The official TaskingAI Python client.
For more information, see the docs at TaskingAI Documentation
The TaskingAI client is compatible with Python 3.8 and above.
Use pip to install the TaskingAI Python client.
# Install the latest version pip install taskingai # Install a specific version pip install taskingai==0.2.2
Before you can use the TaskingAI SDK, you must have your TaskingAI project set up and running. For community version, visit TaskingAI Community to get started. For cloud version, visit TaskingAI Cloud to sign up first.
You need to initialize the TaskingAI Python client with an API key you obtain from the TaskingAI console. You can set the API key as an environment variable or pass it directly to the init function.
Set it as an environment variable on your local system, and the SDK will automatically load the key without passing the api_key parameter in the init function.
export TASKINGAI_API_KEY=$YOUR_API_KEY
When you run your Python script, the SDK will automatically pick up the API key from the environment variable.
import taskingai # taskingai.init() # No need to initialize the SDK with the API key
You can also specify an API key to the SDK by passing it as a parameter to the init function:
import taskingai taskingai.init(api_key="YOUR_API_KEY")
If you use community version, you can set the base URL to the TaskingAI server by passing it to the init function:
import taskingai taskingai.init(api_key="YOUR_API_KEY", host="http://localhost:8080")
The Assistant system in TaskingAI represents a sophisticated framework designed to create and manage AI agents with customizable functionalities.
Here is an example of how to create, update, and delete an assistant:
import taskingai # Initialize your API key if you haven't already set it in the environment taskingai.init(api_key="YOUR_API_KEY") # Create an assistant asst = taskingai.assistant.create_assistant( model_id="YOUR_MODEL_ID", memory={"type": "naive"}, system_prompt_template=["You are a professional assistant."], ) print(f"Assistant created: {asst.assistant_id}") # Get details about the assistant assistant_details = taskingai.assistant.get_assistant(assistant_id=asst.assistant_id) print(f"Assistant details: {assistant_details}") # Update the assistant's description taskingai.assistant.update_assistant( assistant_id=asst.assistant_id, description="Updated description" ) print(f"Assistant updated.") # Delete the assistant when done taskingai.assistant.delete_assistant(assistant_id=asst.assistant_id) print("Assistant deleted successfully.")
TaskingAI offers comprehensive tools for the retrieval system, ranging from straightforward to intricate setups. Here is an example of how to create, add, retrieve, and delete a record in a collection:
import taskingai # Create a collection for storing and retrieving data coll = taskingai.retrieval.create_collection( embedding_model_id="YOUR_EMBEDDING_MODEL_ID", capacity=1000 ) print(f"Collection created: {coll.collection_id}") # Add a record to the collection record = taskingai.retrieval.create_record( collection_id=coll.collection_id, type="text", content="Machine learning is ...", text_splitter={"type": "token", "chunk_size": 200, "chunk_overlap": 20} ) print(f"Record added to collection: {record.record_id}") # Retrieve the record from the collection retrieved_record = taskingai.retrieval.get_record( collection_id=coll.collection_id, record_id=record.record_id ) print(f"Record retrieved: {retrieved_record.content}") # Delete the record taskingai.retrieval.delete_record( collection_id=coll.collection_id, record_id=record.record_id ) print("Record deleted.") # Delete the collection taskingai.retrieval.delete_collection(collection_id=coll.collection_id) print("Collection deleted.")
We welcome contributions of all kinds. Please read our Contributing Guidelines for more information on how to get started.