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How to Query Live FHIR Data in Natural Language in Python using LlamaIndex
Use LlamaIndex to query live FHIR data data in natural language using Python.
Start querying live data from FHIR using the CData Python Connector for FHIR. 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 FHIR data in Python. When you issue complex SQL queries from Python, the driver pushes supported SQL operations, like filters and aggregations, directly to FHIR 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.
Overview
Here's how to query live data with CData's Python connector for FHIR 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 FHIR 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.fhir 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 FHIR using the CData connector using a connection string with the required connection properties.
Set URL to the Service Base URL of the FHIR server. This is the address where the resources are defined in the FHIR server you would like to connect to. Set ConnectionType to a supported connection type. Set ContentType to the format of your documents. Set AuthScheme based on the authentication requirements for your FHIR server.
Generic, Azure-based, AWS-based, and Google-based FHIR server implementations are supported.
Sample Service Base URLs
- Generic: http://my_fhir_server/r4b/
- Azure: https://MY_AZURE_FHIR.azurehealthcareapis.com/
- AWS: https://healthlake.REGION.amazonaws.com/datastore/DATASTORE_ID/r4/
- Google: https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/fhirStores/FHIR_STORE_ID/fhir/
Generic FHIR Instances
The product supports connections to custom instances of FHIR. Authentication to custom FHIR servers is handled via OAuth (read more about OAuth in the Help documentation. Before you can connect to custom FHIR instances, you must set ConnectionType to Generic.
Connecting to FHIR
# Create a database engine using the CData Python Connector for FHIR
engine = create_engine("cdata_fhir_2:///?User=URL=http://test.fhir.org/r4b/;ConnectionType=Generic;ContentType=JSON;AuthScheme=None;")
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 FHIR 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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FHIR Python Connector
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