Python client for the DuoScience API.
Project description
DuoScience API Python Client
The official Python client for the DuoScience API. This SDK simplifies interaction with the API's asynchronous, task-based architecture by handling HTTP requests and Server-Sent Events (SSE) streaming.
Features
- Simple Interface: An intuitive, object-oriented interface for all major API endpoints.
- Asynchronous Handling: Abstracts away the complexity of polling and long-running tasks.
- Real-time Events: Uses an iterator to stream real-time status updates and results via SSE.
- Error Handling: Gracefully manages connection and API errors.
Installation
Install the client from PyPI using pip:
pip install duoscience-client
The package will automatically install the required dependencies:
requestssseclientmarkdownpdfkit
Usage
1. Initialization
First, import and instantiate the client. You can specify the base_url of your DuoScience API instance.
from duoscience.client import DuoScienceClient
# Initialize the client
# Defaults to http://127.0.0.1:8000 if not specified
client = DuoScienceClient(base_url="https://api.duoscience.ai")
2. Core Concept: The Event Iterator
The DuoScience API operates asynchronously. When you initiate a task (like a chat or research query), the API immediately returns a task_id. The client uses this ID to connect to an SSE stream, which delivers real-time updates as the task progresses.
The DuoScienceClient handles this for you. Calling a method like client.chat() returns an iterator. Each item from the iterator is a JSON object representing an event from the backend.
You can simply loop over the iterator to process events as they arrive. The loop will automatically terminate when a final event (e.g., status: "completed" or status: "failed") is received.
Key Event Fields:
status: The current state of the task ("running","completed","failed").source: The backend component emitting the event (e.g.,"agent:WriterAgent").message: A human-readable description of the event or progress update.result: The final output of the operation (only present whenstatusis"completed").
3. Examples
Example 1: Initiating a Chat
This example starts a chat session and prints events as they are received.
import logging
from duoscience.client import DuoScienceClient
# Configure logging to see client-side status updates
logging.basicConfig(level=logging.INFO)
# Initialize the client
client = DuoScienceClient()
print("\n▶️ Initiating a new chat task...")
try:
chat_events = client.chat(
user_id="example_user_123",
chat_id="example_chat_abc",
content="Tell me about the role of mitochondria in cellular respiration.",
domain="biosciences",
effort="low"
)
final_answer = None
for event in chat_events:
status = event.get("status", "unknown")
source = event.get("source", "system")
message = event.get("message", "")
print(f"[{status.upper()}] from [{source}]: {message}")
if status == "completed":
print("\n✅ --- TASK COMPLETED --- ✅")
final_answer = event.get("result") # Final output is in the 'result' field
elif status == "failed":
print(f"❌ --- TASK FAILED --- ❌")
print(f"Error details: {message}")
break
if final_answer:
print(f"\nFinal Answer:\n{final_answer}")
except Exception as e:
logging.error(f"An error occurred: {e}")
print("\n--- Example finished ---")
Example 2: Running a Research Task
The process for initiating a research task is identical. Call the research method with your query.
print("\n▶️ Initiating a new research task...")
try:
research_events = client.research(
user_id="example_user_456",
chat_id="example_research_def",
content="What are the latest advancements in CRISPR gene editing for cancer therapy?",
domain="biosciences",
effort="high" # Use 'high' effort for detailed research
)
# Process events from the iterator (same logic as the chat example)
for event in research_events:
status = event.get("status", "unknown")
source = event.get("source", "system")
message = event.get("message", "")
print(f"[{status.upper()}] from [{source}]: {message}")
if status in ["completed", "failed"]:
break
except Exception as e:
logging.error(f"An error occurred: {e}")
print("\n--- Example finished ---")
Utilities
Markdown to PDF Conversion
The project includes a utility module duoscience.utils for converting Markdown files into high-quality PDFs. This is useful for generating reports or documents from Markdown sources.
The convert_md_to_pdf function supports:
- Custom CSS for styling.
- Syntax highlighting for code blocks via Pygments.
- Embedding a logo into the document.
- Standard Markdown extensions (tables, footnotes, etc.).
Prerequisite: This utility requires the wkhtmltopdf command-line tool to be installed on your system.
Example Usage:
from duoscience.utils import convert_md_to_pdf
import logging
logging.basicConfig(level=logging.INFO)
# Ensure wkhtmltopdf is installed and provide the path to the executable
WKHTMLTOPDF_PATH = '/usr/local/bin/wkhtmltopdf' # Path may vary
try:
convert_md_to_pdf(
md_file_path='local/research.md',
pdf_file_path='local/research.pdf',
wkhtmltopdf_path=WKHTMLTOPDF_PATH,
css_path='assets/style.css', # Optional custom CSS
logo_path='assets/duoscience-logo.png'
)
print("✅ PDF generated successfully.")
except Exception as e:
print(f"❌ Failed to generate PDF: {e}")
Development
To contribute to the development of this client, clone the repository and install the dependencies for local development.
# Clone the repository
git clone https://github.com/duoscience/duoscience-client.git
cd duoscience-client
# Install in editable mode with development dependencies
pip install -e .
Running Build Tasks
The project includes a Makefile for common tasks:
make build: Builds the source distribution and wheel.make publish: Builds and uploads the package to PyPI using Twine.make clean: Removes build artifacts.
License
This project is licensed under the MIT License. See the LICENSE file for details.
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