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updates to readme, docs, guides
This commit is contained in:
Joshua Smith
194
README.md
194
README.md
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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).
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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).
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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).
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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).
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[Watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ)
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It's really helpful to [watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ) to understand how interaction works.
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[](https://www.youtube.com/watch?v=GEXllEH2XiQ)
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[](https://www.youtube.com/watch?v=GEXllEH2XiQ)
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## Configuration
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## Setup and Configuration
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See [the Setup guide](./setup.md).
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This application uses `.env` files for configuration. Copy the [.env.example](.env.example) file to `.env` and update the values:
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## Interaction
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TODO
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```bash
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## Architecture
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cp .env.example .env
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See [the architecture guide](./architecture.md).
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```
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### Agent Goal Configuration
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## Productionalization & Adding Features
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The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file.
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#### Goal: Find an event in Australia / New Zealand, book flights to it and invoice the user for the cost
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- `AGENT_GOAL=goal_event_flight_invoice` (default) - Helps users find events, book flights, and arrange train travel with invoice generation
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- This is the scenario in the video above
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#### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost
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- `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
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- This is a new goal that is part of an upcoming conference talk
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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.
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See the next section for tool configuration for each goal.
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### Tool Configuration
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#### Agent Goal: goal_event_flight_invoice (default)
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* The agent uses a mock function to search for events. This has zero configuration.
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* By default the agent uses a mock function to search for flights.
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* 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.
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* It's free to sign up at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper)
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* 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`
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* Requires a Stripe key for the `create_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env
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* It's free to sign up and get a key at [Stripe](https://stripe.com/)
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* If you're lazy go to `tools/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
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#### Agent Goal: goal_match_train_invoice
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* 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.
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* 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.
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* We use a mock function to search for trains. Start the train API server to use the real API: `python thirdparty/train_api.py`
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* * 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.
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* Requires a Stripe key for the `create_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env
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* It's free to sign up and get a key at [Stripe](https://stripe.com/)
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* If you're lazy go to `tools/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
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### LLM Provider Configuration
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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:
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- `LLM_PROVIDER=openai` for OpenAI's GPT-4o
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- `LLM_PROVIDER=google` for Google Gemini
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- `LLM_PROVIDER=anthropic` for Anthropic Claude
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- `LLM_PROVIDER=deepseek` for DeepSeek-V3
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- `LLM_PROVIDER=ollama` for running LLMs via [Ollama](https://ollama.ai) (not recommended for this use case)
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### Option 1: OpenAI
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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`.
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### Option 2: Google Gemini
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To use Google Gemini:
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1. Obtain a Google API key and set it in the `GOOGLE_API_KEY` environment variable in `.env`.
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2. Set `LLM_PROVIDER=google` in your `.env` file.
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### Option 3: Anthropic Claude (recommended)
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I find that Claude Sonnet 3.5 performs better than the other hosted LLMs for this use case.
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To use Anthropic:
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1. Obtain an Anthropic API key and set it in the `ANTHROPIC_API_KEY` environment variable in `.env`.
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2. Set `LLM_PROVIDER=anthropic` in your `.env` file.
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### Option 4: Deepseek-V3
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To use Deepseek-V3:
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1. Obtain a Deepseek API key and set it in the `DEEPSEEK_API_KEY` environment variable in `.env`.
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2. Set `LLM_PROVIDER=deepseek` in your `.env` file.
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### Option 5: Local LLM via Ollama (not recommended)
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To use a local LLM with Ollama:
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1. Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model.
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- Run `ollama run <OLLAMA_MODEL_NAME>` to start the model. Note that this model is about 9GB to download.
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- Example: `ollama run qwen2.5:14b`
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2. Set `LLM_PROVIDER=ollama` in your `.env` file and `OLLAMA_MODEL_NAME` to the name of the model you installed.
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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.
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## Configuring Temporal Connection
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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.
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### Use Temporal Cloud
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See [.env.example](.env.example) for details on connecting to Temporal Cloud using mTLS or API key authentication.
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[Sign up for Temporal Cloud](https://temporal.io/get-cloud)
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### Use a local Temporal Dev Server
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On a Mac
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```bash
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brew install temporal
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temporal server start-dev
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```
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See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.
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## Running the Application
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### Python Backend
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Requires [Poetry](https://python-poetry.org/) to manage dependencies.
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1. `python -m venv venv`
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2. `source venv/bin/activate`
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3. `poetry install`
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Run the following commands in separate terminal windows:
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1. Start the Temporal worker:
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```bash
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poetry run python scripts/run_worker.py
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```
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2. Start the API server:
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```bash
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poetry run uvicorn api.main:app --reload
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```
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Access the API at `/docs` to see the available endpoints.
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### React UI
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Start the frontend:
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```bash
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cd frontend
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npm install
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npx vite
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```
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Access the UI at `http://localhost:5173`
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### Python Search Trains API
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> Agent Goal: goal_match_train_invoice only
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Required to search and book trains!
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```bash
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poetry run python thirdparty/train_api.py
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# example url
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# http://localhost:8080/api/search?from=london&to=liverpool&outbound_time=2025-04-18T09:00:00&inbound_time=2025-04-20T09:00:00
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```
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### .NET (enterprise) Backend ;)
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> Agent Goal: goal_match_train_invoice only
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We have activities written in C# to call the train APIs.
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```bash
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cd enterprise
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dotnet build # ensure you brew install dotnet@8 first!
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dotnet run
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```
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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`.
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## Customizing the Agent
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- `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them)
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- `goal_registry.py` contains descriptions of goals and the tools used to achieve them
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- The tools themselves are defined in their own files in `/tools`
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- Note the mapping in `tools/__init__.py` to each tool
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## TODO
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- 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.
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- 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.
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- 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.
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- 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.
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- Perhaps the UI should show when the LLM response is being retried (i.e. activity retry attempt because the LLM provided bad output)
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- Perhaps the UI should show when the LLM response is being retried (i.e. activity retry attempt because the LLM provided bad output)
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- Tests would be nice!
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- Tests would be nice!
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See [the todo](./todo.md) for more details.
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See Customization for more details. <-- TODO
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## For Temporal SAs
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Check out the [slides](https://docs.google.com/presentation/d/1wUFY4v17vrtv8llreKEBDPLRtZte3FixxBUn0uWy5NU/edit#slide=id.g3333e5deaa9_0_0) here and the enablement guide here (TODO).
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12
architecture.md
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12
architecture.md
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# Elements
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talk through the pieces
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# Architecture Model
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explain elements
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# Adding features
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link to how to LLM interactions/how to change
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BIN
assets/Architecture_elements.png
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BIN
assets/Architecture_elements.png
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After Width: | Height: | Size: 39 KiB |
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Before Width: | Height: | Size: 376 KiB After Width: | Height: | Size: 376 KiB |
BIN
assets/ai_agent_architecture_model.png
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BIN
assets/ai_agent_architecture_model.png
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After Width: | Height: | Size: 144 KiB |
176
setup.md
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176
setup.md
Normal file
@@ -0,0 +1,176 @@
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|
## Configuration
|
||||||
|
|
||||||
|
This application uses `.env` files for configuration. Copy the [.env.example](.env.example) file to `.env` and update the values:
|
||||||
|
|
||||||
|
```bash
|
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|
cp .env.example .env
|
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|
```
|
||||||
|
|
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|
### 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
|
||||||
53
todo.md
53
todo.md
@@ -1,36 +1,43 @@
|
|||||||
# todo list
|
# todo list
|
||||||
[x] multi-goal <br />
|
|
||||||
[x] set goal to list agents when done <br />
|
|
||||||
[x] make this better/smoother <br />
|
|
||||||
|
|
||||||
[ ] clean up workflow/make functions
|
[ ] clean up workflow/make functions
|
||||||
|
|
||||||
[ ] make the debugging confirms optional <br />
|
[ ] make the debugging confirms optional <br />
|
||||||
[ ] grok integration <br />
|
<br />
|
||||||
[ ] document *why* temporal for ai agents - scalability, durability in the readme <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 />
|
[ ] fix readme: move setup to its own page, demo to its own page, add the why /|\ section <br />
|
||||||
[ ] add architecture to readme <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 tests<br />
|
||||||
|
|
||||||
[ ] create people management scenario <br />
|
[ ] create people management scenario <br />
|
||||||
-- check pay status
|
- check pay status <br />
|
||||||
-- book work travel
|
- book work travel <br />
|
||||||
-- check PTO levels
|
- check PTO levels <br />
|
||||||
-- check insurance coverages
|
- check insurance coverages <br />
|
||||||
-- book PTO around a date (https://developers.google.com/calendar/api/guides/overview)?
|
- book PTO around a date (https://developers.google.com/calendar/api/guides/overview)? <br />
|
||||||
-- scenario should use multiple tools
|
- scenario should use multiple tools <br />
|
||||||
-- expense management
|
- expense management <br />
|
||||||
-- check in on the health of the team
|
- check in on the health of the team <br />
|
||||||
[ ] demo the reasons why:
|
|
||||||
-- Orchestrate interactions across distributed data stores and tools
|
[ ] demo the reasons why: <br />
|
||||||
-- Hold state, potentially over long periods of time
|
- Orchestrate interactions across distributed data stores and tools <br />
|
||||||
-- Ability to ‘self-heal’ and retry until the (probabilistic) LLM returns valid data
|
- Hold state, potentially over long periods of time <br />
|
||||||
-- Support for human intervention such as approvals
|
- Ability to ‘self-heal’ and retry until the (probabilistic) LLM returns valid data <br />
|
||||||
-- Parallel processing for efficiency of data retrieval and tool use
|
- Support for human intervention such as approvals <br />
|
||||||
-- Insight into the agent’s performance
|
- 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 />
|
[ ] customize prompts in [workflow to manage scenario](./workflows/tool_workflow.py)<br />
|
||||||
[ ] add in new tools? <br />
|
[ ] add in new tools? <br />
|
||||||
|
|
||||||
[ ] non-retry the api key error - "Invalid API Key provided: sk_test_**J..." and "AuthenticationError"
|
[ ] 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
|
[ ] make it so you can yeet yourself out of a goal and pick a new one <br />
|
||||||
|
|||||||
Reference in New Issue
Block a user