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OpenAI Assistants API – Course for Beginners

freeCodeCamp.org・179 minutes read

The text explores Open AI's Assistance API through hands-on projects and real-world examples, detailing how to create AI assistants with advanced functionalities and knowledge retrieval. Instructor, Paulo Deson, guides developers through the process, emphasizing the importance of context, model interactions, and thread management for effective AI applications.

Insights

The Assistance API simplifies AI application development by managing infrastructures, data, models, prompts, embeddings, and storage.
Models in the Assistance API are stateless, emphasizing the importance of context for reference as they lack the ability to save history.
Creating assistants in the Open AI dashboard involves naming, providing instructions, selecting a model, and adding tools like the code interpreter.
The Assistant Manager class encapsulates functions for creating and running assistants in an object-oriented manner, enhancing organization and accessibility.
Embeddings, mathematical representations of text, play a crucial role in facilitating search and similarity comparisons within the vector store, ensuring accurate results.

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Recent questions

How can I create powerful AI assistance?
By taking a course on Open AI's Assistance API.

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Summary

00:00

"Developers Master Open AI Assistance API"

Course on Open AI's Assistance API for developers to create powerful AI assistance
Covers integrating API for advanced functionalities, knowledge retrieval, and code interpretation
Hands-on projects with Streamlet and real-world examples for practical skills
Instructor, Paulo Deson, a software engineer with vast teaching experience
Prerequisites include basic programming knowledge, particularly in Python
Detailed instructions provided for setting up Python and Visual Studio Code
Essential to have an Open AI account and create an API key for using the Assistance API
Assistance API extends Open AI API, simplifying AI application development
Solves the complexity of managing infrastructures, data, models, prompts, embeddings, and storage
Features like persistent threading for ongoing conversations, retrieval mechanisms, and code interpreters enhance functionality

15:32

"Open AI APIs: Context, Chat, Assistance, Complexity"

Models are stateless, lacking the ability to save history, making context crucial for reference.
The Assistance API provides context, allowing access to files in various formats.
The Chat Completion API directly queries Open AI models for responses.
The Chat Completion API lacks knowledge of events post-September 2023.
The Assistance API requires creating an assistant with defined capabilities.
Threads are essential for ongoing conversation management in the Assistance API.
The Assistance API setup is more complex compared to the Chat Completion API.
The Assistance API's building blocks include the assistant, threads, messages, and the run entity.
The run entity evaluates queries and processes steps to generate responses.
Creating an assistant in the Open AI dashboard involves naming, providing instructions, selecting a model, and adding tools like the code interpreter.

30:56

Creating and Sending Thread Messages with Metadata

The thread was created with a response containing the thread ID.
Additional metadata was included in the thread creation.
A message was added to the thread, visible in the log.
The message was sent to run, requiring the assistant ID.
The response included an ID, object, creation time, assistant ID, and thread ID.
Various metadata and the model used were sent in the response.
The run status was checked to ensure completion of all steps.
The process involved creating and crafting a message for the assistant.
The response included a JSON format with data fields.
The assistant message was successfully returned to the thread.

46:30

Creating "Personal Trainer" Assistant for Fitness Advice

Created an assistant by assigning the name "personal trainer" and obtaining the ID for this assistant.
Proceeded to create a thread where messages will be stored, using the client beta and the "threads.create" function.
Included a user message within the thread, asking about workout tips to lose fat and build muscles.
Printed out the IDs for the assistant and the thread for reference.
Successfully ran the code to create the assistant and thread, verifying their existence in the backend.
Ensured that hardcoded IDs were used to prevent the creation of duplicate assistants.
Formulated a message asking about the best exercises for lean muscles and strength.
Added this message to the thread using the "messages.create" function.
Ran the assistant by creating a "runs" entity attached to the thread, providing instructions for the assistant to address the user as James Bond.
Implemented a helper function to wait for the completion of the run, retrieving the assistant's response and displaying the elapsed time for the process.

01:02:38

"News Summarizer: API Key Access & Function"

The text discusses printing steps using F strings and accessing step data in a list format.
It demonstrates running a code to determine the number of reps needed for building lean muscles.
The text showcases the details of steps and metadata generated during the process.
It emphasizes the value and leverage of the demonstrated concept.
A new application called the "current news summarizer assist" is introduced.
The application utilizes a function calling tool to access news APIs and summarize news articles.
The text explains the process of obtaining an API key from newsapi.org for the application.
It details the creation of a news summarizer application that fetches news data based on user input topics.
Instructions are provided on setting up the environment, installing necessary packages, and configuring API keys.
A function is defined to fetch news data from the API, parse the JSON response, and extract relevant information for summarization.

01:18:14

Data Retrieval and Formatting for News Articles

To retrieve the status, access the data and navigate to the status field within the object.
Obtain the total results by accessing the data and retrieving the total.
Access the articles by looping through the data to gather all articles as objects or dictionaries.
Ensure consistency in naming fields to accurately retrieve necessary information.
Loop through each article to extract desired information like source, author, title, description, and URL.
Consolidate the extracted information into a formatted string for ease of use.
Compile the extracted information into an array for each article.
Implement error handling to return an empty array if the response status code is not 200.
Utilize the "get news" function to fetch news articles based on a specified topic.
Establish an "assistant manager" class to encapsulate all functions and data within a single class for streamlined access and organization.

01:35:13

"Thread Management and Message Processing Methods"

Set the field "class field self" to the thread object ID.
Print the thread ID using an F string and pass self.thread.ID.
Define the method "add message to thread" to add messages to a thread, passing self reference, role, and content.
Check for the existence of a thread ID before creating a message using self.thread.ID and the passed role and content.
Define the method "run assistant" to run the assistant, passing self reference and instructions.
Check for the existence of a thread ID and assistant before running the thread using self.client.Beta.say_threads.runs.create.
Define the method "process messages" to retrieve and process messages from a thread, passing self reference.
Retrieve messages using self.client.Beta.threads.messages and the thread ID self.thread.ID, storing them in a list named "summary."
Print the summary and role of the last message retrieved, appending the response to the summary list.
Define the method "wait for completed" to wait for the completion of a run, checking the run status and processing messages if completed, or taking action if requires action.

01:52:33

Essential Function for Gathering Tools and News

The function "call required functions" is crucial for the process.
The function requires passing "self" and "required actions."
If "self.run" is not available, the function returns.
"Tools output" is an empty array used to gather required tools.
"Required actions" is a list that includes multiple tools.
The function extracts the function name and action from the required actions.
If the function name is "get news," it retrieves the arguments for the function.
The function "get news" returns a string containing news details.
The string is structured to facilitate summarization by a model.
The tool outputs are submitted back to the assistant using specific IDs and outputs.

02:09:01

"Creating Assistant Manager Class for Efficient Functions"

The text discusses the creation of an assistant manager class to handle various functions.
Key methods include call required functions, process message, Run assistant, create thread, and create assistant.
The assistant manager class allows for the instantiation of objects to run and create assistants in an object-oriented manner.
The get news tool is crucial for the function calling tool in the process.
Streamlit is used to create a web page interface for user interaction.
The user interface includes a text input field for topics and a submit button labeled "run assistant."
Upon clicking the submit button, the assistant manager creates an assistant and thread if they don't exist.
The assistant adds a message to the thread, runs the assistant, waits for completion, and processes messages.
The summary of news articles is displayed on the web page, along with the Run steps.
The process involves retrieving assistant and thread IDs to avoid creating duplicates and ensuring smooth functionality.

02:26:27

"Enhancing Newsletter Creation with Versatile API"

The text discusses the potential use cases of an API for creating a newsletter application based on specific topics.
It highlights the versatility of the API in generating newsletters with main points on various topics.
The API mentioned is the assistance API, which has been utilized for various functions.
A new summarizer tool has been created to enhance the capabilities of the assistant by allowing function calls.
Function calling is emphasized as a powerful tool for extending the assistant's capabilities beyond the existing knowledge base.
The text introduces the concept of knowledge retrieval and the importance of providing specific knowledge to the model for answering questions effectively.
It explains the process of passing documents, like PDF files, to the large language model to enhance its knowledge base.
The text delves into the technical process of document splitting and storing in a vector store for efficient retrieval.
It emphasizes the significance of embeddings, which are mathematical representations of text, in facilitating search and similarity comparisons.
The process of transforming queries into embeddings for effective searching in the vector store is detailed, leading to accurate results.

02:42:59

Uploading PDF files for AI analysis.

The process involves running a program using specific commands and passing necessary parameters like client, thread ID, and Run ID.
Checking Run steps logs is an option to monitor progress.
The goal is to push a cryptocurrency PDF file into OpenAI embeddings.
Running the program should result in successful uploading of the PDF file.
The uploaded file should be visible in the OpenAI backend, confirming the process worked.
The assistant response includes information from the uploaded PDF file, enhancing the knowledge base.
The process involves creating a message with a query related to the uploaded file.
The assistant's response should reflect information extracted from the PDF file.
The successful response indicates the assistant's ability to retrieve information from the uploaded file.
The next step involves building a study buddy by uploading multiple files and engaging in conversations based on their content using Streamlit.

03:01:46

"Enhancing Chat Sessions with Citations and Footnotes"

To initiate a chat session, create a new thread named "chat thread" and set the session for that thread using the thread ID.
Writing a message to the thread involves passing the chat thread ID and handling conditions like on-click events.
Enhancing a model by attaching information from documents to it, including metadata like citations, is crucial for better responses.
A helper function is used to process messages with citations, extracting content and formatting citations as footnotes.
Limitations in retrieving cited files are addressed by using dummy data and hardcoding file names for testing purposes.
Creating footnotes for responses involves retrieving annotations, processing them, and appending them to the citations list.
Setting up the front end interface involves using Streamlit to create a page titled "Study Buddy" and a subheader promoting learning through document chat.
Checking and initializing chat and model sessions ensures smooth functioning before proceeding with user interactions.
User prompts and messages are added to the chat thread, and a spinner is displayed while the assistant generates a response.
Retrieving and processing assistant messages involves listing messages, filtering for assistant responses, and displaying them with citations.

03:22:48

"AI Interface for Cryptocurrency File Management"

A user interface was created for browsing and uploading files, specifically focusing on cryptocurrency files.
The process of uploading a file was successful, resulting in a file ID being generated for reference.
The uploaded files were accessible through Open AI, displaying details like status, size, and file ID.
The user engaged in a chat with an AI assistant, discussing data mining in the context of cryptocurrencies.
An issue arose when attempting to determine the number of cryptocurrencies discussed in a document titled "Cryptor PDF."
Multiple files were uploaded, leading to confusion and the need to delete some files for clarity.
The AI assistant successfully retrieved information on lipids from a biology file, showcasing the functionality of the system.

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