Elasticsearch data with analytics tools such as Crystal Reports, Power BI, and Excel, and leverage our tools to enable a single, federated access layer to all of their data sources, including Elasticsearch.

For more information on CData's Elasticsearch solutions, check out our Knowledge Base article: CData Elasticsearch Driver Features & Differentiators.


Getting Started


Overview

Here's how to query live data with CData's Python connector for Elasticsearch data using LlamaIndex:

  • Import required Python, CData, and LlamaIndex modules for logging, database connectivity, and NLP.
  • Retrieve your OpenAI API key for authenticating API requests from your application.
  • Connect to live Elasticsearch data using the CData Python Connector.
  • Initialize OpenAI and create instances of SQLDatabase and NLSQLTableQueryEngine for handling natural language queries.
  • Create the query engine and specific database instance.
  • Execute natural language queries (e.g., "Who are the top-earning employees?") to get structured responses from the database.
  • Analyze retrieved data to gain insights and inform data-driven decisions.

Import Required Modules

Import the necessary modules CData, database connections, and natural language querying.

import os import logging import sys # Configure logging logging.basicConfig(stream=sys.stdout, level=logging.INFO, force=True) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) # Import required modules for CData and LlamaIndex import cdata.elasticsearch as mod from sqlalchemy import create_engine from llama_index.core.query_engine import NLSQLTableQueryEngine from llama_index.core import SQLDatabase from llama_index.llms.openai import OpenAI

Set Your OpenAI API Key

To use OpenAI's language model, you need to set your API key as an environment variable. Make sure you have your OpenAI API key available in your system's environment variables.

# Retrieve the OpenAI API key from the environment variables OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] ''as an alternative, you can also add your API key directly within your code (though this method is not recommended for production environments due to security risks):'' # Directly set the API key (not recommended for production use) OPENAI_API_KEY = "your-api-key-here"

Create a Database Connection

Next, establish a connection to Elasticsearch using the CData connector using a connection string with the required connection properties.

Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.

Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.

Connecting to Elasticsearch

# Create a database engine using the CData Python Connector for Elasticsearch engine = create_engine("cdata_elasticsearch_2:///?User=Server=127.0.0.1;Port=9200;User=admin;Password=123456;")

Initialize the OpenAI Instance

Create an instance of the OpenAI language model. Here, you can specify parameters like temperature and the model version.

# Initialize the OpenAI language model instance llm = OpenAI(temperature=0.0, model="gpt-3.5-turbo")

Set Up the Database and Query Engine

Now, set up the SQL database and the query engine. The NLSQLTableQueryEngine allows you to perform natural language queries against your SQL database.

# Create a SQL database instance sql_db = SQLDatabase(engine) # This includes all tables # Initialize the query engine for natural language SQL queries query_engine = NLSQLTableQueryEngine(sql_database=sql_db)

Execute a Query

Now, you can execute a natural language query against your live data source. In this example, we will query for the top two earning employees.

# Define your query string query_str = "Who are the top earning employees?" # Get the response from the query engine response = query_engine.query(query_str) # Print the response print(response)

Download a free, 30-day trial of the CData Python Connector for Elasticsearch and start querying your live data seamlessly. Experience the power of natural language processing and unlock valuable insights from your data today.

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