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69 lines
4.0 KiB
Markdown
69 lines
4.0 KiB
Markdown
# AI Agent execution using Temporal
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Work in progress.
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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 create 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.
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## Setup
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* Requires an OpenAI key for the gpt-4o model. Set this in the `OPENAI_API_KEY` environment variable in .env
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* Requires a rapidapi key for sky-scrapper (how we find flights). Set this in the `RAPIDAPI_KEY` environment variable in .env
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* It's free to sign up and get a key at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper)
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* 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.
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* Requires a Stripe key for the `create_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env
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* It's free to sign up and get a key at [Stripe](https://stripe.com/)
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* If you're lazy go to `tools/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
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* See .env_example for the required environment variables.
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* 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.
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* Install the dependencies: `poetry install`
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Deprecated:
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* 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).
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* 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.
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## Running the example
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### Temporal
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From the /scripts directory:
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1. Run the worker: `poetry run python run_worker.py`
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2. In another terminal run the client with a prompt.
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Example: `poetry run python send_message.py 'Can you find events in march in oceania?'`
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3. View the worker's output for the response.
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4. Give followup prompts by signaling the workflow.
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Example: `poetry run python send_message.py 'I want to fly from San Francisco'`
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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.
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You can send a 'confirm' signal using `poetry run python send_confirm.py`
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5. Get the conversation history summary by querying the workflow.
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Example: `poetry run python get_history.py`
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6. To end the chat session, run `poetry run python end_chat.py`
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The chat session will end if it has collected enough information to complete the task or if the user explicitly ends the chat session.
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Run query get_tool_data to see the data the tool has collected so far.
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### API
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- `poetry run uvicorn api.main:app --reload` to start the API server.
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- Access the API at `/docs` to see the available endpoints.
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### UI
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- `cd frontend`
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- `npm install` to install the dependencies.
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- `npm run dev` to start the dev server.
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## Customizing the agent
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- `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them)
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- The tools themselves are defined in their own files in `/tools`
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- Note the mapping in `tools/__init__.py` to each tool
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- See main.py where some tool-specific logic is defined (todo, move this to the tool definition)
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## TODO
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- I should prove this out with other tool definitions outside of the event/flight search case (take advantage of my nice DSL).
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- Currently hardcoded to the Temporal dev server at localhost:7233. Need to support options incl Temporal Cloud.
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- UI: Make prettier |