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fix(setup): add stripe to dep (#39)

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Ka Wo Fong
2025-05-29 11:42:22 -04:00
committed by GitHub
parent 71e54b9ecd
commit f7ef2b1c7e
4 changed files with 33 additions and 15 deletions
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3
Makefile
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@@ -5,6 +5,7 @@ setup: check-python setup-venv install
check-python:
@which python3 >/dev/null 2>&1 || (echo "Python 3 is required. Please install it first." && exit 1)
@which poetry >/dev/null 2>&1 || (echo "Poetry is required. Please install it first." && exit 1)
setup-venv:
python3 -m venv venv
@@ -59,4 +60,4 @@ help:
@echo " make run-legacy-worker - Start the legacy worker"
@echo " make run-enterprise - Build and run the enterprise .NET worker"
@echo " make setup-temporal-mac - Install and start Temporal server on Mac"
@echo " make run-dev - Start all development services (worker, API, frontend) in parallel"
@echo " make run-dev - Start all development services (worker, API, frontend) in parallel"

18
poetry.lock generated
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@@ -2358,6 +2358,22 @@ anyio = ">=3.4.0,<5"
[package.extras]
full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
[[package]]
name = "stripe"
version = "11.6.0"
description = "Python bindings for the Stripe API"
optional = false
python-versions = ">=3.6"
groups = ["main"]
files = [
{file = "stripe-11.6.0-py2.py3-none-any.whl", hash = "sha256:6e6cf09ebb6d5fc2d708401cb8868fd7bff987a6d09a0433caaa92c62f97dbc5"},
{file = "stripe-11.6.0.tar.gz", hash = "sha256:0ced7cce23a6cb1a393c86a1f7f9435c9d83ae7cbd556362868caf62cb44a92c"},
]
[package.dependencies]
requests = {version = ">=2.20", markers = "python_version >= \"3.0\""}
typing-extensions = {version = ">=4.5.0", markers = "python_version >= \"3.7\""}
[[package]]
name = "temporalio"
version = "1.9.0"
@@ -2759,4 +2775,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<4.0"
content-hash = "d9a981a06732679724e07fb4f74b03fc6290f17a3b85bea3356ef72335be377d"
content-hash = "ae5534663e9fa1ab21fb50bd6a7007aa201a22da0c3b729289f8a931434c14bf"

3
pyproject.toml
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@@ -10,7 +10,7 @@ authors = [
]
readme = "README.md"
# By default, Poetry will find packages automatically,
# By default, Poetry will find packages automatically,
# but explicitly including them is fine:
packages = [
{ include = "**/*.py", from = "." }
@@ -38,6 +38,7 @@ uvicorn = "^0.34.0"
python-dotenv = "^1.0.1"
requests = "^2.32.3"
pandas = "^2.2.3"
stripe = "^11.4.1"
gtfs-kit = "^10.1.1"
[tool.poetry.group.dev.dependencies]

24
setup.md
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@@ -47,7 +47,7 @@ If you prefer to run commands manually, follow these steps:
### Agent Goal Configuration
The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file. If unset, default is `goal_choose_agent_type`.
The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file. If unset, default is `goal_choose_agent_type`.
If the first goal is `goal_choose_agent_type` the agent will support multiple goals using goal categories defined by `GOAL_CATEGORIES` in your .env file. If unset, default is all. We recommend starting with `fin`.
```bash
@@ -60,7 +60,7 @@ See the section Goal-Specific Tool Configuration below for tool configuration fo
Note: We recommend using OpenAI's GPT-4o or Claude 3.5 Sonnet for the best results. There can be significant differences in performance and capabilities between models, especially for complex tasks.
The agent uses LiteLLM to interact with various LLM providers. Configure theqfollowing environment variables in your `.env` file:
The agent uses LiteLLM to interact with various LLM providers. Configure the following environment variables in your `.env` file:
- `LLM_MODEL`: The model to use (e.g., "openai/gpt-4o", "anthropic/claude-3-sonnet", "google/gemini-pro", etc.)
- `LLM_KEY`: Your API key for the selected provider
@@ -167,7 +167,7 @@ npm install
npx vite
```
Access the UI at `http://localhost:5173`
## Goal-Specific Tool Configuration
Here is configuration guidance for specific goals. Travel and financial goals have configuration & setup as below.
@@ -175,7 +175,7 @@ Here is configuration guidance for specific goals. Travel and financial goals ha
- `AGENT_GOAL=goal_event_flight_invoice` - Helps users find events, book flights, and arrange train travel with invoice generation
- This is the scenario in the [original video](https://www.youtube.com/watch?v=GEXllEH2XiQ)
#### Configuring Agent Goal: goal_event_flight_invoice
#### Configuring Agent Goal: goal_event_flight_invoice
* The agent uses a mock function to search for events. This has zero configuration.
* By default the agent uses a mock function to search for flights.
* If you want to use the real flights API, go to `tools/search_flights.py` and replace the `search_flights` function with `search_flights_real_api` that exists in the same file.
@@ -190,7 +190,7 @@ Here is configuration guidance for specific goals. Travel and financial goals ha
- `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
- This goal was part of [Temporal's Replay 2025 conference keynote demo](https://www.youtube.com/watch?v=YDxAWrIBQNE)
- Note, there is failure built in to this demo (the train booking step) to show how the agent can handle failures and retry. See Tool Configuration below for details.
#### Configuring Agent Goal: goal_match_train_invoice
#### Configuring Agent Goal: goal_match_train_invoice
NOTE: This goal was developed for an on-stage demo and has failure (and its resolution) built in to show how the agent can handle failures and retry.
* Omit `FOOTBALL_DATA_API_KEY` from .env for the `SearchFixtures` tool to automatically return mock Premier League fixtures. Finding a real match requires a key from [Football Data](https://www.football-data.org). Sign up for a free account, then see the 'My Account' page to get your API token.
* We use a mock function to search for trains. Start the train API server to use the real API: `python thirdparty/train_api.py`
@@ -212,15 +212,15 @@ poetry run python thirdparty/train_api.py
##### Python Train Legacy Worker
> Agent Goal: goal_match_train_invoice only
These are Python activities that fail (raise NotImplemented) to show how Temporal handles a failure. You can run these activities with.
```bash
poetry run python scripts/run_legacy_worker.py
poetry run python scripts/run_legacy_worker.py
```
The activity will fail and be retried infinitely. To rescue the activity (and its corresponding workflows), kill the worker and run the .NET one in the section below.
##### .NET (enterprise) Worker ;)
We have activities written in C# to call the train APIs.
```bash
@@ -233,12 +233,12 @@ If you're running your train API above on a different host/port then change the
#### Goals: FIN - Money Movement and Loan Application
Make sure you have the mock users you want (such as yourself) in [the account mock data file](./tools/data/customer_account_data.json).
- `AGENT_GOAL=goal_fin_move_money` - This scenario _can_ initiate a secondary workflow to move money. Check out [this repo](https://github.com/temporal-sa/temporal-money-transfer-java) - you'll need to get the worker running and connected to the same account as the agentic worker.
- `AGENT_GOAL=goal_fin_move_money` - This scenario _can_ initiate a secondary workflow to move money. Check out [this repo](https://github.com/temporal-sa/temporal-money-transfer-java) - you'll need to get the worker running and connected to the same account as the agentic worker.
By default it will _not_ make a real workflow, it'll just fake it. If you get the worker running and want to start a workflow, in your [.env](./.env):
```bash
FIN_START_REAL_WORKFLOW=FALSE #set this to true to start a real workflow
```
- `AGENT_GOAL=goal_fin_loan_application` - This scenario _can_ initiate a secondary workflow to apply for a loan. Check out [this repo](https://github.com/temporal-sa/temporal-latency-optimization-scenarios) - you'll need to get the worker running and connected to the same account as the agentic worker.
- `AGENT_GOAL=goal_fin_loan_application` - This scenario _can_ initiate a secondary workflow to apply for a loan. Check out [this repo](https://github.com/temporal-sa/temporal-latency-optimization-scenarios) - you'll need to get the worker running and connected to the same account as the agentic worker.
By default it will _not_ make a real workflow, it'll just fake it. If you get the worker running and want to start a workflow, in your [.env](./.env):
```bash
FIN_START_REAL_WORKFLOW=FALSE #set this to true to start a real workflow