https://github.com/tahmidefaz/seedling

🌱 Simple and scalable intent recognition using LLMs
https://github.com/tahmidefaz/seedling

generative-ai intent-detection python-library

Last synced: about 12 hours ago
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🌱 Simple and scalable intent recognition using LLMs

• Host: GitHub
• URL: https://github.com/tahmidefaz/seedling
• Owner: tahmidefaz
• License: apache-2.0
• Created: 2024-07-27T21:54:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-30T02:42:29.000Z (4 months ago)
• Last Synced: 2024-07-31T07:00:50.654Z (4 months ago)
• Topics: generative-ai, intent-detection, python-library
• Language: Python
• Homepage:
• Size: 249 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 🌱 seedling

*A simple and supposedly scalable python library for intent recognition using Large Language Models (LLMs).*

## The Idea

Seedling 🌱 gives you a very simple and scalable way to detect intents from user messages using LLMs.

In order to make things scalable, seedling forces you to group your intents to **topics**.

This also keeps your prompt length short, reduces LLM hallucination, and makes LLM output more accurate.

![An image showing intents grouped together into topics](/topics_and_intents.png)

*Example image showing intents grouped under six different topics*

Seedling supports OpenAI/OpenAI-Complatible APIs out of the box.

The OpenAI-Compatible API server must support `tools` use.

It is also very easy to write your own LLM interface for prediction purposes.

### Defining Topic YAML

Following is a simple topic YAML file for the `map` topic with the related intents grouped under it.

Please note that both the topic and the intent `description` must be as detailed as possible, as these get fed into the LLM.

```yaml

name: "map"

description: "Get directions, find places such as restaurants, shops, places of interest, etc."

intents:

  - name: "get_directions"

    description: "Gets directions between two locations."

  - name: "find_place"

    description: "Finds a specific place like a restaurant, gas station, or store."

  - name: "get_traffic_information"

    description: "Gets real-time traffic information for a route."

```

Each YAML files must NOT contain more than 1 topic.

### One-shot entity extraction

In addition to detecting intents, seedling also allows you to attempt to extract entities in one-shot.

For enitity extraction, the smarter your LLM is, the better.

```yaml

name: "travel"

description: "Plan and manage travel arrangements. Search for hotels, flights, and rental cars."

intents:

  - name: "search_flights"

    description: "Searches for flights based on origin, destination, and dates."

    entities:

      - name: "origin"

        type: "string"

        description: "The origin of the flight. Write 'none' if not provided."

      - name: "destination"

        type: "string"

        description: "The destination of the flight.  Write 'none' if not provided."

      - name: "date"

        type: "string"

        description: "The date of the flight. Write 'none' if not provided."

```

## Basic Usage

```python

from seedling import YAMLreader, LanguageModel, predict

# Path to the directory containing all your topic/intent YAML files

all_topics = YAMLreader("directory_path")

# OpenAI/OpenAI-compatible LLM server (must support tools use)

# check example/main.py for Ollama config

llm = LanguageModel(

    base_url='http://baseurl',

    api_key='apikey',

    model='model_name',

)

# Predict the intent of the user message

user_message = "do some magic on spotify!"

predicted = predict(llm, all_topics, user_message)

print(predicted)

```

## Check out the example

The example is great for wrapping your head around everything.

Follow these steps to run the example:

1. Start a virtual environment

    ```

    python -m venv venv

    source venv/bin/activate

    ```

2. Build the seedling 🌱 library

    ```

    pip install -e .

    ```

3. Install the latest version of [Ollama](https://ollama.com/) and pull down the `llama3.1` model.

   Alternatively, you can also update the example file under `example/main.py` with your OpenAI/OpenAI-compatible API server information.

   ```

   ollama pull llama3.1

   ```

5. Run the example

    ```

    cd example

    python main.py

    ```

## Local Development

* Only tested on `python 3.11` so far

* Currently, the actual library source code can be found under `src/seedling`

* To install all dependencies do `pip install ."[dev]"` in your virtual environment

* Use `make lint` to run the linter