.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorial/task_embedding.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorial_task_embedding.py: .. _embedding: Embedding ========================= In AgentScope, the embedding module provides a unified interface for vector representation generation, which features: - Support **caching embeddings** to avoid redundant API calls - Support **multiple embedding providers** with a consistent API AgentScope has built-in embedding classes for the following API providers: .. list-table:: :header-rows: 1 * - Provider - Class * - OpenAI - ``OpenAITextEmbedding`` * - Gemini - ``GeminiTextEmbedding`` * - DashScope - ``DashScopeTextEmbedding`` * - Ollama - ``OllamaTextEmbedding`` All classes inherit from ``EmbeddingModelBase``, implementing the ``__call__`` method and generating ``EmbeddingResponse`` object with the embeddings and usage information. Taking the DashScope embedding class as an example, you can use it as follows: .. GENERATED FROM PYTHON SOURCE LINES 33-66 .. code-block:: Python import asyncio import os import tempfile from agentscope.embedding import DashScopeTextEmbedding, FileEmbeddingCache async def example_dashscope_embedding() -> None: """Example usage of DashScope text embedding.""" texts = [ "What is the capital of France?", "Paris is the capital city of France.", ] # Initialize the DashScope text embedding instance embedding_model = DashScopeTextEmbedding( model_name="text-embedding-v2", api_key=os.getenv("DASHSCOPE_API_KEY"), ) # Get the embedding from the model response = await embedding_model(texts) print("The embedding ID: ", response.id) print("The embedding create at: ", response.created_at) print("The embedding usage: ", response.usage) print("The embedding:") print(response.embeddings) asyncio.run(example_dashscope_embedding()) .. rst-class:: sphx-glr-script-out .. code-block:: none The embedding ID: 2025-08-15 10:12:34.471_1c3811 The embedding create at: 2025-08-15 10:12:34.471 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GENERATED FROM PYTHON SOURCE LINES 67-80 You can customize your embedding model by subclassing ``EmbeddingModelBase`` and implementing the ``__call__`` method. Embedding Cache --------------------- AgentScope provides a base class ``EmbeddingCacheBase`` for caching embeddings, as well as a file-based implementation ``FileEmbeddingCache``. It works as follows in the embedding module: .. image:: ../../_static/images/embedding_cache.png :align: center :width: 90% To use caching, just pass an instance of ``FileEmbeddingCache`` (or your custom cache) to the embedding model's constructor as follows: .. GENERATED FROM PYTHON SOURCE LINES 80-132 .. code-block:: Python async def example_embedding_cache() -> None: """Demonstrate embedding with cache functionality.""" # Example texts texts = [ "What is the capital of France?", "Paris is the capital city of France.", ] # Create a temporary directory for cache demonstration # In real applications, you might want to use a persistent directory cache_dir = tempfile.mkdtemp(prefix="embedding_cache_") print(f"Using cache directory: {cache_dir}") # Initialize the embedding model with cache # We limit the cache to 100 files and 10MB for demonstration purposes embedder = DashScopeTextEmbedding( model_name="text-embedding-v3", api_key=os.getenv("DASHSCOPE_API_KEY"), embedding_cache=FileEmbeddingCache( cache_dir=cache_dir, max_file_number=100, max_cache_size=10, # Maximum cache size in MB ), ) # First call - will fetch from API and store in cache print("\n=== First API Call (No Cache Hit) ===") start_time = asyncio.get_event_loop().time() response1 = await embedder(texts) elapsed_time1 = asyncio.get_event_loop().time() - start_time print(f"Source: {response1.source}") # Should be 'api' print(f"Time taken: {elapsed_time1:.4f} seconds") print(f"Tokens used: {response1.usage.tokens}") # Second call with the same texts - should use cache print("\n=== Second API Call (Cache Hit Expected) ===") start_time = asyncio.get_event_loop().time() response2 = await embedder(texts) elapsed_time2 = asyncio.get_event_loop().time() - start_time print(f"Source: {response2.source}") # Should be 'cache' print(f"Time taken: {elapsed_time2:.4f} seconds") print( f"Tokens used: {response2.usage.tokens}", ) # Should be 0 for cached results print( f"Speed improvement: {elapsed_time1 / elapsed_time2:.1f}x faster with cache", ) asyncio.run(example_embedding_cache()) .. rst-class:: sphx-glr-script-out .. code-block:: none Using cache directory: /tmp/embedding_cache_tel8d1xm === First API Call (No Cache Hit) === Source: api Time taken: 0.8120 seconds Tokens used: 13 === Second API Call (Cache Hit Expected) === Source: cache Time taken: 0.0003 seconds Tokens used: 0 Speed improvement: 2595.4x faster with cache .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.059 seconds) .. _sphx_glr_download_tutorial_task_embedding.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: task_embedding.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: task_embedding.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: task_embedding.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_