The pizza store project features a fictitious pizza shop built on Shopify. The agent in the system autonomously interacts with the Shopify API. Its primary function is to facilitate customer order placement, processing text input from the broader xRx system. In entire xRx project, this directory contains the code which powers the "Reasoning Agent" which is shown in blue the image below.
Install Docker, Python3, Pip3, redis with homebrew on MacOS or apt-get update on Debian/Ubuntu based Linux systems
brew cask install docker
brew install [email protected]
brew install node@18
brew install redis- Access to an OpenAI compatible endpoint with
chat/completionscapabilities. We recommend using Groq for best performance in this repository. Visit https://console.groq.com/docs/quickstart to create your Groq API key. The following environment variables are the needed for LLM usage.LLM_API_KEYLLM_BASE_URLLLM_MODEL_IDLLM_MODEL_ID_JSON_FIXER(this is the model which helps correct any JSON errors potentially made byLLM_MODEL_ID)
- Access to a Shopify admin API and GraqhQL API. The following environment variables available for Shopify API usage. Create a Shopify store at https://partners.shopify.com/ to retrieve these keys
SHOPIFY_TOKENSHOPIFY_API_KEYSHOPIFY_SHOP
To set up the environment variables, create a .env file. You can use the provided env-example file as a template.
Here's an example of what your .env file should include:
-
Language Model (LLM) Configuration:
LLM_API_KEY: Your API key for the language model service (e.g., OpenAI, Groq)LLM_BASE_URL: The base URL for the language model APILLM_MODEL_ID: The ID of the language model to useLLM_MODEL_ID_JSON_FIXER: The model ID for JSON error correction
-
Shopify Configuration:
SHOPIFY_TOKEN: Your Shopify admin API tokenSHOPIFY_API_KEY: Your Shopify API keySHOPIFY_SHOP: Your Shopify shop name
-
UI Configuration:
NEXT_PUBLIC_UI: Set to either "pizza-agent" or "shoe-agent" depending on the UI you want to display.
docker build -t xrx-reasoning:latest .
docker run -it --rm \
--env-file ./.docker.env \
xrx-reasoning:latestOnce the containers are up and running, you should be able to visit the API docs at http://localhost:8003/docs
redis-server --port 6379If you are running a local redis cluster, you should also set the REDIS_HOST environment variable to the host of your redis cluster. This can be done by adding the following line to your .env file:
REDIS_HOST=localhost
python3 -m venv myenv
source myenv/bin/activatepip install -r requirements.txt
cd app
uvicorn --reload --port 8003 --env-file ../../../.env main:app --log-config logging_config.yamlIf you get the following error, it means you are not in the app directory
ERROR: Error loading ASGI app. Could not import module "main".
Now you have the app/container up and running at http://localhost:8003
Here is an example POST request you can now make to the server
curl --location 'http://127.0.0.1:8003/run-reasoning-agent' \
--header 'Content-Type: application/json' \
--data '{
"session": {
"id": "1234567890"
},
"messages": [
{"role": "user", "content": "What kind of food do you have?"}
]
}'Once you have the API up and running, in a separate terminal, go to shopify-agent/tests directory.
From there, in another fresh python environment, install the dependencies
pip install -r requirements.txtThen run the following command to start the chat interface
python interactive_test.py
You should see a terminal interface which looks like this
Interactive Shopify Agent Test. Type 'quit' to exit.
Customer:
You can then chat with the agent via the Customer: input in the terminal. Happy testing!
To run single turn and multi-turn tests
python -m unittest shopify-agent.test.test.TestShopifyAgent.test_single_turn_agent_responses
python -m unittest shopify-agent.test.test.TestShopifyAgent.test_multi_turn_agent_responses
To run all tests
From within the shopify-agent directory, you can use:
python -m unittest test.test
From the root directory of your project:
python -m unittest shopify-agent.test.test
You can find below a graph of the reasoning structure.
graph TD
A[main.py] --> B[executor.py]
subgraph config["config.py"]
D[Context Manager]
F[Tools]
C[Graph]
B[executor.py]
E[Utils]
B --> D
B <--> C
B --> E
F --> C
subgraph F[Tools]
F1[shopify.py]
subgraph F1[shopify.py]
F11[get_products]
F12[get_product_details]
F13[add_item_to_cart]
F14[delete_item_from_cart]
F15[get_cart_summary]
F16[submit_cart_for_order]
F17[get_order_status]
end
end
subgraph C[Graph]
C1[Routing] --> C2[CustomerResponse]
C1 --> C4[ChooseTool]
C3 --> C1
C4 --> C5[IdentifyToolParams]
C5 --> C6[ExecuteTool]
C6 --> C7[Widget]
C6 --> C8[ConvertNaturalLanguage]
C7 --> C8
C8 --> C1
C1 --> C3[TaskDescriptionResponse]
end
subgraph E[Utils]
E1[llm.py]
E2[shopify.py]
subgraph E1[llm.py]
E11[message format conversion]
E12[make tools description]
E13[initialize llm]
end
subgraph E2[shopify.py]
E21[init_shopify]
E22[get_product_image]
E23[populate_images]
E24[get_cart_summary_from_object]
E25[get_variant_id_from_sku]
E26[make_new_blank_cart]
end
end
end
classDef configClass fill:transparent,stroke:#ADD8E6,stroke-width:2px;
class config configClass;
classDef subgraphClass fill:transparent,stroke:#ADD8E6,stroke-width:1px;
class F,C,E,F1,E1,E2 subgraphClass;
%% Set the background to white and border to light blue
style config fill:#ffffff,stroke:#ADD8E6,stroke-width:2px;