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57 lines
3.1 KiB
Markdown
57 lines
3.1 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 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.
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## Setup
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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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* 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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## Running the example
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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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TODO: Document /frontend react app running instructions.
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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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## TODO
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- The LLM prompts move through 3 mock tools (FindEvents, SearchFlights, CreateInvoice) but I should make them contact real APIs.
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- I should prove this out with other tool definitions (take advantage of my nice DSL).
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- Might need to abstract the json example in the prompt generator to be part of a ToolDefinition (prevent overfitting to the example).
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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
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- UI: Tool Confirmed state could be better represented
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- UI: 'Start new chat' button needs to handle better |