The model effectively handles the entire 25,000 words?

Please, how much text does chatgpt-4 turbo or the more advanced version handle efficiently in the 20 dollar monthly subscription?

In ChatGPT Plus(20 dollar subscription) and ChatGPT Teams plans, excluding ChatGPT Enterprise, you can only handle up to 32K context.

Additionally, in ChatGPT, unlike the API, you cannot specify a particular model.

One must understand: You don’t have the ability to directly input that much information at once into ChatGPT. Its input box is limited. Your 25000 words might be an estimate from its advertised context length, discovered in network specification or described at various tiers as 32k.

For example, I craft a special version of the OpenAI YAML specification, containing just the assistants endpoint and stripping all node.js examples, with other curation, for 29374 tokens. Counted as 11100 words by an online counter.

What I want to know is prompted: “AI TASK: describe the steps to get an AI response from a user input query using Assistants framework API as described.”

  • Direct input into ChatGPT Plus: Denied

  • File upload as .yaml: ChatGPT uses Python to interpret, maximum extraction is the final 32768 characters, 4520 tokens. Poor answer.

  • File upload as .txt: *ChatGPT uses files browser to interpret, with the amount placed into AI context unclear. Answer is almost correct but succinct.

ChatGPT answers about Assistants; 179 tokens

To get an AI response using the Assistants framework API, you would typically follow these steps:

  1. Create an Assistant: Use the POST /assistants endpoint to create an assistant by specifying instructions, the model, and any tools or files it should use.
  2. Create a Thread: With the POST /threads endpoint, create a thread which represents a conversation or a session with the assistant.
  3. Create a Message: Using the POST /threads/{thread_id}/messages endpoint, you can send a user input query to the assistant within a thread.
  4. Retrieve the Response: The assistant’s response can be accessed through the API, typically as a message in the thread.

Each step involves sending a request to the API with the necessary parameters and handling the response. It’s crucial to manage thread and message IDs to track conversations and responses.

An answer that is more like an ephemeral outline than documentation. It hasn’t found the need to use the “run” method.

Now, we use the API to send to gpt-4-turbo-preview, AI that is unfettered. How does it do after sending those 139795 characters? (answering a question you might have also). A response that costs $0.32:


API gpt-4-turbo answer about assistants; 658 tokens

To get an AI response from a user input query using the Assistants framework API, you can follow these steps:

  1. Create an Assistant: First, you need to create an assistant that will process the user’s input. This involves defining the model, instructions, and any tools the assistant might need.

  2. Create a Thread: A thread represents a conversation or a context in which messages (queries and responses) are exchanged. You need to create a thread to start interacting with the assistant.

  3. Create a Message: Once you have a thread, you can create a message within that thread. This message will contain the user’s input query.

  4. Retrieve the Assistant’s Response: After creating the message with the user’s query, the assistant processes it and generates a response. You can retrieve this response by listing the messages in the thread.

Here’s how you can implement these steps using Python with the hypothetical openai Python package:

from openai import OpenAI

# Initialize the OpenAI client with your API key
client = OpenAI(api_key="your_api_key_here")

# Step 1: Create an Assistant
assistant = client.beta.assistants.create(
    model="gpt-4",
    name="My Assistant",
    instructions="You are a helpful assistant designed to answer user queries.",
    tools=[{"type": "code_interpreter"}]  # Example tool, adjust as needed
)
assistant_id = assistant["id"]

# Step 2: Create a Thread
thread = client.beta.threads.create()
thread_id = thread["id"]

# Step 3: Create a Message with the user's input query
user_query = "What is the capital of France?"
message = client.beta.threads.messages.create(
    thread_id=thread_id,
    role="user",
    content=user_query
)

# Step 4: Retrieve the Assistant's Response
# Assuming the assistant responds immediately, list the messages in the thread
# to retrieve the assistant's response. In practice, you might need to poll
# for the response if processing takes time.
messages = client.beta.threads.messages.list(thread_id=thread_id)

# Extracting the assistant's response from the messages
# Note: This is a simplified example. You might need to filter messages based on the role or other criteria.
for msg in messages["data"]:
    if msg["role"] == "assistant":
        print("Assistant's response:", msg["content"][0]["text"]["value"])
        break

This example assumes the existence of an openai Python package and methods that correspond directly to the API endpoints described in the OpenAPI documentation. In reality, you might need to use the requests library or another HTTP client to make API calls, depending on the actual Python SDK provided by OpenAI or if you’re interacting with the API directly.

It’s alright. We have code steps to try. However, it completely overlooks the requirement to set a run into motion and poll for status. gpt-4-turbo once again insults information provided to it by the user as “hypothetical”, as if I’m a liar.


Conclusion: ChatGPT Plus would be better for tasks that only need a subset of provided information, without full understanding. The context length is more about past conversation.

I did not send to full limited-availability GPT-4-32k, where I expect better quality. At $2.00 a run.



Addendum: I can get more of the file into AI conversation history instructing and using hilariously inefficient methods: The AI calls Python, receives a response of 30000 characters, the AI responds to me… then repeat 3x more until the AI quits before the whole file would have been read. Then the quality of the chat context management is what would allow it to answer - incorrectly:

Poor/incorrect answer after iterating file into chat history of function-calling

User:

Iteratively load 30000 characters at a time in steps from the yaml file using python until all is read, thus being placed into AI context memory. Then answer again: describe the steps to get an AI response from a user input query using Assistants framework API as described.

ChatGPT:

The initial portion of the file has been read successfully, and it seems to involve examples of API usage, possibly related to creating and running threads with user messages. To fully understand the steps to get an AI response using the Assistants framework API, I’ll continue loading the file content in 30,000-character increments and integrating it into the context memory. This process will be repeated until the entire file content is read. Let’s proceed with the next chunk.

The process of loading the file in chunks is continuing, and we’re gradually building a more comprehensive view of the content. The latest portion appears to touch upon API endpoints related to assistant files, indicating a more detailed description of how files are managed within this framework.

Let’s proceed to load the next segment of the file to further enrich our understanding.
(python)
The latest content chunk suggests details about configuring certain parameters, such as the number of epochs for a process that could be related to fine-tuning a model. This indicates the comprehensive nature of the API, which might include advanced features for training or adjusting AI models.
(python)
We’ll continue with the next chunk to further our understanding and ensure a complete overview of the steps to get an AI response from a user input query using the Assistants framework API.

The newly loaded content portion seems to detail the structure of messages within threads, including roles (such as “assistant”), content types, and how responses are structured within the framework. This part provides insights into how interactions are managed and how responses are formulated and delivered.
(python)
To ensure a thorough understanding and coverage of the entire document, let’s proceed with loading the next segment.
(python)
The most recent segment of the content appears to conclude with details on listing files, indicating pagination features such as first_id, last_id, and has_more, which suggest the API supports listing items with the ability to navigate through large sets of data. The section ends with a note on security, specifically API key authentication, and a meta tag, followed by the AI task directive.

Now that a significant portion of the file has been loaded and reviewed, let’s summarize the steps to get an AI response from a user input query using the Assistants framework API as described in the content:

  1. Authenticate: Use your API key for authentication, typically included in the request’s header.

  2. Prepare the Query: Format your user input according to the API’s specifications. This might involve setting up a thread with one or more messages, where each message has a role (e.g., “user” for the query and “assistant” for the response) and content.

  3. Send the Query: Make a request to the appropriate endpoint, such as creating a new thread or adding a message to an existing thread. The endpoint and the method (POST, GET, etc.) will depend on the API’s structure.

  4. Include Relevant Files (if necessary): If your query involves specific files, ensure they are correctly attached or referenced in your request as indicated by the file handling sections.

  5. Receive and Process the Response: The API will process the query and return a response, which could be in the form of text, a structured object, or a list of messages within a thread. This response may include the AI’s reply, structured according to the API’s message format.

  6. Pagination and File Listings (if applicable): For responses that include lists of items (like files or messages), use the provided pagination controls (e.g., first_id, last_id, has_more) to navigate through the list if necessary.

  7. Error Handling: Be prepared to handle errors, which might include invalid requests, authentication issues, or server errors. The API should provide error messages to help diagnose issues.

This summary provides a structured approach to interacting with the Assistants framework API, based on the loaded content. If the process involves more specific steps or nuances not covered in the loaded sections, a complete review of the entire file would be necessary for a detailed understanding.

(this is why you don’t put “Assistants” into customer-facing applications, with you paying…)

[quote=“dignity_for_all, post:2, topic:631119, full:true”]
On the ChatGPT Plus ($20 subscription) and ChatGPT Teams plans, excluding ChatGPT Enterprise, it can only handle up to 32K context.

Today I tried with 6000 words and ChatGPTplus no longer remembered the conversation

Hmm, it seems like it’s really not remembered.

But maybe I asked the question in a wrong way.

It remenbered.