elizaos-plugin-local-embedding 2.0.0a5

elizaOS Local Embedding Plugin - Local text embedding and tokenization

Local AI Plugin

This plugin provides local AI model capabilities through the elizaOS platform, supporting text generation, image analysis, speech synthesis, and audio transcription.

Usage

Add the plugin to your character configuration:

"plugins": ["@elizaos-plugins/plugin-local-ai"]

Configuration

The plugin is configured using environment variables (typically set in a .env file or via your deployment settings):

Or in .env file:

# Optional: Specify a custom directory for models (GGUF files)
# MODELS_DIR=/path/to/your/models

# Optional: Specify a custom directory for caching other components (tokenizers, etc.)
# CACHE_DIR=/path/to/your/cache

# Optional: Specify filenames for the text generation and embedding models within the models directory
# LOCAL_SMALL_MODEL=my-custom-small-model.gguf
# LOCAL_LARGE_MODEL=my-custom-large-model.gguf
# LOCAL_EMBEDDING_MODEL=my-custom-embedding-model.gguf

# Optional: Fallback dimension size for embeddings if generation fails. Defaults to the model's default (e.g., 384).
# LOCAL_EMBEDDING_DIMENSIONS=384

Configuration Options

• MODELS_DIR (Optional): Specifies a custom directory for storing model files (GGUF format). If not set, defaults to ~/.eliza/models.
• CACHE_DIR (Optional): Specifies a custom directory for caching other components like tokenizers. If not set, defaults to ~/.eliza/cache.
• LOCAL_SMALL_MODEL (Optional): Specifies the filename for the small text generation model (e.g., DeepHermes-3-Llama-3-3B-Preview-q4.gguf) located in the models directory.
• LOCAL_LARGE_MODEL (Optional): Specifies the filename for the large text generation model (e.g., DeepHermes-3-Llama-3-8B-q4.gguf) located in the models directory.
• LOCAL_EMBEDDING_MODEL (Optional): Specifies the filename for the text embedding model (e.g., bge-small-en-v1.5.Q4_K_M.gguf) located in the models directory.
• LOCAL_EMBEDDING_DIMENSIONS (Optional): Defines the expected dimension size for text embeddings. This is primarily used as a fallback dimension if the embedding model fails to generate an embedding. If not set, it defaults to the embedding model's native dimension size (e.g., 384 for bge-small-en-v1.5.Q4_K_M.gguf).

Features

The plugin provides these model classes:

• TEXT_SMALL: Fast, efficient text generation using smaller models
• TEXT_LARGE: More capable text generation using larger models
• TEXT_EMBEDDING: Generates text embeddings locally.
• IMAGE_DESCRIPTION: Local image analysis using Florence-2 vision model
• TEXT_TO_SPEECH: Local text-to-speech synthesis
• TRANSCRIPTION: Local audio transcription using Whisper

Text Generation

// Using small model
const smallResponse = await runtime.useModel(ModelType.TEXT_SMALL, {
  prompt: 'Generate a short response',
  stopSequences: [],
});

// Using large model
const largeResponse = await runtime.useModel(ModelType.TEXT_LARGE, {
  prompt: 'Generate a detailed response',
  stopSequences: [],
});

Text Embedding

const embedding = await runtime.useModel(ModelType.TEXT_EMBEDDING, {
  text: 'Text to get embedding for',
});

Image Analysis

const { title, description } = await runtime.useModel(
  ModelType.IMAGE_DESCRIPTION,
  'https://example.com/image.jpg'
);

Text-to-Speech

const audioStream = await runtime.useModel(ModelType.TEXT_TO_SPEECH, 'Text to convert to speech');

Audio Transcription

const transcription = await runtime.useModel(ModelType.TRANSCRIPTION, audioBuffer);