Getting Started

This guide will help you get started with the Abacus.AI Python SDK. To get a full list of commands, you can refer to the Oficial Python SDK Documentation.

Prerequisites

• Sign up for Abacus.AI
• Navigate to the API Keys Dashboard and generate an API key
• Have Python 3.6 or newer installed

Setting up your development environmentx

The following steps would allow you to successfully setup your development environment and use the Abacus.AI platform:

• Install the Abacus.AI Library:

python3 -m pip install abacusai

• Initialize a string variable with the API Key generated from the API Keys Dashboard:

api_key = 'API_KEY' # replace API_KEY with the generated key

• Import and initialize the Abacus.AI client:

from abacusai import ApiClient
client = ApiClient(api_key)

If you are working within the platform's Notebooks, then you can initialise the Abacus.AI client without passing an API key:

from abacusai import ApiClient
client = ApiClient()

Using the API

There are a couple of ways to find available APIs easily:

• Try auto-completion features in IDEs. Methods follow expressive language.
• Use the suggest_abacus_api method.
• Official python SDK documentation page

Example of suggest_abacus_api:

apis = client.suggest_abacus_apis("list feature groups in a project", verbosity=2, limit=3)
for api in apis:
    print(f"Method: {api.method}")
    print(f"Docstring: {api.docstring}")
    print("---")
     

When creating custom objects in Abacus.AI, you'll have access to templates that cover most common functionalities. This is why our Python SDK documentation focuses on core concepts rather than extensive code samples.

For practical implementation guidance, refer to the "Machine Learning Samples" section, which contains end-to-end examples demonstrating basic usage patterns.

Additionally, the platform's AI Engineer can provide custom code examples and assist with any specific API implementation needs you may have.

Below is a cheatsheet of most commonly used methods of client:

Method	Explanation
suggest_abacus_apis	Describe what you need, and we will return the methods that will help you achieve it.
describe_project	Describe's project
create_dataset_from_upload	Creates a dataset object from local data
describe_feature_group_by_table_name	Describes the feature group using the table name
describe_feature_group_version	Describes the feature group using the feature group version
list_models	List's models of a project
extract_data_using_llm	Extracts data from a document. Allows you to create a json output and extract specific information from a document
execute_data_query_using_llm	Runs SQL on top of feature groups based on natural language input. Can return both SQL and the result of SQL execution.
get_chat_response	Uses a chatLLM deployment. Can be used to add filters, change LLM and do advanced use cases using an agent on top of a ChatLLM deployment.
get_chat_response_with_binary_data	Same as above, but you can also send a binary dataset
get_conversation_response	Uses a chatLLM deployment with conversation history. Useful when you need to use the API. You create a conversation ID and you send it or you use the one created by Abacus.
get_conversation_response_with_binary_data	Same as above, but you can also send a binary dataset
evaluate_prompt	LLM call for a user query. Can get JSON output using additional arguments
get_matching_documents	Gets the search results for a user query using document retriever directly. Can be used along with evaluate_prompt to create a customized chat LLM like agent
get_relevant_snippets	Creates a doc retriever on the fly for retrieving search results
extract_document_data	Extract data from a PDF, Word document, etc using OCR or using the digital text.
get_docstore_document	Download document from the doc store using their doc_id.
get_docstore_document_data	Get extracted or embedded text from a document using their doc_id.
stream_message	Streams message on the UI for agents
update_feature_group_sql_definition	Updates the SQL definition of a feature group
query_database_connector	Executes a SQL query on top of a database connector. Will only work for connectors that support it.
export_feature_group_version_to_file_connector	Exports a feature group to a file connector
export_feature_group_version_to_database_connector	Exports a feature group to a database connector
create_dataset_version_from_file_connector	Refreshes data from the file connector connected to the file connector.
create_dataset_version_from_database_connector	Refreshes data from the file connector connected to the database connector.