https://github.com/quantgeekdev/keyword-extractor-agent
Takes in a paragraph and returns a list of keywords for SEO
https://github.com/quantgeekdev/keyword-extractor-agent
Last synced: 9 months ago
JSON representation
Takes in a paragraph and returns a list of keywords for SEO
- Host: GitHub
- URL: https://github.com/quantgeekdev/keyword-extractor-agent
- Owner: QuantGeekDev
- Created: 2025-04-19T05:39:53.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-04-19T05:39:56.000Z (10 months ago)
- Last Synced: 2025-05-03T03:01:32.882Z (9 months ago)
- Language: TypeScript
- Size: 74.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🐝 BeeAI Framework Starter
This starter template lets you quickly start working with the [BeeAI Framework](https://github.com/i-am-bee/beeai-framework) in a second.
📚 See the [documentation](https://i-am-bee.github.io/beeai-framework/) to learn more.
## ✨ Key Features
- 🔒 Safely execute an arbitrary Python Code via [Bee Code Interpreter](https://github.com/i-am-bee/bee-code-interpreter).
- 🔎 Get complete visibility into agents' decisions using our [OpenInference Instrumentation for BeeAI](https://github.com/Arize-ai/openinference/tree/main/js/packages/openinference-instrumentation-beeai) package.
- 🚀 Fully fledged TypeScript project setup with linting and formatting.
## 📦 Requirements
- JavaScript runtime [NodeJS > 18](https://nodejs.org/) (ideally installed via [nvm](https://github.com/nvm-sh/nvm)).
- Container system like [Rancher Desktop](https://rancherdesktop.io/), [Podman](https://podman.io/) (VM must be rootfull machine) or [Docker](https://www.docker.com/).
- LLM Provider either external [WatsonX](https://www.ibm.com/watsonx) (OpenAI, Groq, ...) or local [ollama](https://ollama.com).
## 🛠️ Getting started
1. Clone this repository or [use it as a template](https://github.com/new?template_name=beeai-framework-starter&template_owner=i-am-bee).
2. Install dependencies `npm ci`.
3. Configure your project by filling in missing values in the `.env` file (default LLM provider is locally hosted `Ollama`).
4. Run the agent `npm run start src/agent.ts`
To run an agent with a custom prompt, simply do this `npm run start src/agent.ts <<< 'Hello Bee!'`
🧪 More examples can be found [here](https://github.com/i-am-bee/beeai-framework/blob/main/examples).
> [!TIP]
>
> To use Bee agent with [Python Code Interpreter](https://github.com/i-am-bee/bee-code-interpreter) refer to the [Code Interpreter](#code-interpreter) section.
> [!TIP]
>
> To use Bee agent with [OpenInference Instrumentation for BeeAI](https://github.com/Arize-ai/openinference/tree/main/js/packages/openinference-instrumentation-beeai) refer to the [Observability](#observability) section.
## 🏗 Infrastructure
> [!NOTE]
>
> Docker distribution with support for _compose_ is required, the following are supported:
>
> - [Docker](https://www.docker.com/)
> - [Rancher](https://www.rancher.com/) - macOS users may want to use VZ instead of QEMU
> - [Podman](https://podman.io/) - requires [compose](https://podman-desktop.io/docs/compose/setting-up-compose) and **rootful machine** (if your current machine is rootless, please create a new one, also ensure you have enabled Docker compatibility mode).
## 🔒Code interpreter
The [Bee Code Interpreter](https://github.com/i-am-bee/bee-code-interpreter) is a gRPC service that an agent uses to execute an arbitrary Python code safely.
### Instructions
1. Start all services related to the [`Code Interpreter`](https://github.com/i-am-bee/bee-code-interpreter) `npm run infra:start --profile=code_interpreter`
2. Run the agent `npm run start src/agent_code_interpreter.ts`
> [!NOTE]
>
> Code Interpreter runs on `http://127.0.0.1:50081`.
## 🔎 Observability
Get complete visibility of the agent's inner workings via [OpenInference Instrumentation for BeeAI](https://github.com/Arize-ai/openinference/tree/main/js/packages/openinference-instrumentation-beeai).
### Instructions
> Please use node version >= 20 to run this example.
1. (Optional) In order to see spans in [Phoenix](https://github.com/Arize-ai/phoenix), begin running a Phoenix server. This can be done in one command using docker.
```
docker run -p 6006:6006 -i -t arizephoenix/phoenix
```
or via the command line:
```
brew install i-am-bee/beeai/arize-phoenix
brew services start arize-phoenix
```
see https://docs.beeai.dev/observability/agents-traceability for more details.
2. Run the agent `npm run start src/agent_observe.ts`
3. You should see your spans exported in your console. If you've set up a locally running Phoenix server, head to [**localhost:6006**](http://localhost:6006/projects) to see your spans.