Examples

We plan to build more examples but would love to see what you build with AGiXT. If you have an example you would like to share, please submit a pull request to add it to this page.

Chatbot Example

Example of a basic AGiXT chatbot: Set your agent, make it learn whichever urls or files you want, then just keep using that conversation ID to keep a conversation going with the AI where it is aware of the history of your conversation (last 5 interactions). If you want to keep talking to it about the same docs without the history, start a new conversation and keep going with the same agent without any retraining of the documentation. Any conversations you have with the AI will be saved in the agixt/conversations directory and will also be viewable from inside of the AGiXT Streamlit Web UI.

You can open this file in a Jupyter Notebook and run the code cells to see the example in action. https://github.com/Josh-XT/AGiXT/blob/main/examples/Chatbot.ipynb

Voice Chat Example

Example of a basic AGiXT voice chat: Make the agent listen to you saying a specific word that makes it take what you say, send it to the agent, and then execute an AGiXT function. In this example, you can use two different wake functions, chat and instruct. When this example is running, and you say each of the wake words, it will take the words you say after that, send them to the agent, and respond back to you with an audio response.

You can open this file in a Jupyter Notebook and run the code cells to see the example in action. https://github.com/Josh-XT/AGiXT/blob/main/examples/Voice.ipynb

Some Examples of Useful Chains

• Smart Chat
• Smart Instruct
• Smart Task Chain
• Task Chain

OpenAI Style Chat Completions Endpoint Example

See the details of this pull request for example usage of the chat completions endpoint in AGiXT.

• Built in accommodations for uploading audio, files, or images to the pipeline of chat completions.
• Adds support for the gpt-4-vision-preview model allowing images to be uploaded with the same syntax. Follow syntax from OpenAI documentation on how your request should look to send images https://platform.openai.com/docs/guides/vision
• Adds support for vision models being used with ezLocalai using the same OpenAI endpoint syntax mentioned above.
• Audio upload support through the chat completions endpoint has been implemented.
• File upload support through the chat completions endpoint has been implemented.
• Website scraping by giving the URL through the chat completions endpoint as been implemented.

Example of URL scraping, file, image, and audio uploads below in a single endpoint that also prompts the agent:

import openai

response = openai.chat.completions.create(
    model="THE AGENTS NAME GOES HERE",
    messages=[
        {
            "conversation_name": "The conversation name", # The conversation name goes here
            "prompt_category": "Default",  # Optional, default will be "Default"
            "prompt_name": "Chat",  # Optional, the prompt template name goes here, default will be "Chat"
            "context_results": 5,  # Optional, default will be 5
            "websearch": false, # Set to true to enable websearch, default is false
            "websearch_depth": 0, # Set to the number of depth you want to websearch to go (3 would go 3 links deep per link it scrapes)
        },
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "YOUR USER INPUT TO THE AGENT GOES HERE"},
                {
                    "type": "image_url",
                    "image_url": {  # Will download the image and send it to the vision model
                        "url": f"https://www.visualwatermark.com/images/add-text-to-photos/add-text-to-image-3.webp"
                    },
                },
                {
                    "type": "text_url",  # Or just "url"
                    "url": {  # Content of the text or URL for it to be scraped
                        "url": "https://agixt.com"
                    },
                    "collection_number": 0,  # Optional field, defaults to 0.
                },
                {
                    "type": "application_url",
                    "url": {  # Will scrape mime type `application` into the agent's memory
                        "url": "data:application/pdf;base64,base64_encoded_pdf_here"
                    },
                    "collection_number": 0,  # Optional field, defaults to 0.
                },
                {
                    "type": "audio_url",
                    "url": {  # Will transcribe the audio and send it to the agent in the same way as text. Enables easy voice chat.
                        "url": "data:audio/wav;base64,base64_encoded_audio_here"
                    },
                },
            ],
        },
    ],
    max_tokens=4096,
    temperature=0.7,
    top_p=0.9,
)
print(response.choices[0].message.content)