semantica/cookbook/introduction/12_Embedding_Generation.ipynb

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   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/semantica-agi/semantica/blob/main/cookbook/introduction/13_Embedding_Generation.ipynb)\n",
    "\n",
    "# Embedding Generation\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook demonstrates how to generate embeddings from text using Semantica's embedding modules. You'll learn to use `EmbeddingGenerator` and `TextEmbedder` to create vector representations of text.\n",
    "\n",
    "\n",
    "**Documentation**: [API Reference](https://semantica.readthedocs.io/reference/embeddings/)\n",
    "\n",
    "### Learning Objectives\n",
    "\n",
    "- Use `EmbeddingGenerator` to generate embeddings\n",
    "- Use `TextEmbedder` for text embedding generation\n",
    "- Generate embeddings for multiple texts\n",
    "- Understand embedding dimensions\n",
    "\n",
    "## Installation\n",
    "\n",
    "Install Semantica from PyPI:\n",
    "\n",
    "```bash\n",
    "pip install semantica\n",
    "# Or with all optional dependencies:\n",
    "pip install semantica[all]\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "## Step 1: Generate Embeddings\n",
    "\n",
    "Generate embeddings using EmbeddingGenerator.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install semantica\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantica.embeddings import EmbeddingGenerator\n",
    "\n",
    "generator = EmbeddingGenerator()\n",
    "\n",
    "texts = [\n",
    "    \"Apple Inc. is a technology company.\",\n",
    "    \"Microsoft Corporation develops software.\",\n",
    "    \"Amazon provides cloud services.\"\n",
    "]\n",
    "\n",
    "embeddings = generator.generate_embeddings(texts, data_type=\"text\")\n",
    "\n",
    "print(f\"Generated embeddings for {len(texts)} texts\")\n",
    "print(f\"Embeddings shape: {embeddings.shape}\")\n",
    "print(f\"First embedding dimension: {len(embeddings[0]) if len(embeddings) > 0 else 'N/A'}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Text Embedding\n",
    "\n",
    "Use TextEmbedder for text-specific embeddings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantica.embeddings import TextEmbedder\n",
    "\n",
    "text_embedder = TextEmbedder()\n",
    "\n",
    "text = \"Semantic knowledge graphs enable intelligent data processing.\"\n",
    "\n",
    "embedding = text_embedder.embed_text(text)\n",
    "\n",
    "print(f\"Generated embedding for text\")\n",
    "print(f\"First 5 values: {embedding[:5]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Model Selection & Dynamic Switching\n",
    "\n",
    "Semantica allows you to choose between different embedding providers (e.g., Sentence Transformers, FastEmbed) and switch models dynamically.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize with a specific provider and model\n",
    "embedder = TextEmbedder(method=\"sentence_transformers\", model_name=\"all-MiniLM-L6-v2\")\n",
    "print(f\"Current method: {embedder.get_method()}\")\n",
    "\n",
    "# Switch to FastEmbed dynamically\n",
    "try:\n",
    "    embedder.set_model(method=\"fastembed\", model_name=\"BAAI/bge-small-en-v1.5\")\n",
    "    print(f\"Switched to: {embedder.get_method()}\")\n",
    "    print(f\"Model Info: {embedder.get_model_info()}\")\n",
    "except ImportError:\n",
    "    print(\"FastEmbed not installed. Install with: pip install fastembed\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "You've learned how to generate embeddings:\n",
    "\n",
    "- **EmbeddingGenerator**: Generate embeddings for multiple texts\n",
    "- **TextEmbedder**: Generate text-specific embeddings\n",
    "\n",
    "Next: Learn how to store and search vectors in the Vector_Store notebook.\n"
   ]
  }
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