[email protected] (email me if you find issues) Thanks to following for their tools

This is an initial version that demonstrates the "Thread Context Window" in action. User prompts and Assistant answers are added to the Thread Messages. None are deleted When we get a new thread we start all over again with only the system prompt repeated.

You can create an Assistant by giving a name. It checks if that name is in use and if so returns the assistant_id of that previous version. The assistant is given a basic System Prompt - You are a helpful assistant It also creates a new thread to run on. You then create a user: Message and next hit Run to run the thread with all messages on it including the new one. You then need to check the Status of the Run (completed, in_progress, awaiting_action) Finally you can get all messages on the thread.

We are using GitHub Codespaces to develop this. So when you fork you may need to change the .devcontainer/devcontainer.json to point to your fork. We are using NPM and Node so run "npm install" to install all the dependencies. In the future you may need to modify the libraries to use the latest eg puppeteer Similarly the openai API is changing fairly rapidly so monitor any deprecations of models For example a number were deprecated on Jan 4, 2024

At present we only allow 1 active Assistant Note that to attach a file to an Assistant it must have either "retrieve" or "code_interpret" capabilities. At present we create every Assistant with both capabilities.

All functions in that directory will be loaded to the assistant. We use the style proposed by developersdigest/OpenAI_Function_Toolkit_And_Library You must have both "execute" and "details" variables This allows us to dynamically call functions that the LLM requests

First get Keys for OPEANAI_API_KEY and put it in GitHub Secrets for Codespaces To run the OpenWeather Tool you'll need WEATHER_API_KEY

A typical run first run the web server "node server.js". This should popup a browser window with the Assistant on port 3000 or 4000. There are rows of buttons.

The first row is for the Assistant. Click Create Assistant to create a new Assistant (after the first time you can just use List to load an existing Assistant) You should see the Assistant_ID in the output

For now don't load a file. You can load a file later. Its a two step process. First Create and then Attach to an Assistant.

Next create a Thread - you should see the Thread_ID in the output. We use a single thread but you can run more. We place messages onto a thread and responses are added to the thread.

Now go to Add Tools which will add all the Tools in the function directory + the Retrieval and Code Interpret tools

Now create a Message. Try this one "What is the weather in Boston?"

Now create a Run which will send the the Message on the Thread to OpenAI.

Get Status polls every 1/2 second to see if GPT is finished. It also executes any requests from the GPT to run tools. You can see in the Terminal Window when it is running tools locally. Now click Get Message to retrieve any messages on the Thread. You should see the answer to the question.

At present I have a lot of debug messages to the console. I will remove them in the future.

Also there are messages from GPT that are outputted at the botthom of the page in the green area. I give examples of functions you might want to load such as "take screenshot of a web site" OpenWeather function - try "What is the weather in Boston?" The openweather function is a simple function that uses the openweather API to return the weather for a city. It is a simple example of how to use an API. You will need a key from openweather WEATHER_API_KEY to use it. You can get one for free at

The most sophisticated is the crawlDomain function which will crawl the web to find answers to questions. Try "Crawl the Lunarmail.io web site and answer the question "What products does Lunarmail offer?" Its modified from btg5679/javascript-openai-web-crawler It will answer question if possible from stored embeddings. If not it will crawl the web to find the answer. It makes use of RAG to embed the question ## ## tokens and compare them to the embeddings of the web pages. It uses GPT to figure out "Key" tokens in the question. ## It will then use the best match to answer the question. If it can't find an answer it will use GPT to generate an ## answer. It will then store the answer in the database for future use. You may want to delete crawled_urls.csv and contents.csv to start fresh.