mirror of
https://github.com/temporal-community/temporal-ai-agent.git
synced 2026-03-15 14:08:08 +01:00
Joshua Smith
@@ -7,6 +7,9 @@ STRIPE_API_KEY=sk_test_51J...
|
||||
LLM_PROVIDER=openai # default
|
||||
OPENAI_API_KEY=sk-proj-...
|
||||
# or
|
||||
#LLM_PROVIDER=grok
|
||||
#GROK_API_KEY=xai-your-grok-api-key
|
||||
# or
|
||||
# LLM_PROVIDER=ollama
|
||||
# OLLAMA_MODEL_NAME=qwen2.5:14b
|
||||
# or
|
||||
@@ -34,3 +37,6 @@ OPENAI_API_KEY=sk-proj-...
|
||||
|
||||
# Agent Goal Configuration
|
||||
# AGENT_GOAL=goal_event_flight_invoice # (default) or goal_match_train_invoice
|
||||
|
||||
# Set if the UI should force a user confirmation step or not
|
||||
SHOW_CONFIRM=True
|
||||
194
README.md
194
README.md
@@ -2,191 +2,29 @@
|
||||
|
||||
This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The purpose of the agent is to collect information towards a goal, running tools along the way. There's a simple DSL input for collecting information (currently set up to use mock functions to search for public events, search for flights around those events, then create a test Stripe invoice for the trip).
|
||||
|
||||
The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use [ChatGPT 4o](https://openai.com/index/hello-gpt-4o/), [Anthropic Claude](https://www.anthropic.com/claude), [Google Gemini](https://gemini.google.com), [Deepseek-V3](https://www.deepseek.com/) or a local LLM of your choice using [Ollama](https://ollama.com).
|
||||
The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use [ChatGPT 4o](https://openai.com/index/hello-gpt-4o/), [Anthropic Claude](https://www.anthropic.com/claude), [Google Gemini](https://gemini.google.com), [Deepseek-V3](https://www.deepseek.com/), [Grok](https://docs.x.ai/docs/overview) or a local LLM of your choice using [Ollama](https://ollama.com).
|
||||
|
||||
[Watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ)
|
||||
It's really helpful to [watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ) to understand how interaction works.
|
||||
|
||||
[](https://www.youtube.com/watch?v=GEXllEH2XiQ)
|
||||
[](https://www.youtube.com/watch?v=GEXllEH2XiQ)
|
||||
|
||||
## Configuration
|
||||
## Setup and Configuration
|
||||
See [the Setup guide](./setup.md).
|
||||
|
||||
This application uses `.env` files for configuration. Copy the [.env.example](.env.example) file to `.env` and update the values:
|
||||
## Interaction
|
||||
TODO
|
||||
|
||||
```bash
|
||||
cp .env.example .env
|
||||
```
|
||||
## Architecture
|
||||
See [the architecture guide](./architecture.md).
|
||||
|
||||
### Agent Goal Configuration
|
||||
|
||||
The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file.
|
||||
|
||||
#### Goal: Find an event in Australia / New Zealand, book flights to it and invoice the user for the cost
|
||||
- `AGENT_GOAL=goal_event_flight_invoice` (default) - Helps users find events, book flights, and arrange train travel with invoice generation
|
||||
- This is the scenario in the video above
|
||||
|
||||
#### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost
|
||||
- `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
|
||||
- This is a new goal that is part of an upcoming conference talk
|
||||
|
||||
If not specified, the agent defaults to `goal_event_flight_invoice`. Each goal comes with its own set of tools and conversation flows designed for specific use cases. You can examine `tools/goal_registry.py` to see the detailed configuration of each goal.
|
||||
|
||||
See the next section for tool configuration for each goal.
|
||||
|
||||
### Tool Configuration
|
||||
|
||||
#### Agent Goal: goal_event_flight_invoice (default)
|
||||
* 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.
|
||||
* It's free to sign up at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper)
|
||||
* This api might be slow to respond, so you may want to increase the start to close timeout, `TOOL_ACTIVITY_START_TO_CLOSE_TIMEOUT` in `workflows/workflow_helpers.py`
|
||||
* Requires a Stripe key for the `create_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/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
|
||||
|
||||
#### Agent Goal: goal_match_train_invoice
|
||||
|
||||
* Finding a 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. Set `FOOTBALL_DATA_API_KEY` to this value.
|
||||
* If you're lazy go to `tools/search_fixtures.py` and replace the `search_fixtures` function with the mock `search_fixtures_example` that exists in the same file.
|
||||
* We use a mock function to search for trains. Start the train API server to use the real API: `python thirdparty/train_api.py`
|
||||
* * The train activity is 'enterprise' so it's written in C# and requires a .NET runtime. See the [.NET backend](#net-(enterprise)-backend) section for details on running it.
|
||||
* Requires a Stripe key for the `create_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/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
|
||||
|
||||
### LLM Provider Configuration
|
||||
|
||||
The agent can use OpenAI's GPT-4o, Google Gemini, Anthropic Claude, or a local LLM via Ollama. Set the `LLM_PROVIDER` environment variable in your `.env` file to choose the desired provider:
|
||||
|
||||
- `LLM_PROVIDER=openai` for OpenAI's GPT-4o
|
||||
- `LLM_PROVIDER=google` for Google Gemini
|
||||
- `LLM_PROVIDER=anthropic` for Anthropic Claude
|
||||
- `LLM_PROVIDER=deepseek` for DeepSeek-V3
|
||||
- `LLM_PROVIDER=ollama` for running LLMs via [Ollama](https://ollama.ai) (not recommended for this use case)
|
||||
|
||||
### Option 1: OpenAI
|
||||
|
||||
If using OpenAI, ensure you have an OpenAI key for the GPT-4o model. Set this in the `OPENAI_API_KEY` environment variable in `.env`.
|
||||
|
||||
### Option 2: Google Gemini
|
||||
|
||||
To use Google Gemini:
|
||||
|
||||
1. Obtain a Google API key and set it in the `GOOGLE_API_KEY` environment variable in `.env`.
|
||||
2. Set `LLM_PROVIDER=google` in your `.env` file.
|
||||
|
||||
### Option 3: Anthropic Claude (recommended)
|
||||
|
||||
I find that Claude Sonnet 3.5 performs better than the other hosted LLMs for this use case.
|
||||
|
||||
To use Anthropic:
|
||||
|
||||
1. Obtain an Anthropic API key and set it in the `ANTHROPIC_API_KEY` environment variable in `.env`.
|
||||
2. Set `LLM_PROVIDER=anthropic` in your `.env` file.
|
||||
|
||||
### Option 4: Deepseek-V3
|
||||
|
||||
To use Deepseek-V3:
|
||||
|
||||
1. Obtain a Deepseek API key and set it in the `DEEPSEEK_API_KEY` environment variable in `.env`.
|
||||
2. Set `LLM_PROVIDER=deepseek` in your `.env` file.
|
||||
|
||||
### Option 5: Local LLM via Ollama (not recommended)
|
||||
|
||||
To use a local LLM with Ollama:
|
||||
|
||||
1. Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model.
|
||||
- Run `ollama run <OLLAMA_MODEL_NAME>` to start the model. Note that this model is about 9GB to download.
|
||||
- Example: `ollama run qwen2.5:14b`
|
||||
|
||||
2. Set `LLM_PROVIDER=ollama` in your `.env` file and `OLLAMA_MODEL_NAME` to the name of the model you installed.
|
||||
|
||||
Note: I found the other (hosted) LLMs to be MUCH more reliable for this use case. However, you can switch to Ollama if desired, and choose a suitably large model if your computer has the resources.
|
||||
|
||||
## Configuring Temporal Connection
|
||||
|
||||
By default, this application will connect to a local Temporal server (`localhost:7233`) in the default namespace, using the `agent-task-queue` task queue. You can override these settings in your `.env` file.
|
||||
|
||||
### Use Temporal Cloud
|
||||
|
||||
See [.env.example](.env.example) for details on connecting to Temporal Cloud using mTLS or API key authentication.
|
||||
|
||||
[Sign up for Temporal Cloud](https://temporal.io/get-cloud)
|
||||
|
||||
### Use a local Temporal Dev Server
|
||||
|
||||
On a Mac
|
||||
```bash
|
||||
brew install temporal
|
||||
temporal server start-dev
|
||||
```
|
||||
See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.
|
||||
|
||||
|
||||
## Running the Application
|
||||
|
||||
### Python Backend
|
||||
|
||||
Requires [Poetry](https://python-poetry.org/) to manage dependencies.
|
||||
|
||||
1. `python -m venv venv`
|
||||
|
||||
2. `source venv/bin/activate`
|
||||
|
||||
3. `poetry install`
|
||||
|
||||
Run the following commands in separate terminal windows:
|
||||
|
||||
1. Start the Temporal worker:
|
||||
```bash
|
||||
poetry run python scripts/run_worker.py
|
||||
```
|
||||
|
||||
2. Start the API server:
|
||||
```bash
|
||||
poetry run uvicorn api.main:app --reload
|
||||
```
|
||||
Access the API at `/docs` to see the available endpoints.
|
||||
|
||||
### React UI
|
||||
Start the frontend:
|
||||
```bash
|
||||
cd frontend
|
||||
npm install
|
||||
npx vite
|
||||
```
|
||||
Access the UI at `http://localhost:5173`
|
||||
|
||||
### Python Search Trains API
|
||||
> Agent Goal: goal_match_train_invoice only
|
||||
|
||||
Required to search and book trains!
|
||||
```bash
|
||||
poetry run python thirdparty/train_api.py
|
||||
|
||||
# example url
|
||||
# http://localhost:8080/api/search?from=london&to=liverpool&outbound_time=2025-04-18T09:00:00&inbound_time=2025-04-20T09:00:00
|
||||
```
|
||||
|
||||
### .NET (enterprise) Backend ;)
|
||||
> Agent Goal: goal_match_train_invoice only
|
||||
|
||||
We have activities written in C# to call the train APIs.
|
||||
```bash
|
||||
cd enterprise
|
||||
dotnet build # ensure you brew install dotnet@8 first!
|
||||
dotnet run
|
||||
```
|
||||
If you're running your train API above on a different host/port then change the API URL in `Program.cs`. Otherwise, be sure to run it using `python thirdparty/train_api.py`.
|
||||
|
||||
## Customizing the Agent
|
||||
- `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them)
|
||||
- `goal_registry.py` contains descriptions of goals and the tools used to achieve them
|
||||
- The tools themselves are defined in their own files in `/tools`
|
||||
- Note the mapping in `tools/__init__.py` to each tool
|
||||
|
||||
## TODO
|
||||
## Productionalization & Adding Features
|
||||
- In a prod setting, I would need to ensure that payload data is stored separately (e.g. in S3 or a noSQL db - the claim-check pattern), or otherwise 'garbage collected'. Without these techniques, long conversations will fill up the workflow's conversation history, and start to breach Temporal event history payload limits.
|
||||
- Continue-as-new shouldn't be a big consideration for this use case (as it would take many conversational turns to trigger). Regardless, I should ensure that it's able to carry the agent state over to the new workflow execution.
|
||||
- Perhaps the UI should show when the LLM response is being retried (i.e. activity retry attempt because the LLM provided bad output)
|
||||
- Tests would be nice!
|
||||
See [the todo](./todo.md) for more details.
|
||||
|
||||
See Customization for more details. <-- TODO
|
||||
|
||||
## For Temporal SAs
|
||||
Check out the [slides](https://docs.google.com/presentation/d/1wUFY4v17vrtv8llreKEBDPLRtZte3FixxBUn0uWy5NU/edit#slide=id.g3333e5deaa9_0_0) here and the enablement guide here (TODO).
|
||||
|
||||
@@ -34,6 +34,7 @@ class ToolActivities:
|
||||
|
||||
# Initialize client variables (all set to None initially)
|
||||
self.openai_client: Optional[OpenAI] = None
|
||||
self.grok_client: Optional[OpenAI] = None
|
||||
self.anthropic_client: Optional[anthropic.Anthropic] = None
|
||||
self.genai_configured: bool = False
|
||||
self.deepseek_client: Optional[deepseek.DeepSeekAPI] = None
|
||||
@@ -48,6 +49,13 @@ class ToolActivities:
|
||||
else:
|
||||
print("Warning: OPENAI_API_KEY not set but LLM_PROVIDER is 'openai'")
|
||||
|
||||
if self.llm_provider == "grok":
|
||||
if os.environ.get("GROK_API_KEY"):
|
||||
self.grok_client = OpenAI(api_key=os.environ.get("GROK_API_KEY"), base_url="https://api.x.ai/v1")
|
||||
print("Initialized grok client")
|
||||
else:
|
||||
print("Warning: GROK_API_KEY not set but LLM_PROVIDER is 'grok'")
|
||||
|
||||
elif self.llm_provider == "anthropic":
|
||||
if os.environ.get("ANTHROPIC_API_KEY"):
|
||||
self.anthropic_client = anthropic.Anthropic(
|
||||
@@ -195,6 +203,8 @@ class ToolActivities:
|
||||
return self.prompt_llm_anthropic(input)
|
||||
elif self.llm_provider == "deepseek":
|
||||
return self.prompt_llm_deepseek(input)
|
||||
elif self.llm_provider == "grok":
|
||||
return self.prompt_llm_grok(input)
|
||||
else:
|
||||
return self.prompt_llm_openai(input)
|
||||
|
||||
@@ -237,13 +247,47 @@ class ToolActivities:
|
||||
)
|
||||
|
||||
response_content = chat_completion.choices[0].message.content
|
||||
print(f"ChatGPT response: {response_content}")
|
||||
activity.logger.info(f"ChatGPT response: {response_content}")
|
||||
|
||||
# Use the new sanitize function
|
||||
response_content = self.sanitize_json_response(response_content)
|
||||
|
||||
return self.parse_json_response(response_content)
|
||||
|
||||
def prompt_llm_grok(self, input: ToolPromptInput) -> dict:
|
||||
if not self.grok_client:
|
||||
api_key = os.environ.get("GROK_API_KEY")
|
||||
if not api_key:
|
||||
raise ValueError(
|
||||
"GROK_API_KEY is not set in the environment variables but LLM_PROVIDER is 'grok'"
|
||||
)
|
||||
self.grok_client = OpenAI(api_key=api_key, base_url="https://api.x.ai/v1")
|
||||
print("Initialized grok client on demand")
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": input.context_instructions
|
||||
+ ". The current date is "
|
||||
+ datetime.now().strftime("%B %d, %Y"),
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": input.prompt,
|
||||
},
|
||||
]
|
||||
|
||||
chat_completion = self.grok_client.chat.completions.create(
|
||||
model="grok-2-1212", messages=messages
|
||||
)
|
||||
|
||||
response_content = chat_completion.choices[0].message.content
|
||||
activity.logger.info(f"Grok response: {response_content}")
|
||||
|
||||
# Use the new sanitize function
|
||||
response_content = self.sanitize_json_response(response_content)
|
||||
|
||||
return self.parse_json_response(response_content)
|
||||
def prompt_llm_ollama(self, input: ToolPromptInput) -> dict:
|
||||
# If not yet initialized, try to do so now (this is a backup if warm_up_ollama wasn't called or failed)
|
||||
if not self.ollama_initialized:
|
||||
@@ -449,6 +493,7 @@ def dynamic_tool_activity(args: Sequence[RawValue]) -> dict:
|
||||
# Delegate to the relevant function
|
||||
handler = get_handler(tool_name)
|
||||
result = handler(tool_args)
|
||||
print(f"in dynamic tool activity, result: {result}")
|
||||
|
||||
# Optionally log or augment the result
|
||||
activity.logger.info(f"Tool '{tool_name}' result: {result}")
|
||||
|
||||
53
api/main.py
53
api/main.py
@@ -1,3 +1,4 @@
|
||||
import os
|
||||
from fastapi import FastAPI
|
||||
from typing import Optional
|
||||
from temporalio.client import Client
|
||||
@@ -6,11 +7,10 @@ from temporalio.api.enums.v1 import WorkflowExecutionStatus
|
||||
from fastapi import HTTPException
|
||||
from dotenv import load_dotenv
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from workflows.agent_goal_workflow import AgentGoalWorkflow
|
||||
from models.data_types import CombinedInput, AgentGoalWorkflowParams
|
||||
from tools.goal_registry import goal_match_train_invoice, goal_event_flight_invoice
|
||||
from tools.goal_registry import goal_list
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from shared.config import get_temporal_client, TEMPORAL_TASK_QUEUE
|
||||
|
||||
@@ -21,14 +21,12 @@ temporal_client: Optional[Client] = None
|
||||
load_dotenv()
|
||||
|
||||
|
||||
def get_agent_goal():
|
||||
def get_initial_agent_goal():
|
||||
"""Get the agent goal from environment variables."""
|
||||
goal_name = os.getenv("AGENT_GOAL", "goal_match_train_invoice")
|
||||
goals = {
|
||||
"goal_match_train_invoice": goal_match_train_invoice,
|
||||
"goal_event_flight_invoice": goal_event_flight_invoice,
|
||||
}
|
||||
return goals.get(goal_name, goal_event_flight_invoice)
|
||||
env_goal = os.getenv("AGENT_GOAL", "goal_choose_agent_type") #if no goal is set in the env file, default to choosing an agent
|
||||
for listed_goal in goal_list:
|
||||
if listed_goal.id == env_goal:
|
||||
return listed_goal
|
||||
|
||||
|
||||
@app.on_event("startup")
|
||||
@@ -113,18 +111,44 @@ async def get_conversation_history():
|
||||
status_code=404, detail="Workflow worker unavailable or not found."
|
||||
)
|
||||
|
||||
if "workflow not found" in error_message:
|
||||
await start_workflow()
|
||||
return []
|
||||
else:
|
||||
# For other Temporal errors, return a 500
|
||||
raise HTTPException(
|
||||
status_code=500, detail="Internal server error while querying workflow."
|
||||
)
|
||||
|
||||
@app.get("/agent-goal")
|
||||
async def get_agent_goal():
|
||||
"""Calls the workflow's 'get_agent_goal' query."""
|
||||
try:
|
||||
# Get workflow handle
|
||||
handle = temporal_client.get_workflow_handle("agent-workflow")
|
||||
|
||||
# Check if the workflow is completed
|
||||
workflow_status = await handle.describe()
|
||||
if workflow_status.status == 2:
|
||||
# Workflow is completed; return an empty response
|
||||
return {}
|
||||
|
||||
# Query the workflow
|
||||
agent_goal = await handle.query("get_agent_goal")
|
||||
return agent_goal
|
||||
except TemporalError as e:
|
||||
# Workflow not found; return an empty response
|
||||
print(e)
|
||||
return {}
|
||||
|
||||
|
||||
@app.post("/send-prompt")
|
||||
async def send_prompt(prompt: str):
|
||||
# Create combined input with goal from environment
|
||||
combined_input = CombinedInput(
|
||||
tool_params=AgentGoalWorkflowParams(None, None),
|
||||
agent_goal=get_agent_goal(),
|
||||
agent_goal=get_initial_agent_goal(),
|
||||
#change to get from workflow query
|
||||
)
|
||||
|
||||
workflow_id = "agent-workflow"
|
||||
@@ -168,13 +192,12 @@ async def end_chat():
|
||||
|
||||
@app.post("/start-workflow")
|
||||
async def start_workflow():
|
||||
# Get the configured goal
|
||||
agent_goal = get_agent_goal()
|
||||
initial_agent_goal = get_initial_agent_goal()
|
||||
|
||||
# Create combined input
|
||||
combined_input = CombinedInput(
|
||||
tool_params=AgentGoalWorkflowParams(None, None),
|
||||
agent_goal=agent_goal,
|
||||
agent_goal=initial_agent_goal,
|
||||
)
|
||||
|
||||
workflow_id = "agent-workflow"
|
||||
@@ -186,9 +209,9 @@ async def start_workflow():
|
||||
id=workflow_id,
|
||||
task_queue=TEMPORAL_TASK_QUEUE,
|
||||
start_signal="user_prompt",
|
||||
start_signal_args=["### " + agent_goal.starter_prompt],
|
||||
start_signal_args=["### " + initial_agent_goal.starter_prompt],
|
||||
)
|
||||
|
||||
return {
|
||||
"message": f"Workflow started with goal's starter prompt: {agent_goal.starter_prompt}."
|
||||
"message": f"Workflow started with goal's starter prompt: {initial_agent_goal.starter_prompt}."
|
||||
}
|
||||
|
||||
12
architecture.md
Normal file
12
architecture.md
Normal file
@@ -0,0 +1,12 @@
|
||||
# Elements
|
||||
|
||||
|
||||
talk through the pieces
|
||||
|
||||
# Architecture Model
|
||||
|
||||
|
||||
explain elements
|
||||
|
||||
# Adding features
|
||||
link to how to LLM interactions/how to change
|
||||
BIN
assets/Architecture_elements.png
Normal file
BIN
assets/Architecture_elements.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 39 KiB |
|
Before Width: | Height: | Size: 376 KiB After Width: | Height: | Size: 376 KiB |
BIN
assets/ai_agent_architecture_model.png
Normal file
BIN
assets/ai_agent_architecture_model.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 144 KiB |
201
frontend/package-lock.json
generated
201
frontend/package-lock.json
generated
@@ -825,247 +825,228 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@rollup/rollup-android-arm-eabi": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm-eabi/-/rollup-android-arm-eabi-4.29.1.tgz",
|
||||
"integrity": "sha512-ssKhA8RNltTZLpG6/QNkCSge+7mBQGUqJRisZ2MDQcEGaK93QESEgWK2iOpIDZ7k9zPVkG5AS3ksvD5ZWxmItw==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm-eabi/-/rollup-android-arm-eabi-4.34.7.tgz",
|
||||
"integrity": "sha512-l6CtzHYo8D2TQ3J7qJNpp3Q1Iye56ssIAtqbM2H8axxCEEwvN7o8Ze9PuIapbxFL3OHrJU2JBX6FIIVnP/rYyw==",
|
||||
"cpu": [
|
||||
"arm"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"android"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-android-arm64": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm64/-/rollup-android-arm64-4.29.1.tgz",
|
||||
"integrity": "sha512-CaRfrV0cd+NIIcVVN/jx+hVLN+VRqnuzLRmfmlzpOzB87ajixsN/+9L5xNmkaUUvEbI5BmIKS+XTwXsHEb65Ew==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm64/-/rollup-android-arm64-4.34.7.tgz",
|
||||
"integrity": "sha512-KvyJpFUueUnSp53zhAa293QBYqwm94TgYTIfXyOTtidhm5V0LbLCJQRGkQClYiX3FXDQGSvPxOTD/6rPStMMDg==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"android"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-darwin-arm64": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.29.1.tgz",
|
||||
"integrity": "sha512-2ORr7T31Y0Mnk6qNuwtyNmy14MunTAMx06VAPI6/Ju52W10zk1i7i5U3vlDRWjhOI5quBcrvhkCHyF76bI7kEw==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.34.7.tgz",
|
||||
"integrity": "sha512-jq87CjmgL9YIKvs8ybtIC98s/M3HdbqXhllcy9EdLV0yMg1DpxES2gr65nNy7ObNo/vZ/MrOTxt0bE5LinL6mA==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-darwin-x64": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-x64/-/rollup-darwin-x64-4.29.1.tgz",
|
||||
"integrity": "sha512-j/Ej1oanzPjmN0tirRd5K2/nncAhS9W6ICzgxV+9Y5ZsP0hiGhHJXZ2JQ53iSSjj8m6cRY6oB1GMzNn2EUt6Ng==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-x64/-/rollup-darwin-x64-4.34.7.tgz",
|
||||
"integrity": "sha512-rSI/m8OxBjsdnMMg0WEetu/w+LhLAcCDEiL66lmMX4R3oaml3eXz3Dxfvrxs1FbzPbJMaItQiksyMfv1hoIxnA==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-freebsd-arm64": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-arm64/-/rollup-freebsd-arm64-4.29.1.tgz",
|
||||
"integrity": "sha512-91C//G6Dm/cv724tpt7nTyP+JdN12iqeXGFM1SqnljCmi5yTXriH7B1r8AD9dAZByHpKAumqP1Qy2vVNIdLZqw==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-arm64/-/rollup-freebsd-arm64-4.34.7.tgz",
|
||||
"integrity": "sha512-oIoJRy3ZrdsXpFuWDtzsOOa/E/RbRWXVokpVrNnkS7npz8GEG++E1gYbzhYxhxHbO2om1T26BZjVmdIoyN2WtA==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"freebsd"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-freebsd-x64": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-x64/-/rollup-freebsd-x64-4.29.1.tgz",
|
||||
"integrity": "sha512-hEioiEQ9Dec2nIRoeHUP6hr1PSkXzQaCUyqBDQ9I9ik4gCXQZjJMIVzoNLBRGet+hIUb3CISMh9KXuCcWVW/8w==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-x64/-/rollup-freebsd-x64-4.34.7.tgz",
|
||||
"integrity": "sha512-X++QSLm4NZfZ3VXGVwyHdRf58IBbCu9ammgJxuWZYLX0du6kZvdNqPwrjvDfwmi6wFdvfZ/s6K7ia0E5kI7m8Q==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"freebsd"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-arm-gnueabihf": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-gnueabihf/-/rollup-linux-arm-gnueabihf-4.29.1.tgz",
|
||||
"integrity": "sha512-Py5vFd5HWYN9zxBv3WMrLAXY3yYJ6Q/aVERoeUFwiDGiMOWsMs7FokXihSOaT/PMWUty/Pj60XDQndK3eAfE6A==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-gnueabihf/-/rollup-linux-arm-gnueabihf-4.34.7.tgz",
|
||||
"integrity": "sha512-Z0TzhrsNqukTz3ISzrvyshQpFnFRfLunYiXxlCRvcrb3nvC5rVKI+ZXPFG/Aa4jhQa1gHgH3A0exHaRRN4VmdQ==",
|
||||
"cpu": [
|
||||
"arm"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-arm-musleabihf": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-musleabihf/-/rollup-linux-arm-musleabihf-4.29.1.tgz",
|
||||
"integrity": "sha512-RiWpGgbayf7LUcuSNIbahr0ys2YnEERD4gYdISA06wa0i8RALrnzflh9Wxii7zQJEB2/Eh74dX4y/sHKLWp5uQ==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-musleabihf/-/rollup-linux-arm-musleabihf-4.34.7.tgz",
|
||||
"integrity": "sha512-nkznpyXekFAbvFBKBy4nNppSgneB1wwG1yx/hujN3wRnhnkrYVugMTCBXED4+Ni6thoWfQuHNYbFjgGH0MBXtw==",
|
||||
"cpu": [
|
||||
"arm"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-arm64-gnu": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-gnu/-/rollup-linux-arm64-gnu-4.29.1.tgz",
|
||||
"integrity": "sha512-Z80O+taYxTQITWMjm/YqNoe9d10OX6kDh8X5/rFCMuPqsKsSyDilvfg+vd3iXIqtfmp+cnfL1UrYirkaF8SBZA==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-gnu/-/rollup-linux-arm64-gnu-4.34.7.tgz",
|
||||
"integrity": "sha512-KCjlUkcKs6PjOcxolqrXglBDcfCuUCTVlX5BgzgoJHw+1rWH1MCkETLkLe5iLLS9dP5gKC7mp3y6x8c1oGBUtA==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-arm64-musl": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-musl/-/rollup-linux-arm64-musl-4.29.1.tgz",
|
||||
"integrity": "sha512-fOHRtF9gahwJk3QVp01a/GqS4hBEZCV1oKglVVq13kcK3NeVlS4BwIFzOHDbmKzt3i0OuHG4zfRP0YoG5OF/rA==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-musl/-/rollup-linux-arm64-musl-4.34.7.tgz",
|
||||
"integrity": "sha512-uFLJFz6+utmpbR313TTx+NpPuAXbPz4BhTQzgaP0tozlLnGnQ6rCo6tLwaSa6b7l6gRErjLicXQ1iPiXzYotjw==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-loongarch64-gnu": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-loongarch64-gnu/-/rollup-linux-loongarch64-gnu-4.29.1.tgz",
|
||||
"integrity": "sha512-5a7q3tnlbcg0OodyxcAdrrCxFi0DgXJSoOuidFUzHZ2GixZXQs6Tc3CHmlvqKAmOs5eRde+JJxeIf9DonkmYkw==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-loongarch64-gnu/-/rollup-linux-loongarch64-gnu-4.34.7.tgz",
|
||||
"integrity": "sha512-ws8pc68UcJJqCpneDFepnwlsMUFoWvPbWXT/XUrJ7rWUL9vLoIN3GAasgG+nCvq8xrE3pIrd+qLX/jotcLy0Qw==",
|
||||
"cpu": [
|
||||
"loong64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-powerpc64le-gnu": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-powerpc64le-gnu/-/rollup-linux-powerpc64le-gnu-4.29.1.tgz",
|
||||
"integrity": "sha512-9b4Mg5Yfz6mRnlSPIdROcfw1BU22FQxmfjlp/CShWwO3LilKQuMISMTtAu/bxmmrE6A902W2cZJuzx8+gJ8e9w==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-powerpc64le-gnu/-/rollup-linux-powerpc64le-gnu-4.34.7.tgz",
|
||||
"integrity": "sha512-vrDk9JDa/BFkxcS2PbWpr0C/LiiSLxFbNOBgfbW6P8TBe9PPHx9Wqbvx2xgNi1TOAyQHQJ7RZFqBiEohm79r0w==",
|
||||
"cpu": [
|
||||
"ppc64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-riscv64-gnu": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-riscv64-gnu/-/rollup-linux-riscv64-gnu-4.29.1.tgz",
|
||||
"integrity": "sha512-G5pn0NChlbRM8OJWpJFMX4/i8OEU538uiSv0P6roZcbpe/WfhEO+AT8SHVKfp8qhDQzaz7Q+1/ixMy7hBRidnQ==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-riscv64-gnu/-/rollup-linux-riscv64-gnu-4.34.7.tgz",
|
||||
"integrity": "sha512-rB+ejFyjtmSo+g/a4eovDD1lHWHVqizN8P0Hm0RElkINpS0XOdpaXloqM4FBkF9ZWEzg6bezymbpLmeMldfLTw==",
|
||||
"cpu": [
|
||||
"riscv64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-s390x-gnu": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-s390x-gnu/-/rollup-linux-s390x-gnu-4.29.1.tgz",
|
||||
"integrity": "sha512-WM9lIkNdkhVwiArmLxFXpWndFGuOka4oJOZh8EP3Vb8q5lzdSCBuhjavJsw68Q9AKDGeOOIHYzYm4ZFvmWez5g==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-s390x-gnu/-/rollup-linux-s390x-gnu-4.34.7.tgz",
|
||||
"integrity": "sha512-nNXNjo4As6dNqRn7OrsnHzwTgtypfRA3u3AKr0B3sOOo+HkedIbn8ZtFnB+4XyKJojIfqDKmbIzO1QydQ8c+Pw==",
|
||||
"cpu": [
|
||||
"s390x"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-x64-gnu": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-x64-gnu/-/rollup-linux-x64-gnu-4.29.1.tgz",
|
||||
"integrity": "sha512-87xYCwb0cPGZFoGiErT1eDcssByaLX4fc0z2nRM6eMtV9njAfEE6OW3UniAoDhX4Iq5xQVpE6qO9aJbCFumKYQ==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-x64-gnu/-/rollup-linux-x64-gnu-4.34.7.tgz",
|
||||
"integrity": "sha512-9kPVf9ahnpOMSGlCxXGv980wXD0zRR3wyk8+33/MXQIpQEOpaNe7dEHm5LMfyRZRNt9lMEQuH0jUKj15MkM7QA==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-linux-x64-musl": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-x64-musl/-/rollup-linux-x64-musl-4.29.1.tgz",
|
||||
"integrity": "sha512-xufkSNppNOdVRCEC4WKvlR1FBDyqCSCpQeMMgv9ZyXqqtKBfkw1yfGMTUTs9Qsl6WQbJnsGboWCp7pJGkeMhKA==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-linux-x64-musl/-/rollup-linux-x64-musl-4.34.7.tgz",
|
||||
"integrity": "sha512-7wJPXRWTTPtTFDFezA8sle/1sdgxDjuMoRXEKtx97ViRxGGkVQYovem+Q8Pr/2HxiHp74SSRG+o6R0Yq0shPwQ==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-win32-arm64-msvc": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-win32-arm64-msvc/-/rollup-win32-arm64-msvc-4.29.1.tgz",
|
||||
"integrity": "sha512-F2OiJ42m77lSkizZQLuC+jiZ2cgueWQL5YC9tjo3AgaEw+KJmVxHGSyQfDUoYR9cci0lAywv2Clmckzulcq6ig==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-win32-arm64-msvc/-/rollup-win32-arm64-msvc-4.34.7.tgz",
|
||||
"integrity": "sha512-MN7aaBC7mAjsiMEZcsJvwNsQVNZShgES/9SzWp1HC9Yjqb5OpexYnRjF7RmE4itbeesHMYYQiAtUAQaSKs2Rfw==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"win32"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-win32-ia32-msvc": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-win32-ia32-msvc/-/rollup-win32-ia32-msvc-4.29.1.tgz",
|
||||
"integrity": "sha512-rYRe5S0FcjlOBZQHgbTKNrqxCBUmgDJem/VQTCcTnA2KCabYSWQDrytOzX7avb79cAAweNmMUb/Zw18RNd4mng==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-win32-ia32-msvc/-/rollup-win32-ia32-msvc-4.34.7.tgz",
|
||||
"integrity": "sha512-aeawEKYswsFu1LhDM9RIgToobquzdtSc4jSVqHV8uApz4FVvhFl/mKh92wc8WpFc6aYCothV/03UjY6y7yLgbg==",
|
||||
"cpu": [
|
||||
"ia32"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"win32"
|
||||
]
|
||||
},
|
||||
"node_modules/@rollup/rollup-win32-x64-msvc": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-win32-x64-msvc/-/rollup-win32-x64-msvc-4.29.1.tgz",
|
||||
"integrity": "sha512-+10CMg9vt1MoHj6x1pxyjPSMjHTIlqs8/tBztXvPAx24SKs9jwVnKqHJumlH/IzhaPUaj3T6T6wfZr8okdXaIg==",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/@rollup/rollup-win32-x64-msvc/-/rollup-win32-x64-msvc-4.34.7.tgz",
|
||||
"integrity": "sha512-4ZedScpxxIrVO7otcZ8kCX1mZArtH2Wfj3uFCxRJ9NO80gg1XV0U/b2f/MKaGwj2X3QopHfoWiDQ917FRpwY3w==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"win32"
|
||||
@@ -1115,8 +1096,7 @@
|
||||
"node_modules/@types/estree": {
|
||||
"version": "1.0.6",
|
||||
"resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.6.tgz",
|
||||
"integrity": "sha512-AYnb1nQyY49te+VRAVgmzfcgjYS91mY5P0TKUDCLEM+gNnA+3T6rWITXRLYCpahpqSQbN5cE+gHpnPyXjHWxcw==",
|
||||
"license": "MIT"
|
||||
"integrity": "sha512-AYnb1nQyY49te+VRAVgmzfcgjYS91mY5P0TKUDCLEM+gNnA+3T6rWITXRLYCpahpqSQbN5cE+gHpnPyXjHWxcw=="
|
||||
},
|
||||
"node_modules/@vitejs/plugin-react": {
|
||||
"version": "4.3.4",
|
||||
@@ -2084,9 +2064,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/postcss": {
|
||||
"version": "8.4.49",
|
||||
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.49.tgz",
|
||||
"integrity": "sha512-OCVPnIObs4N29kxTjzLfUryOkvZEq+pf8jTF0lg8E7uETuWHA+v7j3c/xJmiqpX450191LlmZfUKkXxkTry7nA==",
|
||||
"version": "8.5.2",
|
||||
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.2.tgz",
|
||||
"integrity": "sha512-MjOadfU3Ys9KYoX0AdkBlFEF1Vx37uCCeN4ZHnmwm9FfpbsGWMZeBLMmmpY+6Ocqod7mkdZ0DT31OlbsFrLlkA==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "opencollective",
|
||||
@@ -2101,9 +2081,8 @@
|
||||
"url": "https://github.com/sponsors/ai"
|
||||
}
|
||||
],
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"nanoid": "^3.3.7",
|
||||
"nanoid": "^3.3.8",
|
||||
"picocolors": "^1.1.1",
|
||||
"source-map-js": "^1.2.1"
|
||||
},
|
||||
@@ -2339,10 +2318,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/rollup": {
|
||||
"version": "4.29.1",
|
||||
"resolved": "https://registry.npmjs.org/rollup/-/rollup-4.29.1.tgz",
|
||||
"integrity": "sha512-RaJ45M/kmJUzSWDs1Nnd5DdV4eerC98idtUOVr6FfKcgxqvjwHmxc5upLF9qZU9EpsVzzhleFahrT3shLuJzIw==",
|
||||
"license": "MIT",
|
||||
"version": "4.34.7",
|
||||
"resolved": "https://registry.npmjs.org/rollup/-/rollup-4.34.7.tgz",
|
||||
"integrity": "sha512-8qhyN0oZ4x0H6wmBgfKxJtxM7qS98YJ0k0kNh5ECVtuchIJ7z9IVVvzpmtQyT10PXKMtBxYr1wQ5Apg8RS8kXQ==",
|
||||
"dependencies": {
|
||||
"@types/estree": "1.0.6"
|
||||
},
|
||||
@@ -2354,25 +2332,25 @@
|
||||
"npm": ">=8.0.0"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@rollup/rollup-android-arm-eabi": "4.29.1",
|
||||
"@rollup/rollup-android-arm64": "4.29.1",
|
||||
"@rollup/rollup-darwin-arm64": "4.29.1",
|
||||
"@rollup/rollup-darwin-x64": "4.29.1",
|
||||
"@rollup/rollup-freebsd-arm64": "4.29.1",
|
||||
"@rollup/rollup-freebsd-x64": "4.29.1",
|
||||
"@rollup/rollup-linux-arm-gnueabihf": "4.29.1",
|
||||
"@rollup/rollup-linux-arm-musleabihf": "4.29.1",
|
||||
"@rollup/rollup-linux-arm64-gnu": "4.29.1",
|
||||
"@rollup/rollup-linux-arm64-musl": "4.29.1",
|
||||
"@rollup/rollup-linux-loongarch64-gnu": "4.29.1",
|
||||
"@rollup/rollup-linux-powerpc64le-gnu": "4.29.1",
|
||||
"@rollup/rollup-linux-riscv64-gnu": "4.29.1",
|
||||
"@rollup/rollup-linux-s390x-gnu": "4.29.1",
|
||||
"@rollup/rollup-linux-x64-gnu": "4.29.1",
|
||||
"@rollup/rollup-linux-x64-musl": "4.29.1",
|
||||
"@rollup/rollup-win32-arm64-msvc": "4.29.1",
|
||||
"@rollup/rollup-win32-ia32-msvc": "4.29.1",
|
||||
"@rollup/rollup-win32-x64-msvc": "4.29.1",
|
||||
"@rollup/rollup-android-arm-eabi": "4.34.7",
|
||||
"@rollup/rollup-android-arm64": "4.34.7",
|
||||
"@rollup/rollup-darwin-arm64": "4.34.7",
|
||||
"@rollup/rollup-darwin-x64": "4.34.7",
|
||||
"@rollup/rollup-freebsd-arm64": "4.34.7",
|
||||
"@rollup/rollup-freebsd-x64": "4.34.7",
|
||||
"@rollup/rollup-linux-arm-gnueabihf": "4.34.7",
|
||||
"@rollup/rollup-linux-arm-musleabihf": "4.34.7",
|
||||
"@rollup/rollup-linux-arm64-gnu": "4.34.7",
|
||||
"@rollup/rollup-linux-arm64-musl": "4.34.7",
|
||||
"@rollup/rollup-linux-loongarch64-gnu": "4.34.7",
|
||||
"@rollup/rollup-linux-powerpc64le-gnu": "4.34.7",
|
||||
"@rollup/rollup-linux-riscv64-gnu": "4.34.7",
|
||||
"@rollup/rollup-linux-s390x-gnu": "4.34.7",
|
||||
"@rollup/rollup-linux-x64-gnu": "4.34.7",
|
||||
"@rollup/rollup-linux-x64-musl": "4.34.7",
|
||||
"@rollup/rollup-win32-arm64-msvc": "4.34.7",
|
||||
"@rollup/rollup-win32-ia32-msvc": "4.34.7",
|
||||
"@rollup/rollup-win32-x64-msvc": "4.34.7",
|
||||
"fsevents": "~2.3.2"
|
||||
}
|
||||
},
|
||||
@@ -2719,14 +2697,13 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/vite": {
|
||||
"version": "6.0.7",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-6.0.7.tgz",
|
||||
"integrity": "sha512-RDt8r/7qx9940f8FcOIAH9PTViRrghKaK2K1jY3RaAURrEUbm9Du1mJ72G+jlhtG3WwodnfzY8ORQZbBavZEAQ==",
|
||||
"license": "MIT",
|
||||
"version": "6.1.0",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz",
|
||||
"integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==",
|
||||
"dependencies": {
|
||||
"esbuild": "^0.24.2",
|
||||
"postcss": "^8.4.49",
|
||||
"rollup": "^4.23.0"
|
||||
"postcss": "^8.5.1",
|
||||
"rollup": "^4.30.1"
|
||||
},
|
||||
"bin": {
|
||||
"vite": "bin/vite.js"
|
||||
|
||||
@@ -27,7 +27,7 @@ const LLMResponse = memo(({ data, onConfirm, isLastMessage, onHeightChange }) =>
|
||||
: data?.response;
|
||||
|
||||
const displayText = (response || '').trim();
|
||||
const requiresConfirm = data.next === "confirm" && isLastMessage;
|
||||
const requiresConfirm = data.force_confirm && data.next === "confirm" && isLastMessage;
|
||||
const defaultText = requiresConfirm
|
||||
? `Agent is ready to run "${data.tool}". Please confirm.`
|
||||
: '';
|
||||
|
||||
@@ -17,7 +17,7 @@ class CombinedInput:
|
||||
|
||||
Message = Dict[str, Union[str, Dict[str, Any]]]
|
||||
ConversationHistory = Dict[str, List[Message]]
|
||||
NextStep = Literal["confirm", "question", "done"]
|
||||
NextStep = Literal["confirm", "question", "pick-new-goal", "done"]
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@@ -15,9 +15,11 @@ class ToolDefinition:
|
||||
description: str
|
||||
arguments: List[ToolArgument]
|
||||
|
||||
|
||||
@dataclass
|
||||
class AgentGoal:
|
||||
id: str
|
||||
agent_name: str
|
||||
agent_friendly_description: str
|
||||
tools: List[ToolDefinition]
|
||||
description: str = "Description of the tools purpose and overall goal"
|
||||
starter_prompt: str = "Initial prompt to start the conversation"
|
||||
|
||||
@@ -68,7 +68,7 @@ def generate_genai_prompt(
|
||||
"Your JSON format must be:\n"
|
||||
"{\n"
|
||||
' "response": "<plain text>",\n'
|
||||
' "next": "<question|confirm|done>",\n'
|
||||
' "next": "<question|confirm|pick-new-goal|done>",\n'
|
||||
' "tool": "<tool_name or null>",\n'
|
||||
' "args": {\n'
|
||||
' "<arg1>": "<value1 or null>",\n'
|
||||
@@ -81,9 +81,8 @@ def generate_genai_prompt(
|
||||
"1) If any required argument is missing, set next='question' and ask the user.\n"
|
||||
"2) If all required arguments are known, set next='confirm' and specify the tool.\n"
|
||||
" The user will confirm before the tool is run.\n"
|
||||
"3) If no more tools are needed (user_confirmed_tool_run has been run for all), set next='done' and tool=null.\n"
|
||||
"3) If no more tools are needed (user_confirmed_tool_run has been run for all), set next='confirm' and tool='ListAgents'.\n"
|
||||
"4) response should be short and user-friendly.\n"
|
||||
"5) Don't set next='done' until the final tool has returned user_confirmed_tool_run.\n"
|
||||
)
|
||||
|
||||
# Validation Task (If raw_json is provided)
|
||||
@@ -123,12 +122,11 @@ def generate_tool_completion_prompt(current_tool: str, dynamic_result: dict) ->
|
||||
return (
|
||||
f"### The '{current_tool}' tool completed successfully with {dynamic_result}. "
|
||||
"INSTRUCTIONS: Parse this tool result as plain text, and use the system prompt containing the list of tools in sequence and the conversation history (and previous tool_results) to figure out next steps, if any. "
|
||||
"You will need to use the tool_results to auto-fill arguments for subsequent tools and also to figure out if all tools have been run."
|
||||
'{"next": "<question|confirm|done>", "tool": "<tool_name or null>", "args": {"<arg1>": "<value1 or null>", "<arg2>": "<value2 or null>}, "response": "<plain text (can include \\n line breaks)>"}'
|
||||
"ONLY return those json keys (next, tool, args, response), nothing else."
|
||||
'Next should only be "done" if all tools have been run (use the system prompt to figure that out).'
|
||||
'Next should be "question" if the tool is not the last one in the sequence.'
|
||||
'Next should NOT be "confirm" at this point.'
|
||||
"You will need to use the tool_results to auto-fill arguments for subsequent tools and also to figure out if all tools have been run. "
|
||||
'{"next": "<question|confirm|pick-new-goal|done>", "tool": "<tool_name or null>", "args": {"<arg1>": "<value1 or null>", "<arg2>": "<value2 or null>}, "response": "<plain text (can include \\n line breaks)>"}'
|
||||
"ONLY return those json keys (next, tool, args, response), nothing else. "
|
||||
'Next should be "question" if the tool is not the last one in the sequence. '
|
||||
'Next should only be "pick-new-goal" if all tools have been run (use the system prompt to figure that out).'
|
||||
)
|
||||
|
||||
def generate_missing_args_prompt(current_tool: str, tool_data: dict, missing_args: list[str]) -> str:
|
||||
|
||||
176
setup.md
Normal file
176
setup.md
Normal file
@@ -0,0 +1,176 @@
|
||||
## Configuration
|
||||
|
||||
This application uses `.env` files for configuration. Copy the [.env.example](.env.example) file to `.env` and update the values:
|
||||
|
||||
```bash
|
||||
cp .env.example .env
|
||||
```
|
||||
|
||||
### Agent Goal Configuration
|
||||
|
||||
The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file.
|
||||
|
||||
#### Goal: Find an event in Australia / New Zealand, book flights to it and invoice the user for the cost
|
||||
- `AGENT_GOAL=goal_event_flight_invoice` (default) - Helps users find events, book flights, and arrange train travel with invoice generation
|
||||
- This is the scenario in the video above
|
||||
|
||||
#### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost
|
||||
- `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
|
||||
- This is a new goal that is part of an upcoming conference talk
|
||||
|
||||
If not specified, the agent defaults to `goal_event_flight_invoice`. Each goal comes with its own set of tools and conversation flows designed for specific use cases. You can examine `tools/goal_registry.py` to see the detailed configuration of each goal.
|
||||
|
||||
See the next section for tool configuration for each goal.
|
||||
|
||||
### Tool Configuration
|
||||
|
||||
#### Agent Goal: goal_event_flight_invoice (default)
|
||||
* 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.
|
||||
* It's free to sign up at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper)
|
||||
* This api might be slow to respond, so you may want to increase the start to close timeout, `TOOL_ACTIVITY_START_TO_CLOSE_TIMEOUT` in `workflows/workflow_helpers.py`
|
||||
* Requires a Stripe key for the `create_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/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
|
||||
|
||||
#### Agent Goal: goal_match_train_invoice
|
||||
|
||||
* Finding a 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. Set `FOOTBALL_DATA_API_KEY` to this value.
|
||||
* If you're lazy go to `tools/search_fixtures.py` and replace the `search_fixtures` function with the mock `search_fixtures_example` that exists in the same file.
|
||||
* We use a mock function to search for trains. Start the train API server to use the real API: `python thirdparty/train_api.py`
|
||||
* * The train activity is 'enterprise' so it's written in C# and requires a .NET runtime. See the [.NET backend](#net-(enterprise)-backend) section for details on running it.
|
||||
* Requires a Stripe key for the `create_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/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
|
||||
|
||||
### LLM Provider Configuration
|
||||
|
||||
The agent can use OpenAI's GPT-4o, Google Gemini, Anthropic Claude, or a local LLM via Ollama. Set the `LLM_PROVIDER` environment variable in your `.env` file to choose the desired provider:
|
||||
|
||||
- `LLM_PROVIDER=openai` for OpenAI's GPT-4o
|
||||
- `LLM_PROVIDER=google` for Google Gemini
|
||||
- `LLM_PROVIDER=anthropic` for Anthropic Claude
|
||||
- `LLM_PROVIDER=deepseek` for DeepSeek-V3
|
||||
- `LLM_PROVIDER=ollama` for running LLMs via [Ollama](https://ollama.ai) (not recommended for this use case)
|
||||
|
||||
### Option 1: OpenAI
|
||||
|
||||
If using OpenAI, ensure you have an OpenAI key for the GPT-4o model. Set this in the `OPENAI_API_KEY` environment variable in `.env`.
|
||||
|
||||
### Option 2: Google Gemini
|
||||
|
||||
To use Google Gemini:
|
||||
|
||||
1. Obtain a Google API key and set it in the `GOOGLE_API_KEY` environment variable in `.env`.
|
||||
2. Set `LLM_PROVIDER=google` in your `.env` file.
|
||||
|
||||
### Option 3: Anthropic Claude (recommended)
|
||||
|
||||
I find that Claude Sonnet 3.5 performs better than the other hosted LLMs for this use case.
|
||||
|
||||
To use Anthropic:
|
||||
|
||||
1. Obtain an Anthropic API key and set it in the `ANTHROPIC_API_KEY` environment variable in `.env`.
|
||||
2. Set `LLM_PROVIDER=anthropic` in your `.env` file.
|
||||
|
||||
### Option 4: Deepseek-V3
|
||||
|
||||
To use Deepseek-V3:
|
||||
|
||||
1. Obtain a Deepseek API key and set it in the `DEEPSEEK_API_KEY` environment variable in `.env`.
|
||||
2. Set `LLM_PROVIDER=deepseek` in your `.env` file.
|
||||
|
||||
### Option 5: Local LLM via Ollama (not recommended)
|
||||
|
||||
To use a local LLM with Ollama:
|
||||
|
||||
1. Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model.
|
||||
- Run `ollama run <OLLAMA_MODEL_NAME>` to start the model. Note that this model is about 9GB to download.
|
||||
- Example: `ollama run qwen2.5:14b`
|
||||
|
||||
2. Set `LLM_PROVIDER=ollama` in your `.env` file and `OLLAMA_MODEL_NAME` to the name of the model you installed.
|
||||
|
||||
Note: I found the other (hosted) LLMs to be MUCH more reliable for this use case. However, you can switch to Ollama if desired, and choose a suitably large model if your computer has the resources.
|
||||
|
||||
## Configuring Temporal Connection
|
||||
|
||||
By default, this application will connect to a local Temporal server (`localhost:7233`) in the default namespace, using the `agent-task-queue` task queue. You can override these settings in your `.env` file.
|
||||
|
||||
### Use Temporal Cloud
|
||||
|
||||
See [.env.example](.env.example) for details on connecting to Temporal Cloud using mTLS or API key authentication.
|
||||
|
||||
[Sign up for Temporal Cloud](https://temporal.io/get-cloud)
|
||||
|
||||
### Use a local Temporal Dev Server
|
||||
|
||||
On a Mac
|
||||
```bash
|
||||
brew install temporal
|
||||
temporal server start-dev
|
||||
```
|
||||
See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.
|
||||
|
||||
|
||||
## Running the Application
|
||||
|
||||
### Python Backend
|
||||
|
||||
Requires [Poetry](https://python-poetry.org/) to manage dependencies.
|
||||
|
||||
1. `python -m venv venv`
|
||||
|
||||
2. `source venv/bin/activate`
|
||||
|
||||
3. `poetry install`
|
||||
|
||||
Run the following commands in separate terminal windows:
|
||||
|
||||
1. Start the Temporal worker:
|
||||
```bash
|
||||
poetry run python scripts/run_worker.py
|
||||
```
|
||||
|
||||
2. Start the API server:
|
||||
```bash
|
||||
poetry run uvicorn api.main:app --reload
|
||||
```
|
||||
Access the API at `/docs` to see the available endpoints.
|
||||
|
||||
### React UI
|
||||
Start the frontend:
|
||||
```bash
|
||||
cd frontend
|
||||
npm install
|
||||
npx vite
|
||||
```
|
||||
Access the UI at `http://localhost:5173`
|
||||
|
||||
### Python Search Trains API
|
||||
> Agent Goal: goal_match_train_invoice only
|
||||
|
||||
Required to search and book trains!
|
||||
```bash
|
||||
poetry run python thirdparty/train_api.py
|
||||
|
||||
# example url
|
||||
# http://localhost:8080/api/search?from=london&to=liverpool&outbound_time=2025-04-18T09:00:00&inbound_time=2025-04-20T09:00:00
|
||||
```
|
||||
|
||||
### .NET (enterprise) Backend ;)
|
||||
> Agent Goal: goal_match_train_invoice only
|
||||
|
||||
We have activities written in C# to call the train APIs.
|
||||
```bash
|
||||
cd enterprise
|
||||
dotnet build # ensure you brew install dotnet@8 first!
|
||||
dotnet run
|
||||
```
|
||||
If you're running your train API above on a different host/port then change the API URL in `Program.cs`. Otherwise, be sure to run it using `python thirdparty/train_api.py`.
|
||||
|
||||
## Customizing the Agent
|
||||
- `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them)
|
||||
- `goal_registry.py` contains descriptions of goals and the tools used to achieve them
|
||||
- The tools themselves are defined in their own files in `/tools`
|
||||
- Note the mapping in `tools/__init__.py` to each tool
|
||||
@@ -16,7 +16,6 @@ TEMPORAL_TLS_CERT = os.getenv("TEMPORAL_TLS_CERT", "")
|
||||
TEMPORAL_TLS_KEY = os.getenv("TEMPORAL_TLS_KEY", "")
|
||||
TEMPORAL_API_KEY = os.getenv("TEMPORAL_API_KEY", "")
|
||||
|
||||
|
||||
async def get_temporal_client() -> Client:
|
||||
"""
|
||||
Creates a Temporal client based on environment configuration.
|
||||
|
||||
55
tests/agent_goal_workflow_test.py
Normal file
55
tests/agent_goal_workflow_test.py
Normal file
@@ -0,0 +1,55 @@
|
||||
import asyncio
|
||||
|
||||
from temporalio.client import Client, WorkflowExecutionStatus
|
||||
from temporalio.worker import Worker
|
||||
from temporalio.testing import TestWorkflowEnvironment
|
||||
from api.main import get_initial_agent_goal
|
||||
from models.data_types import AgentGoalWorkflowParams, CombinedInput
|
||||
from workflows import AgentGoalWorkflow
|
||||
from activities.tool_activities import ToolActivities, dynamic_tool_activity
|
||||
|
||||
|
||||
async def asyncSetUp(self):
|
||||
# Set up the test environment
|
||||
self.env = await TestWorkflowEnvironment.create_local()
|
||||
|
||||
async def asyncTearDown(self):
|
||||
# Clean up after tests
|
||||
await self.env.shutdown()
|
||||
|
||||
async def test_workflow_success(client: Client):
|
||||
# Register the workflow and activity
|
||||
# self.env.register_workflow(AgentGoalWorkflow)
|
||||
# self.env.register_activity(ToolActivities.agent_validatePrompt)
|
||||
# self.env.register_activity(ToolActivities.agent_toolPlanner)
|
||||
# self.env.register_activity(dynamic_tool_activity)
|
||||
|
||||
task_queue_name = "agent-ai-workflow"
|
||||
workflow_id = "agent-workflow"
|
||||
|
||||
initial_agent_goal = get_initial_agent_goal()
|
||||
|
||||
# Create combined input
|
||||
combined_input = CombinedInput(
|
||||
tool_params=AgentGoalWorkflowParams(None, None),
|
||||
agent_goal=initial_agent_goal,
|
||||
)
|
||||
|
||||
workflow_id = "agent-workflow"
|
||||
async with Worker(client, task_queue=task_queue_name, workflows=[AgentGoalWorkflow], activities=[ToolActivities.agent_validatePrompt, ToolActivities.agent_toolPlanner, dynamic_tool_activity]):
|
||||
handle = await client.start_workflow(
|
||||
AgentGoalWorkflow.run, id=workflow_id, task_queue=task_queue_name
|
||||
)
|
||||
# todo fix signals
|
||||
await handle.signal(AgentGoalWorkflow.submit_greeting, "user1")
|
||||
await handle.signal(AgentGoalWorkflow.submit_greeting, "user2")
|
||||
assert WorkflowExecutionStatus.RUNNING == (await handle.describe()).status
|
||||
|
||||
await handle.signal(AgentGoalWorkflow.exit)
|
||||
assert ["Hello, user1", "Hello, user2"] == await handle.result()
|
||||
assert WorkflowExecutionStatus.COMPLETED == (await handle.describe()).status
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
45
todo.md
Normal file
45
todo.md
Normal file
@@ -0,0 +1,45 @@
|
||||
# todo list
|
||||
[ ] clean up workflow/make functions
|
||||
|
||||
[ ] make the debugging confirms optional <br />
|
||||
<br />
|
||||
[ ] document *why* temporal for ai agents - scalability, durability, visibility in the readme <br />
|
||||
[ ] fix readme: move setup to its own page, demo to its own page, add the why /|\ section <br />
|
||||
[ ] add architecture to readme <br />
|
||||
- elements of app <br />
|
||||
- dive into llm interaction <br />
|
||||
- workflow breakdown - interactive loop <br />
|
||||
- why temporal <br />
|
||||
|
||||
[ ] setup readme, why readme, architecture readme, what this is in main readme with temporal value props and pictures <br />
|
||||
[ ] how to add more scenarios, tools <br />
|
||||
<br />
|
||||
<br />
|
||||
[ ] create tests<br />
|
||||
|
||||
[ ] create people management scenario <br />
|
||||
- check pay status <br />
|
||||
- book work travel <br />
|
||||
- check PTO levels <br />
|
||||
- check insurance coverages <br />
|
||||
- book PTO around a date (https://developers.google.com/calendar/api/guides/overview)? <br />
|
||||
- scenario should use multiple tools <br />
|
||||
- expense management <br />
|
||||
- check in on the health of the team <br />
|
||||
|
||||
[ ] demo the reasons why: <br />
|
||||
- Orchestrate interactions across distributed data stores and tools <br />
|
||||
- Hold state, potentially over long periods of time <br />
|
||||
- Ability to ‘self-heal’ and retry until the (probabilistic) LLM returns valid data <br />
|
||||
- Support for human intervention such as approvals <br />
|
||||
- Parallel processing for efficiency of data retrieval and tool use <br />
|
||||
- Insight into the agent’s performance <br />
|
||||
- ask the ai agent how it did at the end of the conversation, was it efficient? successful? insert a search attribute to document that before return
|
||||
|
||||
[ ] customize prompts in [workflow to manage scenario](./workflows/tool_workflow.py)<br />
|
||||
[ ] add in new tools? <br />
|
||||
|
||||
[ ] non-retry the api key error - "Invalid API Key provided: sk_test_**J..." and "AuthenticationError" <br />
|
||||
[ ] make it so you can yeet yourself out of a goal and pick a new one <br />
|
||||
|
||||
[ ] add visual feedback when workflow starting
|
||||
@@ -4,6 +4,9 @@ from .search_trains import search_trains
|
||||
from .search_trains import book_trains
|
||||
from .create_invoice import create_invoice
|
||||
from .find_events import find_events
|
||||
from .list_agents import list_agents
|
||||
from .change_goal import change_goal
|
||||
from .transfer_control import transfer_control
|
||||
|
||||
|
||||
def get_handler(tool_name: str):
|
||||
@@ -19,5 +22,11 @@ def get_handler(tool_name: str):
|
||||
return create_invoice
|
||||
if tool_name == "FindEvents":
|
||||
return find_events
|
||||
if tool_name == "ListAgents":
|
||||
return list_agents
|
||||
if tool_name == "ChangeGoal":
|
||||
return change_goal
|
||||
if tool_name == "TransferControl":
|
||||
return transfer_control
|
||||
|
||||
raise ValueError(f"Unknown tool: {tool_name}")
|
||||
|
||||
9
tools/change_goal.py
Normal file
9
tools/change_goal.py
Normal file
@@ -0,0 +1,9 @@
|
||||
def change_goal(args: dict) -> dict:
|
||||
|
||||
new_goal = args.get("goalID")
|
||||
if new_goal is None:
|
||||
new_goal = "goal_choose_agent_type"
|
||||
|
||||
return {
|
||||
"new_goal": new_goal,
|
||||
}
|
||||
@@ -4,7 +4,7 @@ from dotenv import load_dotenv
|
||||
|
||||
load_dotenv(override=True) # Load environment variables from a .env file
|
||||
|
||||
stripe.api_key = os.getenv("STRIPE_API_KEY", "YOUR_DEFAULT_KEY")
|
||||
stripe.api_key = os.getenv("STRIPE_API_KEY")
|
||||
|
||||
|
||||
def ensure_customer_exists(
|
||||
@@ -26,7 +26,8 @@ def ensure_customer_exists(
|
||||
|
||||
def create_invoice(args: dict) -> dict:
|
||||
"""Create and finalize a Stripe invoice."""
|
||||
# Find or create customer
|
||||
# If an API key exists in the env file, find or create customer
|
||||
if stripe.api_key is not None:
|
||||
customer_id = ensure_customer_exists(
|
||||
args.get("customer_id"), args.get("email", "default@example.com")
|
||||
)
|
||||
@@ -60,7 +61,14 @@ def create_invoice(args: dict) -> dict:
|
||||
"invoiceURL": finalized_invoice.hosted_invoice_url,
|
||||
"reference": finalized_invoice.number,
|
||||
}
|
||||
|
||||
# if no API key is in the env file, return dummy info
|
||||
else:
|
||||
print("[CreateInvoice] Creating invoice with:", args)
|
||||
return {
|
||||
"invoiceStatus": "generated",
|
||||
"invoiceURL": "https://pay.example.com/invoice/12345",
|
||||
"reference": "INV-12345",
|
||||
}
|
||||
|
||||
def create_invoice_example(args: dict) -> dict:
|
||||
"""
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
from typing import List
|
||||
from models.tool_definitions import AgentGoal
|
||||
from tools.tool_registry import (
|
||||
search_fixtures_tool,
|
||||
@@ -6,14 +7,50 @@ from tools.tool_registry import (
|
||||
book_trains_tool,
|
||||
create_invoice_tool,
|
||||
find_events_tool,
|
||||
change_goal_tool,
|
||||
list_agents_tool
|
||||
)
|
||||
|
||||
starter_prompt_generic = "Welcome me, give me a description of what you can do, then ask me for the details you need to do your job"
|
||||
|
||||
goal_choose_agent_type = AgentGoal(
|
||||
id = "goal_choose_agent_type",
|
||||
agent_name="Choose Agent",
|
||||
agent_friendly_description="Choose the type of agent to assist you today.",
|
||||
tools=[
|
||||
list_agents_tool,
|
||||
change_goal_tool,
|
||||
],
|
||||
description="The user wants to choose which type of agent they will interact with. "
|
||||
"Help the user gather args for these tools, in order: "
|
||||
"1. ListAgents: List agents available to interact with "
|
||||
"2. ChangeGoal: Change goal of agent "
|
||||
"After these tools are complete, change your goal to the new goal as chosen by the user. ",
|
||||
starter_prompt=starter_prompt_generic,
|
||||
example_conversation_history="\n ".join(
|
||||
[
|
||||
"user: I'd like to choose an agent",
|
||||
"agent: Sure! Would you like me to list the available agents?",
|
||||
"user_confirmed_tool_run: <user clicks confirm on ListAgents tool>",
|
||||
"tool_result: { 'agent_name': 'Event Flight Finder', 'goal_id': 'goal_event_flight_invoice', 'agent_description': 'Helps users find interesting events and arrange travel to them' }",
|
||||
"agent: The available agents are: 1. Event Flight Finder. Which agent would you like to speak to?",
|
||||
"user: 1",
|
||||
"user_confirmed_tool_run: <user clicks confirm on ChangeGoal tool>",
|
||||
"tool_result: { 'new_goal': 'goal_event_flight_invoice' }",
|
||||
]
|
||||
),
|
||||
)
|
||||
|
||||
goal_match_train_invoice = AgentGoal(
|
||||
id = "goal_match_train_invoice",
|
||||
agent_name="UK Premier League Match Trip Booking",
|
||||
agent_friendly_description="Book a trip to a city in the UK around the dates of a premier league match.",
|
||||
tools=[
|
||||
search_fixtures_tool,
|
||||
search_trains_tool,
|
||||
book_trains_tool,
|
||||
create_invoice_tool,
|
||||
list_agents_tool, #last tool must be list_agents to fasciliate changing back to picking an agent again at the end
|
||||
],
|
||||
description="The user wants to book a trip to a city in the UK around the dates of a premier league match. "
|
||||
"Help the user find a premier league match to attend, search and book trains for that match and offers to invoice them for the cost of train tickets. "
|
||||
@@ -23,7 +60,7 @@ goal_match_train_invoice = AgentGoal(
|
||||
"2. SearchTrains: Search for trains to the city of the match and list them for the customer to choose from "
|
||||
"3. BookTrains: Book the train tickets, used to invoice the user for the cost of the train tickets "
|
||||
"4. CreateInvoice: Invoices the user for the cost of train tickets, with total and details inferred from the conversation history ",
|
||||
starter_prompt="Welcome me, give me a description of what you can do, then ask me for the details you need to begin your job as an agent ",
|
||||
starter_prompt=starter_prompt_generic,
|
||||
example_conversation_history="\n ".join(
|
||||
[
|
||||
"user: I'd like to travel to a premier league match",
|
||||
@@ -51,18 +88,21 @@ goal_match_train_invoice = AgentGoal(
|
||||
),
|
||||
)
|
||||
|
||||
# unused
|
||||
goal_event_flight_invoice = AgentGoal(
|
||||
id = "goal_event_flight_invoice",
|
||||
agent_name="Australia and New Zealand Event Flight Booking",
|
||||
agent_friendly_description="Book a trip to a city in Australia or New Zealand around the dates of events in that city.",
|
||||
tools=[
|
||||
find_events_tool,
|
||||
search_flights_tool,
|
||||
create_invoice_tool,
|
||||
list_agents_tool, #last tool must be list_agents to fasciliate changing back to picking an agent again at the end
|
||||
],
|
||||
description="Help the user gather args for these tools in order: "
|
||||
"1. FindEvents: Find an event to travel to "
|
||||
"2. SearchFlights: search for a flight around the event dates "
|
||||
"3. CreateInvoice: Create a simple invoice for the cost of that flight ",
|
||||
starter_prompt="Welcome me, give me a description of what you can do, then ask me for the details you need to do your job",
|
||||
starter_prompt=starter_prompt_generic,
|
||||
example_conversation_history="\n ".join(
|
||||
[
|
||||
"user: I'd like to travel to an event",
|
||||
@@ -85,3 +125,9 @@ goal_event_flight_invoice = AgentGoal(
|
||||
]
|
||||
),
|
||||
)
|
||||
|
||||
#Add the goals to a list for more generic processing, like listing available agents
|
||||
goal_list: List[AgentGoal] = []
|
||||
goal_list.append(goal_choose_agent_type)
|
||||
goal_list.append(goal_event_flight_invoice)
|
||||
goal_list.append(goal_match_train_invoice)
|
||||
|
||||
17
tools/list_agents.py
Normal file
17
tools/list_agents.py
Normal file
@@ -0,0 +1,17 @@
|
||||
import tools.goal_registry as goals
|
||||
|
||||
def list_agents(args: dict) -> dict:
|
||||
|
||||
agents = []
|
||||
if goals.goal_list is not None:
|
||||
for goal in goals.goal_list:
|
||||
agents.append(
|
||||
{
|
||||
"agent_name": goal.agent_name,
|
||||
"goal_id": goal.id,
|
||||
"agent_description": goal.agent_friendly_description,
|
||||
}
|
||||
)
|
||||
return {
|
||||
"agents": agents,
|
||||
}
|
||||
@@ -1,5 +1,23 @@
|
||||
from models.tool_definitions import ToolDefinition, ToolArgument
|
||||
|
||||
list_agents_tool = ToolDefinition(
|
||||
name="ListAgents",
|
||||
description="List available agents to interact with, pulled from goal_registry. ",
|
||||
arguments=[],
|
||||
)
|
||||
|
||||
change_goal_tool = ToolDefinition(
|
||||
name="ChangeGoal",
|
||||
description="Change the goal of the active agent. ",
|
||||
arguments=[
|
||||
ToolArgument(
|
||||
name="goalID",
|
||||
type="string",
|
||||
description="Which goal to change to",
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
search_flights_tool = ToolDefinition(
|
||||
name="SearchFlights",
|
||||
description="Search for return flights from an origin to a destination within a date range (dateDepart, dateReturn).",
|
||||
|
||||
7
tools/transfer_control.py
Normal file
7
tools/transfer_control.py
Normal file
@@ -0,0 +1,7 @@
|
||||
import shared.config
|
||||
|
||||
def transfer_control(args: dict) -> dict:
|
||||
|
||||
return {
|
||||
"new_goal": shared.config.AGENT_GOAL,
|
||||
}
|
||||
@@ -1,11 +1,13 @@
|
||||
from collections import deque
|
||||
from datetime import timedelta
|
||||
import os
|
||||
from typing import Dict, Any, Union, List, Optional, Deque, TypedDict
|
||||
|
||||
from temporalio.common import RetryPolicy
|
||||
from temporalio import workflow
|
||||
|
||||
from models.data_types import ConversationHistory, NextStep, ValidationInput
|
||||
from models.tool_definitions import AgentGoal
|
||||
from workflows.workflow_helpers import LLM_ACTIVITY_START_TO_CLOSE_TIMEOUT, \
|
||||
LLM_ACTIVITY_SCHEDULE_TO_CLOSE_TIMEOUT
|
||||
from workflows import workflow_helpers as helpers
|
||||
@@ -19,15 +21,24 @@ with workflow.unsafe.imports_passed_through():
|
||||
CombinedInput,
|
||||
ToolPromptInput,
|
||||
)
|
||||
from tools.goal_registry import goal_list
|
||||
|
||||
# Constants
|
||||
MAX_TURNS_BEFORE_CONTINUE = 250
|
||||
|
||||
SHOW_CONFIRM = True
|
||||
show_confirm_env = os.getenv("SHOW_CONFIRM")
|
||||
if show_confirm_env is not None:
|
||||
if show_confirm_env == "False":
|
||||
SHOW_CONFIRM = False
|
||||
|
||||
#ToolData as part of the workflow is what's accessible to the UI - see LLMResponse.jsx for example
|
||||
class ToolData(TypedDict, total=False):
|
||||
next: NextStep
|
||||
tool: str
|
||||
args: Dict[str, Any]
|
||||
response: str
|
||||
force_confirm: bool = True
|
||||
|
||||
@workflow.defn
|
||||
class AgentGoalWorkflow:
|
||||
@@ -41,13 +52,18 @@ class AgentGoalWorkflow:
|
||||
self.tool_data: Optional[ToolData] = None
|
||||
self.confirm: bool = False
|
||||
self.tool_results: List[Dict[str, Any]] = []
|
||||
self.goal: AgentGoal = {"tools": []}
|
||||
|
||||
# see ../api/main.py#temporal_client.start_workflow() for how the input parameters are set
|
||||
@workflow.run
|
||||
async def run(self, combined_input: CombinedInput) -> str:
|
||||
"""Main workflow execution method."""
|
||||
params = combined_input.tool_params
|
||||
agent_goal = combined_input.agent_goal
|
||||
|
||||
"""Main workflow execution method."""
|
||||
# setup phase, starts with blank tool_params and agent_goal prompt as defined in tools/goal_registry.py
|
||||
params = combined_input.tool_params
|
||||
self.goal = combined_input.agent_goal
|
||||
|
||||
# add message from sample conversation provided in tools/goal_registry.py, if it exists
|
||||
if params and params.conversation_summary:
|
||||
self.add_message("conversation_summary", params.conversation_summary)
|
||||
self.conversation_summary = params.conversation_summary
|
||||
@@ -58,16 +74,26 @@ class AgentGoalWorkflow:
|
||||
waiting_for_confirm = False
|
||||
current_tool = None
|
||||
|
||||
# This is the main interactive loop. Main responsibilities:
|
||||
# - Selecting and changing goals as directed by the user
|
||||
# - reacting to user input (from signals)
|
||||
# - calling activities to determine next steps and prompts
|
||||
# - executing the selected tools
|
||||
while True:
|
||||
# wait indefinitely for input from signals - user_prompt, end_chat, or confirm as defined below
|
||||
await workflow.wait_condition(
|
||||
lambda: bool(self.prompt_queue) or self.chat_ended or self.confirm
|
||||
)
|
||||
|
||||
# handle chat-end signal
|
||||
if self.chat_ended:
|
||||
workflow.logger.warning(f"workflow step: chat-end signal received, ending")
|
||||
workflow.logger.info("Chat ended.")
|
||||
return f"{self.conversation_history}"
|
||||
|
||||
# Execute the tool
|
||||
if self.confirm and waiting_for_confirm and current_tool and self.tool_data:
|
||||
workflow.logger.warning(f"workflow step: user has confirmed, executing the tool {current_tool}")
|
||||
self.confirm = False
|
||||
waiting_for_confirm = False
|
||||
|
||||
@@ -75,6 +101,7 @@ class AgentGoalWorkflow:
|
||||
confirmed_tool_data["next"] = "user_confirmed_tool_run"
|
||||
self.add_message("user_confirmed_tool_run", confirmed_tool_data)
|
||||
|
||||
# execute the tool by key as defined in tools/__init__.py
|
||||
await helpers.handle_tool_execution(
|
||||
current_tool,
|
||||
self.tool_data,
|
||||
@@ -82,18 +109,30 @@ class AgentGoalWorkflow:
|
||||
self.add_message,
|
||||
self.prompt_queue
|
||||
)
|
||||
|
||||
#set new goal if we should
|
||||
if len(self.tool_results) > 0:
|
||||
if "ChangeGoal" in self.tool_results[-1].values() and "new_goal" in self.tool_results[-1].keys():
|
||||
new_goal = self.tool_results[-1].get("new_goal")
|
||||
workflow.logger.warning(f"Booya new goal!: {new_goal}")
|
||||
self.change_goal(new_goal)
|
||||
elif "ListAgents" in self.tool_results[-1].values() and self.goal.id != "goal_choose_agent_type":
|
||||
workflow.logger.warning("setting goal to goal_choose_agent_type")
|
||||
self.change_goal("goal_choose_agent_type")
|
||||
continue
|
||||
|
||||
# if we've received messages to be processed on the prompt queue...
|
||||
if self.prompt_queue:
|
||||
prompt = self.prompt_queue.popleft()
|
||||
if not prompt.startswith("###"):
|
||||
workflow.logger.warning(f"workflow step: processing message on the prompt queue, message is {prompt}")
|
||||
if not prompt.startswith("###"): #if the message isn't from the LLM but is instead from the user
|
||||
self.add_message("user", prompt)
|
||||
|
||||
# Validate the prompt before proceeding
|
||||
validation_input = ValidationInput(
|
||||
prompt=prompt,
|
||||
conversation_history=self.conversation_history,
|
||||
agent_goal=agent_goal,
|
||||
agent_goal=self.goal,
|
||||
)
|
||||
validation_result = await workflow.execute_activity(
|
||||
ToolActivities.agent_validatePrompt,
|
||||
@@ -105,25 +144,17 @@ class AgentGoalWorkflow:
|
||||
),
|
||||
)
|
||||
|
||||
#If validation fails, provide that feedback to the user - i.e., "your words make no sense, puny human" end this iteration of processing
|
||||
if not validation_result.validationResult:
|
||||
workflow.logger.warning(
|
||||
f"Prompt validation failed: {validation_result.validationFailedReason}"
|
||||
)
|
||||
self.add_message(
|
||||
"agent", validation_result.validationFailedReason
|
||||
)
|
||||
workflow.logger.warning(f"Prompt validation failed: {validation_result.validationFailedReason}")
|
||||
self.add_message("agent", validation_result.validationFailedReason)
|
||||
continue
|
||||
|
||||
# Proceed with generating the context and prompt
|
||||
context_instructions = generate_genai_prompt(
|
||||
agent_goal, self.conversation_history, self.tool_data
|
||||
)
|
||||
|
||||
prompt_input = ToolPromptInput(
|
||||
prompt=prompt,
|
||||
context_instructions=context_instructions,
|
||||
)
|
||||
# If valid, proceed with generating the context and prompt
|
||||
context_instructions = generate_genai_prompt(self.goal, self.conversation_history, self.tool_data)
|
||||
prompt_input = ToolPromptInput(prompt=prompt, context_instructions=context_instructions)
|
||||
|
||||
# connect to LLM and execute to get next steps
|
||||
tool_data = await workflow.execute_activity(
|
||||
ToolActivities.agent_toolPlanner,
|
||||
prompt_input,
|
||||
@@ -133,51 +164,73 @@ class AgentGoalWorkflow:
|
||||
initial_interval=timedelta(seconds=5), backoff_coefficient=1
|
||||
),
|
||||
)
|
||||
tool_data["force_confirm"] = SHOW_CONFIRM
|
||||
self.tool_data = tool_data
|
||||
|
||||
# process the tool as dictated by the prompt response - what to do next, and with which tool
|
||||
next_step = tool_data.get("next")
|
||||
current_tool = tool_data.get("tool")
|
||||
|
||||
workflow.logger.warning(f"next_step: {next_step}, current tool is {current_tool}")
|
||||
#if the next step is to confirm...
|
||||
if next_step == "confirm" and current_tool:
|
||||
args = tool_data.get("args", {})
|
||||
#if we're missing arguments, go back to the top of the loop
|
||||
if await helpers.handle_missing_args(current_tool, args, tool_data, self.prompt_queue):
|
||||
continue
|
||||
|
||||
#...otherwise, if we want to force the user to confirm, set that up
|
||||
waiting_for_confirm = True
|
||||
if SHOW_CONFIRM:
|
||||
self.confirm = False
|
||||
workflow.logger.info("Waiting for user confirm signal...")
|
||||
else:
|
||||
#theory - set self.confirm to true bc that's the signal, so we can get around the signal??
|
||||
self.confirm = True
|
||||
|
||||
# else if the next step is to pick a new goal...
|
||||
elif next_step == "pick-new-goal":
|
||||
workflow.logger.info("All steps completed. Resetting goal.")
|
||||
self.change_goal("goal_choose_agent_type")
|
||||
|
||||
# else if the next step is to be done - this should only happen if the user requests it via "end conversation"
|
||||
elif next_step == "done":
|
||||
workflow.logger.info("All steps completed. Exiting workflow.")
|
||||
self.add_message("agent", tool_data)
|
||||
# end the workflow
|
||||
return str(self.conversation_history)
|
||||
|
||||
self.add_message("agent", tool_data)
|
||||
await helpers.continue_as_new_if_needed(
|
||||
self.conversation_history,
|
||||
self.prompt_queue,
|
||||
agent_goal,
|
||||
self.goal,
|
||||
MAX_TURNS_BEFORE_CONTINUE,
|
||||
self.add_message
|
||||
)
|
||||
|
||||
#Signal that comes from api/main.py via a post to /send-prompt
|
||||
@workflow.signal
|
||||
async def user_prompt(self, prompt: str) -> None:
|
||||
"""Signal handler for receiving user prompts."""
|
||||
workflow.logger.warning(f"signal received: user_prompt, prompt is {prompt}")
|
||||
if self.chat_ended:
|
||||
workflow.logger.warn(f"Message dropped due to chat closed: {prompt}")
|
||||
workflow.logger.warning(f"Message dropped due to chat closed: {prompt}")
|
||||
return
|
||||
self.prompt_queue.append(prompt)
|
||||
|
||||
#Signal that comes from api/main.py via a post to /confirm
|
||||
@workflow.signal
|
||||
async def confirm(self) -> None:
|
||||
"""Signal handler for user confirmation of tool execution."""
|
||||
workflow.logger.info("Received user confirmation")
|
||||
workflow.logger.warning(f"signal recieved: confirm")
|
||||
self.confirm = True
|
||||
|
||||
#Signal that comes from api/main.py via a post to /end-chat
|
||||
@workflow.signal
|
||||
async def end_chat(self) -> None:
|
||||
"""Signal handler for ending the chat session."""
|
||||
workflow.logger.warning("signal received: end_chat")
|
||||
self.chat_ended = True
|
||||
|
||||
@workflow.query
|
||||
@@ -185,6 +238,11 @@ class AgentGoalWorkflow:
|
||||
"""Query handler to retrieve the full conversation history."""
|
||||
return self.conversation_history
|
||||
|
||||
@workflow.query
|
||||
def get_agent_goal(self) -> AgentGoal:
|
||||
"""Query handler to retrieve the current goal of the agent."""
|
||||
return self.goal
|
||||
|
||||
@workflow.query
|
||||
def get_summary_from_history(self) -> Optional[str]:
|
||||
"""Query handler to retrieve the conversation summary if available.
|
||||
@@ -212,3 +270,18 @@ class AgentGoalWorkflow:
|
||||
self.conversation_history["messages"].append(
|
||||
{"actor": actor, "response": response}
|
||||
)
|
||||
|
||||
def change_goal(self, goal: str) -> None:
|
||||
'''goalsLocal = {
|
||||
"goal_match_train_invoice": goal_match_train_invoice,
|
||||
"goal_event_flight_invoice": goal_event_flight_invoice,
|
||||
"goal_choose_agent_type": goal_choose_agent_type,
|
||||
}'''
|
||||
|
||||
if goal is not None:
|
||||
for listed_goal in goal_list:
|
||||
if listed_goal.id == goal:
|
||||
self.goal = listed_goal
|
||||
# self.goal = goals.get(goal)
|
||||
workflow.logger.warning("Changed goal to " + goal)
|
||||
#todo reset goal or tools if this doesn't work or whatever
|
||||
|
||||
Reference in New Issue
Block a user