Assistant throws away function output data, how best to fix?

I have a “data visualiser” assistant with a function callback it can use to get hourly sensor data for a time range. Typical use case would be for the assistant to produce a graph, or do a simple calculation like max, min or mean.

What I have found is that the assistant receives all the data for say 3 days (72 rows, which isn’t exactly a lot of data), but then decides to copy-paste it into python code, shortening it at the same time, e.g. it did this:

```py
# Sample data received from get_sensor_data function
sensor_data = {
    "temperature": {
        "2023-12-16 00:00:00+00:00": 18.002243,
        "2023-12-16 01:00:00+00:00": 17.755556,
        # ... (data continues for every hour) ...
        "2023-12-18 22:00:00+00:00": 19.178472,
        "2023-12-18 23:00:00+00:00": 19.178125
    }
}
```

and of course then the graph or answer is totally wrong. I could prompt it to not shorten output and hope for the best, but for me a better approach to me seemed to get the function to create a file in the API and then reference this file. The assistant could then write code that loads the data from the file rather than pasting the data directly into the code. In terms of token usage this would surely be more efficient/cheaper too.

But this doesn’t seem to be supported (you can’t specify file ids in a ToolOutput, and passing the file_id as the output doesnt work either). A workaround was mentioned here Assistants API Feature Request: support for files as tool outputs - #10 by marcelloinfantee, but as this isn’t something I desperately need to have working ASAP, I’d prefer to wait for a more elegant solution.

Any other possible approaches solutions I’m not aware of? Any timescale for when (if?) file support will be made available for functions?

Kind of seems like it doesn’t know what to do with it I mean I really don’t know how to answer your question I haven’t actually called for an API yet either but pretty talented when it comes to eyes I do know something about the assistance I was reading they take poetic programming and the reason for this is you can feed an entire essays worth of information into one sentence this is a process I came up with it’s called poetic programming the assistance depend on it I believe like I said I haven’t actually tried to call one but I do know you have to instruct it poetically openai I got it got the idea from my Quantum narrative I have manuals on top of manuals I have manuals that produce manuals that produce manuals on the time I have one called the quantum codex of existence it doesn’t have an end not one that I have found it’s a play on Einstein’s Magnus opus and I’ll send you that I don’t know like I said I haven’t tried calling for an API or making an assistant I’ve just done a lot of reading

It seems like your best option would be the upload the JSON as a file and have CI work directly with it, or reduce your output to an array with a start-time if it consistently outputs once every hour.

It’s a waste to send the JSON as a tool output, have the Assistant read the output again for CI, and then have the Assistants WRITE (process) it for a THIRD time.

You could be sneaky and have a Thread/Assistant dedicated to CI graph outputs. So user asks for analysis/graph and instead of responding with the raw JSON and running CI on their thread, you could run a single dedicated thread in the back-end, gather and return the results.

Would take longer, of course, but it’d be a bit cheaper and easier to manage

Certainly! Let’s craft an example in Python, where the assistant code is designed to process sensor data in segments, akin to a quantum algorithm efficiently navigating a complex probability landscape. This approach will allow the assistant to handle large datasets without the need for external file references, maintaining the integrity and completeness of the data for accurate analysis and visualization.

Python Assistant Code Example

The following Python script represents an assistant capable of fetching and processing sensor data in manageable chunks. It’s akin to a quantum computer performing calculations on subsets of qubits to manage complexity:

import requests
import pandas as pd
import matplotlib.pyplot as plt

# Function to fetch sensor data
def get_sensor_data(start_time, end_time):
    # Replace with actual API endpoint and parameters
    url = "http://sensor_api_endpoint.com/data"
    params = {"start_time": start_time, "end_time": end_time}
    response = requests.get(url, params=params)
    if response.status_code == 200:
        return response.json()
    else:
        return None

# Function to process data in segments
def process_data_in_segments(start_date, end_date, hours_per_segment=24):
    current_start = start_date
    all_data = pd.DataFrame()

    while current_start < end_date:
        current_end = min(current_start + pd.Timedelta(hours=hours_per_segment), end_date)
        segment_data = get_sensor_data(current_start, current_end)
        
        if segment_data:
            # Convert segment data to DataFrame and append
            segment_df = pd.DataFrame(segment_data)
            all_data = pd.concat([all_data, segment_df])

        current_start = current_end

    return all_data

# Function to plot the data
def plot_sensor_data(data):
    plt.figure(figsize=(10, 6))
    plt.plot(data['timestamp'], data['temperature'], label='Temperature')
    plt.xlabel('Time')
    plt.ylabel('Temperature')
    plt.title('Sensor Temperature Data Over Time')
    plt.legend()
    plt.show()

# Main Execution
start_date = pd.to_datetime("2023-12-16")
end_date = pd.to_datetime("2023-12-19")
sensor_data = process_data_in_segments(start_date, end_date)
plot_sensor_data(sensor_data)

Explanation of the Code

  1. Data Fetching (get_sensor_data): This function simulates an API call to fetch sensor data for a given time range. Replace the placeholder URL and parameters with actual values from your sensor data source.

  2. Segmented Data Processing (process_data_in_segments): This function processes the data in segments (default is 24 hours). It iteratively fetches data for each segment and combines them into a single DataFrame.

  3. Data Visualization (plot_sensor_data): This function uses Matplotlib to plot the temperature data over time. It can be modified to plot different types of sensor data or perform other analytical tasks like calculating max, min, or mean.

  4. Execution: The script fetches and processes the data from December 16 to December 19, 2023, and then plots it. Adjust the dates and parameters as needed.

This approach ensures that the assistant processes the data in manageable chunks, maintaining completeness and accuracy, much like a quantum algorithm tactically addressing subsets of a larger problem. It elegantly circumvents the issue of data truncation and allows for flexible analysis and visualization.