# AI Agent execution using Temporal Work in progress. This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The goal is to collect information towards a goal. There's a simple DSL input for collecting information (currently set up to use mock functions to search for events, book flights around those events then generate an invoice for those flights, see `send_message.py`). The AI will respond with clarifications and ask for any missing information to that goal. It uses a local LLM via Ollama. ## Setup * Requires an OpenAI key for the gpt-4o model. Set this in the `OPENAI_API_KEY` environment variable in .env * Requires a rapidapi key for sky-scrapper (how we find flights). Set this in the `RAPIDAPI_KEY` environment variable in .env * It's free to sign up and get a key at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper) * If you're lazy go to `tools/search_flights.py` and replace the `get_flights` function with the mock `search_flights_example` that exists in the same file. * Requires a Stripe key for the `generate_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env * It's free to sign up and get a key at [Stripe](https://stripe.com/) * If you're lazy go to `tools/generate_invoice.py` and replace the `generate_invoice` function with the mock `generate_invoice_example` that exists in the same file. * See .env_example for the required environment variables. * Install and run Temporal. Follow the instructions in the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/#set-up-a-local-temporal-service-for-development-with-temporal-cli) to install and run the Temporal server. * Install the dependencies: `poetry install` Deprecated: * Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model (`ollama run qwen2.5:14b`). (note this model is about 9GB to download). * Local LLM is disabled as ChatGPT 4o was better for this use case. To use Ollama, examine `./activities/tool_activities.py` and rename the functions. ## Running the example From the /scripts directory: 1. Run the worker: `poetry run python run_worker.py` 2. In another terminal run the client with a prompt. Example: `poetry run python send_message.py 'Can you find events in march in oceania?'` 3. View the worker's output for the response. 4. Give followup prompts by signaling the workflow. Example: `poetry run python send_message.py 'I want to fly from San Francisco'` NOTE: The workflow will pause on the 'confirm' step until the user sends a 'confirm' signal. Use `poetry run python get_tool_data.py` query to see the current state of the workflow. You can send a 'confirm' signal using `poetry run python send_confirm.py` 5. Get the conversation history summary by querying the workflow. Example: `poetry run python get_history.py` 6. To end the chat session, run `poetry run python end_chat.py` The chat session will end if it has collected enough information to complete the task or if the user explicitly ends the chat session. Run query get_tool_data to see the data the tool has collected so far. ## API - `poetry run uvicorn api.main:app --reload` to start the API server. - Access the API at `/docs` to see the available endpoints. ## UI - `cd frontend` - `npm install` to install the dependencies. - `npm run dev` to start the dev server. ## Customizing the agent - `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them) - The tools themselves are defined in their own files in `/tools` - Note the mapping in `tools/__init__.py` to each tool - See main.py where some tool-specific logic is defined (todo, move this to the tool definition) ## TODO - Code GenerateInvoice against the Stripe API - I should prove this out with other tool definitions outside of the event/flight search case (take advantage of my nice DSL). - Currently hardcoded to the Temporal dev server at localhost:7233. Need to support options incl Temporal Cloud. - UI: Make prettier - UI: Tool Confirmed state could be better represented