TaskingAI
Project description
TaskingAI-client
The TaskingAI Python client for creating and managing AI-driven applications.
For more information, see the docs at TaskingAI Documentation
Installation
Install the latest released version using pip:
pip3 install taskingai
Quickstart
Here's how you can quickly start building and managing AI-driven applications using the TaskingAI client.
Assistants
Explore the ease of creating and customizing your own AI assistants with TaskingAI to enhance user interactions.
import taskingai
from taskingai.assistant import *
from taskingai.assistant.memory import AssistantNaiveMemory
# Initialize your API key if you haven't already set it in the environment
taskingai.init(api_key="YOUR_API_KEY")
# Create an assistant
assistant = create_assistant(
model_id="YOUR_MODEL_ID",
memory=AssistantNaiveMemory(),
system_prompt_template=["You are a professional assistant."],
)
print(f"Assistant created: {assistant.id}")
# Get details about the assistant
assistant_details = get_assistant(assistant_id=assistant.id)
print(f"Assistant details: {assistant_details}")
# Update the assistant's description
update_assistant(
assistant_id=assistant.id,
description="An updated description for my assistant."
)
print(f"Assistant updated.")
# Delete the assistant when done
delete_assistant(assistant_id=assistant.id)
print("Assistant deleted successfully.")
Retrieval
Leverage TaskingAI's retrieval capabilities to store, manage, and extract information, making your applications smarter and more responsive.
import taskingai
from taskingai.retrieval import *
# Create a collection for storing and retrieving data
collection = create_collection(
embedding_model_id="YOUR_MODEL_ID",
capacity=1000
)
print(f"Collection created: {collection.id}")
# Add a record to the collection
record = create_record(
collection_id=collection.id,
content="Example text for machine learning.",
text_splitter=TokenTextSplitter(chunk_size=200, chunk_overlap=20),
)
print(f"Record added to collection: {record.id}")
# Retrieve the record from the collection
retrieved_record = get_record(
collection_id=collection.id,
record_id=record.id
)
print(f"Record retrieved: {retrieved_record.text}")
# Delete the record
delete_record(
collection_id=collection.id,
record_id=record.id
)
print("Record deleted.")
# Delete the collection
delete_collection(collection_id=collection.id)
print("Collection deleted.")
Tools
Utilize TaskingAI's tools to create actions that enable your assistant to interact with external APIs and services, enriching the user experience.
import taskingai
from taskingai.tool import *
# Define a schema for the tool action
NUMBERS_API_SCHEMA = {
# Schema definition goes here
}
# Create a tool action based on the defined schema
actions = bulk_create_actions(
openapi_schema=NUMBERS_API_SCHEMA,
authentication=ActionAuthentication(type=ActionAuthenticationType.NONE)
)
action = actions[0]
print(f"Action created: {action.id}")
# Run the action for a test purpose
result = run_action(
action_id=action.id,
parameters={"number": 42}
)
print(f"Action result: {result}")
# Delete the action when done
delete_action(action_id=action.id)
print("Action deleted.")
Contributing
We welcome contributions of all kinds. Please read our Contributing Guidelines for more information on how to get started.
Project details
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